Predicting ICU mortality in patients with abdominal aortic aneurysm: a nomogram based on MIMIC-IV and eICU-CRD

Abstract

<b>Background:</b> Abdominal aortic aneurysm (AAA), characterized by pathological aortic dilation, carries high mortality in intensive care unit (ICU) settings. However, existing severity scores (e.g., SAPS III, SOFA) poorly capture AAA-specific mortality predictors. We aimed to develop a focused prognostic tool to improve short-term risk stratification in ICU-admitted AAA patients. <b>Objective:</b> To develop and validate a machine learning-based nomogram model using the Medical Information Mart for Intensive Care IV (MIMIC-IV; 2008-2019) and the eICU Collaborative Research Database (eICU-CRD; 2014-2015) for early mortality prediction (≤7 days) in critically ill patients with AAA, addressing limitations of conventional ICU scoring systems by integrating AAA-specific predictors and ensuring generalizability through external validation. <b>Methods:</b> Using two independent datasets from MIMIC-IV and eICU-CRD databases, we identified patients with AAA with complete ICU records and lab data within 24 hours of admission. Critical predictors were selected via a dual approach: least absolute shrinkage and selection operator (LASSO) regression to eliminate collinearity and support vector machine-recursive feature elimination (SVM-RFE) to rank feature importance. MIMIC-IV served as the training dataset, while eICU-CRD was used for external validation. A Cox regression-based nomogram was constructed using the training set and tested for 7-, 14-, and 28-day mortality prediction. Model performance was evaluated using area under the ROC curve (AUC), concordance index (C-index), calibration plots, and decision curve analysis. <b>Results:</b> Six key variables independently predicted mortality including age, sepsis, blood urea nitrogen (BUN), antihypertensive drug use, average percutaneous arterial oxygen saturation (SpO₂), and anion gap. The nomogram demonstrated optimal predictive accuracy for 7-day mortality (AUC: 0.730 [training] and 0.718 [validation]; C-indices: 0.717 and 0.731), with reduced performance for 14-day and 28-day outcomes. Calibration curves displayed strong agreement at both 7 and 14 days, and DCA indicated that the model provides significant clinical value. External validation in eICU-CRD mirrored these trends (7-day AUC: 0.723), supporting model generalizability. <b>Conclusion:</b> This multicohort-derived nomogram provides a simple yet reliable tool to predict early mortality (≤7 days) in critically ill AAA patients, which may guide time-sensitive interventions in critical care settings.

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