1
|
Hu J, Chen C, Li X, Zang X, Ke J, Zhou S, Mai H, Gong C. Risk of Systemic Inflammatory Response Syndrome Following Preoperative Glucocorticoids Administration in Patients After Percutaneous Nephrolithotomy: A Retrospective Cohort Study. Drug Saf 2024; 47:465-474. [PMID: 38441749 DOI: 10.1007/s40264-024-01402-y] [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] [Accepted: 01/31/2024] [Indexed: 04/17/2024]
Abstract
INTRODUCTION Systemic inflammatory response syndrome (SIRS) is one of the most serious complications in patients undergoing percutaneous nephrolithotomy (PCNL). Although glucocorticoids are increasingly used during PCNL, few studies have been concerned about the association between glucocorticoids and postoperative SIRS. The study aims to explore whether preoperative use of glucocorticoids is associated with SIRS after PCNL. METHODS A total of 1259 patients who underwent PCNL between January 2015 and April 2021 were enrolled in the retrospective cohort study. Risk factors for post-PCNL SIRS were identified by univariate and multivariate regression analysis. To further explore the association between preoperative administration of glucocorticoids and SIRS, 113 pairs of patients were matched for the confounding factors using propensity score matching (PSM) analysis. The odds ratios (OR) and 95 % confidence intervals (CI) for the above variables were analyzed. RESULTS The incidence of SIRS after PCNL was 9.6 % (121/1259) and the patients who suffered from postoperative SIRS had longer hospital stays and higher hospital costs (all p < 0.05). Multivariate logistic regression analysis indicated that female, preoperative leukocyte count, insertion of central vein catheter, serum albumin, preoperative high-sensitive C-reactive protein/albumin ratio, preoperative transfusion, preoperative administration of glucocorticoids were independent risk factors for SIRS (all p < 0.05). After minimization, the effects of confounding factors by PSM, preoperative administration of glucocorticoids was significantly correlated with SIRS in patients after PCNL (OR=2.44, 95 %CI: 1.31-4.55, p = 0.005). CONCLUSION Preoperative administration of glucocorticoids is an independent risk factor for SIRS in patients undergoing PCNL.
Collapse
Affiliation(s)
- Jingping Hu
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Chaojin Chen
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Xiaoyue Li
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Xiangyang Zang
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China
| | - Jie Ke
- Guangzhou AID Cloud Technology Co., Ltd, Guangzhou, 510000, China
| | - Shaoli Zhou
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
| | - Haiyan Mai
- Department of Pharmacy, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, 510630, Guangdong, China.
| | - Chulian Gong
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Chongo G, Soldera J. Use of machine learning models for the prognostication of liver transplantation: A systematic review. World J Transplant 2024; 14:88891. [PMID: 38576762 PMCID: PMC10989468 DOI: 10.5500/wjt.v14.i1.88891] [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: 10/13/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 03/15/2024] Open
Abstract
BACKGROUND Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models. AIM To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws. RESULTS Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI. CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.
