Construction of a prediction model for risk of early neurological deterioration following intravenous thrombolysis in patients with acute ischemic stroke

Abstract

BACKGROUND:

The etiology of early neurological deterioration (END) occurring after intravenous thrombolysis is unclear.

OBJECTIVE:

To investigate the factors associated with END following intravenous thrombolysis in patients with acute ischemic stroke, and to construct a prediction model.

METHODS:

We selected a total of 321 patients with acute ischemic stroke, who were divided into two groups: the END group ( $n=$ 91) and the non-END group ( $n=$ 230). They were compared for their demographics, onset-to-needle time (ONT), door-to-needle time (DNT), related score results, and other data. The risk factors of the END group were identified using logistic regression analysis, and we constructed a nomogram model using the R software. A calibration curve was used to evaluate the calibration of the nomogram, and we assessed its clinical applicability using decision curve analysis (DCA).

RESULTS:

In our multivariate logistic regression analysis, we found that four indexes, namely, complication with atrial fibrillation, post-thrombolysis National Institutes of Health Stroke Scale (NIHSS) score, pre-thrombolysis systolic blood pressure (SBP), and serum albumin level, were independent risk factors for END following intravenous thrombolysis in the patients ( $P<$ 0.05). We constructed an individualized nomogram prediction model using the above four predictors. The AUC value of the nomogram model was 0.785 (95% CI: 0.727–0.845) after internal validation, and the mean absolute error (MAE) in the calibration curve was 0.011, which indicated that the nomogram model had good prediction value. The decision curve analysis indicated that the nomogram model was clinically relevant.

CONCLUSION:

The model was found to have excellent value in clinical application and prediction of END. This will be beneficial for healthcare providers to develop individualized prevention measures for END in advance, and thus reduce the incidence of END following intravenous thrombolysis.