Putra Agina Widyaswara Suwaryo, Barkah Waladani, Ernawati Ernawati, Endah Setianingsih
Gulhane Medical Journal - 2025;67(4):259-267
Aims: This study aims to develop and implement a predictive model to enhance the early detection of stroke in patients with comorbidities, thereby enabling clinicians to identify high-risk patients more quickly and effectively. Methods: This study used a prospective cohort design involving 235 patients treated in the stroke care unit. Data were collected over six months through direct interviews, physical examinations, and laboratory analyses. Statistical analysis was conducted using multivariate logistic regression to identify the main predictive factors of stroke. The evaluation model was conducted using the area under the curve (AUC) to measure predictive accuracy. Results: A total of 235 patients were included in the analysis, with a mean age of 56.4+/-12.7 years; 76.6% were male and 23.4% female. Significant predictive factors for stroke occurrence included diabetes mellitus, hypertension, rheumatoid arthritis, physical activity, family health history, random blood sugar levels, uric acid levels, and salt consumption (p<0.05). The developed model achieved an AUC of 98.7%, which-based on comparisons with established models such as the Framingham Stroke Risk Profile (AUC~0.78) and the QStroke algorithm (AUC~0.80)-demonstrates substantially higher discriminative ability in distinguishing between patients with and without stroke risk. Conclusions: This predictive model has demonstrated a high capacity for detecting stroke risk in patients with comorbidities. The implementation of this model in clinical practice is expected to enhance the effectiveness of stroke screening and accelerate early intervention.
