Stroke Prognostic Scores and Data-Driven Prediction of Clinical Outcomes After Acute Ischemic Stroke

Abstract

Background and Purpose- Several stroke prognostic scores have been developed to predict clinical outcomes after stroke. This study aimed to develop and validate novel data-driven predictive models for clinical outcomes by referring to previous prognostic scores in patients with acute ischemic stroke in a real-world setting. Methods- We used retrospective data of 4237 patients with acute ischemic stroke who were hospitalized in a single stroke center in Japan between January 2012 and August 2017. We first validated point-based stroke prognostic scores (preadmission comorbidities, level of consciousness, age, and neurological deficit [PLAN] score, ischemic stroke predictive risk score [IScore], and acute stroke registry and analysis of Lausanne [ASTRAL] score in all patients; Houston intraarterial recanalization therapy [HIAT] score, totaled health risks in vascular events [THRIVE] score, and stroke prognostication using age and National Institutes of Health Stroke Scale-100 [SPAN-100] in patients who received reperfusion therapy) in our cohort. We then developed predictive models using all available data by linear regression or decision tree ensembles (random forest and gradient boosting decision tree) and evaluated their area under the receiver operating characteristic curve for clinical outcomes after repeated random splits. Results- The mean (SD) age of the patients was 74.7 (12.9) years and 58.3% were men. Area under the receiver operating characteristic curves (95% CIs) of prognostic scores in our cohort were 0.92 PLAN score (0.90-0.93), 0.86 for IScore (0.85-0.87), 0.85 for ASTRAL score (0.83-0.86), 0.69 for HIAT score (0.62-0.75), 0.70 for THRIVE score (0.64-0.76), and 0.70 for SPAN-100 (0.63-0.76) for poor functional outcomes, and 0.87 for PLAN score (0.85-0.90), 0.88 for IScore (0.86-0.91), and 0.88 ASTRAL score (0.85-0.91) for in-hospital mortality. Internal validation of data-driven prediction models showed that their area under the receiver operating characteristic curves ranged between 0.88 and 0.94 for poor functional outcomes and between 0.84 and 0.88 for in-hospital mortality. Ensemble models of a decision tree tended to outperform linear regression models in predicting poor functional outcomes but not in predicting in-hospital mortality. Conclusions- Stroke prognostic scores perform well in predicting clinical outcomes after stroke. Data-driven models may be an alternative tool for predicting poststroke clinical outcomes in a real-world setting.

Keywords: brain infarction; decision tree; in-hospital mortality; reperfusion; stroke.