Modeling and simulation to predict the degree of disability over time in acute ischemic stroke patients

Disability in patients with acute stroke varies over time, with the prediction of outcomes being critical for proper management. This study aimed to develop a model to predict the cumulative probability of each modified Rankin Scale (mRS) score over time with inclusion of significant covariates. Longitudinal data obtained from 193 patients, 1-24 months after onset of acute ischemic stroke, were included for a modeling analysis using nonlinear mixed-effect modeling (NONMEM). After selecting a model that best described the time course of the probability of different mRS scores, potential covariates were tested. Visual predicted check plots, parameter estimates, and decreases in minimum objective function values were used for model evaluation. The inclusion of disease progression (DP) in the baseline proportional odds cumulative logit model significantly improved the model compared to the baseline model without DP. An inhibitory maximum effect (E_max ) model was determined to be the best DP model for describing the probability of specific mRS scores over time. In the final model, DP was multiplied with the baseline cumulative logit probability with a baseline adjustment. In addition to differences in lesion volume (DLV), the final model included comorbid diabetes mellitus (DM) and baseline National Institutes of Health Stroke Scale (NIHSS) scores on E_max as statistically significant covariates. This study developed a model including DLV, NIHSS score, and comorbid DM for predicting the disability time course in patients with acute ischemic stroke. This model may help to predict disease outcomes and to develop more appropriate management plans for patients with acute stroke.