2026-10-12T07:30:23Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33979534 2026-09-24T04:33:47Z category_363 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Co-recorded conditions in ischemic and hemorrhagic stroke: a 20-year national analysis of 598,186 natural deaths with machine learning.docx Junlin Ge (22225468) Hangang Jiang (25102024) Yu Fu (16582) Hua Tian (220297) Wencheng Yuan (25102027) Lingyun Sun (255929) Neurology and Neuromuscular Diseases association rule mining co-recorded condition hemorrhagic stroke ischemic stroke machine learning public health stroke <p>The purpose of this study was to characterize the co-occurrence patterns of co-recorded conditions in ischemic and hemorrhagic stroke and identify co-recorded conditions using a large national dataset of fatal, subtype-confirmed stroke cases from Brazil. We analyzed 598,186 natural deaths recorded in the Brazilian Mortality Information System (SIM) between 2000 and 2019, comprising 288,724 (48.3%) hemorrhagic and 309,462 (51.7%) ischemic stroke. Hierarchical clustering was performed to identify co-recorded condition clusters, while association rule mining using the Apriori algorithm uncovered hidden patterns of disease co-occurrence. Logistic regression, random forest, and XGBoost models were developed to classify stroke subtype based on demographic characteristics and co-recorded condition profiles. Ischemic stroke patients had significantly higher co-recorded condition burden (median 2, IQR 1–3) compared to hemorrhagic stroke (median 1, IQR 1–2; p < 0.001). For hemorrhagic stroke, 12 association rules were identified; the strongest involved co-occurrence of infectious and respiratory diseases (e.g., {hypertension = Yes, infectious_disease = Yes} → {respiratory_disease = Yes}, lift = 2.57) and of endocrine/metabolic conditions with hypertension (lift = 1.67), whereas the rule {mental_disorders = Yes} → {hypertension = Yes} (lift = 1.27, confidence = 53.9%) was the weakest of the 12 rules. In contrast, ischemic stroke yielded 8 rules reflecting associations among infectious, respiratory, genitourinary, and endocrine/metabolic conditions. The logistic regression model achieved an AUC of 0.768 for predicting stroke subtype, with consistent performance across sensitivity analyses. Age-standardized analyses showed that ischemic stroke proportion increased from 48.8 to 51.5% over 20 years, with approximately half of the crude increase attributable to population aging (crude ischemic proportion reached 54.7%). Co-recorded condition burden increased substantially in both subtypes (≥3 co-recorded conditions in ischemic stroke: 24.8–33.3%). We observed that hemorrhagic and ischemic stroke have different co-occurrence patterns of co-recorded conditions: hemorrhagic stroke exhibited simpler co-occurrence patterns centered on hypertension together with infectious, respiratory, and endocrine/metabolic conditions, while ischemic stroke was characterized by clustered cardiovascular and metabolic multimorbidity. These findings warrant further investigation in prospective studies.</p> 2026-09-24T04:33:47Z Dataset Dataset 10.3389/fneur.2026.1854246.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Co-recorded_conditions_in_ischemic_and_hemorrhagic_stroke_a_20-year_national_analysis_of_598_186_natural_deaths_with_machine_learning_docx/33979534 CC BY 4.0