Veerawat Phongtankuel1, P Johnson2, M C Reid1, R D Adelman1, Z Grinspan2,3, M A Unruh2, E Abramson2,3. 1. 1 Department of Medicine, Division of Geriatrics and Palliative Medicine, Joan and Sanford I Weill Medical College of Cornell University, New York, NY, USA. 2. 2 Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA. 3. 3 Department of Pediatrics, Weill Cornell Medical College, New York, NY, USA.
Abstract
BACKGROUND: Over 10% of hospice patients experience at least 1 care transition 6 months prior to death. Transitions at the end of life, particularly from hospice to hospital, result in burdensome and fragmented care for patients and families. Little is known about factors that predict hospitalization in this population. OBJECTIVES: To develop and validate a model predictive of hospitalization after enrollment into home hospice using prehospice admission risk factors. DESIGN: Retrospective cohort study using Medicare fee-for-service claims. PARTICIPANTS: Patients enrolled into the Medicare hospice benefit were ≥18 years old in 2012. OUTCOME MEASURED: Hospitalization within 2 days from a hospice discharge. RESULTS: We developed a predictive model using 61 947 hospice enrollments, of which 3347 (5.4%) underwent a hospitalization. Seven variables were associated with hospitalization: age 18 to 55 years old (adjusted odds ratio [95% confidence interval]: 2.94 [2.41-3.59]), black race (2.13 [1.93-2.34]), east region (1.97 [1.73-2.24]), a noncancer diagnosis (1.32 [1.21-1.45]), 4 or more chronic conditions (8.11 [7.19-9.14]), 2 or more prior hospice enrollments (1.75 [1.35-2.26]), and enrollment in a not-for-profit hospice (2.01 [1.86-2.18]). A risk scoring tool ranging from 0 to 29 was developed, and a cutoff score of 18 identified hospitalized patients with a positive predictive value of 22%. CONCLUSIONS: Reasons for hospitalization among home hospice patients are complex. Patients who are younger, belong to a minority group, and have a greater number of chronic conditions are at increased odds of hospitalization. Our newly developed predictive tool identifies patients at risk for hospitalization and can serve as a benchmark for future model development.
BACKGROUND: Over 10% of hospice patients experience at least 1 care transition 6 months prior to death. Transitions at the end of life, particularly from hospice to hospital, result in burdensome and fragmented care for patients and families. Little is known about factors that predict hospitalization in this population. OBJECTIVES: To develop and validate a model predictive of hospitalization after enrollment into home hospice using prehospice admission risk factors. DESIGN: Retrospective cohort study using Medicare fee-for-service claims. PARTICIPANTS: Patients enrolled into the Medicare hospice benefit were ≥18 years old in 2012. OUTCOME MEASURED: Hospitalization within 2 days from a hospice discharge. RESULTS: We developed a predictive model using 61 947 hospice enrollments, of which 3347 (5.4%) underwent a hospitalization. Seven variables were associated with hospitalization: age 18 to 55 years old (adjusted odds ratio [95% confidence interval]: 2.94 [2.41-3.59]), black race (2.13 [1.93-2.34]), east region (1.97 [1.73-2.24]), a noncancer diagnosis (1.32 [1.21-1.45]), 4 or more chronic conditions (8.11 [7.19-9.14]), 2 or more prior hospice enrollments (1.75 [1.35-2.26]), and enrollment in a not-for-profit hospice (2.01 [1.86-2.18]). A risk scoring tool ranging from 0 to 29 was developed, and a cutoff score of 18 identified hospitalized patients with a positive predictive value of 22%. CONCLUSIONS: Reasons for hospitalization among home hospice patients are complex. Patients who are younger, belong to a minority group, and have a greater number of chronic conditions are at increased odds of hospitalization. Our newly developed predictive tool identifies patients at risk for hospitalization and can serve as a benchmark for future model development.
Authors: David Casarett; Joan Harrold; Pamela S Harris; Laura Bender; Sue Farrington; Eugenia Smither; Kevin Ache; Joan Teno Journal: J Pain Symptom Manage Date: 2015-04-30 Impact factor: 3.612
Authors: Joan M Teno; Jason Bowman; Michael Plotzke; Pedro L Gozalo; Thomas Christian; Susan C Miller; Cindy Williams; Vincent Mor Journal: J Pain Symptom Manage Date: 2015-05-21 Impact factor: 3.612
Authors: Kathleen T Unroe; Melissa A Greiner; Kimberly S Johnson; Lesley H Curtis; Soko Setoguchi Journal: Am Heart J Date: 2012-06 Impact factor: 4.749
Authors: Alexander K Smith; Jonathan Fisher; Mara A Schonberg; Daniel J Pallin; Susan D Block; Lachlan Forrow; Russell S Phillips; Ellen P McCarthy Journal: Ann Emerg Med Date: 2008-10-18 Impact factor: 5.721
Authors: T A Brennan; L L Leape; N M Laird; L Hebert; A R Localio; A G Lawthers; J P Newhouse; P C Weiler; H H Hiatt Journal: Qual Saf Health Care Date: 2004-04
Authors: Veerawat Phongtankuel; Benjamin A Scherban; Manney C Reid; Amanda Finley; Angela Martin; Jeanne Dennis; Ronald D Adelman Journal: J Palliat Med Date: 2016-01 Impact factor: 2.947
Authors: Tony Rosen; Yuhua Bao; Yiye Zhang; Sunday Clark; Katherine Wen; Alyssa Elman; Philip Jeng; Elizabeth Bloemen; Daniel Lindberg; Richard Krugman; Jacquelyn Campbell; Ronet Bachman; Terry Fulmer; Karl Pillemer; Mark Lachs Journal: BMJ Open Date: 2021-02-05 Impact factor: 2.692