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Hu Z, Hu Y, Zhang S, Dong L, Chen X, Yang H, Su L, Hou X, Huang X, Shen X, Ye C, Tu W, Chen Y, Chen Y, Cai S, Zhong J, Dong L. Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study. Chin Med J (Engl) 2024; 137:1811-1822. [PMID: 38863118 DOI: 10.1097/cm9.0000000000003025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD. METHODS In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort. RESULTS In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone. CONCLUSION ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE. TRIAL REGISTRATION Chictr.org.cn : ChiCTR2200059599.
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Affiliation(s)
- Ziwei Hu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yangyang Hu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shuoqi Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Li Dong
- Department of Rheumatology and Immunology, Jingzhou Central Hospital, Yangtze University, Jinzhou, Hubei 434020, China
| | - Xiaoqi Chen
- Department of Rheumatology and Immunology, Zhongnan Hospital, Wuhan University, Wuhan, Hubei 430071, China
| | - Huiqin Yang
- Department of Rheumatology, Wuhan No.1 Hospital, Wuhan, Hubei 430022, China
| | - Linchong Su
- Department of Rheumatology, Minda Hospital of Hubei Minzu University, Enshi, Hubei 445000, China
| | - Xiaoqiang Hou
- Department of Rheumatology and Immunology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443003, China
| | - Xia Huang
- Department of Rheumatology, Minda Hospital of Hubei Minzu University, Enshi, Hubei 445000, China
| | - Xiaolan Shen
- Department of Rheumatology and Immunology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443003, China
| | - Cong Ye
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Wei Tu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yu Chen
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yuxue Chen
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shaozhe Cai
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jixin Zhong
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lingli Dong
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
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Liu C, Zhang K, Yang X, Meng B, Lou J, Liu Y, Cao J, Liu K, Mi W, Li H. Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study. JMIR Aging 2024; 7:e54872. [PMID: 39087583 PMCID: PMC11294761 DOI: 10.2196/54872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 04/01/2024] [Accepted: 05/24/2024] [Indexed: 08/02/2024] Open
Abstract
Background Myocardial injury after noncardiac surgery (MINS) is an easily overlooked complication but closely related to postoperative cardiovascular adverse outcomes; therefore, the early diagnosis and prediction are particularly important. Objective We aimed to develop and validate an explainable machine learning (ML) model for predicting MINS among older patients undergoing noncardiac surgery. Methods The retrospective cohort study included older patients who had noncardiac surgery from 1 northern center and 1 southern center in China. The data sets from center 1 were divided into a training set and an internal validation set. The data set from center 2 was used as an external validation set. Before modeling, the least absolute shrinkage and selection operator and recursive feature elimination methods were used to reduce dimensions of data and select key features from all variables. Prediction models were developed based on the extracted features using several ML algorithms, including category boosting, random forest, logistic regression, naïve Bayes, light gradient boosting machine, extreme gradient boosting, support vector machine, and decision tree. Prediction performance was assessed by the area under the receiver operating characteristic (AUROC) curve as the main evaluation metric to select the best algorithms. The model performance was verified by internal and external validation data sets with the best algorithm and compared to the Revised Cardiac Risk Index. The Shapley Additive Explanations (SHAP) method was applied to calculate values for each feature, representing the contribution to the predicted risk of complication, and generate personalized explanations. Results A total of 19,463 eligible patients were included; among those, 12,464 patients in center 1 were included as the training set; 4754 patients in center 1 were included as the internal validation set; and 2245 in center 2 were included as the external validation set. The best-performing model for prediction was the CatBoost algorithm, achieving the highest AUROC of 0.805 (95% CI 0.778-0.831) in the training set, validating with an AUROC of 0.780 in the internal validation set and 0.70 in external validation set. Additionally, CatBoost demonstrated superior performance compared to the Revised Cardiac Risk Index (AUROC 0.636; P<.001). The SHAP values indicated the ranking of the level of importance of each variable, with preoperative serum creatinine concentration, red blood cell distribution width, and age accounting for the top three. The results from the SHAP method can predict events with positive values or nonevents with negative values, providing an explicit explanation of individualized risk predictions. Conclusions The ML models can provide a personalized and fairly accurate risk prediction of MINS, and the explainable perspective can help identify potentially modifiable sources of risk at the patient level.
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Affiliation(s)
- Chang Liu
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Kai Zhang
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Xiaodong Yang
- Institute of Computing Technology Chinese Academy of Science, Beijing, China
| | - Bingbing Meng
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jingsheng Lou
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yanhong Liu
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jiangbei Cao
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Kexuan Liu
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Weidong Mi
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Hao Li
- Department of Anesthesiology, The First Medical Center, Chinese People's Liberation Army General Hospital, 28th Fuxing Road, Haidian District, Beijing, 100853, China, 86 15010665099
- Medical School of Chinese People's Liberation Army General Hospital, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
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Abdelhack M, Tripathi S, Chen Y, Avidan MS, King CR. Social vulnerability and surgery outcomes: a cross-sectional analysis. BMC Public Health 2024; 24:1907. [PMID: 39014400 PMCID: PMC11253435 DOI: 10.1186/s12889-024-19418-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 07/09/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND Post-operative complications present a challenge to the healthcare system due to the high unpredictability of their incidence. Socioeconomic conditions have been established as social determinants of health. However, their contribution relating to postoperative complications is still unclear as it can be heterogeneous based on community, type of surgical services, and sex and gender. Uncovering these relations can enable improved public health policy to reduce such complications. METHODS In this study, we conducted a large population cross-sectional analysis of social vulnerability and the odds of various post-surgical complications. We collected electronic health records data from over 50,000 surgeries that happened between 2012 and 2018 at a quaternary health center in St. Louis, Missouri, United States and the corresponding zip code of the patients. We built statistical logistic regression models of postsurgical complications with the social vulnerability index of the tract consisting of the zip codes of the patient as the independent variable along with sex and race interaction. RESULTS Our sample from the St. Louis area exhibited high variance in social vulnerability with notable rapid increase in vulnerability from the south west to the north of the Mississippi river indicating high levels of inequality. Our sample had more females than males, and females had slightly higher social vulnerability index. Postoperative complication incidence ranged from 0.75% to 41% with lower incidence rate among females. We found that social vulnerability was associated with abnormal heart rhythm with socioeconomic status and housing status being the main association factors. We also found associations of the interaction of social vulnerability and female sex with an increase in odds of heart attack and surgical wound infection. Those associations disappeared when controlling for general health and comorbidities. CONCLUSIONS Our results indicate that social vulnerability measures such as socioeconomic status and housing conditions could affect postsurgical outcomes through preoperative health. This suggests that the domains of preventive medicine and public health should place social vulnerability as a priority to achieve better health outcomes of surgical interventions.
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Affiliation(s)
- Mohamed Abdelhack
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA.
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Sandhya Tripathi
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA.
| | - Yixin Chen
- Department of Computer Science, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Christopher R King
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA.
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Zhang Z, Shao B, Liu H, Huang B, Gao X, Qiu J, Wang C. Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting. J Inflamm Res 2024; 17:4163-4174. [PMID: 38973999 PMCID: PMC11226989 DOI: 10.2147/jir.s464489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 06/25/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed. Patients and Methods 3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD risk assessment, the receiver operating characteristics (ROC) curve and precision-recall (PR) curve were conducted to evaluate their predictive accuracy. The optimal model was interpreted by Shapley additive explanations (SHAP) analysis and assessed by the ROC curve, calibration curve, and decision curve analysis (DCA) and validated by two external cohorts. Results Using LASSO filtration, all included variables were considered to be statistically significant. Four machine learning models were constructed based on these features and the results of ROC and PR curve implied that the XGBoost model exhibited the highest predictive performance, which yielded a high area of ROC curve (AUC) of 0.988 (95% CI: 0.986-0.991) to distinguish CAD patients from controls with a sensitivity of 94.6% and a specificity of 94.6%. The calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a better net benefit across a wide range of threshold probabilities. External validation of the model also exhibited favorable discriminatory performance, with an AUC, sensitivity, and specificity of 0.953 (95% CI: 0.945-0.960), 89.9%, and 87.1% in the validation cohort, and 0.935 (95% CI: 0.915-0.955), 82.0%, and 90.3% in the replication cohort. Conclusion Our model is highly informative for clinical practice and will be conducive to primary prevention and tailoring the precise management for CAD patients.
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Affiliation(s)
- Zheng Zhang
- Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, People’s Republic of China
- Center for Gene Diagnosis, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China
| | - Binbin Shao
- Department of Prenatal Diagnosis, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing, Jiangsu Province, People’s Republic of China
| | - Hongzhou Liu
- Center for Gene Diagnosis, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China
- School of Clinical Medicine, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuang Province, People’s Republic of China
| | - Ben Huang
- Center for Gene Diagnosis, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of China
| | - Xuechen Gao
- Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, People’s Republic of China
| | - Jun Qiu
- Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, People’s Republic of China
| | - Chen Wang
- Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, People’s Republic of China
- Center for Gene Diagnosis, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China
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Arredondo Montero J. From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery. World J Gastrointest Surg 2024; 16:1517-1520. [PMID: 38983356 PMCID: PMC11230006 DOI: 10.4240/wjgs.v16.i6.1517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 06/27/2024] Open
Abstract
Recent medical literature shows that the application of artificial intelligence (AI) models in gastrointestinal pathology is an exponentially growing field, with promising models that show very high performances. Regarding inflammatory bowel disease (IBD), recent reviews demonstrate promising diagnostic and prognostic AI models. However, studies are generally at high risk of bias (especially in AI models that are image-based). The creation of specific AI models that improve diagnostic performance and allow the establishment of a general prognostic forecast in IBD is of great interest, as it may allow the stratification of patients into subgroups and, in turn, allow the creation of different diagnostic and therapeutic protocols for these patients. Regarding surgical models, predictive models of postoperative complications have shown great potential in large-scale studies. In this work, the authors present the development of a predictive algorithm for early post-surgical complications in Crohn's disease based on a Random Forest model with exceptional predictive ability for complications within the cohort. The present work, based on logical and reasoned, clinical, and applicable aspects, lays a solid foundation for future prospective work to further develop post-surgical prognostic tools for IBD. The next step is to develop in a prospective and multicenter way, a collaborative path to optimize this line of research and make it applicable to our patients.
