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Zang H, Hu A, Xu X, Ren H, Xu L. Development of machine learning models to predict perioperative blood transfusion in hip surgery. BMC Med Inform Decis Mak 2024; 24:158. [PMID: 38840126 PMCID: PMC11155147 DOI: 10.1186/s12911-024-02555-7] [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: 08/21/2023] [Accepted: 05/28/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk is necessary for minimizing blood product waste and preoperative decision-making. The study aimed to develop machine learning models for predicting perioperative blood transfusion in hip surgery and identify significant risk factors. METHODS Data of patients undergoing hip surgery between January 2013 and October 2021 in the Peking Union Medical College Hospital were collected to train and test predictive models. The primary outcome was perioperative red blood cell (RBC) transfusion within 72 h of surgery. Fourteen machine learning algorithms were established to predict blood transfusion risk incorporating patient demographic characteristics, preoperative laboratory tests, and surgical information. Discrimination, calibration, and decision curve analysis were used to evaluate machine learning models. SHapley Additive exPlanations (SHAP) was performed to interpret models. RESULTS In this study, 2431 hip surgeries were included. The Ridge Classifier performed the best with an AUC = 0.85 (95% CI, 0.81 to 0.88) and a Brier score = 0.21. Patient-related risk factors included lower preoperative hemoglobin, American Society of Anesthesiologists (ASA) Physical Status > 2, anemia, lower preoperative fibrinogen, and lower preoperative albumin. Surgery-related risk factors included longer operation time, total hip arthroplasty, and autotransfusion. CONCLUSIONS The machine learning model developed in this study achieved high predictive performance using available variables for perioperative blood transfusion in hip surgery. The predictors identified could be helpful for risk stratification, preoperative optimization, and outcomes improvement.
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Affiliation(s)
- Han Zang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Ai Hu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Xuanqi Xu
- Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, 100084, China
- School of Computer Science, Peking University, Beijing, 100084, China
| | - He Ren
- Beijing HealSci Technology Co., Ltd., Beijing, 100176, China
| | - Li Xu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
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Su M, Wu H, Chen H, Guo J, Chen Z, Qiu J, Huang J. Early prediction of sepsis-induced respiratory tract infection using a biomarker-based machine-learning algorithm. Scand J Clin Lab Invest 2024; 84:202-210. [PMID: 38683948 DOI: 10.1080/00365513.2024.2346914] [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: 11/11/2023] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
Early and differential diagnosis of sepsis is essential to avoid unnecessary antibiotic use and further reduce patient morbidity and mortality. Here, we aimed to identify predictors of sepsis and advance a machine-learning strategy to predict sepsis-induced respiratory tract infection (RTI). Patients with sepsis and RTI were selected via retrospective analysis, and essential population characteristics and laboratory parameters were recorded. To improve the performance of the primary model and avoid over-fitting, a recursive feature elimination with cross-validation (RFECV) strategy was used to screen the optimal subset of biomarkers and construct nine machine-learning models based on this subset; the average accuracy, precision, recall, and F1-score were used for evaluation of the models. We identified 430 patients with sepsis and 686 patients with RTI. A total of 39 features were collected, with 23 features identified for initial model construction. Using the RFECV algorithm, we found that the XGBoost classifier, which only needed to include seven biomarkers, demonstrated the best performance among all prediction models, with an average accuracy of 89.24 ± 2.28, while the Ridge classifier, which included 11 biomarkers, had an average accuracy of only 83.87 ± 4.69. The remaining models had prediction accuracies greater than 88%. We developed nine models for predicting sepsis using a strategy that combined RFECV with machine learning. Among these models, the XGBoost classifier, which included seven biomarkers, showed the best performance and highest accuracy for predicting sepsis and may be a promising tool for the timely identification of sepsis.
