Abstract
Background and Purpose: Prediction models for functional outcomes after ischemic stroke are useful for statistical analyses in clinical trials and guiding patient expectations. While there are models predicting dichotomous functional outcomes after ischemic stroke, there are no models that predict ordinal mRS outcomes. We aimed to create a model that predicts, at the time of hospital discharge, a patient's modified Rankin Scale (mRS) score on day 90 after ischemic stroke.Methods: We used data from three multi-center prospective studies: CRISP, DEFUSE 2, and DEFUSE 3 to derive and validate an ordinal logistic regression model that predicts the 90-day mRS score based on variables available during the stroke hospitalization. Forward selection was used to retain independent significant variables in the multivariable model.Results: The prediction model was derived using data on 297 stroke patients from the CRISP and DEFUSE 2 studies. National Institutes of Health Stroke Scale (NIHSS) at discharge and age were retained as significant (p < 0.001) independent predictors of the 90-day mRS score. When applied to the external validation set (DEFUSE 3, n = 160), the model accurately predicted the 90-day mRS score within one point for 78% of the patients in the validation cohort.Conclusions: A simple model using age and NIHSS score at time of discharge can predict 90-day mRS scores in patients with ischemic stroke. This model can be useful for prognostication in routine clinical care and to impute missing data in clinical trials.
Highlights
Prediction models of functional outcome after ischemic stroke can aid clinical decision making for providers, patients, and families by guiding rehabilitation goals, discharge planning, and patient expectations [1,2,3]
These models stroke have generally focused on predicting a dichotomization of the modified Rankin Scale such as functional independence vs. functional dependency or death, or alive vs. dead [4,5,6]
We aimed to develop an ordinal logistic regression model that predicts the 90-day modified Rankin Scale (mRS) score based on variables available at the time of hospital discharge
Summary
Prediction models of functional outcome after ischemic stroke can aid clinical decision making for providers, patients, and families by guiding rehabilitation goals, discharge planning, and patient expectations [1,2,3]. They can be useful for imputing missing data in clinical trials. These models stroke have generally focused on predicting a dichotomization of the modified Rankin Scale (mRS) such as functional independence (mRS 0–2) vs functional dependency or death (mRS 3–6), or alive (mRS 0–5) vs dead (mRS 6) [4,5,6]. We aimed to create a model that predicts, at the time of hospital discharge, a patient’s modified Rankin Scale (mRS) score on day 90 after ischemic stroke
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