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Predicting one-year mortality risk in ICU patients with ischemic stroke using multi-algorithm machine learning and a

Jian Huang1, Yalin Dong2, Xiaozhu Liu3

  • 1Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Hangzhou, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

A new nomogram predicts one-year mortality risk in intensive care unit (ICU) patients with ischemic stroke. This tool integrates key clinical factors, outperforming existing scoring systems for better risk stratification.

Keywords:
ICUIschemic strokeMIMIC-IVmachine learningmortality predictionnomogram

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Area of Science:

  • Neurology
  • Critical Care Medicine
  • Biostatistics

Background:

  • Ischemic stroke is a major cause of death.
  • Patients admitted to the intensive care unit (ICU) after ischemic stroke have a poor prognosis.
  • Accurate risk stratification is crucial for managing these high-risk patients.

Purpose of the Study:

  • To develop and validate a predictive model for estimating one-year mortality risk in ICU-admitted ischemic stroke patients.
  • To create a practical tool for clinical use in risk stratification.

Main Methods:

  • Retrospective cohort study using the MIMIC-IV database (1974 patients).
  • Data split into training (80%) and test (20%) sets.
  • Machine learning algorithms used for feature selection, followed by multivariate logistic regression and nomogram construction.
  • Model performance evaluated using discrimination, calibration, and clinical utility metrics, compared against established scoring systems.

Main Results:

  • A nomogram was developed incorporating nine predictors: age, heart rate, weight, glucose, anion gap, calcium, alkaline phosphatase (ALP), red cell distribution width (RDW), and mean corpuscular hemoglobin concentration (MCHC).
  • The model achieved an AUC of 0.739 in the training set and 0.737 in the test set.
  • The nomogram demonstrated good calibration, favorable clinical net benefit, and superior discriminatory ability compared to SOFA, SAPS II, LODS, OASIS, and GCS scores.

Conclusions:

  • A validated, practical nomogram effectively predicts one-year mortality risk in ICU patients with ischemic stroke.
  • The nomogram integrates key clinical variables for robust risk stratification.
  • This tool has potential clinical utility for improving patient management and outcomes.