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Prognosis of Individual-Level Mobility and Self-Care Stroke Recovery during Inpatient Rehabilitation, Part 1: A Proof-of-Concept Single Group Retrospective Cohort Study

Published:December 31, 2022DOI:https://doi.org/10.1016/j.apmr.2022.12.189

      Abstract

      Objective

      To demonstrate feasibility of generating predictive short-term individual trajectory recovery models following acute stroke by extracting clinical data from an electronic medical record system (EMR).

      Design

      Single-group retrospective patient cohort design

      Setting

      Stroke rehabilitation unit at an independent inpatient rehabilitation facility (IRF).

      Participants

      Cohort of 1408 inpatients with acute ischemic or hemorrhagic stroke with a mean (SD) age of 66 (14.5) years admitted between April 2014 and October 2019.

      Interventions

      Not applicable

      Main Outcome Measures

      0-100 Rasch-scaled FIM™ Instrument (FIM) Mobility and elf-Care subscales.

      Results

      Unconditional models were best-fit on FIM Mobility and Self-care subscales by spline fixed-effect functions with knots at weeks 1 and 2, and random effects on the baseline (FIM 0-100 Rasch score at IRF admission), initial rate (slope at time zero), and 2nd knot (change in slope pre-to-post week 2) parameters. The final Mobility multivariable model had intercept associations with Private/Other Insurance, Ischemic Stroke, Serum Albumin, Motricity Index Lower Extremity, and FIM Cognition; and initial slope associations with Ischemic Stroke, Private/Other and Medicaid Insurance, and FIM Cognition. The final Self-Care multivariable model had intercept associations with Private/Other Insurance, Ischemic Stroke, Living with One or More persons, Serum Albumin, and FIM Cognition; and initial slope associations with Ischemic Stroke, Private/Other and Medicaid Insurance, and FIM Cognition. Final models explained 52% and 27% of the variance compared to unconditional Mobility and Self-Care models. However, some EMR data elements had apparent coding errors or missing data, and desired elements from acute care were not available. Also, unbalanced outcome data may have biased trajectories.

      Conclusions

      We demonstrate the feasibility of developing individual-level prognostic models from EMR data; however, some data elements were poorly defined, subject to error, or missing for some or all cases. Development of prognostic models from EMR will require improvements in EMR data collection and standardization.

      Keywords

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