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Affiliation(s)
- Mingkuan Su
- Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, China
| | - Haiying Wu
- Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, China
| | - Hongbin Chen
- Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, China
| | - Jianfeng Guo
- Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, China
| | - Zongyun Chen
- Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, China
| | - Jie Qiu
- Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, China
| | - Jiancheng Huang
- Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, China
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Rui M, Jiang L, Pan JJ, Huang XT, Cui JF, Zhang SJ, He SM, Han HH, Chen X, Wang DD. Effects of tacrolimus on proteinuria in Chinese and Indian patients with idiopathic membranous nephropathy: the results of machine learning study. Int Urol Nephrol 2024:10.1007/s11255-024-04056-y. [PMID: 38642210 DOI: 10.1007/s11255-024-04056-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 04/08/2024] [Indexed: 04/22/2024]
Abstract
PURPOSE The present study aims to explore the effects of tacrolimus on proteinuria in patients with idiopathic membranous nephropathy (IMN) and recommend an appropriate dosage schedule via machine learning method. METHODS The Emax model was constructed to analyze the effects of tacrolimus on proteinuria in patients with IMN. Data were mined from published literature and machine learning was built up with Emax model, among which the efficacy indicator was proteinuria change rates from baseline. 463 IMN patients were included for modeling, and tacrolimus therapeutic window concentrations were 4-10 ng/ml. RESULTS In machine learning model, the Emax from tacrolimus effecting proteinuria in IMN patients was -72.7%, the ET50 was 0.43 months, and the time to achieving 25% Emax, 50% Emax, 75% Emax, and 80% (plateau) Emax of tacrolimus on proteinuria in patients with IMN were 0.15, 0.43, 1.29, and 1.72 months, respectively. CONCLUSION For achieving better therapeutic effects from tacrolimus on proteinuria in patients with IMN, tacrolimus concentration range need to be maintained at 4-10 ng/ml for at least 1.72 months.
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Affiliation(s)
- Min Rui
- Department of Orthopaedics, The Affiliated Jiangyin Clinical College of Xuzhou Medical University, Jiangyin, 214400, Jiangsu, China
| | - Lei Jiang
- Department of Pharmacy, Taixing People's Hospital, Taixing, 225400, Jiangsu, China
| | - Jia-Jun Pan
- Department of Thoracic Cardiovascular Surgery, The Affiliated Xuzhou Municipal Hospital of Xuzhou Medical University, Xuzhou, 221100, Jiangsu, China
| | - Xue-Ting Huang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jia-Fang Cui
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shi-Jia Zhang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Su-Mei He
- Department of Pharmacy, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, 215153, Jiangsu, China.
| | - Huan-Huan Han
- Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, 222000, Jiangsu, China.
| | - Xiao Chen
- School of Nursing, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| | - Dong-Dong Wang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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Tu JB, Liao WJ, Long SP, Li MP, Gao XH. Construction and validation of a machine learning model for the diagnosis of juvenile idiopathic arthritis based on fecal microbiota. Front Cell Infect Microbiol 2024; 14:1371371. [PMID: 38524178 PMCID: PMC10957563 DOI: 10.3389/fcimb.2024.1371371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/26/2024] [Indexed: 03/26/2024] Open
Abstract
Purpose Human gut microbiota has been shown to be significantly associated with various inflammatory diseases. Therefore, this study aimed to develop an excellent auxiliary tool for the diagnosis of juvenile idiopathic arthritis (JIA) based on fecal microbial biomarkers. Method The fecal metagenomic sequencing data associated with JIA were extracted from NCBI, and the sequencing data were transformed into the relative abundance of microorganisms by professional data cleaning (KneadData, Trimmomatic and Bowtie2) and comparison software (Kraken2 and Bracken). After that, the fecal microbes with high abundance were extracted for subsequent analysis. The extracted fecal microbes were further screened by least absolute shrinkage and selection operator (LASSO) regression, and the selected fecal microbe biomarkers were used for model training. In this study, we constructed six different machine learning (ML) models, and then selected the best model for constructing a JIA diagnostic tool by comparing the performance of the models based on a combined consideration of area under receiver operating characteristic curve (AUC), accuracy, specificity, F1 score, calibration curves and clinical decision curves. In addition, to further explain the model, Permutation Importance analysis and Shapley Additive Explanations (SHAP) were performed to understand the contribution of each biomarker in the prediction process. Result A total of 231 individuals were included in this study, including 203 JIA patients and Non-JIA individuals. In the analysis of diversity at the genus level, the alpha diversity represented by Shannon value was not significantly different between the two groups, while the belt diversity was slightly different. After selection by LASSO regression, 10 fecal microbe biomarkers were selected for model training. By comparing six different models, the XGB model showed the best performance, which average AUC, accuracy and F1 score were 0.976, 0.914 and 0.952, respectively, thus being used to construct the final JIA diagnosis model. Conclusion A JIA diagnosis model based on XGB algorithm was constructed with excellent performance, which may assist physicians in early detection of JIA patients and improve the prognosis of JIA patients.
