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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Article info
Publication history
Accepted:
December 20,
2022
Received in revised form:
November 1,
2022
Received:
April 11,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2023 by the American Congress of Rehabilitation Medicine.
