Associations of International Normalized Ratio and Hematocrit With Stroke Status and Machine Learning Mortality Prediction in Critical Care Databases

Stroke is a major cause of mortality and long-term disability. This retrospective cohort study evaluated associations of international normalized ratio (INR) and hematocrit (Hct) with stroke status in 29,831 adult intensive care unit (ICU) patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). Stroke status was identified using predefined International Classification of Diseases, Ninth and Tenth Revision codes. INR and Hct were defined using the first measurements within 24 h after ICU admission. Associations were evaluated using multivariable logistic regression, quartile analyses, restricted cubic splines (RCS), subgroup analyses, and sensitivity analyses. Primary analyses used complete-case cohorts, with multiple imputation as a sensitivity analysis. A post hoc exploratory secondary analysis among patients with stroke used Cox regression to identify six prognostic variables for machine learning-based prediction of ICU mortality. Stroke proportions decreased across increasing INR quartiles and increased across increasing Hct quartiles. After full adjustment, each 1-unit increase in INR was associated with lower stroke odds, with odds ratios (ORs) of 0.105-0.395 across cohorts, whereas each 1-percentage-point increase in Hct was associated with higher stroke odds (ORs, 1.076-1.103). RCS analyses supported overall associations but not consistent nonlinearity after full adjustment. Multiple-imputation results were directionally consistent. The gradient boosting machine (GBM) model yielded areas under the receiver operating characteristic curve (AUCs) of 0.717 (95% confidence interval [CI], 0.669-0.766), 0.670 (95% CI, 0.603-0.737), and 0.630 (95% CI, 0.557-0.703) in the training, internal validation, and external validation cohorts, respectively. INR was inversely associated and Hct positively associated with stroke status. These observational associations require cautious interpretation; the exploratory ICU mortality analysis provides a complementary prognostic perspective among patients with stroke.

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Publication Details

Journal
Journal of Visualized Experiments
Published
2026-10-27
DOI
https://doi.org/10.3791/73411
Primary Topic
Machine Learning in Healthcare
Type
article
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article

Associations of International Normalized Ratio and Hematocrit With Stroke Status and Machine Learning Mortality Prediction in Critical Care Databases

Chunyan Lei, Linming Zhang, Juan Han
Journal of Visualized Experiments
Machine Learning in Healthcare
article

Associations of International Normalized Ratio and Hematocrit With Stroke Status and Machine Learning Mortality Prediction in Critical Care Databases

Chunyan Lei, Linming Zhang, Juan Han
article en

Abstract

Stroke is a major cause of mortality and long-term disability. This retrospective cohort study evaluated associations of international normalized ratio (INR) and hematocrit (Hct) with stroke status in 29,831 adult intensive care unit (ICU) patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). Stroke status was identified using predefined International Classification of Diseases, Ninth and Tenth Revision codes. INR and Hct were defined using the first measurements within 24 h after ICU admission. Associations were evaluated using multivariable logistic regression, quartile analyses, restricted cubic splines (RCS), subgroup analyses, and sensitivity analyses. Primary analyses used complete-case cohorts, with multiple imputation as a sensitivity analysis. A post hoc exploratory secondary analysis among patients with stroke used Cox regression to identify six prognostic variables for machine learning-based prediction of ICU mortality. Stroke proportions decreased across increasing INR quartiles and increased across increasing Hct quartiles. After full adjustment, each 1-unit increase in INR was associated with lower stroke odds, with odds ratios (ORs) of 0.105-0.395 across cohorts, whereas each 1-percentage-point increase in Hct was associated with higher stroke odds (ORs, 1.076-1.103). RCS analyses supported overall associations but not consistent nonlinearity after full adjustment. Multiple-imputation results were directionally consistent. The gradient boosting machine (GBM) model yielded areas under the receiver operating characteristic curve (AUCs) of 0.717 (95% confidence interval [CI], 0.669-0.766), 0.670 (95% CI, 0.603-0.737), and 0.630 (95% CI, 0.557-0.703) in the training, internal validation, and external validation cohorts, respectively. INR was inversely associated and Hct positively associated with stroke status. These observational associations require cautious interpretation; the exploratory ICU mortality analysis provides a complementary prognostic perspective among patients with stroke.

Journal of Visualized Experiments(236)
Kunming Medical University (CN)
Good health and well-being
Openalex Percentile: Top 13%
Machine Learning in Healthcare
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