Data for: Prognostic Model of In-hospital Ischemic Stroke Mortality Based on an Electronic Health Record Cohort in Indonesia
Description
Background: Stroke patients rarely have satisfactory survival, which worsens further if comorbidities develop in such patients. Limited data availability from South-east Asia countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate predictors of in-hospital mortality in patients with ischemic stroke (IS). Methods: This retrospective observational study used IS medical records from the National Brain Centre Hospital, Jakarta, Indonesia. A theoretically driven logistic regression model was established by controlling for age and sex to calculate the odds ratio of each plausible risk factor for predicting post-stroke mortality. Findings: This study included 3,479 patients with IS, 999 (28.72%) of whom had cardiovascular disease, 421 (12.1%) had renal disease, and 511 (14.69%) were verbally incoherent. Bivariate exploratory analysis revealed lower blood levels of triglycerides, low density lipoprotein, and total cholesterol in patients with post-stroke mortality. The average age of patients with post-stroke mortality was 64 ± 12 years, with a mean body mass index (BMI) of 24 ± 3.5 kg/m2 and a median Glasgow Coma Scale (GCS) score of 12 ± 5. Cardiovascular disease was more prevalent than renal disease (28.72% vs. 12.1%), and both contributed to a 4.5-times increase in the mortality risk. Comorbidities, such as cardiovascular disease (odds ratio [OR]=2.66, 95% confidence interval [CI]: 1.82–3.91) and renal disease (OR=2.63, 95% CI: 1.77–3.89), caused higher odds of post-stroke mortality. However, the factors contributing to lower odds of mortality were BMI (OR=0.94, 95% CI: 0.89–0.99) and GCS (OR=0.67, 95% CI: 0.67–0.72). Conclusion: After controlling for age and sex, our study reported that cardiovascular diseases, renal disease, BMI, and GCS on admission were strong predictors of in-hospital mortality in patients with IS. Keywords: ischemic stroke; cardiovascular disease; renal disease; BMI; GCS and prognostic model
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Follows the step provided in `analysis.R`