Collapse
Affiliation(s)
- Gidion Chongo
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
| | - Jonathan Soldera
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
| |
Collapse
|
4
|
Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [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: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
Collapse
Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
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
Collapse
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
| |
Collapse
|
7
|
Chen C, Chen B, Yang J, Li X, Peng X, Feng Y, Guo R, Zou F, Zhou S, Hei Z. Development and validation of a practical machine learning model to predict sepsis after liver transplantation. Ann Med 2023; 55:624-633. [PMID: 36790357 PMCID: PMC9937004 DOI: 10.1080/07853890.2023.2179104] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Postoperative sepsis is one of the main causes of mortality after liver transplantation (LT). Our study aimed to develop and validate a predictive model for postoperative sepsis within 7 d in LT recipients using machine learning (ML) technology. METHODS Data of 786 patients received LT from January 2015 to January 2020 was retrospectively extracted from the big data platform of Third Affiliated Hospital of Sun Yat-sen University. Seven ML models were developed to predict postoperative sepsis. The area under the receiver-operating curve (AUC), sensitivity, specificity, accuracy, and f1-score were evaluated as the model performances. The model with the best performance was validated in an independent dataset involving 118 adult LT cases from February 2020 to April 2021. The postoperative sepsis-associated outcomes were also explored in the study. RESULTS After excluding 109 patients according to the exclusion criteria, 677 patients underwent LT were finally included in the analysis. Among them, 216 (31.9%) were diagnosed with sepsis after LT, which were related to more perioperative complications, increased postoperative hospital stay and mortality after LT (all p < .05). Our results revealed that a larger volume of red blood cell infusion, ascitic removal, blood loss and gastric drainage, less volume of crystalloid infusion and urine, longer anesthesia time, higher level of preoperative TBIL were the top 8 important variables contributing to the prediction of post-LT sepsis. The Random Forest Classifier (RF) model showed the best overall performance to predict sepsis after LT among the seven ML models developed in the study, with an AUC of 0.731, an accuracy of 71.6%, the sensitivity of 62.1%, and specificity of 76.1% in the internal validation set, and a comparable AUC of 0.755 in the external validation set. CONCLUSIONS Our study enrolled eight pre- and intra-operative variables to develop an RF-based predictive model of post-LT sepsis to assist clinical decision-making procedure.
Collapse
Affiliation(s)
- Chaojin Chen
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Bingcheng Chen
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jing Yang
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Xiaoyue Li
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Xiaorong Peng
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yawei Feng
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Rongchang Guo
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, People's Republic of China
| | - Fengyuan Zou
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, People's Republic of China
| | - Shaoli Zhou
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Ziqing Hei
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| |
Collapse
|
8
|
Wang R, Cai L, Liu Y, Zhang J, Ou X, Xu J. Machine learning algorithms for prediction of ventilator associated pneumonia in traumatic brain injury patients from the MIMIC-III database. Heart Lung 2023; 62:225-232. [PMID: 37595390 DOI: 10.1016/j.hrtlng.2023.08.002] [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: 04/10/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND Ventilator associated pneumonia (VAP) is a common complication and associated with poor prognosis of traumatic brain injury (TBI) patients. OBJECTIVES This study was conducted to explore the predictive performance of different machine-learning algorithms for VAP in TBI patients. METHODS TBI patients receiving mechanical ventilation more than 48 hours from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for the study. The VAP was confirmed based on the ICD-9 code. Included patients were separated to the training cohort and the validation cohort with a ratio of 7:3. Predictive models based on different machine learning algorithms were developed using 5-fold cross validation in the training cohort and then verified in the validation cohort by evaluating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy and F score. RESULTS 786 TBI patients from the MIMIC-III were finally included with the VAP incidence of 44.0%. The random forest performed the best on predicting VAP in the training cohort with a AUC of 1.000. The XGBoost and AdaBoost were ranked the second and the third with a AUC of 0.915 and 0.789 in the training cohort. While the AdaBoost performed the best on predicting VAP in the validation cohort with a AUC of 0.706. The XGBoost and random forest were ranked the second and the third with the AUC of 0.685 and 0.683 in the validation cohort. Generally, the random forest and XGBoost were likely to be over-fitting while the AdaBoost was relatively stable in predicting the VAP. CONCLUSIONS The AdaBoost performed well and stably on predicting the VAP in TBI patients. Developing programs using AdaBoost in portable electronic devices may effectively assist physicians in assessing the risk of VAP in TBI.
Collapse
Affiliation(s)
- Ruoran Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan province, China
| | - Linrui Cai
- Institute of Drug Clinical Trial·GCP, West China Second University Hospital, Sichuan University, Chengdu, China; Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China
| | - Yan Liu
- Laboratory Animal Center of Sichuan University, Chengdu, China
| | - Jing Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan province, China
| | - Xiaofeng Ou
- Department of Critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan province, China.
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan province, China.
| |
Collapse
|
9
|
Guo J, He Q, Peng C, Dai R, Li W, Su Z, Li Y. Machine learning algorithms to predict risk of postoperative pneumonia in elderly with hip fracture. J Orthop Surg Res 2023; 18:571. [PMID: 37543618 PMCID: PMC10403839 DOI: 10.1186/s13018-023-04049-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Hip fracture (HF) is one of the most common fractures in the elderly and is significantly associated with high mortality and unfavorable prognosis. Postoperative pneumonia (POP), the most common postoperative complication of HF, can seriously affect patient prognosis and increase the burden on the healthcare system. The aim of this study was to develop machine learning models for identifying elderly patients at high risk of pneumonia after hip fracture surgery. METHODS From May 2016 to November 2022, patients admitted to a single central hospital for HF served as the study population. We extracted data that could be collected within 24 h of patient admission. The dataset was divided into training and validation sets according to 70:30. Based on the screened risk factors, prediction models were developed using seven machine learning algorithms, namely CART, GBM, KNN, LR, NNet, RF, and XGBoost, and their performance was evaluated. RESULTS Eight hundred five patients were finally included in the analysis and 75 (9.3%) patients suffered from POP. Age, CI, COPD, WBC, HB, GLU, STB, GLOB, Ka+ which are used as features to build machine learning models. By evaluating the model's AUC value, accuracy, sensitivity, specificity, Kappa value, MCC value, Brier score value, calibration curve, and DCA curve, the model constructed by XGBoost algorithm has the best and near-perfect performance. CONCLUSION The machine learning model we created is ideal for detecting elderly patients at high risk of POP after HF at an early stage.
Collapse
Affiliation(s)
- Jiale Guo
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Qionghan He
- Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Caiju Peng
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Ru Dai
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Wei Li
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Zhichao Su
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Yehai Li
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China.
| |
Collapse
|
10
|
Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol 2023; 78:1216-1233. [PMID: 37208107 DOI: 10.1016/j.jhep.2023.01.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 05/21/2023]
Abstract
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.
Collapse
Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Beatriz Sordi Chara
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
11
|
Li D, Ding L, Luo J, Li QG. Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning. Front Immunol 2023; 14:1192369. [PMID: 37304293 PMCID: PMC10248221 DOI: 10.3389/fimmu.2023.1192369] [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: 03/23/2023] [Accepted: 05/04/2023] [Indexed: 06/13/2023] Open
Abstract
Objectives The assessment of accurate mortality risk is essential for managing pneumonia patients with connective tissue disease (CTD) treated with glucocorticoids or/and immunosuppressants. This study aimed to construct a nomogram for predicting 90-day mortality in pneumonia patients using machine learning. Methods Data were obtained from the DRYAD database. Pneumonia patients with CTD were screened. The samples were randomly divided into a training cohort (70%) and a validation cohort (30%). A univariate Cox regression analysis was used to screen for prognostic variables in the training cohort. Prognostic variables were entered into the least absolute shrinkage and selection operator (Lasso) and a random survival forest (RSF) analysis was used to screen important prognostic variables. The overlapping prognostic variables of the two algorithms were entered into the stepwise Cox regression analysis to screen the main prognostic variables and construct a model. Model predictive power was assessed using the C-index, the calibration curve, and the clinical subgroup analysis (age, gender, interstitial lung disease, diabetes mellitus). The clinical benefits of the model were assessed using a decision curve analysis (DCA). Similarly, the C-index was calculated and the calibration curve was plotted to verify the model stability in the validation cohort. Results A total of 368 pneumonia patients with CTD (training cohort: 247; validation cohort: 121) treated with glucocorticoids or/and immunosuppressants were included. The univariate Cox regression analysis obtained 19 prognostic variables. Lasso and RSF algorithms obtained eight overlapping variables. The overlapping variables were entered into a stepwise Cox regression to obtain five variables (fever, cyanosis, blood urea nitrogen, ganciclovir treatment, and anti-pseudomonas treatment), and a prognostic model was constructed based on the five variables. The C-index of the construction nomogram of the training cohort was 0.808. The calibration curve, DCA results, and clinical subgroup analysis showed that the model also had good predictive power. Similarly, the C-index of the model in the validation cohort was 0.762 and the calibration curve had good predictive value. Conclusion In this study, the nomogram developed performed well in predicting the 90-day risk of death in pneumonia patients with CTD treated with glucocorticoids or/and immunosuppressants.