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Affiliation(s)
- Jun-Bo Tu
- Department of Orthopaedics, Xinfeng County People’s Hospital, Xinfeng, Jiangxi, China
| | - Wei-Jie Liao
- Department of ICU, GanZhou People’s Hospital, GanZhou, Jiangxi, China
| | - Si-Ping Long
- The First Clinical Medical College of Nanchang University, Nanchang, China
| | - Meng-Pan Li
- The First Clinical Medical College of Nanchang University, Nanchang, China
- Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xing-Hua Gao
- Department of Orthopaedics, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, China
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Zhou R, Li Z, Liu J, Qian D, Meng X, Guan L, Sun X, Li H, Yu M. Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort. Front Cardiovasc Med 2024; 11:1344170. [PMID: 38486703 PMCID: PMC10937389 DOI: 10.3389/fcvm.2024.1344170] [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: 11/25/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
Background Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort. Methods A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model. Results The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662-0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534-0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model. Conclusions This study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.
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Affiliation(s)
- Ren Zhou
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai Institute of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhaolong Li
- Department of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Liu
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dewei Qian
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangdong Meng
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lichun Guan
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinxin Sun
- Department of Cardiovascular Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Haiqing Li
- Department of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Yu
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Thakur SK, Sinha AK, Negi DK, Singh S. Forecasting demand for blood products: Towards inventory management of a perishable product. Bioinformation 2024; 20:20-28. [PMID: 38352907 PMCID: PMC10859947 DOI: 10.6026/973206300200020] [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: 01/01/2024] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Forecasting consumption of blood products can reduce their order frequency by 60% and inventory level by 40%. This also prevents shortage by balancing demand and supply. The study aimed to establish a "Simple Average with Mean Annual Increment" (SAMAI) method of time series forecasting and to compare its results with those of ARIMA, ratio to trend, and simple average to forecast demand of blood products. Monthly demand data of blood component from January 2017 to December 2022 (data set I) was used for creating a forecasting model. To avoid the effect of COVID19 pandemic instead of actual data of year 2020 and 2021, average monthly values of previous three years were used (data set II). The data from January to July 2023 were used as testing data set. To assess the fitness of model MAPE (Mean Absolute Percentage Error) was used. By SAMAI method MAPE were 18.82%, 13.392%, 14.516% and 27.637% respectively for of blood donation, blood issue, RDP issue and FFP issue for data set I. By Simple Average method MAPE were 20.05%, 12.09%, 29.06% and 34.85%, respectably. By Ratio-to-Trend method MAPE were 21.08%, 21.65%, 25.62% and 39.95% respectively. By SARIMA method MAPE were 12.99%, 19.59%, 37.15% and 31.94% respectively. The average MAPE was lower in data set II by all tested method and overall MAPE was lower by SAMAI method. The SAMAI method is simple and easy to perform. It can be used in the forecasting of blood components demand in medical institution without knowledge of advanced statistics.
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Affiliation(s)
- Sanjay Kumar Thakur
- P.G. Department of Zoology, Veer Kunwar Singh University, Ara, Bihar-802301, India
- Department of Regional Blood Transfusion Centre and Department of Pathology, Hindu Rao Hospital and NDM Medical College, Delhi-110007, India
| | - Anil Kumar Sinha
- P.G. Department of Zoology, Veer Kunwar Singh University, Ara, Bihar-802301, India
| | - Dinesh Kumar Negi
- Department of Regional Blood Transfusion Centre and Department of Pathology, Hindu Rao Hospital and NDM Medical College, Delhi-110007, India
| | - Sompal Singh
- Department of Regional Blood Transfusion Centre and Department of Pathology, Hindu Rao Hospital and NDM Medical College, Delhi-110007, India
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Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, Stanworth SJ. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64:162-184. [PMID: 37950535 DOI: 10.1111/trf.17582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Suzanne Maynard
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Joseph Farrington
- Institute of Health Informatics, University College London, London, UK
| | - Samah Alimam
- Haematology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Hayley Evans
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London, UK
| | - Wai Keong Wong
- Director of Digital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Simon J Stanworth
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Lima CMDF, Rebouças TO, Carlos LMDB, Oliveira JBF, Silva ELD, Alves JS, Lima CAD, Mesquita FP, Ribeiro JKC, Aquino PEAD, Brunetta DM, Garcia JHP, Viana Júnior AB. Assessment of the need for routine intraoperative cell salvage in liver transplantation. Acta Cir Bras 2023; 38:e386423. [PMID: 38055399 DOI: 10.1590/acb386423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/19/2023] [Indexed: 12/08/2023] Open
Abstract
PURPOSE This study aimed to assess the necessity of routine intraoperative cell salvage in liver transplantations. METHODS A total of 327 liver transplants performed between 2014 and 2016 was included in the analysis. Patient data, including pre-transplant examinations, intraoperative red blood cell transfusions, and procedural information, were collected. RESULTS The median age of the patients was 54 years old, with 67% (219) being male. The most prevalent ABO blood type was O, accounting for 48% (155) of cases. The leading causes of liver disease were hepatitis C (113 cases, 34.6%) and alcohol-related liver disease (97 cases, 29.7%). Out of the 327 liver transplants, allogeneic red blood cell transfusions were administered in 110 cases (34%) with a median of two units of red blood cells per case. Cell salvage was employed in 237 transplants (73%), and successful blood recovery was achieved in 221 cases (93%). Among the group that recovered more than 200 mL of blood, the median volume of recovered blood was 417 mL, with no transfusion of allogeneic blood required. A total of 90 transplants was performed without utilizing cell salvage, and, among these cases, 19 required blood transfusions, with a median of zero units transfused. CONCLUSIONS This study suggests that routine cell salvage is unnecessary for all liver transplantations. The most suitable indication for its use is in patients presenting with portal vein thrombosis and abnormal creatinine levels.
