Ordinal Prediction Model of 90-Day Modified Rankin Scale in Ischemic Stroke

 Just what the fuck does predictions of failure to recover do for survivors?

 

Ordinal Prediction Model of 90-Day Modified Rankin Scale in Ischemic Stroke

Michelle Y. Zhang¹, Michael Mlynash², Kristin L. Sainani³, Gregory W. Albers² and Maarten G. Lansberg^2*

• ¹Stanford University School of Medicine, Stanford, CA, United States
• ²Department of Neurology and Neurological Sciences and the Stanford Stroke Center, Stanford University Medical Center, Stanford, CA, United States
• ³Department of Epidemiology and Population Health, Stanford University, Stanford, CA, United States

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.

Introduction

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 (1–3). They can also 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) (4–6). While these dichotomizations are meaningful, a model that could predict outcome across the entire spectrum of the mRS would be more informative. For example, for patients who have less severe strokes, a model predicting mortality may be less useful than a model that predicts the exact score on the mRS (7). Such a model could also be used to impute missing data in clinical trials when patients are lost to follow-up or when outcome data is not yet available.

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