Collapse
Affiliation(s)
- Dongdong Li
- Medical College of Nanchang University, Nanchang, Jiangxi, China
- Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Liting Ding
- Department of Rheumatology and Clinical Immunology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- JXHC Key Laboratory of Rheumatology and Immunology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Jiao Luo
- Department of Rheumatology and Clinical Immunology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Qiu-Gen Li
- Medical College of Nanchang University, Nanchang, Jiangxi, China
- Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| |
Collapse
|
12
|
Kim JH, Kim Y, Yoo K, Kim M, Kang SS, Kwon YS, Lee JJ. Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning. J Clin Med 2023; 12:jcm12051804. [PMID: 36902590 PMCID: PMC10003313 DOI: 10.3390/jcm12051804] [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: 01/30/2023] [Revised: 02/16/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023] Open
Abstract
Postoperative pulmonary edema (PPE) is a well-known postoperative complication. We hypothesized that a machine learning model could predict PPE risk using pre- and intraoperative data, thereby improving postoperative management. This retrospective study analyzed the medical records of patients aged > 18 years who underwent surgery between January 2011 and November 2021 at five South Korean hospitals. Data from four hospitals (n = 221,908) were used as the training dataset, whereas data from the remaining hospital (n = 34,991) were used as the test dataset. The machine learning algorithms used were extreme gradient boosting, light-gradient boosting machine, multilayer perceptron, logistic regression, and balanced random forest (BRF). The prediction abilities of the machine learning models were assessed using the area under the receiver operating characteristic curve, feature importance, and average precisions of precision-recall curve, precision, recall, f1 score, and accuracy. PPE occurred in 3584 (1.6%) and 1896 (5.4%) patients in the training and test sets, respectively. The BRF model exhibited the best performance (area under the receiver operating characteristic curve: 0.91, 95% confidence interval: 0.84-0.98). However, its precision and f1 score metrics were not good. The five major features included arterial line monitoring, American Society of Anesthesiologists physical status, urine output, age, and Foley catheter status. Machine learning models (e.g., BRF) could predict PPE risk and improve clinical decision-making, thereby enhancing postoperative management.
Collapse
Affiliation(s)
- Jong Ho Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
| | - Youngmi Kim
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
| | - Kookhyun Yoo
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
| | - Minguan Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
| | - Seong Sik Kang
- Department of Anesthesiology and Pain Medicine, College of Medicine, Kangwon National University, Chuncheon-si 24341, Republic of Korea
| | - Young-Suk Kwon
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
- Correspondence: (Y.-S.K.); (J.J.L.); Tel.: +82-33-240-5271 (Y.-S.K. & J.J.L.)
| | - Jae Jun Lee
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
- Correspondence: (Y.-S.K.); (J.J.L.); Tel.: +82-33-240-5271 (Y.-S.K. & J.J.L.)
