Journal of Gastroenterology and Hepatology.

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

Paris Charilaou, Sonmoon Mohapatra, Sotirios Doukas, Maanit Kohli, Dhruvil Radadiya, Kalpit Devani, Arkady Broder, Olivier Elemento, Dana J Lukin, Robert Battat. Journal of Gastroenterology and Hepatology, (2022).

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 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.

doi: https://doi.org/10.1111/jgh.16029

This publication is open access.

Anyone can read, download, distribute, or cite this paper. You can opt out at any time. We’re cookieless, and our privacy policy is actually easy to read.

Try the QLattice.

Experience the future of AI, where accuracy meets simplicity and explainability.

Models developed by the QLattice have unparalleled accuracy, even with very little data, and are uniquely simple to understand.

The QLattice: Explainable AI

Share this publication.

The QLattice accelerates discoveries with explainable insights.​

Researchers and and scientists cite Abzu’s QLattice symbolic AI in industry-leading journals for introducing a new standard of performance and explainability to data sets.

Subscribe for
notifications from Abzu.

You can opt out at any time. We’re cookieless, and our privacy policy is actually easy to read.