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Affiliation(s)
- Javier Arredondo Montero
- Department of Pediatric Surgery, Complejo Asistencial Universitario de León, Castilla y León, León 24008, Spain
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Han C, Kim HI, Soh S, Choi JW, Song JW, Yoon D. Machine learning with clinical and intraoperative biosignal data for predicting postoperative delirium after cardiac surgery. iScience 2024; 27:109932. [PMID: 38799563 PMCID: PMC11126810 DOI: 10.1016/j.isci.2024.109932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/25/2024] [Accepted: 05/05/2024] [Indexed: 05/29/2024] Open
Abstract
Early identification of patients at high risk of delirium is crucial for its prevention. Our study aimed to develop machine learning models to predict delirium after cardiac surgery using intraoperative biosignals and clinical data. We introduced a novel approach to extract relevant features from continuously measured intraoperative biosignals. These features reflect the patient's overall or baseline status, the extent of unfavorable conditions encountered intraoperatively, and beat-to-beat variability within the data. We developed a soft voting ensemble machine learning model using retrospective data from 1,912 patients. The model was then prospectively validated with data from 202 additional patients, achieving a high performance with an area under the receiver operating characteristic curve of 0.887 and an accuracy of 0.881. According to the SHapley Additive exPlanation method, several intraoperative biosignal features had high feature importance, suggesting that intraoperative patient management plays a crucial role in preventing delirium after cardiac surgery.
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Affiliation(s)
- Changho Han
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Hyun Il Kim
- Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sarah Soh
- Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ja Woo Choi
- Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Wook Song
- Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
- Institute for Innovation in Digital Healthcare (IIDH), Severance Hospital, Seoul, Republic of Korea
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Wang X, Xu X, Wang Y, Liu L, Xu Y, Liu J, Hu B, Li X. Evaluation of the clinical value of 10 estimating glomerular filtration rate equations and construction of a prediction model for kidney damage in adults from central China. Front Mol Biosci 2024; 11:1408503. [PMID: 38939508 PMCID: PMC11208320 DOI: 10.3389/fmolb.2024.1408503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/24/2024] [Indexed: 06/29/2024] Open
Abstract
Objectives This study aimed to evaluate 10 estimating glomerular filtration rate (eGFR) equations in central China population and construct a diagnostic prediction model for assessing the kidney damage severity. Methods The concordance of 10 eGFR equations was investigated in healthy individuals from central China, and their clinical effectiveness in diagnosing kidney injury was evaluated. Subsequently, relevant clinical indicators were selected to develop a clinical prediction model for kidney damage. Results The overall concordance between CKD-EPIASR-Scr and CKD-EPI2021-Scr was the highest (weightedκ = 0.964) in healthy population. The CG formula, CKD-EPIASR-Scr and CKD-EPI2021-Scr performed better than others in terms of concordance with referenced GFR (rGFR), but had poor ability to distinguish between rGFR < 90 or < 60 mL/min·1.73 m2. This finding was basically consistent across subgroups. Finally, two logistic regression prediction models were constructed based on rGFR < 90 or 60 mL/min·1.73 m2. The area under the curve of receiver operating characteristic values of two prediction models were 0.811 vs 0.846 in training set and 0.812 vs 0.800 in testing set. Conclusion The concordance of CKD-EPIASR-Scr and CKD-EPI2021-Scr was the highest in the central China population. The Cockcroft-Gault formula, CKD-EPIASR-Scr, and CKD-EPI2021-Scr more accurately reflected true kidney function, while performed poorly in the staging diagnosis of CKD. The diagnostic prediction models showed the good clinical application performance in identifying mild or moderate kidney injury. These findings lay a solid foundation for future research on renal function assessment and predictive equations.
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Affiliation(s)
- Xian Wang
- Department of Nephrology, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
- Center for Scientific Research, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
| | - Xingcheng Xu
- Department of Nephrology, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
| | - Yongsheng Wang
- School of Computer and Information Engineering, Fuyang Normal University, Fuyang, Anhui, China
| | - Lei Liu
- Department of Nephrology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Ying Xu
- Department of Nuclear Medicine, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
| | - Jun Liu
- Health Management Center, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
| | - Benjin Hu
- Department of Nephrology, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
| | - Xiaowei Li
- Department of Nephrology, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
- Center for Scientific Research, Anhui Medical University, Fuyang People’s Hospital of Anhui Medical University, Fuyang, Anhui, China
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Balian J, Sakowitz S, Verma A, Vadlakonda A, Cruz E, Ali K, Benharash P. Machine learning based predictive modeling of readmissions following extracorporeal membrane oxygenation hospitalizations. Surg Open Sci 2024; 19:125-130. [PMID: 38655069 PMCID: PMC11035075 DOI: 10.1016/j.sopen.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024] Open
Abstract
Background Despite increasing utilization and survival benefit over the last decade, extracorporeal membrane oxygenation (ECMO) remains resource-intensive with significant complications and rehospitalization risk. We thus utilized machine learning (ML) to develop prediction models for 90-day nonelective readmission following ECMO. Methods All adult patients receiving ECMO who survived index hospitalization were tabulated from the 2016-2020 Nationwide Readmissions Database. Extreme Gradient Boosting (XGBoost) models were developed to identify features associated with readmission following ECMO. Area under the receiver operating characteristic (AUROC), mean Average Precision (mAP), and the Brier score were calculated to estimate model performance relative to logistic regression (LR). Shapley Additive Explanation summary (SHAP) plots evaluated the relative impact of each factor on the model. An additional sensitivity analysis solely included patient comorbidities and indication for ECMO as potential model covariates. Results Of ∼22,947 patients, 4495 (19.6 %) were readmitted nonelectively within 90 days. The XGBoost model exhibited superior discrimination (AUROC 0.64 vs 0.49), classification accuracy (mAP 0.30 vs 0.20) and calibration (Brier score 0.154 vs 0.165, all P < 0.001) in predicting readmission compared to LR. SHAP plots identified duration of index hospitalization, undergoing heart/lung transplantation, and Medicare insurance to be associated with increased odds of readmission. Upon sub-analysis, XGBoost demonstrated superior disclination compared to LR (AUROC 0.61 vs 0.60, P < 0.05). Chronic liver disease and frailty were linked with increased odds of nonelective readmission. Conclusions ML outperformed LR in predicting readmission following ECMO. Future work is needed to identify other factors linked with readmission and further optimize post-ECMO care among this cohort.
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Affiliation(s)
- Jeffrey Balian
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California, Los Angeles, CA, United States of America
| | - Sara Sakowitz
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California, Los Angeles, CA, United States of America
| | - Arjun Verma
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California, Los Angeles, CA, United States of America
| | - Amulya Vadlakonda
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California, Los Angeles, CA, United States of America
| | - Emma Cruz
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California, Los Angeles, CA, United States of America
| | - Konmal Ali
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California, Los Angeles, CA, United States of America
| | - Peyman Benharash
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California, Los Angeles, CA, United States of America
- Division of Cardiac Surgery, Department of Surgery, University of California, Los Angeles, CA, United States of America
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Li P, Gao S, Wang Y, Zhou R, Chen G, Li W, Hao X, Zhu T. Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications. Br J Anaesth 2024; 132:1315-1326. [PMID: 38637267 DOI: 10.1016/j.bja.2024.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/20/2024] [Accepted: 02/23/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that use intraoperative respiratory features to predict PPCs. METHODS We analysed perioperative data from a cohort comprising patients aged 65 yr and older at an academic medical centre from 2019 to 2023. Two linear and four nonlinear learning models were developed and compared with the current gold-standard risk assessment tool ARISCAT (Assess Respiratory Risk in Surgical Patients in Catalonia Tool). The Shapley additive explanation of artificial intelligence was utilised to interpret feature importance and interactions. RESULTS Perioperative data were obtained from 10 284 patients who underwent 10 484 operations (mean age [range] 71 [65-98] yr; 42% female). An optimised XGBoost model that used preoperative variables and intraoperative respiratory variables had area under the receiver operating characteristic curves (AUROCs) of 0.878 (0.866-0.891) and 0.881 (0.879-0.883) in the validation and prospective cohorts, respectively. These models outperformed ARISCAT (AUROC: 0.496-0.533). The intraoperative dynamic features of respiratory dynamic system compliance, mechanical power, and driving pressure were identified as key modifiable contributors to PPCs. A simplified model based on XGBoost including 20 variables generated an AUROC of 0.864 (0.852-0.875) in an internal testing cohort. This has been developed into a web-based tool for further external validation (https://aorm.wchscu.cn/). CONCLUSIONS These findings suggest that real-time identification of surgical patients' risk of postoperative pulmonary complications could help personalise intraoperative ventilatory strategies and reduce postoperative pulmonary complications.
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Affiliation(s)
- Peiyi Li
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shuanliang Gao
- College of Software Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Yaqiang Wang
- College of Software Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China; Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service, Chengdu, Sichuan, China
| | - RuiHao Zhou
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Guo Chen
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China; State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Xuechao Hao
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Tao Zhu
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Gunaratne C, Ison R, Price CC, Modave F, Tighe P. Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 249:108143. [PMID: 38552333 DOI: 10.1016/j.cmpb.2024.108143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/21/2024]
Abstract
BACKGROUND Blood pressure is a vital sign for organ perfusion that anesthesiologists measure and modulate during surgery. However, current decision-making processes rely heavily on clinicians' experience, which can lead to variability in treatment across surgeries. With the advent of machine learning, we can now create models to predict the outcomes of interventions and guide perioperative decision-making. The first step in this process involves translating the clinical decision-making process into a framework understood by an algorithm. Probabilistic Boolean networks (PBNs) provide an information-rich approach to this problem. A PBN trends toward a steady state, and its decisions are easily understood via its Boolean predictor functions. We hypothesize that a PBN can be developed that corrects hemodynamic instability in patients by selecting clinical interventions to maintain blood pressure within a given range. METHODS Data on patients over the age of 65 undergoing surgery with general anesthesia from 2018 to 2020 were drawn from the UF Health PRECEDE data set with IRB approval (IRB201700747). Parameters examined included heart rate, blood pressure, and frequency of medications given 15 min after anesthetic induction and 15 min before awakening. The medication frequency data were truncated into a 66/33 split for the training and validation set used in the PBN. The model was coded using Python 3 and evaluated by comparing the frequency of medications chosen by the program to the values in the testing set via linear regression analysis. RESULTS The network developed successfully models a hemodynamically unstable patient and corrects the imbalance by administering medications. This is evidenced by the model achieving a stable, steady state matrix in all iterations. However, the model's ability to emulate clinical drug selection was variable. It was successful with its use of vasodilator selection but struggled with the appropriate selection of vasopressors. CONCLUSIONS The PBN has demonstrated the ability to choose appropriate interventions based on a patient's current vitals. Additional work must be done to have the network emulate the frequency at which drugs are selected from in clinical practice. In its current state, the model provides an understanding of how a PBN behaves in the context of correcting hemodynamic instability and can aid in developing more robust models in the future.
