Abstract
OBJECTIVES: Hemorrhagic transformation (HT) is significantly related to poor neurological outcomes and mortality. Although variables and models that predict HT have been reported in the literature, the need for a model with high diagnostic performance continues. We aimed to propose a model that can accurately predict symptomatic HT within 7 days of acute ischemic stroke (AIS). METHODS: Patients with AIS admitted to the emergency department of a tertiary training and research hospital between November 07, 2021, and August 26, 2022, were included in this single-center retrospective study. For the model, binary logistics with the forced-entry method was used and the model was validated with 3-fold cross-validation. After the final model was created, the optimal cutoff point was determined with Youden's index. Another cut-off point was determined at which the sensitivity was the highest. RESULTS: The mean age of the 423 patients included in the study was 70 (60-81) and 53.7% (n = 227) of the patients were male. Symptomatic HT was present in 31 (7.3%) patients. Mechanical thrombectomy, atrial fibrillation, and diabetes mellitus were the independent predictors (P < 0.001, P = 0.003, P = 0.006, respectively). The mean area under the curve of the receiver operating characteristics of the model was 0.916 (95% confidence interval [CI] = 0.876-0.957). The sensitivity for the optimal cut-off point was 90.3% (95% CI = 74.3%-97.9%) and specificity was 80.6% (95% CI = 76.4%-84.4%). For the second cutoff point where the sensitivity was 100%, the specificity was 60.5% (95% CI = 55.4%-65.3%). CONCLUSION: The diagnostic performance of our model was satisfactory and it seems to be promising for symptomatic HT. External validation studies are required to implement our results into clinical use.