Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach
Research objectives Clostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mor...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2021-10-01
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| Series: | BMJ Open Gastroenterology |
| Online Access: | https://bmjopengastro.bmj.com/content/8/1/e000761.full |
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| _version_ | 1846138113742602240 |
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| author | Mengru Liu Mengling Feng Kewin Tien Ho Siah Wesley Yeung Hao Du Valencia Zhang Ru-Yan Readon Teh Christopher Yu En Tan Christina Scaduto Sarah Bolongaita Maria Teresa Kasunuran Cruz Xiaohao Lin Yan Yuan Tan |
| author_facet | Mengru Liu Mengling Feng Kewin Tien Ho Siah Wesley Yeung Hao Du Valencia Zhang Ru-Yan Readon Teh Christopher Yu En Tan Christina Scaduto Sarah Bolongaita Maria Teresa Kasunuran Cruz Xiaohao Lin Yan Yuan Tan |
| author_sort | Mengru Liu |
| collection | DOAJ |
| description | Research objectives Clostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.Methodology The demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of results From 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.Conclusion Our machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI. |
| format | Article |
| id | doaj-art-a6f2772c8e4540158c4cf83b3c199340 |
| institution | Kabale University |
| issn | 2054-4774 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Gastroenterology |
| spelling | doaj-art-a6f2772c8e4540158c4cf83b3c1993402024-12-07T08:40:12ZengBMJ Publishing GroupBMJ Open Gastroenterology2054-47742021-10-018110.1136/bmjgast-2021-000761Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approachMengru Liu0Mengling Feng1Kewin Tien Ho Siah2Wesley Yeung3Hao Du4Valencia Zhang Ru-Yan5Readon Teh6Christopher Yu En Tan7Christina Scaduto8Sarah Bolongaita9Maria Teresa Kasunuran Cruz10Xiaohao Lin11Yan Yuan Tan12School of Computing and Information Systems, Singapore Management University, SingaporeSaw Swee Hock School of Public Health, National University Health System, National University of Singapore, SingaporeDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, SingaporeLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USASaw Swee Hock School of Public Health, National University Health System, National University of Singapore, SingaporeUniversity Medicine Cluster, National University Hospital, SingaporeUniversity Medicine Cluster, National University Hospital, SingaporeDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, SingaporeDepartment of Global Health and Population, Harvard TH Chan School of Public Health, Boston, Massachusetts, USAHealth, Nutrition and Population, The World Bank Group, Washington, DC, USAUniversity Medicine Cluster, National University Hospital, SingaporeMachine Intellection Department, Institute for Infocomm Research, Agency for Science Technology and Research, SingaporeAlliance Healthcare Group, SingaporeResearch objectives Clostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.Methodology The demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of results From 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.Conclusion Our machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.https://bmjopengastro.bmj.com/content/8/1/e000761.full |
| spellingShingle | Mengru Liu Mengling Feng Kewin Tien Ho Siah Wesley Yeung Hao Du Valencia Zhang Ru-Yan Readon Teh Christopher Yu En Tan Christina Scaduto Sarah Bolongaita Maria Teresa Kasunuran Cruz Xiaohao Lin Yan Yuan Tan Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach BMJ Open Gastroenterology |
| title | Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach |
| title_full | Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach |
| title_fullStr | Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach |
| title_full_unstemmed | Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach |
| title_short | Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach |
| title_sort | prediction of in hospital mortality of clostriodiodes difficile infection using critical care database a big data driven machine learning approach |
| url | https://bmjopengastro.bmj.com/content/8/1/e000761.full |
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