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Affiliation(s)
- Chamara Gunaratne
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA.
| | - Ron Ison
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Catherine C Price
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA; Department of Clinical and Health Psychology, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL 32610, USA
| | - Francois Modave
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Patrick Tighe
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
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Abstract
OBJECTIVE To summarize the current research progress of machine learning and venous thromboembolism. METHODS The literature on risk factors, diagnosis, prevention and prognosis of machine learning and venous thromboembolism in recent years was reviewed. RESULTS Machine learning is the future of biomedical research, personalized medicine, and computer-aided diagnosis, and will significantly promote the development of biomedical research and healthcare. However, many medical professionals are not familiar with it. In this review, we will introduce several commonly used machine learning algorithms in medicine, discuss the application of machine learning in venous thromboembolism, and reveal the challenges and opportunities of machine learning in medicine. CONCLUSION The incidence of venous thromboembolism is high, the diagnostic measures are diverse, and it is necessary to classify and treat machine learning, and machine learning as a research tool, it is more necessary to strengthen the special research of venous thromboembolism and machine learning.
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Affiliation(s)
- Shirong Zou
- West China Hospital of Medicine, West China Hospital Operation Room /West China School of Nursing, Sichuan University, Chengdu, China
| | - Zhoupeng Wu
- Department of vascular surgery, West China Hospital, Sichuan University, Chengdu, China
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12
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Fischer L. Applying Artificial Intelligence to Perioperative Nursing Practice. AORN J 2024; 119:P1-P4. [PMID: 38804724 DOI: 10.1002/aorn.14156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 05/29/2024]
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Fritz BA, King CR, Abdelhack M, Chen Y, Kronzer A, Abraham J, Tripathi S, Abdallah AB, Kannampallil T, Budelier TP, Helsten D, Montes de Oca A, Mehta D, Sontha P, Higo O, Kerby P, Gregory SH, Wildes TS, Avidan MS. Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.22.24307754. [PMID: 38826471 PMCID: PMC11142290 DOI: 10.1101/2024.05.22.24307754] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration ClinicalTrials.gov NCT05042804.
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Affiliation(s)
- Bradley A Fritz
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Christopher R King
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Mohamed Abdelhack
- Department of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, USA
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, USA
| | - Alex Kronzer
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, USA
| | - Sandhya Tripathi
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, USA
| | - Thaddeus P Budelier
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Daniel Helsten
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Arianna Montes de Oca
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Divya Mehta
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Pratyush Sontha
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Omokhaye Higo
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Paul Kerby
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Stephen H. Gregory
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
| | - Troy S. Wildes
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine, Saint Louis, USA
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King CR, Fritz BA, Gregory SH, Budelier TP, Ben Abdallah A, Kronzer A, Helsten DL, Torres B, McKinnon SL, Tripathi S, Abdelhack M, Goswami S, Montes de Oca A, Mehta D, Valdez MA, Karanikolas E, Higo O, Kerby P, Henrichs B, Wildes TS, Politi MC, Abraham J, Avidan MS, Kannampallil T. Effect of Telemedicine Support for Intraoperative Anaesthesia Care on Postoperative Outcomes: The TECTONICS Randomised Clinical Trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.21.24307593. [PMID: 38826207 PMCID: PMC11142280 DOI: 10.1101/2024.05.21.24307593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Novel applications of telemedicine can improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied. Methods This single centre randomised clinical trial ( clinicaltrials.gov NCT03923699 ) of unselected adult surgical patients was conducted between July 1, 2019 and January 31, 2023. Patients received usual care or decision support from a telemedicine service, the Anesthesiology Control Tower (ACT). The ACT provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews, machine-learning forecasting, and physiologic alerts. ORs were randomised 1:1. Co-primary outcomes of 30-day all-cause mortality, respiratory failure, acute kidney injury (AKI), and delirium were analysed as intention-to-treat. Results The trial completed planned enrolment with 71927 surgeries (35956 ACT; 35971 usual care). After multiple testing correction, there was no significant effect of the ACT vs. usual care on 30-day mortality [641/35956 (1.8%) vs 638/35971 (1.8%), risk difference 0.0% (95% CI -0.2% to 0.3%), p=0.96], respiratory failure [1089/34613 (3.1%) vs 1112/34619 (3.2%), risk difference -0.1% (95% CI -0.4% to 0.3%), p=0.96], AKI [2357/33897 (7%) vs 2391/33795 (7.1%), risk difference -0.1% (-0.6% to 0.4%), p=0.96], or delirium [1283/3928 (32.7%) vs 1279/3989 (32.1%), risk difference 0.6% (-2.0% to 3.2%), p=0.96]. There were no significant differences in secondary outcomes or in sensitivity analyses. Conclusions In this large RCT of a novel application of telemedicine-based remote monitoring and decision support using real-time alerts and case reviews, we found no significant differences in postoperative outcomes. Large-scale intraoperative telemedicine is feasible, and we suggest future avenues where it may be impactful.
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Danilatou V, Dimopoulos D, Kostoulas T, Douketis J. Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review. Thromb Haemost 2024. [PMID: 38574756 DOI: 10.1055/a-2299-4758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
BACKGROUND Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. METHODS Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. RESULTS Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. CONCLUSION ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.
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Affiliation(s)
- Vasiliki Danilatou
- School of Medicine, European University of Cyprus, Nicosia, Cyprus
- Healthcare Division, Sphynx Technology Solutions, Nicosia, Cyprus
| | - Dimitrios Dimopoulos
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - Theodoros Kostoulas
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - James Douketis
- Department of Medicine, McMaster University, Hamilton, Canada
- Department of Medicine, St. Joseph's Healthcare Hamilton, Ontario, Canada
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Sun R, Li S, Wei Y, Hu L, Xu Q, Zhan G, Yan X, He Y, Wang Y, Li X, Luo A, Zhou Z. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study. Int J Surg 2024; 110:2950-2962. [PMID: 38445452 DOI: 10.1097/js9.0000000000001237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. MATERIALS AND METHODS Adult patients undergoing noncardiac surgery were retrospectively included in the study (76 457 patients in the discovery cohort and 11 910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. RESULTS The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. CONCLUSIONS The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.
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Affiliation(s)
- Rao Sun
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Shiyong Li
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yuna Wei
- Yidu Cloud Technology Inc, Beijing, People's Republic of China
| | - Liu Hu
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei
| | - Qiaoqiao Xu
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Gaofeng Zhan
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Xu Yan
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yuqin He
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yao Wang
- Yidu Cloud Technology Inc, Beijing, People's Republic of China
| | - Xinhua Li
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Ailin Luo
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Zhiqiang Zhou
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
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Huang Y, Wang X, Cao Y, Li M, Li L, Chen H, Tang S, Lan X, Jiang F, Zhang J. Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis. Diagn Interv Imaging 2024; 105:191-205. [PMID: 38272773 DOI: 10.1016/j.diii.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Abstract
PURPOSE The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis. MATERIAL AND METHODS Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis. RESULTS A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively. CONCLUSION Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.
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Affiliation(s)
- Yao Huang
- School of Medicine, Chongqing University, Chongqing, 400030, China; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Ying Cao
- School of Medicine, Chongqing University, Chongqing, 400030, China; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Mengfei Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Lan Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Huifang Chen
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Sun Tang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Fujie Jiang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China.
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Staiger RD, Mehra T, Haile SR, Domenghino A, Kümmerli C, Abbassi F, Kozbur D, Dutkowski P, Puhan MA, Clavien PA. Experts vs. machine - comparison of machine learning to expert-informed prediction of outcome after major liver surgery. HPB (Oxford) 2024; 26:674-681. [PMID: 38423890 DOI: 10.1016/j.hpb.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 02/01/2024] [Accepted: 02/11/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Machine learning (ML) has been successfully implemented for classification tasks (e.g., cancer diagnosis). ML performance for more challenging predictions is largely unexplored. This study's objective was to compare machine learning vs. expert-informed predictions for surgical outcome in patients undergoing major liver surgery. METHODS Single tertiary center data on preoperative parameters and postoperative complications for elective hepatic surgery patients were included (2008-2021). Expert-informed prediction models were established on 14 parameters identified by two expert liver surgeons to impact on postoperative outcome. ML models used all available preoperative patient variables (n = 62). Model performance was compared for predicting 3-month postoperative overall morbidity. Temporal validation and additional analysis in major liver resection patients were conducted. RESULTS 889 patients included. Expert-informed models showed low average bias (2-5 CCI points) with high over/underprediction. ML models performed similarly: average prediction 5-10 points higher than observed CCI values with high variability (95% CI -30 to 50). No performance improvement for major liver surgery patients. CONCLUSION No clinical relevance in the application of ML for predicting postoperative overall morbidity was found. Despite being a novel hype, ML has the potential for application in clinical practice. However, at this stage it does not replace established approaches of prediction modelling.
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Affiliation(s)
- Roxane D Staiger
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland.
| | - Tarun Mehra
- Department of Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | - Sarah R Haile
- Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Anja Domenghino
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
| | | | - Fariba Abbassi
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Damian Kozbur
- Department of Economics, University of Zurich, Zurich, Switzerland
| | - Philipp Dutkowski
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Milo A Puhan
- Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Pierre-Alain Clavien
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
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19
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Chen Y, Yu Y, Yang D, Zhang W, Kouritas V, Chen X. Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease. J Thorac Dis 2024; 16:2482-2498. [PMID: 38738219 PMCID: PMC11087607 DOI: 10.21037/jtd-24-416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024]
Abstract
Background Frailty is a medical syndrome caused by multiple factors, characterized by decreased strength, endurance, and diminished physiological function, resulting in increased susceptibility to dependence and/or death. Patients with chronic obstructive pulmonary disease (COPD) tend to be more vulnerable to frailty due to their physical and psychological burdens. Therefore, the aim of this study was to develop a reliable and accurate vulnerability risk prediction model for frailty in patients with COPD in order to improve the identification and prediction of patient frailty. The specific objectives of this study were to determine the prevalence of frailty in patients with COPD and develop a prediction model and evaluate its predictive power. Methods Clinical information was analyzed using data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, and 34 indicators, including behavioral factors, health status, mental health parameters, and various sociodemographic variables, were examined in the study. The adaptive synthetic sampling technique was used for unbalanced data. Three methods, ridge regressor, extreme gradient boosting (XGBoost) classifier, and random forest (RF) regressor, were used to filter predictors. Seven machine learning (ML) techniques including logistic regression (LR), support vector machines (SVM), multilayer perceptron, light gradient-boosting machine, XGBoost, RF, and K-nearest neighbors were used to analyze and determine the optimal model. For customized risk assessment, an online predictive risk modeling website was created, along with Shapley additive explanation (SHAP) interpretations. Results Depression, smoking, gender, social activities, dyslipidemia, asthma, and residence type (urban vs. rural) were predictors for the development of frailty in patients with COPD. In the test set, the XGBoost model had an area under the curve of 0.942 (95% confidence interval: 0.925-0.959), an accuracy of 0.915, a sensitivity of 0.873, and a specificity of 0.911, indicating that it was the best model. Conclusions The ML predictive model developed in this study is a useful and easy-to-use instrument for assessing the vulnerability risk of patients with COPD and may aid clinical physicians in screening high-risk patients.
