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<p>This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients admitted to the intensive care unit (ICU) using only routine clinical variables, without requiring National Institutes of Health Stroke Scale (NIHSS) scores or neuroimaging data. This retrospective study included 3385 ICU-admitted AIS patients from a large tertiary hospital in China. Sixteen predictors available within 48&#xa0;h of admission were selected, explicitly excluding NIHSS and neuroimaging features. Six models were compared using discrimination, calibration, and decision curve analysis. SHapley Additive exPlanations (SHAP) provided interpretability. Test-set area under the receiver operating characteristic curves (AUCs) ranged from 0.845 to 0.855 with overlapping confidence intervals, suggesting comparable discrimination across models. Gradient Boosting achieved the best overall performance with an AUC of 0.855 (95% CI: 0.817–0.892), good calibration (Brier score = 0.083), and the broadest net benefit range on decision curve analysis (0.07–0.59); by contrast, the remaining models showed substantially poorer calibration (Brier scores 0.132–0.150) and narrower clinically useful threshold ranges. SHAP analysis identified mechanical ventilation (21.2%), intubation (19.8%), pulmonary infection (14.8%), and broad-spectrum antibiotics (14.2%) as top contributors, collectively accounting for approximately 70% of model output. A gradient boosting model using 16 routine clinical variables achieves strong in-hospital mortality prediction (AUC = 0.855) without requiring NIHSS or neuroimaging. SHAP-based individualized risk decomposition enables clinicians to identify dominant risk drivers at the patient level, supporting early triage, targeted monitoring, and goals-of-care discussions—particularly in settings where neurological scoring or imaging resources are unavailable or delayed.</p>

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Beyond NIHSS and neuroimaging: an interpretable gradient boosting model for predicting in-hospital mortality in ICU patients with acute ischemic stroke

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Limin Yang,
  • Yue Song,
  • Guangxin Gu,
  • Ruiwen Wang,
  • Peng Chen,
  • Lianhui Fan

摘要

This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients admitted to the intensive care unit (ICU) using only routine clinical variables, without requiring National Institutes of Health Stroke Scale (NIHSS) scores or neuroimaging data. This retrospective study included 3385 ICU-admitted AIS patients from a large tertiary hospital in China. Sixteen predictors available within 48 h of admission were selected, explicitly excluding NIHSS and neuroimaging features. Six models were compared using discrimination, calibration, and decision curve analysis. SHapley Additive exPlanations (SHAP) provided interpretability. Test-set area under the receiver operating characteristic curves (AUCs) ranged from 0.845 to 0.855 with overlapping confidence intervals, suggesting comparable discrimination across models. Gradient Boosting achieved the best overall performance with an AUC of 0.855 (95% CI: 0.817–0.892), good calibration (Brier score = 0.083), and the broadest net benefit range on decision curve analysis (0.07–0.59); by contrast, the remaining models showed substantially poorer calibration (Brier scores 0.132–0.150) and narrower clinically useful threshold ranges. SHAP analysis identified mechanical ventilation (21.2%), intubation (19.8%), pulmonary infection (14.8%), and broad-spectrum antibiotics (14.2%) as top contributors, collectively accounting for approximately 70% of model output. A gradient boosting model using 16 routine clinical variables achieves strong in-hospital mortality prediction (AUC = 0.855) without requiring NIHSS or neuroimaging. SHAP-based individualized risk decomposition enables clinicians to identify dominant risk drivers at the patient level, supporting early triage, targeted monitoring, and goals-of-care discussions—particularly in settings where neurological scoring or imaging resources are unavailable or delayed.