Advertisement

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

      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

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Pain and Symptom Management
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Elfiky AA
        • Pany MJ
        • Parikh RB
        • Obermeyer Z.
        Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy.
        JAMA Netw Open. 2018; 1e180926
        • Bertsimas D
        • Dunn J
        • Pawlowski C
        • et al.
        Applied informatics decision support tool for mortality predictions in patients with cancer.
        JCO Clin Cancer Inform. 2018; 2: 1-11
      1. James G, Witten D, Hastie T, Tibshirani R. New York, NY: Springer, 2013.

        • Sahni N
        • Simon G
        • Arora R
        Development and validation of machine learning models for prediction of 1-year mortality utilizing electronic medical record data available at the end of hospitalization in multicondition patients: a proof-of-concept study.
        J Gen Intern Med. 2018; 33: 921-928
        • Weng SF
        • Reps J
        • Kai J
        • Garibaldi JM
        • Qureshi N.
        Can machine-learning improve cardiovascular risk prediction using routine clinical data?.
        PLoS One. 2017; 12e0174944
        • Tabak YP
        • Sun X
        • Nunez CM
        • Johannes RS.
        Using electronic health record data to develop inpatient mortality predictive model: acute Laboratory Risk of Mortality Score (ALaRMS).
        J Am Med Inform Assoc. 2014; 21: 455-463
        • Schwartz N
        • Sakhnini A
        • Bisharat N.
        Predictive modeling of inpatient mortality in departments of internal medicine.
        Intern Emerg Med. 2018; 13: 205-211
        • Nakas CT
        • Schutz N
        • Werners M
        • Leichtle AB.
        Accuracy and calibration of computational approaches for inpatient mortality predictive modeling.
        PLoS One. 2016; 11e0159046
        • Brajer N
        • Cozzi B
        • Gao M
        • et al.
        Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission.
        JAMA Netw Open. 2020; 3e1920733
        • Parikh RB
        • Manz C
        • Chivers C
        • et al.
        Machine learning approaches to predict 6-month mortality among patients with cancer.
        JAMA Netw Open. 2019; 2e1915997
        • Manz CR
        • Chen J
        • Liu M
        • et al.
        Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer.
        JAMA Oncol. 2020; 6: 1723-1730
        • Manz CR
        • Parikh RB
        • Small DS
        • et al.
        Effect of integrating machine learning mortality estimates with behavioral nudges to clinicians on serious illness conversations among patients with cancer: a stepped-wedge cluster randomized clinical trial.
        JAMA Oncol. 2020; e204759
        • Bernacki R
        • Paladino J
        • Neville BA
        • et al.
        Effect of the serious illness care program in outpatient oncology: a cluster randomized clinical trial.
        JAMA Int Med. 2019; 179: 751-759
        • Paladino J
        • Bernacki R
        • Neville BA
        • et al.
        Evaluating an intervention to improve communication between oncology clinicians and patients with life-limiting cancer: a cluster randomized clinical trial of the serious illness care program.
        JAMA Oncol. 2019; 5: 801-809
      2. Committee on Approaching Death: Addressing Key End of Life I, Institute of M: Dying in America: Improving Quality and Honoring Individual Preferences Near the End of Life. Washington (DC), National Academies Press (US), 2015

        • Courtright KR
        • Chivers C
        • Becker M
        • et al.
        Electronic health record mortality prediction model for targeted palliative care among hospitalized medical patients: a pilot Quasi-experimental study.
        J Gen Intern Med. 2019; 34: 1841-1847
        • Parchure P
        • Joshi H
        • Dharmarajan K
        • et al.
        Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19.
        BMJ Supportive Palliat Care. 2020;
        • Murphree DH
        • Wilson PM
        • Asai SW
        • et al.
        Improving the delivery of palliative care through predictive modeling and healthcare informatics.
        J Am Med Informat Assoc: JAMIA. 2021; 28: 1065-1073
        • Porter AS
        • Harman S
        • Lakin JR.
        Power and perils of prediction in palliative care.
        Lancet (London, England). 2020; 395: 680-681
        • Collins GS
        • Reitsma JB
        • Altman DG
        • Moons KG.
        Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).
        Ann Intern Med. 2015; 162: 735-736
        • Wang L
        • Sha L
        • Lakin JR
        • et al.
        Development and validation of a deep learning algorithm for mortality prediction in selecting patients with dementia for earlier palliative care interventions.
        JAMA network open. 2019; 2e196972
        • Fried TR
        • Bradley EH
        • Towle VR
        • Allore H.
        Understanding the treatment preferences of seriously ill patients.
        N Engl J Med. 2002; 346: 1061-1066
        • Bakitas M
        • Lyons KD
        • Hegel MT
        • et al.
        Effects of a palliative care intervention on clinical outcomes in patients with advanced cancer: the Project ENABLE II randomized controlled trial.
        JAMA. 2009; 302: 741-749
        • Temel JS
        • Greer JA
        • Muzikansky A
        • et al.
        Early palliative care for patients with metastatic non-small-cell lung cancer.
        N Engl J Med. 2010; 363: 733-742
        • Kelley AS
        • Morrison RS.
        Palliative care for the seriously Ill.
        N Engl J Med. 2015; 373: 747-755
        • Quill TE
        • Abernethy AP.
        Generalist plus specialist palliative care–creating a more sustainable model.
        N Engl J Med. 2013; 368: 1173-1175
        • Kaasa S
        • Loge JH
        • Aapro M
        • et al.
        Integration of oncology and palliative care: a lancet oncology commission.
        Lancet Oncol. 2018; 19: e588-e653
        • Tang M
        • Bruera E.
        Hospital deaths a poor quality metric for patients with cancer.
        JAMA Oncol. 2020; 6: 1861-1862