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Affiliation(s)
- Yong Chen
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yonglin Yu
- Department of Stomatology, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Dongmei Yang
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Wenbo Zhang
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Vasileios Kouritas
- Department of Thoracic Surgery, Norfolk and Norwich University Hospital, Norwich, UK
| | - Xiaoju Chen
- Department of Respiratory and Critical Care Medicine, Clinical Medical College & Affiliated Hospital of Chengdu University, Chengdu, China
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20
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Wang W, Dai J, Li J, Du X. Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach. Sci Rep 2024; 14:9717. [PMID: 38678066 PMCID: PMC11055901 DOI: 10.1038/s41598-024-60463-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/23/2024] [Indexed: 04/29/2024] Open
Abstract
In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively reduce intracranial pressure and hematoma volume, their significant risk of causing postoperative rehemorrhage necessitates early detection and management to improve patient prognosis. This study sought to develop and validate machine learning (ML) models leveraging clinical data and noncontrast CT radiomics to pinpoint patients at risk of postoperative rehemorrhage, equipping clinicians with an early detection tool for prompt intervention. The study conducted a retrospective analysis on 609 HICH patients, dividing them into training and external verification cohorts. These patients were categorized into groups with and without postoperative rehemorrhage. Radiomics features from noncontrast CT images were extracted, standardized, and employed to create several ML models. These models underwent internal validation using both radiomics and clinical data, with the best model's feature significance assessed via the Shapley additive explanations (SHAP) method, then externally validated. In the study of 609 patients, postoperative rehemorrhage rates were similar in the training (18.8%, 80/426) and external verification (17.5%, 32/183) cohorts. Six significant noncontrast CT radiomics features were identified, with the support vector machine (SVM) model outperforming others in both internal and external validations. SHAP analysis highlighted five critical predictors of postoperative rehemorrhage risk, encompassing three radiomics features from noncontrast CT and two clinical data indicators. This study highlights the effectiveness of an SVM model combining radiomics features from noncontrast CT and clinical parameters in predicting postoperative rehemorrhage among HICH patients. This approach enables timely and effective interventions, thereby improving patient outcomes.
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Affiliation(s)
- Weigong Wang
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China
| | - Jinlong Dai
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China
| | - Jibo Li
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China
| | - Xiangyang Du
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China.
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21
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Li X, Zhang C, Wang J, Ye C, Zhu J, Zhuge Q. Development and performance assessment of novel machine learning models for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage patients: external validation in MIMIC-IV. Front Neurol 2024; 15:1341252. [PMID: 38685951 PMCID: PMC11056519 DOI: 10.3389/fneur.2024.1341252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/28/2024] [Indexed: 05/02/2024] Open
Abstract
Background Postoperative pneumonia (POP) is one of the primary complications after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with postoperative mortality, extended hospital stay, and increased medical fee. Early identification of pneumonia and more aggressive treatment can improve patient outcomes. We aimed to develop a model to predict POP in aSAH patients using machine learning (ML) methods. Methods This internal cohort study included 706 patients with aSAH undergoing intracranial aneurysm embolization or aneurysm clipping. The cohort was randomly split into a train set (80%) and a testing set (20%). Perioperative information was collected from participants to establish 6 machine learning models for predicting POP after surgical treatment. The area under the receiver operating characteristic curve (AUC), precision-recall curve were used to assess the accuracy, discriminative power, and clinical validity of the predictions. The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Results In this study, 15.01% of patients in the training set and 12.06% in the testing set with POP after underwent surgery. Multivariate logistic regression analysis showed that mechanical ventilation time (MVT), Glasgow Coma Scale (GCS), Smoking history, albumin level, neutrophil-to-albumin Ratio (NAR), c-reactive protein (CRP)-to-albumin ratio (CAR) were independent predictors of POP. The logistic regression (LR) model presented significantly better predictive performance (AUC: 0.91) than other models and also performed well in the external validation set (AUC: 0.89). Conclusion A machine learning model for predicting POP in aSAH patients was successfully developed using a machine learning algorithm based on six perioperative variables, which could guide high-risk POP patients to take appropriate preventive measures.
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Affiliation(s)
- Xinbo Li
- Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Wenzhou Medical University, Wenzhou, China
| | - Chengwei Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Wenzhou Medical University, Wenzhou, China
| | - Jiale Wang
- Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Wenzhou Medical University, Wenzhou, China
| | - Chengxing Ye
- Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Wenzhou Medical University, Wenzhou, China
| | | | - Qichuan Zhuge
- Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Wenzhou Medical University, Wenzhou, China
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22
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Fritz BA, Pugazenthi S, Budelier TP, Tellor Pennington BR, King CR, Avidan MS, Abraham J. User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications. Anesth Analg 2024; 138:804-813. [PMID: 37339083 PMCID: PMC10730770 DOI: 10.1213/ane.0000000000006577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
BACKGROUND Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.
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Affiliation(s)
| | | | | | | | | | | | - Joanna Abraham
- From the Department of Anesthesiology
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri
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23
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Zhuang Y, Dyas A, Meguid RA, Henderson WG, Bronsert M, Madsen H, Colborn KL. Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data. Ann Surg 2024; 279:720-726. [PMID: 37753703 DOI: 10.1097/sla.0000000000006106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
OBJECTIVE To estimate preoperative risk of postoperative infections using structured electronic health record (EHR) data. BACKGROUND Surveillance and reporting of postoperative infections is primarily done through costly, labor-intensive manual chart reviews on a small sample of patients. Automated methods using statistical models applied to postoperative EHR data have shown promise to augment manual review as they can cover all operations in a timely manner. However, there are no specific models for risk-adjusting infectious complication rates using EHR data. METHODS Preoperative EHR data from 30,639 patients (2013-2019) were linked to the American College of Surgeons National Surgical Quality Improvement Program preoperative data and postoperative infection outcomes data from 5 hospitals in the University of Colorado Health System. EHR data included diagnoses, procedures, operative variables, patient characteristics, and medications. Lasso and the knockoff filter were used to perform controlled variable selection. Outcomes included surgical site infection, urinary tract infection, sepsis/septic shock, and pneumonia up to 30 days postoperatively. RESULTS Among >15,000 candidate predictors, 7 were chosen for the surgical site infection model and 6 for each of the urinary tract infection, sepsis, and pneumonia models. Important variables included preoperative presence of the specific outcome, wound classification, comorbidities, and American Society of Anesthesiologists physical status classification. The area under the receiver operating characteristic curve for each model ranged from 0.73 to 0.89. CONCLUSIONS Parsimonious preoperative models for predicting postoperative infection risk using EHR data were developed and showed comparable performance to existing American College of Surgeons National Surgical Quality Improvement Program risk models that use manual chart review. These models can be used to estimate risk-adjusted postoperative infection rates applied to large volumes of EHR data in a timely manner.
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Affiliation(s)
- Yaxu Zhuang
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Biostatistics and Informatics, Colorado School of Public Health
| | - Adam Dyas
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
| | - Robert A Meguid
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
- Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - William G Henderson
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
| | - Michael Bronsert
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Helen Madsen
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
| | - Kathryn L Colborn
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Biostatistics and Informatics, Colorado School of Public Health
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
- Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO
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Shahin MH, Barth A, Podichetty JT, Liu Q, Goyal N, Jin JY, Ouellet D. Artificial Intelligence: From Buzzword to Useful Tool in Clinical Pharmacology. Clin Pharmacol Ther 2024; 115:698-709. [PMID: 37881133 DOI: 10.1002/cpt.3083] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/06/2023] [Indexed: 10/27/2023]
Abstract
The advent of artificial intelligence (AI) in clinical pharmacology and drug development is akin to the dawning of a new era. Previously dismissed as merely technological hype, these approaches have emerged as promising tools in different domains, including health care, demonstrating their potential to empower clinical pharmacology decision making, revolutionize the drug development landscape, and advance patient care. Although challenges remain, the remarkable progress already made signals that the leap from hype to reality is well underway, and AI promises to offer clinical pharmacology new tools and possibilities for optimizing patient care is gradually coming to fruition. This review dives into the burgeoning world of AI and machine learning (ML), showcasing different applications of AI in clinical pharmacology and the impact of successful AI/ML implementation on drug development and/or regulatory decisions. This review also highlights recommendations for areas of opportunity in clinical pharmacology, including data analysis (e.g., handling large data sets, screening to identify important covariates, and optimizing patient population) and efficiencies (e.g., automation, translation, literature curation, and training). Realizing the benefits of AI in drug development and understanding its value will lead to the successful integration of AI tools in our clinical pharmacology and pharmacometrics armamentarium.
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Affiliation(s)
- Mohamed H Shahin
- Clinical Pharmacology and Bioanalytics, Pfizer Inc., Groton, Connecticut, USA
| | - Aline Barth
- Clinical Pharmacology and Bioanalytics, Pfizer Inc., Groton, Connecticut, USA
| | | | - Qi Liu
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Navin Goyal
- Clinical Pharmacology and Pharmacometrics, Janssen Research and Development, LLC., Spring House, Pennsylvania, USA
| | - Jin Y Jin
- Department of Clinical Pharmacology, Genentech, South San Francisco, California, USA
| | - Daniele Ouellet
- Clinical Pharmacology and Pharmacometrics, Janssen Research and Development, LLC., Spring House, Pennsylvania, USA
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Wang FT, Lin Y, Yuan XQ, Gao RY, Wu XC, Xu WW, Wu TQ, Xia K, Jiao YR, Yin L, Chen CQ. Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study. World J Gastrointest Surg 2024; 16:717-730. [PMID: 38577067 PMCID: PMC10989335 DOI: 10.4240/wjgs.v16.i3.717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/12/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Due to the complexity and numerous comorbidities associated with Crohn's disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care. AIM To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR. METHODS A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models. RESULTS Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model. CONCLUSION Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.
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Affiliation(s)
- Fang-Tao Wang
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Yin Lin
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Xiao-Qi Yuan
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Ren-Yuan Gao
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Xiao-Cai Wu
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Wei-Wei Xu
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Tian-Qi Wu
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Kai Xia
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Yi-Ran Jiao
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Lu Yin
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Chun-Qiu Chen
- Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
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Zhang S, Li H, Jing Q, Shen W, Luo W, Dai R. Anesthesia decision analysis using a cloud-based big data platform. Eur J Med Res 2024; 29:201. [PMID: 38528564 DOI: 10.1186/s40001-024-01764-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/01/2024] [Indexed: 03/27/2024] Open
Abstract
Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.
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Affiliation(s)
- Shuiting Zhang
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Hui Li
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Qiancheng Jing
- Department of Otolaryngology Head and Neck Surgery, Hengyang Medical School, The Affiliated Changsha Central Hospital, University of South China, Changsha, 410000, Hunan, China
| | - Weiyun Shen
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Wei Luo
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Ruping Dai
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China.