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Affiliation(s)
| | | | | | | | | | | | - Clébia Azevedo de Lima
- Universidade Federal do Ceará - Hospital Universitário Walter Cantídio - Fortaleza (CE), Brazil
| | | | | | | | - Denise Menezes Brunetta
- Centro de Hematologia e Hemoterapia do Ceará - Fortaleza (CE), Brazil
- Universidade Federal do Ceará - Hospital Universitário Walter Cantídio - Fortaleza (CE), Brazil
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Li MP, Liu WC, Wu JB, Luo K, Liu Y, Zhang Y, Xiao SN, Liu ZL, Huang SH, Liu JM. Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3825-3835. [PMID: 37195363 DOI: 10.1007/s00586-023-07772-8] [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: 02/25/2023] [Revised: 04/20/2023] [Accepted: 05/05/2023] [Indexed: 05/18/2023]
Abstract
PURPOSE The purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment decisions. METHODS Patients with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the model. RESULTS A total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on RF algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656). CONCLUSION Age, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on RF algorithm had the best performance.
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Affiliation(s)
- Meng-Pan Li
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- The First Clinical Medical College of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Wen-Cai Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- The First Clinical Medical College of Nanchang University, Nanchang, 330006, People's Republic of China
- Department of Orthopaedics, Shanghai Jiao Tong University Affifiliated Sixth People's Hospital, Shanghai, China
| | - Jia-Bao Wu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Kun Luo
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Yu Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Yu Zhang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Shi-Ning Xiao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Zhi-Li Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Shan-Hu Huang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
| | - Jia-Ming Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
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Chen H, Cao B, Yang J, Ren H, Xia X, Zhang X, Yan W, Liang X, Li C. Construction and effect evaluation of prediction model for red blood cell transfusion requirement in cesarean section based on artificial intelligence. BMC Med Inform Decis Mak 2023; 23:213. [PMID: 37828543 PMCID: PMC10568840 DOI: 10.1186/s12911-023-02286-1] [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: 12/19/2022] [Accepted: 09/04/2023] [Indexed: 10/14/2023] Open
Abstract
OBJECTIVES This study intends to build an artificial intelligence model for obstetric cesarean section surgery to evaluate the intraoperative blood transfusion volume before operation, and compare the model prediction results with the actual results to evaluate the accuracy of the artificial intelligence prediction model for intraoperative red blood cell transfusion in obstetrics. The advantages and disadvantages of intraoperative blood demand and identification of high-risk groups for blood transfusion provide data support and improvement suggestions for the realization of accurate blood management of obstetric cesarean section patients during the perioperative period. METHODS Using a machine learning algorithm, an intraoperative blood transfusion prediction model was trained. The differences between the predicted results and the actual results were compared by means of blood transfusion or not, blood transfusion volume, and blood transfusion volume targeting postoperative hemoglobin (Hb). RESULTS Area under curve of the model is 0.89. The accuracy of the model for blood transfusion was 96.85%. The statistical standard for the accuracy of the model blood transfusion volume is the calculation of 1U absolute error, the accuracy rate is 86.56%, and the accuracy rate of the blood transfusion population is 45.00%. In the simulation prediction results, 93.67% of the predicted and actual cases in no blood transfusion surgery; 63.45% of the same predicted blood transfusion in blood transfusion surgery, and only 20.00% of the blood transfusion volume is the same. CONCLUSIONS In conclusion, this study used machine learning algorithm to process, analyze and predict the results of a large sample of cesarean section clinical data, and found that the important predictors of blood transfusion during cesarean section included preoperative RBC, surgical method, the site of surgery, coagulation-related indicators, and other factors. At the same time, it was found that the overall accuracy of the AI model was higher than actual blood using. Although the prediction of blood transfusion volume was not well matched with the actual blood using, the model provided a perspective of preoperative identification of high blood transfusion risks. The results can provide good auxiliary decision support for preoperative evaluation of obstetric cesarean section, and then promote the realization of accurate perioperative blood management for obstetric cesarean section patients.
