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. 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.
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.
Authors: Jennifer S Temel; Joseph A Greer; Alona Muzikansky; Emily R Gallagher; Sonal Admane; Vicki A Jackson; Constance M Dahlin; Craig D Blinderman; Juliet Jacobsen; William F Pirl; J Andrew Billings; Thomas J Lynch Journal: N Engl J Med Date: 2010-08-19 Impact factor: 91.245
Authors: Rachelle Bernacki; Joanna Paladino; Bridget A Neville; Mathilde Hutchings; Jane Kavanagh; Olaf P Geerse; Joshua Lakin; Justin J Sanders; Kate Miller; Stuart Lipsitz; Atul A Gawande; Susan D Block Journal: JAMA Intern Med Date: 2019-06-01 Impact factor: 21.873
Authors: Katherine R Courtright; Corey Chivers; Michael Becker; Susan H Regli; Linnea C Pepper; Michael E Draugelis; Nina R O'Connor Journal: J Gen Intern Med Date: 2019-07-16 Impact factor: 5.128
Authors: Stein Kaasa; Jon H Loge; Matti Aapro; Tit Albreht; Rebecca Anderson; Eduardo Bruera; Cinzia Brunelli; Augusto Caraceni; Andrés Cervantes; David C Currow; Luc Deliens; Marie Fallon; Xavier Gómez-Batiste; Kjersti S Grotmol; Breffni Hannon; Dagny F Haugen; Irene J Higginson; Marianne J Hjermstad; David Hui; Karin Jordan; Geana P Kurita; Philip J Larkin; Guido Miccinesi; Friedemann Nauck; Rade Pribakovic; Gary Rodin; Per Sjøgren; Patrick Stone; Camilla Zimmermann; Tonje Lundeby Journal: Lancet Oncol Date: 2018-10-18 Impact factor: 41.316
Authors: Christopher R Manz; Jinbo Chen; Manqing Liu; Corey Chivers; Susan Harkness Regli; Jennifer Braun; Michael Draugelis; C William Hanson; Lawrence N Shulman; Lynn M Schuchter; Nina O'Connor; Justin E Bekelman; Mitesh S Patel; Ravi B Parikh Journal: JAMA Oncol Date: 2020-11-01 Impact factor: 31.777
Authors: Dennis H Murphree; Patrick M Wilson; Shusaku W Asai; Daniel J Quest; Yaxiong Lin; Piyush Mukherjee; Nirmal Chhugani; Jacob J Strand; Gabriel Demuth; David Mead; Brian Wright; Andrew Harrison; Jalal Soleimani; Vitaly Herasevich; Brian W Pickering; Curtis B Storlie Journal: J Am Med Inform Assoc Date: 2021-06-12 Impact factor: 4.497
Authors: Christopher R Manz; Ravi B Parikh; Dylan S Small; Chalanda N Evans; Corey Chivers; Susan H Regli; C William Hanson; Justin E Bekelman; Charles A L Rareshide; Nina O'Connor; Lynn M Schuchter; Lawrence N Shulman; Mitesh S Patel Journal: JAMA Oncol Date: 2020-12-10 Impact factor: 31.777