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Kowadlo G, Mittelberg Y, Ghomlaghi M, Stiglitz DK, Kishore K, Guha R, Nazareth J, Weinberg L. Development and validation of 'Patient Optimizer' (POP) algorithms for predicting surgical risk with machine learning. BMC Med Inform Decis Mak 2024; 24:70. [PMID: 38468330 DOI: 10.1186/s12911-024-02463-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate collaborative decision-making and operational planning. OBJECTIVE To develop effective pre-operative risk assessment algorithms (referred to as Patient Optimizer or POP) using Machine Learning (ML) that predict the development of post-operative complications and provide pilot data to inform the design of a larger prospective study. METHODS After institutional ethics approval, we developed a base model that encapsulates the standard manual approach of combining patient-risk and procedure-risk. In an automated process, additional variables were included and tested with 10-fold cross-validation, and the best performing features were selected. The models were evaluated and confidence intervals calculated using bootstrapping. Clinical expertise was used to restrict the cardinality of categorical variables (e.g. pathology results) by including the most clinically relevant values. The models were created with logistic regression (LR) and extreme gradient-boosted trees using XGBoost (Chen and Guestrin, 2016). We evaluated performance using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Data was obtained from a metropolitan university teaching hospital from January 2015 to July 2020. Data collection was restricted to adult patients undergoing elective surgery. RESULTS A total of 11,475 adult admissions were included. The performance of XGBoost and LR was very similar across endpoints and metrics. For predicting the risk of any post-operative complication, kidney failure and length-of-stay (LOS), POP with XGBoost achieved an AUROC (95%CI) of 0.755 (0.744, 0.767), 0.869 (0.846, 0.891) and 0.841 (0.833, 0.847) respectively and AUPRC of 0.651 (0.632, 0.669), 0.336 (0.282, 0.390) and 0.741 (0.729, 0.753) respectively. For 30-day readmission and in-patient mortality, POP with XGBoost achieved an AUROC (95%CI) of 0.610 (0.587, 0.635) and 0.866 (0.777, 0.943) respectively and AUPRC of 0.116 (0.104, 0.132) and 0.031 (0.015, 0.072) respectively. CONCLUSION The POP algorithms effectively predicted any post-operative complication, kidney failure and LOS in the sample population. A larger study is justified to improve the algorithm to better predict complications and length of hospital stay. A larger dataset may also improve the prediction of additional specific complications, readmission and mortality.
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Affiliation(s)
| | | | | | - Daniel K Stiglitz
- Atidia Health, Melbourne, Australia
- Department of Anaesthesiology and Perioperative Medicine, Alfred Health, Melbourne, Australia
| | - Kartik Kishore
- Data Analytics Research and Evaluation Centre, Austin Health, Melbourne, Australia
| | - Ranjan Guha
- Department of Anaesthesia, Austin Health, Heidelberg, Australia
| | - Justin Nazareth
- Department of Anaesthesia, Austin Health, Heidelberg, Australia
| | - Laurence Weinberg
- Department of Anaesthesia, Austin Health, Heidelberg, Australia
- Department of Critical Care, The University of Melbourne, Austin Health, Heidelberg, Australia
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Zeng F, Su X, Liang X, Liao M, Zhong H, Xu J, Gou W, Zhang X, Shen L, Zheng JS, Chen YM. Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults. BMC Med 2024; 22:104. [PMID: 38454425 PMCID: PMC10921631 DOI: 10.1186/s12916-024-03317-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 02/23/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD. METHODS The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018. We collected 272 metadata points from 1546 individuals. The metadata were input into four interpretable machine learning models to identify important gut microbiota associated with NAFLD. These models were subsequently applied to two validation cohorts [the internal validation cohort (n = 377), and the prospective validation cohort (n = 749)] to assess generalizability. We constructed an individual microbiome risk score (MRS) based on the identified gut microbiota and conducted animal faecal microbiome transplantation experiment using faecal samples from individuals with different levels of MRS to determine the relationship between MRS and NAFLD. Additionally, we conducted targeted metabolomic sequencing of faecal samples to analyse potential metabolites. RESULTS Among the four machine learning models used, the lightGBM algorithm achieved the best performance. A total of 12 taxa-related features of the microbiota were selected by the lightGBM algorithm and further used to calculate the MRS. Increased MRS was positively associated with the presence of NAFLD, with odds ratio (OR) of 1.86 (1.72, 2.02) per 1-unit increase in MRS. An elevated abundance of the faecal microbiota (f__veillonellaceae) was associated with increased NAFLD risk, whereas f__rikenellaceae, f__barnesiellaceae, and s__adolescentis were associated with a decreased presence of NAFLD. Higher levels of specific gut microbiota-derived metabolites of bile acids (taurocholic acid) might be positively associated with both a higher MRS and NAFLD risk. FMT in mice further confirmed a causal association between a higher MRS and the development of NAFLD. CONCLUSIONS We confirmed that an alteration in the composition of the core gut microbiota might be biologically relevant to NAFLD development. Our work demonstrated the role of the microbiota in the development of NAFLD.
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Affiliation(s)
- Fangfang Zeng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No.601 Huangpu Road West, Guangzhou, 510632, China.
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China.
| | - Xin Su
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No.601 Huangpu Road West, Guangzhou, 510632, China
| | - Xinxiu Liang
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China
| | - Minqi Liao
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Haili Zhong
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Jinjian Xu
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Wanglong Gou
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China
| | - Xiangzhou Zhang
- Big Data Decision Institute, Jinan University, No.601 Huangpu Road West, Guangzhou, 510632, China
| | - Luqi Shen
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China
| | - Ju-Sheng Zheng
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China.
| | - Yu-Ming Chen
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China.
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Xiao Y, Xiao L, Zhang Y, Xu X, Guan X, Guo Y, Shen Y, Lei X, Dou Y, Yu J. Prediction of tumor lysis syndrome in childhood acute lymphoblastic leukemia based on machine learning models: a retrospective study. Front Oncol 2024; 14:1337295. [PMID: 38515564 PMCID: PMC10955075 DOI: 10.3389/fonc.2024.1337295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 02/19/2024] [Indexed: 03/23/2024] Open
Abstract
Background Tumor lysis syndrome (TLS) often occurs early after induction chemotherapy for acute lymphoblastic leukemia (ALL) and can rapidly progress. This study aimed to construct a machine learning model to predict the risk of TLS using clinical indicators at the time of ALL diagnosis. Methods This observational cohort study was conducted at the National Clinical Research Center for Child Health and Disease. Data were collected from pediatric ALL patients diagnosed between December 2008 and December 2021. Four machine learning models were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) to select key clinical indicators for model construction. Results The study included 2,243 pediatric ALL patients, and the occurrence of TLS was 8.87%. A total of 33 indicators with missing values ≤30% were collected, and 12 risk factors were selected through LASSO regression analysis. The CatBoost model with the best performance after feature screening was selected to predict the TLS of ALL patients. The CatBoost model had an AUC of 0.832 and an accuracy of 0.758. The risk factors most associated with TLS were the absence of potassium, phosphorus, aspartate transaminase (AST), white blood cell count (WBC), and urea levels. Conclusion We developed the first TLS prediction model for pediatric ALL to assist clinicians in risk stratification at diagnosis and in developing personalized treatment protocols. This study is registered on the China Clinical Trials Registry platform (ChiCTR2200060616). Clinical trial registration https://www.chictr.org.cn/, identifier ChiCTR2200060616.
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Affiliation(s)
- Yao Xiao
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Li Xiao
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Yang Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Ximing Xu
- Big Data Engineering Center for Children’s Medical Care, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Xianmin Guan
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Yuxia Guo
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Yali Shen
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - XiaoYing Lei
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Ying Dou
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Jie Yu
- Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
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Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial Intelligence in Operating Room Management. J Med Syst 2024; 48:19. [PMID: 38353755 PMCID: PMC10867065 DOI: 10.1007/s10916-024-02038-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Michele Russo
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Tania Domenichetti
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Matteo Panizzi
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Simone Allai
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy.
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Yang T, Yang H, Liu Y, Liu X, Ding YJ, Li R, Mao AQ, Huang Y, Li XL, Zhang Y, Yu FX. Postoperative delirium prediction after cardiac surgery using machine learning models. Comput Biol Med 2024; 169:107818. [PMID: 38134752 DOI: 10.1016/j.compbiomed.2023.107818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 11/03/2023] [Accepted: 12/03/2023] [Indexed: 12/24/2023]
Abstract
OBJECTIVE Postoperative delirium (POD) is a common postoperative complication in elderly patients, especially those undergoing cardiac surgery, which seriously affects the short- and long-term prognosis of patients. Early identification of risk factors for the development of POD can help improve the perioperative management of surgical patients. In the present study, five machine learning models were developed to predict patients at high risk of delirium after cardiac surgery and their performance was compared. METHODS A total of 367 patients who underwent cardiac surgery were retrospectively included in this study. Using single-factor analysis, 21 risk factors for POD were selected for inclusion in machine learning. The dataset was divided using 10-fold cross-validation for model training and testing. Five machine learning models (random forest (RF), support vector machine (SVM), radial based kernel neural network (RBFNN), K-nearest neighbour (KNN), and Kernel ridge regression (KRR)) were compared using area under the receiver operating characteristic curve (AUC-ROC), accuracy (ACC), sensitivity (SN), specificity (SPE), and Matthews coefficient (MCC). RESULTS Among 367 patients, 105 patients developed POD, the incidence of delirium was 28.6 %. Among the five ML models, RF had the best performance in ACC (87.99 %), SN (69.27 %), SPE (95.38 %), MCC (70.00 %) and AUC (0.9202), which was far superior to the other four models. CONCLUSION Delirium is common in patients after cardiac surgery. This analysis confirms the importance of the computational ML models in predicting the occurrence of delirium after cardiac surgery, especially the outstanding performance of the RF model, which has practical clinical applications for early identification of patients at risk of developing POD.
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Affiliation(s)
- Tan Yang
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Hai Yang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yan Liu
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Xiao Liu
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yi-Jie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 324000 Quzhou, Zhejiang, China
| | - Run Li
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - An-Qiong Mao
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yue Huang
- Department of Anesthesiology, Zigong First People's Hospital, Zi Gong, 644099, Sichuan, China
| | - Xiao-Liang Li
- Department of Cardiothoracic Surgery, First Peoples Hospital of Neijiang, Nei Jiang, 641000, Sichuan, China
| | - Ying Zhang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
| | - Feng-Xu Yu
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
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Feinstein M, Katz D, Demaria S, Hofer IS. Remote Monitoring and Artificial Intelligence: Outlook for 2050. Anesth Analg 2024; 138:350-357. [PMID: 38215713 PMCID: PMC10794024 DOI: 10.1213/ane.0000000000006712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively. This will free up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions. This will also enable the continued integration of remote monitoring and control towers having profound effects on coverage and practice models. In the PACU and ICU, the technology will lead to the development of early warning systems that can identify patients who are at risk of complications, enabling early interventions and more proactive care. The integration of augmented reality will allow for better integration of diverse types of data and better decision-making. Postoperatively, the proliferation of wearable devices that can monitor patient vital signs and track their progress will allow patients to be discharged from the hospital sooner and receive care at home. This will require increased use of telemedicine, which will allow patients to consult with doctors remotely. All of these advances will require changes to legal and regulatory frameworks that will enable new workflows that are different from those familiar to today's providers.