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Affiliation(s)
- Hang Chen
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
- Department of Information Service, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - Bowei Cao
- Department of Information Service, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - Jiangcun Yang
- Department of Transfusion Medicine, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - He Ren
- Beijing HealSci Technology Co., Ltd, Beijing, 100022, China
| | - Xingqiu Xia
- Beijing HealSci Technology Co., Ltd, Beijing, 100022, China
| | - Xiaowen Zhang
- Department of Information Service, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - Wei Yan
- Department of Information Service, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - Xiaodan Liang
- Department of Information Service, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - Chen Li
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
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Multivariable statistical models to predict red cell transfusion in elective surgery. BLOOD TRANSFUSION = TRASFUSIONE DEL SANGUE 2023; 21:42-49. [PMID: 35302483 PMCID: PMC9918382 DOI: 10.2450/2022.0295-21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 02/16/2021] [Indexed: 02/12/2023]
Abstract
BACKGROUND Predicting red cell transfusion may assist in identifying those most likely to benefit from patient blood management strategies. Our objective was to identify a simple statistical model to predict transfusion in elective surgery from routinely available data. MATERIALS AND METHODS Our final multicentre cohort consisted of 42,546 patients and contained the following potential predictors of red cell transfusion known prior to admission: patient age, sex, pre-admission hemoglobin, surgical procedure, and comorbidities. Missing data were handled by multiple imputation methods. The outcome measure of interest was administration of a red cell transfusion. We used multivariable logistic regression models to predict transfusion, and evaluated the performance by applying a 10-fold cross-validation. Model accuracy was assessed by comparing the area under the receiver operating characteristics curve. After applying an optimal probability cut-off we measured model accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS 7.0% (n=2,993) of the study population received a red cell transfusion. Our most simple model predicted red cell transfusion based on admission hemoglobin and surgical procedure with a multiply imputed estimated area under the curve of 0.862 (0.856, 0.864). The estimated accuracy, sensitivity, specificity, positive predictive, and negative predictive values at the probability cut-off of 0.4 were 0.934, 0.257, 0.986, 0.573, and 0.946 respectively. DISCUSSION A small number of variables available prior to admission can predict red cell transfusion with very good accuracy. Our model can be used to flag high-risk patients most likely to benefit from pre-operative patient blood management measures.
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Chen Y, Cai X, Cao Z, Lin J, Huang W, Zhuang Y, Xiao L, Guan X, Wang Y, Xia X, Jiao F, Du X, Jiang G, Wang D. Prediction of red blood cell transfusion after orthopedic surgery using an interpretable machine learning framework. Front Surg 2023; 10:1047558. [PMID: 36936651 PMCID: PMC10017874 DOI: 10.3389/fsurg.2023.1047558] [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: 10/13/2022] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Objective Postoperative red blood cell (RBC) transfusion is widely used during the perioperative period but is often associated with a high risk of infection and complications. However, prediction models for RBC transfusion in patients with orthopedic surgery have not yet been developed. We aimed to identify predictors and constructed prediction models for RBC transfusion after orthopedic surgery using interpretable machine learning algorithms. Methods This retrospective cohort study reviewed a total of 59,605 patients undergoing orthopedic surgery from June 2013 to January 2019 across 7 tertiary hospitals in China. Patients were randomly split into training (80%) and test subsets (20%). The feature selection method of recursive feature elimination (RFE) was used to identify an optimal feature subset from thirty preoperative variables, and six machine learning algorithms were applied to develop prediction models. The Shapley Additive exPlanations (SHAP) value was employed to evaluate the contribution of each predictor towards the prediction of postoperative RBC transfusion. For simplicity of the clinical utility, a risk score system was further established using the top risk factors identified by machine learning models. Results Of the 59,605 patients with orthopedic surgery, 19,921 (33.40%) underwent postoperative RBC transfusion. The CatBoost model exhibited an AUC of 0.831 (95% CI: 0.824-0.836) on the test subset, which significantly outperformed five other prediction models. The risk of RBC transfusion was associated with old age (>60 years) and low RBC count (<4.0 × 1012/L) with clear threshold effects. Extremes of BMI, low albumin, prolonged activated partial thromboplastin time, repair and plastic operations on joint structures were additional top predictors for RBC transfusion. The risk score system derived from six risk factors performed well with an AUC of 0.801 (95% CI: 0.794-0.807) on the test subset. Conclusion By applying an interpretable machine learning framework in a large-scale multicenter retrospective cohort, we identified novel modifiable risk factors and developed prediction models with good performance for postoperative RBC transfusion in patients undergoing orthopedic surgery. Our findings may allow more precise identification of high-risk patients for optimal control of risk factors and achieve personalized RBC transfusion for orthopedic patients.
