In Journal of gastroenterology and hepatology ; h5-index 51.0
BACKGROUND & AIM : Data are lacking on predicting inpatient mortality (IM) in patients admitted for inflammatory bowel disease (IBD). IM is a critical outcome, however, difficulty in its prediction exists due to infrequent occurrence. We assessed IM predictors and developed a predictive model for IM using machine-learning (ML).
METHODS : Using the National Inpatient Sample (NIS) database (2005-2017), we extracted adults admitted for IBD. After ML-guided predictor selection, we trained and internally validated multiple algorithms, targeting minimum sensitivity and positive likelihood ratio (+LR) ≥80% and ≥3, respectively. Diagnostic odds ratio (DOR) compared algorithm performance. The best-performing algorithm was additionally trained and validated for an IBD-related surgery sub-cohort. External validation was done using NIS-2018.
RESULTS : In 398,426 adult IBD admissions, IM was 0.32% overall, and 0.87% among the surgical cohort (n=40,784). Increasing age, ulcerative colitis, IBD-related surgery, pneumonia, chronic lung disease, acute kidney injury, malnutrition, frailty, heart failure, blood transfusion, sepsis/septic shock and thromboembolism were associated with increased IM. The QLattice algorithm, provided the highest performance model (+LR:3.2,95% CI 3.0-3.3; area-under-curve [AUC]:0.87, 85% sensitivity, 73% specificity), distinguishing IM patients by 15.6-fold when comparing high to low-risk patients. The surgical cohort model (+LR:8.5,AUC:0.94, 85% sensitivity, 90% specificity), distinguished IM patients by 49-fold. Both models performed excellently in external validation. An online calculator (https://clinicalc.ai/im-ibd/) was developed allowing bedside model predictions.
CONCLUSIONS : An online prediction-model calculator captured >80% IM cases during IBD-related admissions, with high discriminatory effectiveness. This allows for risk stratification and provides a basis for assessing interventions to reduce mortality in high-risk patients.
Charilaou Paris, Mohapatra Sonmoon, Doukas Sotirios, Kohli Maanit, Radadiya Dhruvil, Devani Kalpit, Broder Arkady, Elemento Olivier, Lukin Dana J, Battat Robert
2022-Oct-18
artificial intelligence, calculator, hospitalized patients, ibd, machine learning, prediction model
