Machine learning modeling for outcome prediction of hospitalized patients with aneurysmal subarachnoid hemorrhage

Abstract

Purpose

Aneurysmal rupture and subarachnoid hemorrhage (SAH) have an exceptionally high mortality and morbidity burden. The aim of this study was to develop interpretable machine learning models for predicting short-term poor outcomes defined by the National Inpatient Sample Subarachnoid Hemorrhage Outcome Measure (NIS-SOM).

Methods

The National Inpatient Sample (NIS) database was queried from 2008 to 2018 to identify patients diagnosed with SAH who had undergone endovascular coiling or clipping for intracranial aneurysm. Demographic, comorbidity, risk factor, and hospital characteristic variables were recorded. Variables were preprocessed, and the feature space was reduced to include the most important features. To predict poor outcomes, machine learning models were trained and cross-validated before being evaluated on a separate testing set. Shapley Additive exPlanations of the best performing model was used for general and local model interpretation.

Results

Among 18,149 admissions (mean age 55 ± 14 years, 68.8% women), 52.9% had a poor outcome. Test-set AUCs ranged 0.74–0.80; a multilayer perceptron performed best (AUC 0.80, precision 0.74, recall 0.82). SHAP ranked the ten most influential variables: age, neurological comorbidity, paralysis, Medicare insurance, smoking status, Elixhauser burden, fluid-electrolyte disorders, weight loss, arrhythmia, and heart failure.

Conclusions

The modeling predicted nationwide aSAH prognosis with decent accuracy and highlighted clinical, socioeconomic, and system-level drivers of determinants of poor short-term outcome. These results support the potential of explainable ML tools as complementary tools for early risk stratification, guiding resource allocation, and informing prospective multi-center validation and implementation studies.