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Affiliation(s)
- Yifeng Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Xiaoyu Cai
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zicheng Cao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Jie Lin
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wenyu Huang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yuan Zhuang
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lehan Xiao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Xiaozhen Guan
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ying Wang
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | | | - Feng Jiao
- Guangzhou Centre for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
- Correspondence: Guozhi Jiang Deqing Wang
| | - Deqing Wang
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Correspondence: Guozhi Jiang Deqing Wang
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Wu HY, Li ZG, Sun XK, Bai WM, Wang AD, Ma YC, Diao RH, Fan EY, Zhao F, Liu YQ, Hong YZ, Guo MH, Xue H, Liang WB. Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks. Sci Rep 2022; 12:19165. [PMID: 36357435 PMCID: PMC9647248 DOI: 10.1038/s41598-022-21215-2] [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: 05/04/2022] [Accepted: 09/23/2022] [Indexed: 11/11/2022] Open
Abstract
Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.
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Affiliation(s)
- Hong-yun Wu
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Zheng-gang Li
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Xin-kai Sun
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Wei-min Bai
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - An-di Wang
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Yu-chi Ma
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Ren-hua Diao
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Eng-yong Fan
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Fang Zhao
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Yun-qi Liu
- grid.499290.f0000 0004 6026 514XNanjing Foreign Language School, Nanjing, Jiangsu People’s Republic of China
| | - Yi-zhou Hong
- grid.499290.f0000 0004 6026 514XNanjing Foreign Language School, Nanjing, Jiangsu People’s Republic of China
| | - Ming-hua Guo
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Hui Xue
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Wen-biao Liang
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
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Wang DD, Li YF, Zhang C, He SM, Chen X. Predicting the effect of sirolimus on disease activity in patients with systemic lupus erythematosus using machine learning. J Clin Pharm Ther 2022; 47:1845-1850. [PMID: 36131617 DOI: 10.1111/jcpt.13778] [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: 06/21/2022] [Revised: 08/03/2022] [Accepted: 09/04/2022] [Indexed: 11/30/2022]
Abstract
WHAT IS KNOWN AND OBJECTIVES The present study aimed to predict the effect of sirolimus on disease activity in patients with systemic lupus erythematosus (SLE) using machine learning and to recommend appropriate sirolimus dosage regimen for patients with SLE. METHODS The Emax model was selected for machine learning, where the evaluation indicator was the change rate of systemic lupus erythematosus disease activity index from baseline value. RESULTS A total 103 patients with SLE were included for modelling, where the Emax , ET50 were -53.9%, 1.53 months in the final model respectively, and the evaluation of the final model was good. Further simulation found that the follow-up time to achieve 25%, 50%, 75% and 80% (plateau) Emax of sirolimus effecting on disease activity in patients with SLE were 0.51, 1.53, 4.59 and 6.12 months, respectively. In addition, the sirolimus dosage was flexible and adjusted according to drug concentration, where the intersection of sirolimus concentration range included in this study was about 8-10 ng/ml. WHAT IS NEW AND CONCLUSIONS This study was the first time to predict the effect of sirolimus on disease activity in patients with SLE and in order to achieve better therapeutic effect maintaining a concentration of 8-10 ng/ml sirolimus for at least 6.12 months was necessary.
