Semantic Visualization in Functional Recovery Prediction of Intravenous Thrombolysis following Acute Ischemic Stroke in Patients by Using Biostatistics: An Exploratory Study

Abstract

(1) Background: Intravenous thrombolysis following acute ischemic stroke (AIS) can reduce disability and increase the survival rate. We designed a functional recovery analysis by using semantic visualization to predict the recovery probability in AIS patients receiving intravenous thrombolysis; (2) Methods: We enrolled 131 AIS patients undergoing intravenous thrombolysis from 2011 to 2015 at the Medical Center in northern Taiwan. An additional 54 AIS patients were enrolled from another community hospital. A modified Rankin Score ≤2 after 3 months of follow-up was defined as favorable recovery. We used multivariable logistic regression with forward selection to construct a nomogram; (3) Results: The model included age and the National Institutes of Health Stroke Scale (NIHSS) score as immediate pretreatment parameters. A 5.23% increase in the functional recovery probability occurred for every 1-year reduction in age, and a 13.57% increase in the functional recovery probability occurred for every NIHSS score reduction. The sensitivity, specificity, and accuracy of the model in the validation dataset were 71.79%, 86.67%, and 75.93%, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.867; (4) Conclusions: Semantic visualization-based functional recovery prediction models may help physicians assess the recovery probability before patients undergo emergency intravenous thrombolysis.

Keywords: acute ischemic stroke; functional recovery; intravenous thrombolysis.

Figure 1

Flow chart of intravenous thrombolysis prognosis.

Figure 2

Receiver operating characteristic curve of the training and validation datasets.

Figure 3

(a) Nomogram for functional recovery probability after intravenous thrombolysis. (b) Nomogram application of recovery probability before thrombolysis. A line was drawn straight upward with an up arrow to obtain the points for the variables. For example, a patient aged 60 years (43 points) had an NIHSS score of 12 (62 points). The total point score was the sum of age and NIHSS scores with 105. Drawing a straight line downward from total points 105 to the lowest line then corresponds to an estimated recovery probability of 67%.

Figure 4

The box plots of score distribution in both event and no-event groups in the training and validation datasets.

Figure 5

Decision curve analysis of the training dataset.