Predicting inpatient mortality in patients with inflammatory bowel disease: A machine learning approach.

ABSTRACT:

Background and 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

SUBMITTER: Charilaou P 

PROVIDER: S-EPMC10099396 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature