Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults

Rajiv Agarwal; Henry J Domenico; Sreenivasa R Balla; Daniel W Byrne; Jennifer G Whisenant; Marcella C Woods; Barbara J Martin; Mohana B Karlekar; Marc L Bennett

doi:10.1016/j.jpainsymman.2022.01.013

Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults

J Pain Symptom Manage. 2022 May;63(5):645-653. doi: 10.1016/j.jpainsymman.2022.01.013. Epub 2022 Jan 23.

Authors

Rajiv Agarwal¹, Henry J Domenico², Sreenivasa R Balla³, Daniel W Byrne⁴, Jennifer G Whisenant⁵, Marcella C Woods⁶, Barbara J Martin⁶, Mohana B Karlekar⁷, Marc L Bennett⁸

Affiliations

¹ Department of Medicine (R.A., J.G.W., M.B.K.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt-Ingram Cancer Center (R.A., J.G.W.), Nashville, Tennessee, USA. Electronic address: rajiv.agarwal@vumc.org.
² Department of Biostatistics (H.J.D., D.W.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Office of Quality, Safety, and Risk Prevention (H.J.D., M.C.W., B.J.M., M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
³ Health Information Technology (S.R.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁴ Department of Biostatistics (H.J.D., D.W.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁵ Department of Medicine (R.A., J.G.W., M.B.K.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt-Ingram Cancer Center (R.A., J.G.W.), Nashville, Tennessee, USA.
⁶ Vanderbilt Office of Quality, Safety, and Risk Prevention (H.J.D., M.C.W., B.J.M., M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁷ Department of Medicine (R.A., J.G.W., M.B.K.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁸ Vanderbilt Office of Quality, Safety, and Risk Prevention (H.J.D., M.C.W., B.J.M., M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Otolaryngology Head and Neck Surgery (M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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.

Keywords: Palliative care; end-of-life; goals of care; predictive modeling.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Adult
Cohort Studies
Female
Hospitalization
Humans
Machine Learning*
Male
Middle Aged
Palliative Care*
Retrospective Studies
Risk Assessment

Grants and funding

K12 CA090625/CA/NCI NIH HHS/United States