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Affiliation(s)
- Dong-Dong Wang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ya-Feng Li
- Department of Pharmacy, Feng Xian People's Hospital, Xuzhou, Jiangsu, China
| | - Cun Zhang
- Department of Pharmacy, Xuzhou Oriental Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Su-Mei He
- Department of Pharmacy, Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Xiao Chen
- School of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China
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Wang DD, Li YF, Mao YZ, He SM, Zhu P, Wei QL. A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome. Front Nutr 2022; 9:851275. [PMID: 36034907 PMCID: PMC9399747 DOI: 10.3389/fnut.2022.851275] [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: 01/09/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
The present study aimed to explore the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predict an appropriate dosage schedule using a machine-learning approach. Data were obtained from literature mining and the rates of body weight change from the initial values were selected as the therapeutic index. The maximal effect (Emax) model was built up as the machine-learning model. A total of 242 patients with PCOS were included for analysis. In the machine-learning model, the Emax of carnitine supplementation on body weight was -3.92%, the ET50 was 3.6 weeks, and the treatment times to realize 25%, 50%, 75%, and 80% (plateau) Emax of carnitine supplementation on body weight were 1.2, 3.6, 10.8, and 14.4 weeks, respectively. In addition, no significant relationship of dose-response was found in the dosage range of carnitine supplementation used in the present study, indicating the lower limit of carnitine supplementation dosage, 250 mg/day, could be used as a suitable dosage. The present study first explored the effect of carnitine supplementation on body weight in patients with PCOS, and in order to realize the optimal therapeutic effect, carnitine supplementation needs 250 mg/day for at least 14.4 weeks.
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Affiliation(s)
- Dong-Dong Wang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Ya-Feng Li
- Department of Pharmacy, Feng Xian People's Hospital, Xuzhou, China
| | - Yi-Zhen Mao
- School Infirmary, Jiangsu Normal University, Xuzhou, China
| | - Su-Mei He
- Department of Pharmacy, Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Ping Zhu
- Department of Endocrinology, Huaian Hospital of Huaian City, Huaian, China
| | - Qun-Li Wei
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, China
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Development of Machine Learning Models Predicting Estimated Blood Loss during Liver Transplant Surgery. J Pers Med 2022; 12:jpm12071028. [PMID: 35887525 PMCID: PMC9320884 DOI: 10.3390/jpm12071028] [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: 04/05/2022] [Revised: 06/03/2022] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753).
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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.
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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.
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Guo K, Song S, Qiu L, Wang X, Ma S. Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China. Front Med (Lausanne) 2022; 9:706284. [PMID: 35665347 PMCID: PMC9162489 DOI: 10.3389/fmed.2022.706284] [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: 05/07/2021] [Accepted: 04/26/2022] [Indexed: 11/25/2022] Open
Abstract
Background Red blood cells (RBCs) are an essential factor to consider for modern medicine, but planning the future collection of RBCs and supply efforts for coping with fluctuating demands is still a major challenge. Objectives This study aimed to explore the feasibility of the time-series model in predicting the clinical demand of RBCs for pediatric patients each month. Methods Our study collected clinical RBC transfusion data from years 2014 to 2019 in the National Center for Children's Health (Beijing) in China, with the goal of constructing a time-series, autoregressive integrated moving average (ARIMA) model by fitting the monthly usage of RBCs from 2014 to 2018. Furthermore, the optimal model was used to forecast the monthly usage of RBCs in 2019, and we subsequently compared the data with actual values to verify the validity of the model. Results The seasonal multiplicative model SARIMA (0, 1, 1) (1, 1, 0)12 (normalized BIC = 8.740, R2 = 0.730) was the best prediction model and could better fit and predict the monthly usage of RBCs for pediatric patients in this medical center in 2019. The model residual sequence was white noise (Ljung-Box Q(18) = 15.127, P > 0.05), and its autocorrelation function (ACF) and partial autocorrelation function (PACF) coefficients also fell within the 95% confidence intervals (CIs). The parameter test results were statistically significant (P < 0.05). 91.67% of the actual values were within the 95% CIs of the forecasted values of the model, and the average relative error of the forecasted and actual values was 6.44%, within 10%. Conclusions The SARIMA model can simulate the changing trend in monthly usage of RBCs of pediatric patients in a time-series aspect, which represents a short-term prediction model with high accuracy. The continuously revised SARIMA model may better serve the clinical environments and aid with planning for RBC demand. A clinical study including more data on blood use should be conducted in the future to confirm these results.
