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

Background

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.

Objective

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.

Methods

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.

Results

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.

Conclusion

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.