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Affiliation(s)
- Max Feinstein
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Daniel Katz
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Samuel Demaria
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Ira S. Hofer
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
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Song Y, Zhang D, Wang Q, Liu Y, Chen K, Sun J, Shi L, Li B, Yang X, Mi W, Cao J. Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations. Transl Psychiatry 2024; 14:57. [PMID: 38267405 PMCID: PMC10808214 DOI: 10.1038/s41398-024-02762-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients.
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Affiliation(s)
- Yuxiang Song
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Di Zhang
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Qian Wang
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Yuqing Liu
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Kunsha Chen
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Jingjia Sun
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Likai Shi
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Baowei Li
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China
| | - Xiaodong Yang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Weidong Mi
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China.
- National Clinical Research Center for Geriatric Diseases, People's Liberation Army General Hospital, 100853, Beijing, China.
| | - Jiangbei Cao
- Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China.
- National Clinical Research Center for Geriatric Diseases, People's Liberation Army General Hospital, 100853, Beijing, China.
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Tong C, Du X, Chen Y, Zhang K, Shan M, Shen Z, Zhang H, Zheng J. Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery-a retrospective cohort study. Int J Surg 2024; 110:01279778-990000000-01006. [PMID: 38265429 PMCID: PMC11020051 DOI: 10.1097/js9.0000000000001112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/09/2024] [Indexed: 01/25/2024]
Abstract
BACKGROUND Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicting four major APOs after pediatric congenital heart surgery and their clinically meaningful model interpretations. METHODS Between August 2014 and December 2021, 23,000 consecutive pediatric patients receiving congenital heart surgery were enrolled. Based on the split date of 1 January 2019, we selected 13,927 participants for the training cohort, and 9,073 participants for the testing cohort. Four predefined major APOs including low cardiac output syndrome (LCOS), pneumonia, renal failure, and deep venous thrombosis (DVT) were investigated. 39 clinical and laboratory features were inputted in five ML models: light gradient boosting machine (LightGBM), logistic regression (LR), support vector machine, random forest, and CatBoost. The performance and interpretations of ML models were evaluated using the area under the receiver operating characteristic curve (AUC) and Shapley Additive Explanations (SHAP). RESULTS In the training cohort, CatBoost algorithms outperformed others with the mean AUCs of 0.908 for LCOS and 0.957 for renal failure, while LightGBM and LR achieved the best mean AUCs of 0.886 for pneumonia and 0.942 for DVT, respectively. In the testing cohort, the best-performing ML model for each major APOs with the following mean AUCs: LCOS (LightGBM), 0.893 (95% confidence interval (CI), 0.884-0.895); pneumonia (LR), 0.929 (95% CI, 0.926-0.931); renal failure (LightGBM), 0.963 (95% CI, 0.947-0.979), and DVT (LightGBM), 0.970 (95% CI, 0.953-0.982). The performance of ML models using only clinical variables was slightly lower than those using combined data, with the mean AUCs of 0.873 for LCOS, 0.894 for pneumonia, 0.953 for renal failure, and 0.933 for DVT. The SHAP showed that mechanical ventilation time was the most important contributor of four major APOs. CONCLUSIONS In pediatric congenital heart surgery, the established ML model can accurately predict the risk of four major APOs, providing reliable interpretations for high-risk contributor identification and informed clinical decisions making.
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Affiliation(s)
| | - Xinwei Du
- Pediatric Thoracic and Cardiovascular Surgery, Shanghai Children’s Medical Center, School of Medicine and National Children’s Medical Center, Shanghai Jiao Tong University
| | | | | | | | - Ziyun Shen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, People’s Republic of China
| | - Haibo Zhang
- Pediatric Thoracic and Cardiovascular Surgery, Shanghai Children’s Medical Center, School of Medicine and National Children’s Medical Center, Shanghai Jiao Tong University
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Tian Y, Li R, Wang G, Xu K, Li H, He L. Prediction of postoperative infectious complications in elderly patients with colorectal cancer: a study based on improved machine learning. BMC Med Inform Decis Mak 2024; 24:11. [PMID: 38184556 PMCID: PMC10770876 DOI: 10.1186/s12911-023-02411-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 12/18/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND Infectious complications after colorectal cancer (CRC) surgery increase perioperative mortality and are significantly associated with poor prognosis. We aimed to develop a model for predicting infectious complications after colorectal cancer surgery in elderly patients based on improved machine learning (ML) using inflammatory and nutritional indicators. METHODS The data of 512 elderly patients with colorectal cancer in the Third Affiliated Hospital of Anhui Medical University from March 2018 to April 2022 were retrospectively collected and randomly divided into a training set and validation set. The optimal cutoff values of NLR (3.80), PLR (238.50), PNI (48.48), LCR (0.52), and LMR (2.46) were determined by receiver operating characteristic (ROC) curve; Six conventional machine learning models were constructed using patient data in the training set: Linear Regression, Random Forest, Support Vector Machine (SVM), BP Neural Network (BP), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost) and an improved moderately greedy XGBoost (MGA-XGBoost) model. The performance of the seven models was evaluated by area under the receiver operator characteristic curve, accuracy (ACC), precision, recall, and F1-score of the validation set. RESULTS Five hundred twelve cases were included in this study; 125 cases (24%) had postoperative infectious complications. Postoperative infectious complications were notably associated with 10 items features: American Society of Anesthesiologists scores (ASA), operation time, diabetes, presence of stomy, tumor location, NLR, PLR, PNI, LCR, and LMR. MGA-XGBoost reached the highest AUC (0.862) on the validation set, which was the best model for predicting postoperative infectious complications in elderly patients with colorectal cancer. Among the importance of the internal characteristics of the model, LCR accounted for the highest proportion. CONCLUSIONS This study demonstrates for the first time that the MGA-XGBoost model with 10 risk factors might predict postoperative infectious complications in elderly CRC patients.
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Affiliation(s)
- Yuan Tian
- Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Anhui Medical University (The first people's Hospital of Hefei), Hefei, Anhui, China
| | - Rui Li
- Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Anhui Medical University (The first people's Hospital of Hefei), Hefei, Anhui, China
| | - Guanlong Wang
- Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Anhui Medical University (The first people's Hospital of Hefei), Hefei, Anhui, China
| | - Kai Xu
- Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Anhui Medical University (The first people's Hospital of Hefei), Hefei, Anhui, China
| | - Hongxia Li
- Department of Oncology, The Third Affiliated Hospital of Anhui Medical University (The first people's Hospital of Hefei), Hefei, Anhui, China
| | - Lei He
- Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Anhui Medical University (The first people's Hospital of Hefei), Hefei, Anhui, China.
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Vannucci M, Niyishaka P, Collins T, Rodríguez-Luna MR, Mascagni P, Hostettler A, Marescaux J, Perretta S. Machine learning models to predict success of endoscopic sleeve gastroplasty using total and excess weight loss percent achievement: a multicentre study. Surg Endosc 2024; 38:229-239. [PMID: 37973639 PMCID: PMC10776503 DOI: 10.1007/s00464-023-10520-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/09/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND The large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making. METHODS ML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python's scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots. RESULTS Multicenter external validation: ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC: TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data. Single-center internal validation: Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results. CONCLUSIONS Although preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.
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Affiliation(s)
- Maria Vannucci
- General Surgery Department, University of Torino, Turin, Italy.
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France.
- , Turin, Italy.
| | | | - Toby Collins
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Research Institute Against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - María Rita Rodríguez-Luna
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- ICube Laboratory, Photonics Instrumentation for Health, Strasbourg, France
| | - Pietro Mascagni
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Research Group CAMMA, University of Strasbourg, Strasbourg, France
| | - Alexandre Hostettler
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Research Institute Against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - Jacques Marescaux
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Research Institute Against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - Silvana Perretta
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Department of Digestive and Endocrine Surgery, University of Strasbourg, Strasbourg, France
- IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France
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Chen J, Yang L, Han J, Wang L, Wu T, Zhao D. Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients. Clin Appl Thromb Hemost 2024; 30:10760296241259784. [PMID: 38825589 PMCID: PMC11146004 DOI: 10.1177/10760296241259784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/08/2024] [Accepted: 05/20/2024] [Indexed: 06/04/2024] Open
Abstract
BACKGROUND Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions for at-risk individuals during this critical period. OBJECTIVE Our study aims to develop and validate an interpretable machine learning (ML) model that integrates peripheral immune cell data with conventional clinical markers. Our goal is to accurately predict 90-day readmission or mortality in patients AHF. METHODS In our study, we conducted a retrospective analysis on 1210 AHF patients, segregating them into training and external validation cohorts. Patients were categorized based on their 90-day outcomes post-discharge into groups of 'with readmission/mortality' and 'without readmission/mortality'. We developed various ML models using data from peripheral immune cells, traditional clinical indicators, or both, which were then internally validated. The feature importance of the most promising model was examined through the Shapley Additive Explanations (SHAP) method, culminating in external validation. RESULTS In our cohort of 1210 patients, 28.4% (344) faced readmission or mortality within 90 days post-discharge. Our study pinpointed 10 significant indicators-spanning peripheral immune cells and traditional clinical metrics-that predict these outcomes, with the support vector machine (SVM) model showing superior performance. SHAP analysis further distilled these predictors to five key determinants, including three clinical indicators and two immune cell types, essential for assessing 90-day readmission or mortality risks. CONCLUSION Our analysis identified the SVM model, which merges traditional clinical indicators and peripheral immune cells, as the most effective for predicting 90-day readmission or mortality in AHF patients. This innovative approach promises to refine risk assessment and enable more targeted interventions for at-risk individuals through continuous improvement.
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Affiliation(s)
- Junming Chen
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, China
| | - Liting Yang
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, China
| | - Jiangchuan Han
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, China
| | - Liang Wang
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, China
| | - Tingting Wu
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, China
| | - Dongsheng Zhao
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, China
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 PMCID: PMC11146190 DOI: 10.1097/aln.0000000000004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R. Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A. Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Chiasakul T, Lam BD, McNichol M, Robertson W, Rosovsky RP, Lake L, Vlachos IS, Adamski A, Reyes N, Abe K, Zwicker JI, Patell R. Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis. Eur J Haematol 2023; 111:951-962. [PMID: 37794526 PMCID: PMC10900245 DOI: 10.1111/ejh.14110] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.
