JMIR Publications

ABSTRACT

Background:

Stroke patients have high rates of all-cause rehospitalization and case fatality. Limited information is available on how to predict these outcomes.

Objective:

We assessed whether adding initial National Institutes of Health Stroke Scale (NIHSS) score or modified Rankin scale (mRS) score at discharge improved predictive models of 30-day non-elective rehospitalization and/or mortality post-stroke.

Methods:

Using a cohort of ischemic stroke patients in a large multi-ethnic integrated healthcare system from 6/15/2018 to 4/29/2020, we tested 2 predictive models for a composite outcome (30-day non-elective hospitalization/death). The models were based on administrative data (Length of stay, Acuity, Charlson, Emergency department use score; LACE) as well as a comprehensive model (Transition Support Level, TSL). The models, initial NIHSS score and mRS scores at discharge, were tested independently and in combination with age and sex. We assessed model performance using the area under the receiver operator characteristic (c-statistic), Nagelkerke pseudo-R2, and Brier score.

Results:

The study cohort included 4,843 patients with 5,014 stroke hospitalizations. Average age was 71.9±14 years, 50.6% female, and 52.1% White. Median initial NIHSS score was 4. There were 538 (10.7%) non-elective readmissions and 150 (3.9%) deaths within 30 days. The logistic models revealed that the best performing models were TSL (c-statistic=0.685) and TSL plus mRS score at discharge (c-statistic=0.694).

Conclusions:

We found that neither the initial NIHSS score nor mRS score at discharge significantly enhanced the predictive ability of the LACE or TSL models. Future efforts at prediction of short-term stroke outcomes will need to incorporate new data elements.