| |
Collapse
|
13
|
Song X, Li H, Chen Q, Zhang T, Huang G, Zou L, Du D. Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches. Front Surg 2023; 9:1060691. [PMID: 36684357 PMCID: PMC9852626 DOI: 10.3389/fsurg.2022.1060691] [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: 10/03/2022] [Accepted: 11/14/2022] [Indexed: 01/07/2023] Open
Abstract
Objective Pneumonia is a common pulmonary complication of flail chest, causing high morbidity and mortality rates in affected patients. The existing methods for identifying pneumonia have low accuracy, and their use may delay antimicrobial therapy. However, machine learning can be combined with electronic medical record systems to identify information and assist in quick clinical decision-making. Our study aimed to develop a novel machine-learning model to predict pneumonia risk in flail chest patients. Methods From January 2011 to December 2021, the electronic medical records of 169 adult patients with flail chest at a tertiary teaching hospital in an urban level I Trauma Centre in Chongqing were retrospectively analysed. Then, the patients were randomly divided into training and test sets at a ratio of 7:3. Using the Fisher score, the best subset of variables was chosen. The performance of the seven models was evaluated by computing the area under the receiver operating characteristic curve (AUC). The output of the XGBoost model was shown using the Shapley Additive exPlanation (SHAP) method. Results Of 802 multiple rib fracture patients, 169 flail chest patients were eventually included, and 86 (50.80%) were diagnosed with pneumonia. The XGBoost model performed the best among all seven machine-learning models. The AUC of the XGBoost model was 0.895 (sensitivity: 84.3%; specificity: 80.0%).Pneumonia in flail chest patients was associated with several features: systolic blood pressure, pH value, blood transfusion, and ISS. Conclusion Our study demonstrated that the XGBoost model with 32 variables had high reliability in assessing risk indicators of pneumonia in flail chest patients. The SHAP method can identify vital pneumonia risk factors, making the XGBoost model's output clinically meaningful.
Collapse
Affiliation(s)
- Xiaolin Song
- School of Medicine, Chongqing University, Chongqing, China,Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Hui Li
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Qingsong Chen
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Tao Zhang
- School of Medicine, Chongqing University, Chongqing, China,Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Guangbin Huang
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Lingyun Zou
- Clinical Data Research Center, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China,Correspondence: Dingyuan Du Lingyun Zou
| | - Dingyuan Du
- Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China,Correspondence: Dingyuan Du Lingyun Zou
| |
Collapse
|
14
|
Peng X, Zhu T, Chen G, Wang Y, Hao X. A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model. Front Surg 2022; 9:976536. [PMID: 36017511 PMCID: PMC9395933 DOI: 10.3389/fsurg.2022.976536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
AimPostoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients.MethodsWe consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation.ResultsThe derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655–0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883–0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632–0.633) and the AUROC was 0.889(95%CI, 0.888–0.889).ConclusionsThis study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.
Collapse
Affiliation(s)
- Xiran Peng
- Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, ChengduChina
- The Research Units of West China (2018RU012) -Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, ChengduChina
| | - Tao Zhu
- Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, ChengduChina
- The Research Units of West China (2018RU012) -Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, ChengduChina
| | - Guo Chen
- Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, ChengduChina
- The Research Units of West China (2018RU012) -Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, ChengduChina
| | - Yaqiang Wang
- College of Software Engineering, Chengdu University of Information Technology, ChengduChina
| | - Xuechao Hao
- Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, ChengduChina
- The Research Units of West China (2018RU012) -Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, ChengduChina
- Correspondence: Xuechao Hao
| |
Collapse
|
15
|
Tran J, Sharma D, Gotlieb N, Xu W, Bhat M. Application of machine learning in liver transplantation: a review. Hepatol Int 2022; 16:495-508. [PMID: 35020154 DOI: 10.1007/s12072-021-10291-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
Collapse
Affiliation(s)
- Jason Tran
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Divya Sharma
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology, Department of Medicine, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
| |
Collapse
|
16
|
