Abstract
Context
The optimal strategy for implementing mortality-predicting algorithms to facilitate
clinical care, prognostic discussions, and palliative care interventions remains unknown.
Objectives
To develop and validate a real-time predictive model for 180 day mortality using routinely
available clinical and laboratory admission data and determine if palliative care
exposure varies with predicted mortality risk.
Methods
Adult admissions between October 1, 2013 and October.1, 2017 were included for the
model derivation. A separate cohort was collected between January 1, 2018 and July
31, 2020 for validation. Patients were followed for 180 days from discharge, and logistic
regression with selected variables was used to estimate patients’ risk for mortality.
Results
In the model derivation cohort, 7963 events of 180 day mortality (4.5% event rate)
were observed. Median age was 53.0 (IQR 24.0-66.0) with 92,734 females (52.5%). Variables
with strongest association with 180 day mortality included: Braden Score (OR 0.83;
95% CI 0.82-0.84); admission Do Not Resuscitate orders (OR 2.61; 95% CI 2.43-2.79);
admission service and admission status. The model yielded excellent discriminatory
ability in both the derivation (c-statistic 0.873; 95% CI 0.870-0.877; Brier score
0.04) and validation cohorts (c-statistic 0.844; 95% CI 0.840-0.847; Brier score 0.072).
Inpatient palliative care consultations increased from 3% of minimal-risk encounters
to 41% of high-risk encounters (P < 0.01).
Conclusion
We developed and temporally validated a predictive mortality model for adults from
a large retrospective cohort, which helps quantify the potential need for palliative
care referrals based on risk strata. Machine learning algorithms for mortality require
clinical interpretation, and additional studies are needed to design patient-centered
and risk-specific interventions.
Key Words
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Article info
Publication history
Published online: January 23, 2022
Accepted:
January 18,
2022
Identification
Copyright
© 2022 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.
