Predicting early in-hospital mortality in acute hemorrhagic stroke: Implications for improving stroke care and health outcomes in low-income settings - Harnold, Shefina P.; Syahrul, Syahrul; Imran, Imran; Musadir, Nasrul; Yani, Muhammad (Narra Sains Indonesia, 2026)
Stroke is a leading cause of death and disability worldwide, and mortality in acute intracerebral hemorrhagic (ICH) stroke is influenced by many factors, and early identification of high-risk patients is crucial for guiding clinical management. This study aimed to evaluate the role of blood pressure, blood glucose level, and Glasgow coma scale (GCS) on admission as predictors of 10-day in-hospital mortality and to develop a predictive scoring system in patients with acute ICH stroke. A cross-sectional study was conducted at Dr. Zainoel Abidin Hospital, a provincial referral hospital in Banda Aceh, Indonesia, in 2025. Patients with acute ICH were consecutively recruited. Clinical parameters on admission, including systolic and diastolic blood pressure, random blood glucose level, and GCS, were recorded. Associations with 10-day mortality were assessed with a Chi-squared test, and a predictive scoring system was developed based on independent predictors. A total of 62 patients were included in this study. Higher systolic blood pressure (≥140 mmHg), diastolic blood pressure (≥90 mmHg) and GCS <9 on admission were significantly associated with 10-day mortality (p=0.031, p=0.023 and p<0.001, respectively). Multivariate analysis identified that GCS <9 was the only independent predictor. A predictive scoring system assigned 8 points for GCS <9, 5 points for systolic ≥140 mmHg, 4 points for diastolic ≥90 mmHg, and 1 point for random blood glucose ≥200 mg/dL, estimating patient-specific mortality risk, highest when all risk factors were present. This study indicates that GCS <9 and elevated blood pressure on hospital admission are key predictors of 10-day mortality in acute ICH. The developed scoring system may assist in early risk stratification and management, and further exploration of predictive models is warranted to optimize clinical outcomes.