Zhang Y, Chen Y. Stratification From Heterogeneity of the Cell-Death Signal Enables Prognosis Prediction and Immune Microenvironment Characterization in Esophageal Squamous Cell Carcinoma. Front Cell Dev Biol 2022; 10:855404. [PMID: 35493093 PMCID: PMC9040162 DOI: 10.3389/fcell.2022.855404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is the primary subtype of esophageal cancer (EC) characterized by a high incidence rate and extremely poor prognosis worldwide. Previous studies suggested that the specific cell death signal was linked to different immune subtypes in multiple cancers, while a comprehensive investigation on ESCC is to be performed yet. In the current study, we dissected different cell death signals in ESCC tumors and then integrated that functional information to stratify ESCC patients into different immunogenic cell death (ICD) subtypes. By systematically analyzing the transcriptomes of 857 patients and proteomic profile of 124 patients, we found that the signals of necroptosis, pyroptosis, and ferroptosis are positively associated with activated immunity in ESCC. We identified two ICD pattern terms, namely, ICD-high and ICD-low subtypes that positively correlated to both progression-free survival and overall survival. In addition, cell fraction deconvolution analysis revealed that more infiltrated leukocytes were enriched in ICD-high types, especially antigen-presenting cells, such as dendritic cells and macrophages. With the XGBoost algorithm, we further developed a 14-gene signature which can simplify the subtyping for allocating new samples, by which we validated the prognosis value of the signature and proved that the ICD score scheme could serve as a promising biomarker for stratifying patients with immunotherapy in several immune checkpoint blockade treatment cohorts. Collectively, we successfully constructed the ICD scheme, which enables predicting of the prognosis or immunotherapy efficacy in ESCC patients and uncovered the critical interplay between cell death signals and immune status in ESCC.
Collapse
Affiliation(s)
- Yiyuan Zhang
- Department of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- *Correspondence: Yiyuan Zhang,
| | - Yanxing Chen
- State Key Laboratory of Oncology in South China, Department of Medical Oncology, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| |
Collapse
|
17
|
Yoon HK, Yang HL, Jung CW, Lee HC. Artificial intelligence in perioperative medicine - a narrative review. Korean J Anesthesiol 2022; 75:202-215. [PMID: 35345305 PMCID: PMC9171545 DOI: 10.4097/kja.22157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 11/23/2022] Open
Abstract
Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.
Collapse
Affiliation(s)
- Hyun-Kyu Yoon
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun-Lim Yang
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
18
|
Chen Y. Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4714898. [PMID: 34900191 PMCID: PMC8654524 DOI: 10.1155/2021/4714898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/19/2021] [Accepted: 11/20/2021] [Indexed: 11/18/2022]
Abstract
An improved nonlinear weighted extreme gradient boosting (XGBoost) technique is developed to forecast length of stay for patients with imbalance data. The algorithm first chooses an effective technique for fitting the duration of stay and determining the distribution law and then optimizes the negative log likelihood loss function using a heuristic nonlinear weighting method based on sample percentage. Theoretical and practical results reveal that, when compared to existing algorithms, the XGBoost method based on nonlinear weighting may achieve higher classification accuracy and better prediction performance, which is beneficial in treating more patients with fewer hospital beds.
Collapse
Affiliation(s)
- Yong Chen
- The Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
| |
Collapse
|
19
|
DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10070452] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Soil erosion is a form of land degradation. It is the process of moving surface soil with the action of external forces such as wind or water. Tillage also causes soil erosion. As outlined by the United Nations Sustainable Development Goal (UN SDG) #15, it is a global challenge to “combat desertification, and halt and reverse land degradation and halt biodiversity loss.” In order to advance this goal, we studied and modeled the soil erosion depth of a typical watershed in Taiwan using 26 morphometric factors derived from a digital elevation model (DEM) and 10 environmental factors. Feature selection was performed using the Boruta algorithm to determine 15 factors with confirmed importance and one tentative factor. Then, machine learning models, including the random forest (RF) and gradient boosting machine (GBM), were used to create prediction models validated by erosion pin measurements. The results show that GBM, coupled with 15 important factors (confirmed), achieved the best result in the context of root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Finally, we present the maps of soil erosion depth using the two machine learning models. The maps are useful for conservation planning and mitigating future soil erosion.
Collapse
|