Abstract
Objectives
To evaluate a machine learning model designed to predict mortality for Medicare beneficiaries
aged >65 years treated for hip fracture in Inpatient Rehabilitation Facilities (IRFs).
Design
Retrospective design/cohort analysis of Centers for Medicare & Medicaid Services Inpatient
Rehabilitation Facility–Patient Assessment Instrument data.
Setting and Participants
A total of 17,140 persons admitted to Medicare-certified IRFs in 2015 following hospitalization
for hip fracture.
Measures
Patient characteristics include sociodemographic (age, gender, race, and social support)
and clinical factors (functional status at admission, chronic conditions) and IRF
length of stay. Outcomes were 30-day and 1-year all-cause mortality. We trained and
evaluated 2 classification models, logistic regression and a multilayer perceptron
(MLP), to predict the probability of 30-day and 1-year mortality and evaluated the
calibration, discrimination, and precision of the models.
Results
For 30-day mortality, MLP performed well [acc = 0.74, area under the receiver operating
characteristic curve (AUROC) = 0.76, avg prec = 0.10, slope = 1.14] as did logistic
regression (acc = 0.78, AUROC = 0.76, avg prec = 0.09, slope = 1.20). For 1-year mortality,
the performances were similar for both MLP (acc = 0.68, AUROC = 0.75, avg prec = 0.32,
slope = 0.96) and logistic regression (acc = 0.68, AUROC = 0.75, avg prec = 0.32,
slope = 0.95).
Conclusion and Implications
A scoring system based on logistic regression may be more feasible to run in current
electronic medical records. But MLP models may reduce cognitive burden and increase
ability to calibrate to local data, yielding clinical specificity in mortality prediction
so that palliative care resources may be allocated more effectively.
Keywords
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Article info
Publication history
Published online: October 29, 2020
Footnotes
Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (KL2TR002554). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. A portion of Dr. Colon-Emeric’s time (K24 AG049077-02) was supported by grants from the National Institute on Aging.
The authors declare no conflicts of interest.
Identification
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© 2020 AMDA - The Society for Post-Acute and Long-Term Care Medicine.
