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Resiliency Groups Following Hip Fracture in Older Adults
Author(s) -
ColónEmeric Cathleen,
Whitson Heather E.,
Pieper Carl F.,
Sloane Richard,
Orwig Denise,
Huffman Kim M.,
Bettger Janet Prvu,
Parker Daniel,
Crabtree Donna M.,
GruberBaldini Ann,
Magaziner Jay
Publication year - 2019
Publication title -
journal of the american geriatrics society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.992
H-Index - 232
eISSN - 1532-5415
pISSN - 0002-8614
DOI - 10.1111/jgs.16152
Subject(s) - medicine , psychosocial , stressor , latent class model , hip fracture , gerontology , psychological resilience , logistic regression , cohort study , proportional hazards model , prospective cohort study , cohort , physical therapy , demography , clinical psychology , psychiatry , psychology , osteoporosis , surgery , statistics , mathematics , psychotherapist , sociology
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