Cathleen Colón-Emeric1,2,3, Heather E Whitson1,2,3, Carl F Pieper1,2,3, Richard Sloane1,2,3, Denise Orwig4, Kim M Huffman5, Janet Prvu Bettger6, Daniel Parker1,3, Donna M Crabtree7, Ann Gruber-Baldini4, Jay Magaziner4. 1. Division of Geriatrics, Duke University Medical Center, Durham, North Carolina. 2. Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina. 3. Duke University Center for the Study of Aging and Human Development, Duke University, Durham, North Carolina. 4. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland. 5. Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina. 6. Department of Orthopedic Surgery, Duke University Medical Center, Durham, North Carolina. 7. Duke Office of Clinical Research, School of Medicine, Duke University Medical Center, Durham, North Carolina.
Abstract
OBJECTIVES: Defining common patterns of recovery after an acute health stressor (resiliency groups) has both clinical and research implications. We sought to identify groups of patients with similar recovery patterns across 10 outcomes following hip fracture (stressor) and to determine the most important predictors of resiliency group membership. DESIGN: Secondary analysis of three prospective cohort studies. SETTING: Participants were recruited from various hospitals in the Baltimore Hip Studies network and followed for up to 1 year in their residence (home or facility). PARTICIPANTS: Community-dwelling adults aged 65 years or older with recent surgical repair of a hip fracture (n = 541). MEASUREMENTS: Self-reported physical function and activity measures using validated scales were collected at baseline (within 15-22 d of fracture), 2, 6, and 12 months. Physical performance tests were administered at all follow-up visits. Stressor characteristics, comorbidities, and psychosocial and environmental factors were collected at baseline via participant report and chart abstraction. Latent class profile analysis was used to identify resiliency groups based on recovery trajectories across 10 outcome measures and logistic regression models to identify factors associated with those groups. RESULTS: Latent profile analysis identified three resiliency groups that had similar patterns across the 10 outcome measures and were defined as "high resilience" (n = 163 [30.1%]), "medium resilience" (n = 242 [44.7%]), and "low resilience" (n = 136 [25.2%]). Recovery trajectories for the outcome measures are presented for each resiliency group. Comparing highest with the medium- and low-resilience groups, self-reported pre-fracture function was by far the strongest predictor of high-resilience group membership with area under the curve (AUC) of .84. Demographic factors, comorbidities, stressor characteristics, environmental factors, and psychosocial characteristics were less predictive, but several factors remained significant in a multivariable model (AUC = .88). CONCLUSION: These three resiliency groups following hip fracture may be useful for understanding mediators of physical resilience. They may provide a more detailed description of recovery patterns in multiple outcomes for use in clinical decision making. J Am Geriatr Soc 67:2519-2527, 2019.
OBJECTIVES: Defining common patterns of recovery after an acute health stressor (resiliency groups) has both clinical and research implications. We sought to identify groups of patients with similar recovery patterns across 10 outcomes following hip fracture (stressor) and to determine the most important predictors of resiliency group membership. DESIGN: Secondary analysis of three prospective cohort studies. SETTING:Participants were recruited from various hospitals in the Baltimore Hip Studies network and followed for up to 1 year in their residence (home or facility). PARTICIPANTS: Community-dwelling adults aged 65 years or older with recent surgical repair of a hip fracture (n = 541). MEASUREMENTS: Self-reported physical function and activity measures using validated scales were collected at baseline (within 15-22 d of fracture), 2, 6, and 12 months. Physical performance tests were administered at all follow-up visits. Stressor characteristics, comorbidities, and psychosocial and environmental factors were collected at baseline via participant report and chart abstraction. Latent class profile analysis was used to identify resiliency groups based on recovery trajectories across 10 outcome measures and logistic regression models to identify factors associated with those groups. RESULTS: Latent profile analysis identified three resiliency groups that had similar patterns across the 10 outcome measures and were defined as "high resilience" (n = 163 [30.1%]), "medium resilience" (n = 242 [44.7%]), and "low resilience" (n = 136 [25.2%]). Recovery trajectories for the outcome measures are presented for each resiliency group. Comparing highest with the medium- and low-resilience groups, self-reported pre-fracture function was by far the strongest predictor of high-resilience group membership with area under the curve (AUC) of .84. Demographic factors, comorbidities, stressor characteristics, environmental factors, and psychosocial characteristics were less predictive, but several factors remained significant in a multivariable model (AUC = .88). CONCLUSION: These three resiliency groups following hip fracture may be useful for understanding mediators of physical resilience. They may provide a more detailed description of recovery patterns in multiple outcomes for use in clinical decision making. J Am Geriatr Soc 67:2519-2527, 2019.