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Affiliation(s)
- Thita Chiasakul
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hematology, Faculty of Medicine, Department of Medicine, Center of Excellence in Translational Hematology, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Barbara D Lam
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Megan McNichol
- Division of Knowledge Services, Department of Information Services (M.M.), Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - William Robertson
- National Blood Clot Alliance, Philadelphia, Pennsylvania, USA
- Department of Emergency Healthcare, College of Health Professions, Weber State University, Ogden, Utah, USA
| | - Rachel P Rosovsky
- Division of Hematology/Oncology, Department of Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leslie Lake
- National Blood Clot Alliance, Philadelphia, Pennsylvania, USA
| | - Ioannis S Vlachos
- Department of Pathology, Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Alys Adamski
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Nimia Reyes
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Karon Abe
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jeffrey I Zwicker
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Hematology Service, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Rushad Patell
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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Nagata C, Hata M, Miyazaki Y, Masuda H, Wada T, Kimura T, Fujii M, Sakurai Y, Matsubara Y, Yoshida K, Miyagawa S, Ikeda M, Ueno T. Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms. Sci Rep 2023; 13:21090. [PMID: 38036664 PMCID: PMC10689441 DOI: 10.1038/s41598-023-48418-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023] Open
Abstract
Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data.Trial registration: UMIN-CTR (ID; UMIN000049390).
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Affiliation(s)
- Chie Nagata
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Yuki Miyazaki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hirotada Masuda
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Tamiki Wada
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Tasuku Kimura
- SANKEN (The Institution of Scientific and Industrial Research), Osaka University, Ibaraki, Osaka, Japan
| | - Makoto Fujii
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yasushi Sakurai
- SANKEN (The Institution of Scientific and Industrial Research), Osaka University, Ibaraki, Osaka, Japan
| | - Yasuko Matsubara
- SANKEN (The Institution of Scientific and Industrial Research), Osaka University, Ibaraki, Osaka, Japan
| | - Kiyoshi Yoshida
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shigeru Miyagawa
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takayoshi Ueno
- Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
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Jiang Z, Bo L, Wang L, Xie Y, Cao J, Yao Y, Lu W, Deng X, Yang T, Bian J. Interpretable machine-learning model for real-time, clustered risk factor analysis of sepsis and septic death in critical care. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107772. [PMID: 37657148 DOI: 10.1016/j.cmpb.2023.107772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 07/25/2023] [Accepted: 08/19/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Interpretable and real-time prediction of sepsis and risk factor analysis could enable timely treatment by clinicians and improve patient outcomes. To develop an interpretable machine-learning model for the prediction and risk factor analysis of sepsis and septic death. METHODS This is a retrospective observational cohort study based on the Medical Information Mart for Intensive Care (MIMIC-IV) dataset; 69,619 patients from the database were screened. The two outcomes include patients diagnosed with sepsis and the death of septic patients. Clinical variables from ICU admission to outcomes were analyzed: demographic data, vital signs, Glasgow Coma Scale scores, laboratory test results, and results for arterial blood gasses (ABGs). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Model interpretations were based on the Shapley additive explanations (SHAP), and the clustered analysis was based on the combination of K-means and dimensionality reduction algorithms of t-SNE and PCA. RESULTS For the analysis of sepsis and septic death, 47,185 and 2480 patients were enrolled, respectively. The XGBoost model achieved a predictive value of area under the curve (AUC): 0.745 [0.731-0.759] for sepsis prediction and 0.8 [0.77, 0.828] for septic death prediction. The real-time prediction model was trained to predict by day and visualize the individual or combined risk factor effects on the outcomes based on SHAP values. Clustered analysis separated the two phenotypes with distinct risk factors among patients with septic death. CONCLUSION The proposed real-time, clustered prediction model for sepsis and septic death exhibited superior performance in predicting the outcomes and visualizing the risk factors in a real-time and interpretable manner to distinguish and mitigate patient risks, thus promising immense potential in effective clinical decision making and comprehensive understanding of complex diseases such as sepsis.
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Affiliation(s)
- Zhengyu Jiang
- Faculty of Anesthesiology, Changhai Hospital, Naval Medical University of PLA, Shanghai 200433, China; Department of Anesthesiology, Naval Medical Center, Naval Medical University of PLA, Shanghai 200052, China
| | - Lulong Bo
- Faculty of Anesthesiology, Changhai Hospital, Naval Medical University of PLA, Shanghai 200433, China
| | - Lei Wang
- Heal Sci Technology Co., Ltd, 1606, Tower 5, 2 Rong Hua South Road, BDA, Beijing 100176, China
| | - Yan Xie
- Heal Sci Technology Co., Ltd, 1606, Tower 5, 2 Rong Hua South Road, BDA, Beijing 100176, China
| | - Jianping Cao
- Department of Anesthesiology, Naval Medical Center, Naval Medical University of PLA, Shanghai 200052, China
| | - Ying Yao
- Department of Anesthesiology, Naval Medical Center, Naval Medical University of PLA, Shanghai 200052, China
| | - Wenbin Lu
- Faculty of Anesthesiology, Changhai Hospital, Naval Medical University of PLA, Shanghai 200433, China
| | - Xiaoming Deng
- Faculty of Anesthesiology, Changhai Hospital, Naval Medical University of PLA, Shanghai 200433, China
| | - Tao Yang
- Faculty of Anesthesiology, Changhai Hospital, Naval Medical University of PLA, Shanghai 200433, China
| | - Jinjun Bian
- Faculty of Anesthesiology, Changhai Hospital, Naval Medical University of PLA, Shanghai 200433, China.
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Matsumoto K, Nohara Y, Sakaguchi M, Takayama Y, Fukushige S, Soejima H, Nakashima N, Kamouchi M. Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study. JMIR Perioper Med 2023; 6:e50895. [PMID: 37883164 PMCID: PMC10636625 DOI: 10.2196/50895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/24/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. OBJECTIVE The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. METHODS The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. RESULTS A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. CONCLUSIONS The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.
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Affiliation(s)
| | - Yasunobu Nohara
- Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Mikako Sakaguchi
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Yohei Takayama
- Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Syota Fukushige
- Department of Inspection, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Hidehisa Soejima
- Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Naoki Nakashima
- Medical Information Center, Kyushu University Hospital, Fukuoka, Japan
| | - Masahiro Kamouchi
- Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Kim JH, Chung KM, Lee JJ, Choi HJ, Kwon YS. Predictive Modeling and Integrated Risk Assessment of Postoperative Mortality and Pneumonia in Traumatic Brain Injury Patients through Clustering and Machine Learning: Retrospective Study. Biomedicines 2023; 11:2880. [PMID: 38001880 PMCID: PMC10669264 DOI: 10.3390/biomedicines11112880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/15/2023] [Accepted: 10/19/2023] [Indexed: 11/26/2023] Open
Abstract
This study harnessed machine learning to forecast postoperative mortality (POM) and postoperative pneumonia (PPN) among surgical traumatic brain injury (TBI) patients. Our analysis centered on the following key variables: Glasgow Coma Scale (GCS), midline brain shift (MSB), and time from injury to emergency room arrival (TIE). Additionally, we introduced innovative clustered variables to enhance predictive accuracy and risk assessment. Exploring data from 617 patients spanning 2012 to 2022, we observed that 22.9% encountered postoperative mortality, while 30.0% faced postoperative pneumonia (PPN). Sensitivity for POM and PPN prediction, before incorporating clustering, was in the ranges of 0.43-0.82 (POM) and 0.54-0.76 (PPN). Following clustering, sensitivity values were 0.47-0.76 (POM) and 0.61-0.77 (PPN). Accuracy was in the ranges of 0.67-0.76 (POM) and 0.70-0.81 (PPN) prior to clustering and 0.42-0.73 (POM) and 0.55-0.73 (PPN) after clustering. Clusters characterized by low GCS, small MSB, and short TIE exhibited a 3.2-fold higher POM risk compared to clusters with high GCS, small MSB, and short TIE. In summary, leveraging clustered variables offers a novel avenue for predicting POM and PPN in TBI patients. Assessing the amalgamated impact of GCS, MSB, and TIE characteristics provides valuable insights for clinical decision making.
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Affiliation(s)
- Jong-Ho Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (J.-J.L.)
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea
| | - Kyung-Min Chung
- Department of Neurosurgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea;
| | - Jae-Jun Lee
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (J.-J.L.)
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea
| | - Hyuk-Jai Choi
- Department of Neurosurgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea;
| | - Young-Suk Kwon
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (J.-J.L.)
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea
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Kobayashi Y, Peng YC, Yu E, Bush B, Jung YH, Murphy Z, Goeddel L, Whitman G, Venkataraman A, Brown CH. Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches. Front Med (Lausanne) 2023; 10:1165912. [PMID: 37790131 PMCID: PMC10543087 DOI: 10.3389/fmed.2023.1165912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/23/2023] [Indexed: 10/05/2023] Open
Abstract
Background Although conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance. Methods Adults who underwent cardiac surgery with cardiopulmonary bypass were eligible. The primary outcome was maximum lactate concentration within 24 h postoperatively. We considered three classes of predictive models, using the performance metric of mean absolute error across testing folds: (1) static models using baseline preoperative variables, (2) augmentation of the static models with intraoperative statistics, and (3) a dynamic approach that integrates preoperative variables with intraoperative time series data. Results 2,187 patients were included. For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). The inclusion of intraoperative summary statistics (including intraoperative lactate concentration) improved model performance, with the prediction error ranging from a median of 2.09 mmol/L (IQR 2.04, 2.14) to 2.12 mmol/L (IQR 2.06, 2.16). For two modelling approaches (recurrent neural network, transformer) that can utilize intraoperative time-series data, the lowest prediction error was obtained with a range of median 1.96 mmol/L (IQR 1.87, 2.05) to 1.97 mmol/L (IQR 1.92, 2.05). Intraoperative lactate concentration was the most important predictive feature based on Shapley additive values. Anemia and weight were also important predictors, but there was heterogeneity in the importance of other features. Conclusion Postoperative lactate concentrations can be predicted using baseline and intraoperative data with moderate accuracy. These results reflect the value of intraoperative data in the prediction of clinically relevant outcomes to guide perioperative management.
