Cumulative Frameworks as a Pragmatic Alternative to Multivariable Modeling in Rare-Event Clinical Settings: A Coronary Care Unit Case Study

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Background: Risk stratification models are widely used in clinical research; however, their development becomes methodologically challenging in settings characterized by low outcome incidence. In coronary care unit (CCU) populations, healthcare-associated infections (HAIs) occur relatively infrequently, limiting the feasibility of conventional multivariable predictive modeling. Methods: A retrospectively assembled CCU cohort comprising 870 patients with 16 HAI events (1.8%) was used as an illustrative example to examine methodological constraints associated with low events-per-variable (EPV) ratios. The implications of limited event frequency for multivariable logistic regression were evaluated, including risks of overfitting, coefficient instability, and reduced reproducibility. As an alternative strategy, a prespecified cumulative additive framework integrating baseline vulnerability and exposure-related variables was conceptually and analytically explored. Results: With four candidate predictors and 16 outcome events, the resulting EPV was approximately four, indicating a high risk of instability for conventional multivariable modeling. The cumulative framework allowed structured cumulative stratification without coefficient optimization. Infection occurrence increased progressively across cumulative framework levels, illustrating a graded pattern of increasing HAI occurrence with accumulating vulnerability and exposure-related burden. Conclusions: In clinical datasets with limited outcome events, modeling strategies should be aligned with the informational capacity of the data. Cumulative additive frameworks may represent a pragmatic structural alternative to coefficient-based modeling approaches in rare-event clinical settings. The present work provides a structured methodological framework for risk stratification under low-events-per-variable conditions rather than proposing a novel clinical scoring system.

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