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BackgroundMortality prediction in critically ill patients with cardiogenic shock can guide triage and selection of potentially high‐risk treatment options. Methods and ResultsWe developed and externally validated a checklist risk score to predict in‐hospital mortality among adults admitted to the cardiac intensive care unit with Society for Cardiovascular Angiography & Interventions Shock Stage C or greater cardiogenic shock using 2 real‐world data sets and Risk‐Calibrated Super‐sparse Linear Integer Modeling (RiskSLIM). We compared this model to those developed using conventional penalized logistic regression and published cardiogenic shock and intensive care unit mortality prediction models. There were 8815 patients in our training cohort (in‐hospital mortality 13.4%) and 2237 patients in our validation cohort (in‐hospital mortality 22.8%), and there were 39 candidate predictor variables. The final risk score (termed BOS,MA₂) included maximum blood urea nitrogen ≥25 mg/dL, minimum oxygen saturation <88%, minimum systolic blood pressure <80 mm Hg, use of mechanical ventilation, age ≥60 years, and maximum anion gap ≥14 mmol/L, based on values recorded during the first 24 hours of intensive care unit stay. Predicted in‐hospital mortality ranged from 0.5% for a score of 0 to 70.2% for a score of 6. The area under the receiver operating curve was 0.83 (0.82–0.84) in training and 0.76 (0.73–0.78) in validation, and the expected calibration error was 0.9% in training and 2.6% in validation. ConclusionsDeveloped using a novel machine learning method and the largest cardiogenic shock cohorts among published models, BOS,MA₂is a simple, clinically interpretable risk score that has improved performance compared with existing cardiogenic‐shock risk scores and better calibration than general intensive care unit risk scores. 

Predefined demographic groups often overlook the subpopulations most impacted by model errors, leading to a growing emphasis on data-driven methods that pinpoint where models underperform. The emerging field of multi-group fairness addresses this by ensuring models perform well across a wide range of group-defining functions, rather than relying on fixed demographic categories. We demonstrate that recently introduced notions of multi-group fairness can be equivalently formulated as integral probability metrics (IPM). IPMs are the common information-theoretic tool that underlie definitions such as multiaccuracy, multicalibration, and outcome indistinguishably. For multiaccuracy, this connection leads to a simple, yet powerful procedure for achieving multiaccuracy with respect to an infinite-dimensional class of functions defined by a reproducing kernel Hilbert space (RKHS): first perform a kernel regression of a model’s errors, then subtract the resulting function from a model’s predictions. We combine these results to develop a post-processing method that improves multiaccuracy with respect to bounded-norm functions in an RKHS, enjoys provable performance guarantees, and, in binary classification benchmarks, achieves favorable multiaccuracy relative to competing methods.