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Cao M, Wang G, He H, Yue R, Zhao Y, Pan L, Huang W, Guo Y, Yin T, Ma L, Zhang D, Huang X. Hemoglobin-Based Oxygen Carriers: Potential Applications in Solid Organ Preservation. Front Pharmacol 2021; 12:760215. [PMID: 34916938 PMCID: PMC8670084 DOI: 10.3389/fphar.2021.760215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/10/2021] [Indexed: 12/30/2022] Open
Abstract
Ameliorating graft injury induced by ischemia and hypoxia, expanding the donor pool, and improving graft quality and recipient prognosis are still goals pursued by the transplant community. The preservation of organs during this process from donor to recipient is critical to the prognosis of both the graft and the recipient. At present, static cold storage, which is most widely used in clinical practice, not only reduces cell metabolism and oxygen demand through low temperature but also prevents cell edema and resists apoptosis through the application of traditional preservation solutions, but these do not improve hypoxia and increase oxygenation of the donor organ. In recent years, improving the ischemia and hypoxia of grafts during preservation and repairing the quality of marginal donor organs have been of great concern. Hemoglobin-based oxygen carriers (HBOCs) are “made of” natural hemoglobins that were originally developed as blood substitutes but have been extended to a variety of hypoxic clinical situations due to their ability to release oxygen. Compared with traditional preservation protocols, the addition of HBOCs to traditional preservation protocols provides more oxygen to organs to meet their energy metabolic needs, prolong preservation time, reduce ischemia–reperfusion injury to grafts, improve graft quality, and even increase the number of transplantable donors. The focus of the present study was to review the potential applications of HBOCs in solid organ preservation and provide new approaches to understanding the mechanism of the promising strategies for organ preservation.
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Affiliation(s)
- Min Cao
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Guoqing Wang
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongli He
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruiming Yue
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Zhao
- Anesthesiology, Southwest Medicine University, Luzhou, China
| | - Lingai Pan
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Weiwei Huang
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Guo
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Yin
- Surgical Department, Chengdu Second People's Hospital, Chengdu, China
| | - Lina Ma
- Health Inspection and Quarantine, Chengdu Medical College, Chengdu, China
| | - Dingding Zhang
- Sichuan Provincial Key Laboratory for Disease Gene Study, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Huang
- Department of Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Huang X, Wang Y, Chen B, Huang Y, Wang X, Chen L, Gui R, Ma X. Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China. Front Med (Lausanne) 2021; 8:694733. [PMID: 34485333 PMCID: PMC8415266 DOI: 10.3389/fmed.2021.694733] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/20/2021] [Indexed: 01/20/2023] Open
Abstract
Background: Predicting the perioperative requirement for red blood cells (RBCs) transfusion in patients with the pelvic fracture may be challenging. In this study, we constructed a perioperative RBCs transfusion predictive model (ternary classifications) based on a machine learning algorithm. Materials and Methods: This study included perioperative adult patients with pelvic trauma hospitalized across six Chinese centers between September 2012 and June 2019. An extreme gradient boosting (XGBoost) algorithm was used to predict the need for perioperative RBCs transfusion, with data being split into training test (80%), which was subjected to 5-fold cross-validation, and test set (20%). The ability of the predictive transfusion model was compared with blood preparation based on surgeons' experience and other predictive models, including random forest, gradient boosting decision tree, K-nearest neighbor, logistic regression, and Gaussian naïve Bayes classifier models. Data of 33 patients from one of the hospitals were prospectively collected for model validation. Results: Among 510 patients, 192 (37.65%) have not received any perioperative RBCs transfusion, 127 (24.90%) received less-transfusion (RBCs < 4U), and 191 (37.45%) received more-transfusion (RBCs ≥ 4U). Machine learning-based transfusion predictive model produced the best performance with the accuracy of 83.34%, and Kappa coefficient of 0.7967 compared with other methods (blood preparation based on surgeons' experience with the accuracy of 65.94%, and Kappa coefficient of 0.5704; the random forest method with an accuracy of 82.35%, and Kappa coefficient of 0.7858; the gradient boosting decision tree with an accuracy of 79.41%, and Kappa coefficient of 0.7742; the K-nearest neighbor with an accuracy of 53.92%, and Kappa coefficient of 0.3341). In the prospective dataset, it also had a food performance with accuracy 81.82%. Conclusion: This multicenter retrospective cohort study described the construction of an accurate model that could predict perioperative RBCs transfusion in patients with pelvic fractures.
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Affiliation(s)
- Xueyuan Huang
- Department of Blood Transfusion, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yongjun Wang
- Department of Blood Transfusion, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Bingyu Chen
- Department of Transfusion, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Yuanshuai Huang
- Department of Transfusion, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xinhua Wang
- Department of Transfusion, Beijing Aerospace Center Hospital, Beijing, China
| | - Linfeng Chen
- Department of Transfusion, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Rong Gui
- Department of Blood Transfusion, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xianjun Ma
- Department of Blood Transfusion, Qilu Hospital of Shandong University, Jinan, China
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