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 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.

Systematic review on the definition and predictors of severe Clostridiodes difficile infection.

Clostridiodes difficile infection (CDI) is one of the most common hospital-acquired infections with high mortality rates. Optimal management of CDI depends on early recognition of severity. However, currently, there is no acceptable standard of prediction. We reviewed severe CDI predictors in published literature and its definition according to clinical guidelines. We systematically reviewed studies describing clinical predictors for severe CDI in medical databases (Cochrane, EMBASE, Global Health Library, and MEDLINE/PubMed). They were independently evaluated by two reviewers. Six hundred thirty-three titles and abstracts were screened, and 31 studies were included. We excluded studies that were restricted to a specific patient population. There were 16 articles that examined mortality in CDI, as compared with 15 articles investigating non-mortality outcomes of CDI. The commonest risk factors identified were comorbidities, white blood cell count, serum albumin level, age, serum creatinine level and intensive care unit admission. Generally, the studies had small patient populations, were retrospective in nature, and mostly from Western centers. The commonest severe CDI criteria in clinical guidelines were raised white blood cell count, followed by low serum albumin and raised serum creatinine levels. There was no commonly agreed upon definition of severe CDI severity in the literature. Current clinical guidelines' definitions for severe CDI are heterogeneous. Hence, there is a need for prospective multi-center studies using standardized protocol for biospecimen investigation collection and shared data on outcomes of patients in order to devise a universally accepted definition for severe CDI.