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Affiliation(s)
| | - Yu-Chung Peng
- Johns Hopkins University, Baltimore, MD, United States
| | - Evan Yu
- Johns Hopkins University, Baltimore, MD, United States
| | - Brian Bush
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Youn-Hoa Jung
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Zachary Murphy
- Department of Anesthesiology & Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Lee Goeddel
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Glenn Whitman
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States
| | - Charles H. Brown
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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King CR, Gregory S, Fritz BA, Budelier TP, Ben Abdallah A, Kronzer A, Helsten DL, Torres B, McKinnon S, Goswami S, Mehta D, Higo O, Kerby P, Henrichs B, Wildes TS, Politi MC, Abraham J, Avidan MS, Kannampallil T. An Intraoperative Telemedicine Program to Improve Perioperative Quality Measures: The ACTFAST-3 Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2332517. [PMID: 37738052 PMCID: PMC10517374 DOI: 10.1001/jamanetworkopen.2023.32517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/30/2023] [Indexed: 09/23/2023] Open
Abstract
Importance Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed. Objective To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures. Design, Setting, and Participants This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US. A total of 26 254 adult surgical patients were randomized to receive either usual intraoperative care (control group; n = 12 980) or usual care augmented by telemedicine decision support (intervention group; n = 13 274). Data were initially analyzed from April 22 to May 19, 2021, with updates in November 2022 and February 2023. Intervention Patients received either usual care (medical direction from the anesthesia care team) or intraoperative anesthesia care monitored and augmented by decision support from the Anesthesiology Control Tower (ACT), a real-time, live telemedicine intervention. The ACT incorporated remote monitoring of operating rooms by a team of anesthesia clinicians with customized analysis software. The ACT reviewed alerts and electronic health record data to inform recommendations to operating room clinicians. Main Outcomes and Measures The primary outcomes were avoidance of postoperative hypothermia (defined as the proportion of patients with a final recorded intraoperative core temperature >36 °C) and hyperglycemia (defined as the proportion of patients with diabetes who had a blood glucose level ≤180 mg/dL on arrival to the postanesthesia recovery area). Secondary outcomes included intraoperative hypotension, temperature monitoring, timely antibiotic redosing, intraoperative glucose evaluation and management, neuromuscular blockade documentation, ventilator management, and volatile anesthetic overuse. Results Among 26 254 participants, 13 393 (51.0%) were female and 20 169 (76.8%) were White, with a median (IQR) age of 60 (47-69) years. There was no treatment effect on avoidance of hyperglycemia (7445 of 8676 patients [85.8%] in the intervention group vs 7559 of 8815 [85.8%] in the control group; rate ratio [RR], 1.00; 95% CI, 0.99-1.01) or hypothermia (7602 of 11 447 patients [66.4%] in the intervention group vs 7783 of 11 672 [66.7.%] in the control group; RR, 1.00; 95% CI, 0.97-1.02). Intraoperative glucose measurement was more common among patients with diabetes in the intervention group (RR, 1.07; 95% CI, 1.01-1.15), but other secondary outcomes were not significantly different. Conclusions and Relevance In this randomized clinical trial, anesthesia care quality measures did not differ between groups, with high confidence in the findings. These results suggest that the intervention did not affect the targeted care practices. Further streamlining of clinical decision support and workflows may help the intraoperative telemedicine program achieve improvement in targeted clinical measures. Trial Registration ClinicalTrials.gov Identifier: NCT02830126.
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Affiliation(s)
- Christopher R. King
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Stephen Gregory
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Bradley A. Fritz
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Thaddeus P. Budelier
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Alex Kronzer
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Daniel L. Helsten
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Brian Torres
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Sherry McKinnon
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Shreya Goswami
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Divya Mehta
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Omokhaye Higo
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Paul Kerby
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Bernadette Henrichs
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Troy S. Wildes
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha
| | - Mary C. Politi
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
- Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Michael S. Avidan
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
- Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, Missouri
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Bellini V, Russo M, Lanza R, Domenichetti T, Compagnone C, Maggiore SM, Cammarota G, Pelosi P, Vetrugno L, Bignami EG. Artificial intelligence and "the Art of Kintsugi" in Anesthesiology: ten influential papers for clinical users. Minerva Anestesiol 2023; 89:804-811. [PMID: 37194240 DOI: 10.23736/s0375-9393.23.17279-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.
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Affiliation(s)
- Valentina Bellini
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Michele Russo
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Roberto Lanza
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Tania Domenichetti
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Christian Compagnone
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Salvatore M Maggiore
- Department of Anesthesiology, Critical Care Medicine and Emergency, SS. Annunziata Hospital, Chieti, Italy
- University Department of Innovative Technologies in Medicine and Dentistry, Gabriele D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Gianmaria Cammarota
- Department of Anesthesia and Intensive Care Medicine, University of Perugia, Perugia, Italy
| | - Paolo Pelosi
- Department of Anesthesia and Intensive Care, IRCCS San Martino Polyclinic Hospital, University of Genoa, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Luigi Vetrugno
- Department of Medical, Oral and Biotechnological Sciences, University of Chieti-Pescara, Chieti, Italy
| | - Elena G Bignami
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy -
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Kim JH, Cheon BR, Kim MG, Hwang SM, Lim SY, Lee JJ, Kwon YS. Harnessing Machine Learning for Prediction of Postoperative Pulmonary Complications: Retrospective Cohort Design. J Clin Med 2023; 12:5681. [PMID: 37685748 PMCID: PMC10488713 DOI: 10.3390/jcm12175681] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
Postoperative pulmonary complications (PPCs) are significant causes of postoperative morbidity and mortality. This study presents the utilization of machine learning for predicting PPCs and aims to identify the important features of the prediction models. This study used a retrospective cohort design and collected data from two hospitals. The dataset included perioperative variables such as patient characteristics, preexisting diseases, and intraoperative factors. Various algorithms, including logistic regression, random forest, light-gradient boosting machines, extreme-gradient boosting machines, and multilayer perceptrons, have been employed for model development and evaluation. This study enrolled 111,212 adult patients, with an overall incidence rate of 8.6% for developing PPCs. The area under the receiver-operating characteristic curve (AUROC) of the models was 0.699-0.767, and the f1 score was 0.446-0.526. In the prediction models, except for multilayer perceptron, the 10 most important features were obtained. In feature-reduced models, including 10 important features, the AUROC was 0.627-0.749, and the f1 score was 0.365-0.485. The number of packed red cells, urine, and rocuronium doses were similar in the three models. In conclusion, machine learning provides valuable insights into PPC prediction, significant features for prediction, and the feasibility of models that reduce the number of features.
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Affiliation(s)
- Jong-Ho Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (B.-R.C.); (M.-G.K.); (S.-M.H.); (S.-Y.L.); (J.-J.L.)
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea
| | - Bo-Reum Cheon
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (B.-R.C.); (M.-G.K.); (S.-M.H.); (S.-Y.L.); (J.-J.L.)
| | - Min-Guan Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (B.-R.C.); (M.-G.K.); (S.-M.H.); (S.-Y.L.); (J.-J.L.)
| | - Sung-Mi Hwang
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (B.-R.C.); (M.-G.K.); (S.-M.H.); (S.-Y.L.); (J.-J.L.)
| | - So-Young Lim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (B.-R.C.); (M.-G.K.); (S.-M.H.); (S.-Y.L.); (J.-J.L.)
| | - Jae-Jun Lee
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (B.-R.C.); (M.-G.K.); (S.-M.H.); (S.-Y.L.); (J.-J.L.)
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea
| | - Young-Suk Kwon
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea; (J.-H.K.); (B.-R.C.); (M.-G.K.); (S.-M.H.); (S.-Y.L.); (J.-J.L.)
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea
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48
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Abraham J, Rosen M, Greilich PE. Improving Perioperative Handoffs: Moving Beyond Standardized Checklists and Protocols. Jt Comm J Qual Patient Saf 2023; 49:341-344. [PMID: 37353400 PMCID: PMC10754391 DOI: 10.1016/j.jcjq.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2023]
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Yi F, Yang H, Chen D, Qin Y, Han H, Cui J, Bai W, Ma Y, Zhang R, Yu H. XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease. BMC Med Inform Decis Mak 2023; 23:137. [PMID: 37491248 PMCID: PMC10369804 DOI: 10.1186/s12911-023-02238-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 07/13/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD. METHODS We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset. RESULTS Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset. CONCLUSIONS The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.
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Affiliation(s)
- Fuliang Yi
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Hui Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Hongjuan Han
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Wenlin Bai
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Yifei Ma
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Rong Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
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Daza JF, Bartoszko J, Van Klei W, Ladha KS, McCluskey SA, Wijeysundera DN. Improved Re-estimation of Perioperative Cardiac Risk Using the Surgical Apgar Score: A Retrospective Cohort Study. Ann Surg 2023; 278:65-71. [PMID: 35801710 DOI: 10.1097/sla.0000000000005509] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To assess whether the Surgical Apgar Score (SAS) improves re-estimation of perioperative cardiac risk. BACKGROUND The SAS is a novel risk index that integrates three relevant and easily measurable intraoperative parameters (blood loss, heart rate, mean arterial pressure) to predict outcomes. The incremental prognostic value of the SAS when used in combination with standard preoperative risk indices is unclear. METHODS We conducted a retrospective cohort study of adults (18 years and older) who underwent elective noncardiac surgery at a quaternary care hospital in Canada (2009-2014). The primary outcome was postoperative acute myocardial injury. The SAS (range 0-10) was calculated based on intraoperative estimated blood loss, lowest mean arterial pressure, and lowest heart rate documented in electronic medical records. Incremental prognostic value of the SAS when combined with the Revised Cardiac Risk Index was assessed based on discrimination (c-statistic), reclassification (integrated discrimination improvement, net reclassification index), and clinical utility (decision curve analysis). RESULTS The cohort included 16,835 patients, of whom 607 (3.6%) patients had acute postoperative myocardial injury. Addition of the SAS to the Revised Cardiac Risk Index improved risk estimation based on the integrated discrimination improvement [2.0%; 95% confidence interval (CI): 1.5%-2.4%], continuous net reclassification index (54%; 95% CI: 46%-62%), and c-index, which increased from 0.68 (95% CI: 0.65-0.70) to 0.75 (95% CI: 0.73-0.77). On decision curve analysis, addition of the SAS to the Revised Cardiac Risk Index resulted in a higher net benefit at all decision thresholds. CONCLUSIONS When combined with a validated preoperative risk index, the SAS improved the accuracy of cardiac risk assessment for noncardiac surgery. Further research is needed to delineate how intraoperative data can better guide postoperative decision-making.
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Affiliation(s)
- Julian F Daza
- Division of General Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Justyna Bartoszko
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
- Department of Anesthesia and Pain Management, Toronto General Hospital-University Health Network, Toronto, ON, Canada
| | - Wilton Van Klei
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
- Department of Anesthesia and Pain Management, Toronto General Hospital-University Health Network, Toronto, ON, Canada
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Karim S Ladha
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
- Department of Anesthesia and Pain Management, Toronto General Hospital-University Health Network, Toronto, ON, Canada
- Department of Anesthesia, St. Michael's Hospital, Toronto, ON, Canada
| | - Stuart A McCluskey
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
- Department of Anesthesia and Pain Management, Toronto General Hospital-University Health Network, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
- Department of Anesthesia and Pain Management, Toronto General Hospital-University Health Network, Toronto, ON, Canada
- Department of Anesthesia, St. Michael's Hospital, Toronto, ON, Canada
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