Authors: Natasha H Dolgin; Paulo N A Martins; Babak Movahedi; Kate L Lapane; Fred A Anderson; Adel Bozorgzadeh Journal: Clin Transplant Date: 2016-10-20 Impact factor: 2.863
Authors: D Orwig; M C Hochberg; A L Gruber-Baldini; B Resnick; R R Miller; G E Hicks; A R Cappola; M Shardell; R Sterling; J R Hebel; R Johnson; J Magaziner Journal: J Frailty Aging Date: 2018
Authors: Heather E Whitson; Harvey J Cohen; Kenneth E Schmader; Miriam C Morey; George Kuchel; Cathleen S Colon-Emeric Journal: J Am Geriatr Soc Date: 2018-03-25 Impact factor: 5.562
Authors: Sheryl Zimmerman; William G Hawkes; J Richard Hebel; Kathleen M Fox; Eva Lydick; Jay Magaziner Journal: Arch Phys Med Rehabil Date: 2006-03 Impact factor: 3.966
Authors: Denise L Orwig; Marc Hochberg; Janet Yu-Yahiro; Barbara Resnick; William G Hawkes; Michelle Shardell; J Richard Hebel; Perry Colvin; Ram R Miller; Justine Golden; Sheryl Zimmerman; Jay Magaziner Journal: Arch Intern Med Date: 2011-02-28
Authors: Brian K Kennedy; Shelley L Berger; Anne Brunet; Judith Campisi; Ana Maria Cuervo; Elissa S Epel; Claudio Franceschi; Gordon J Lithgow; Richard I Morimoto; Jeffrey E Pessin; Thomas A Rando; Arlan Richardson; Eric E Schadt; Tony Wyss-Coray; Felipe Sierra Journal: Cell Date: 2014-11-06 Impact factor: 41.582
Authors: Elizabeth A Eastwood; Jay Magaziner; Jason Wang; Stacey B Silberzweig; Edward L Hannan; Elton Strauss; Albert L Siu Journal: J Am Geriatr Soc Date: 2002-07 Impact factor: 5.562
Authors: Mark D Neuman; Jeffrey H Silber; Jay S Magaziner; Molly A Passarella; Samir Mehta; Rachel M Werner Journal: JAMA Intern Med Date: 2014-08 Impact factor: 21.873
Authors: Svetlana Ukraintseva; Konstantin Arbeev; Matt Duan; Igor Akushevich; Alexander Kulminski; Eric Stallard; Anatoliy Yashin Journal: Mech Ageing Dev Date: 2020-12-16 Impact factor: 5.432
Authors: Thomas Laskow; Jiafeng Zhu; Brian Buta; Julius Oni; Frederick Sieber; Karen Bandeen-Roche; Jeremy Walston; Patricia D Franklin; Ravi Varadhan Journal: J Gerontol A Biol Sci Med Sci Date: 2022-09-01 Impact factor: 6.591
Authors: Carolyn J Presley; Nicole A Arrato; Peter G Shields; David P Carbone; Melisa L Wong; Jason Benedict; Sarah A Reisinger; Ling Han; Thomas M Gill; Heather Allore; Barbara L Andersen; Sarah Janse Journal: JTO Clin Res Rep Date: 2022-05-17
Authors: Cristina González de Villaumbrosia; Pilar Sáez López; Isaac Martín de Diego; Carmen Lancho Martín; Marina Cuesta Santa Teresa; Teresa Alarcón; Cristina Ojeda Thies; Rocío Queipo Matas; Juan Ignacio González-Montalvo Journal: Int J Environ Res Public Health Date: 2021-04-06 Impact factor: 3.390