Development of a simple clinical score to predict early neurological improvement after mechanical thrombectomy: a...

Early neurological improvement (ENI) after endovascular thrombectomy (EVT) is a clinically relevant early outcome and has been associated with subsequent functional recovery. However, simple bedside approaches for estimating the likelihood of ENI using routinely available clinical variables remain limited. We therefore sought to develop and internally evaluate a pragmatic prediction model for ENI after EVT in a real-world stroke cohort.

We performed a single-centre retrospective cohort study at Shanxi Provincial People's Hospital. A total of 314 EVT-treated patients were initially screened from hospital records. After preliminary data verification and assembly of the research database, 253 patients remained in the final study database available for variable-level assessment, of whom 185 with complete data on the primary outcome and prespecified key model variables were included in the primary complete-case analysis. ENI was defined as either a reduction in NIHSS score of at least 8 points from baseline to 1 week or an absolute 1-week NIHSS score of 1 or less. Candidate predictors included age, sex, baseline NIHSS, diabetes, cardioembolic aetiology, prior cerebrovascular disease, and door-to-puncture time (DPT). A multivariable logistic regression model was developed and translated into a simplified bedside score based on baseline NIHSS category and cardioembolic aetiology. Model performance was assessed using discrimination, calibration, Brier score, decision curve analysis, and bootstrap internal validation. Sensitivity analyses included an alternative ENI-4 definition, 48-hour neurological improvement as an alternative early outcome, alternative DPT thresholds, and multiple imputation for incomplete baseline covariates only.

Among the 185 patients in the primary analytical cohort, 53 (28.6%) achieved ENI. Baseline NIHSS was the dominant predictor of ENI in both univariable and multivariable analyses, whereas the additional contribution of other candidate predictors was modest. In the full model (Model 2), each 1-point increase in baseline NIHSS was associated with a 13% increase in the odds of ENI (adjusted OR 1.13, 95% CI 1.05-1.21; p < 0.001). The full model showed an apparent AUC of 0.706 and an optimism-corrected AUC of 0.657 after 1,000 bootstrap resamples; the corresponding Brier scores were 0.181 and 0.197. Bootstrap-corrected calibration suggested some overfitting (intercept - 0.335, slope 0.591). The simplified bedside score yielded an apparent and optimism-corrected AUC of 0.677, while the NIHSS-only model showed an apparent AUC of 0.673 and an optimism-corrected AUC of 0.674. Missing 1-week NIHSS was associated with higher baseline NIHSS, shorter length of stay, lower availability of 48-hour NIHSS, and worse discharge outcomes, suggesting that missing outcome data were unlikely to be completely random. Sensitivity analyses using alternative outcome definitions, alternative DPT thresholds, and multiple imputation for incomplete baseline covariates were broadly supportive of the primary findings, although some smaller-effect covariates were unstable in restricted subsets.

In this single-centre real-world EVT cohort, baseline NIHSS emerged as the main predictor of early neurological improvement. A parsimonious model based on routinely available clinical variables showed only moderate discrimination, and the derived simplified bedside score may be useful for exploratory early risk stratification rather than as a stand-alone clinical decision tool. Given the substantial missingness in 1-week NIHSS, the possibility of selection bias, evidence of overfitting, and the absence of external validation, the model should be considered exploratory and requires independent validation before routine clinical use.