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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).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.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.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.

Background Mounting evidence suggests the safety and efficacy of medical marijuana (MM) in treating chronic ailments, including chronic pain, epilepsy, and anorexia. Despite incremental use of medical and recreational cannabinoids, current limited evidence shows generalized unpreparedness of medical providers to discuss or recommend these substances to their patients. Herein, the present study aims to examine internal medicine residents’ knowledge of marijuana and their attitude towards its medical use. Methods This is a descriptive cross-sectional study. A survey with 12 standardized queries was created and distributed among the internal medicine residents from Mount Sinai Morningside-West (MSMW) program from July 2020 to December 2020. Participants included preliminary and categorical residents from post-graduate years one to three. The survey consisted of self-assessment of residents’ knowledge on the indication, contraindication, adverse effects of MM. Results Eighty-six (59%) out of 145 residents completed the questionnaire. Despite most trainees (70%) having considered certifying the use of MM for their patients, over 90% reported none to little knowledge on its use. Approximately 80% of the surveyed residents expressed willingness to receive an appropriate educational curriculum. Conclusion To the best of our knowledge, this is the first study that indicated a critical lack of medical marijuana-related knowledge in surveyed internal medicine residents. In a population with growing cannabis consumption, physician training on the indication, toxicity, and drug interaction of cannabinoids is warranted.