Validation of the FRAiL model to predict non-vertebral and hip fractures in nursing home residents

The risk of fractures after entering long-term care facilities

BACKGROUND

Stratifying residents at increased risk for fractures in long-term care facilities (LTCFs) can potentially improve awareness and facilitate the delivery of targeted interventions to reduce risk. Although several fracture risk assessment tools exist, most are not suitable for individuals entering LTCF. Moreover, existing tools do not examine risk profiles of individuals at key periods in their aged care journey, specifically at entry into LTCFs.

PURPOSE

Our objectives were to identify fracture predictors, develop a fracture risk prognostic model for new LTCF residents and compare its performance to the Fracture Risk Assessment in Long term care (FRAiL) model using the Registry of Senior Australians (ROSA) Historical National Cohort, which contains integrated health and aged care information for individuals receiving long term care services.

METHODS

Individuals aged ≥65 years old who entered 2079 facilities in three Australian states between 01/01/2009 and 31/12/2016 were examined. Fractures (any) within 365 days of LTCF entry were the outcome of interest. Individual, medication, health care, facility and system-related factors were examined as predictors. A fracture prognostic model was developed using elastic nets penalised regression and Fine-Gray models. Model discrimination was examined using area under the receiver operating characteristics curve (AUC) from the 20 % testing dataset. Model performance was compared to an existing risk model (i.e., FRAiL model).

RESULTS

Of the 238,782 individuals studied, 62.3 % (N = 148,838) were women, 49.7 % (N = 118,598) had dementia and the median age was 84 (interquartile range 79-89). Within 365 days of LTCF entry, 7.2 % (N = 17,110) of individuals experienced a fracture. The strongest fracture predictors included: complex health care rating (no vs high care needs, sub-distribution hazard ratio (sHR) = 1.52, 95 % confidence interval (CI) 1.39-1.67), nutrition rating (moderate vs worst, sHR = 1.48, 95%CI 1.38-1.59), prior fractures (sHR ranging from 1.24 to 1.41 depending on fracture site/type), one year history of general practitioner attendances (≥16 attendances vs none, sHR = 1.35, 95%CI 1.18-1.54), use of dopa and dopa derivative antiparkinsonian medications (sHR = 1.28, 95%CI 1.19-1.38), history of osteoporosis (sHR = 1.22, 95%CI 1.16-1.27), dementia (sHR = 1.22, 95%CI 1.17-1.28) and falls (sHR = 1.21, 95%CI 1.17-1.25). The model AUC in the testing cohort was 0.62 (95%CI 0.61-0.63) and performed similar to the FRAiL model (AUC = 0.61, 95%CI 0.60-0.62).

CONCLUSIONS

Critical information captured during transition into LTCF can be effectively leveraged to inform fracture risk profiling. New fracture predictors including complex health care needs, recent emergency department encounters, general practitioner and consultant physician attendances, were identified.

Development and validation of the fall-related injury risk in nursing homes (INJURE-NH) prediction tool

BACKGROUND

Existing models to predict fall-related injuries (FRI) in nursing homes (NH) focus on hip fractures, yet hip fractures comprise less than half of all FRIs. We developed and validated a series of models to predict the absolute risk of FRIs in NH residents.

METHODS

Retrospective cohort study of long-stay US NH residents (≥100 days in the same facility) between January 1, 2016 and December 31, 2017 (n = 733,427) using Medicare claims and Minimum Data Set v3.0 clinical assessments. Predictors of FRIs were selected through LASSO logistic regression in a 2/3 random derivation sample and tested in a 1/3 validation sample. Sub-distribution hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated for 6-month and 2-year follow-up. Discrimination was evaluated via C-statistic, and calibration compared the predicted rate of FRI to the observed rate. To develop a parsimonious clinical tool, we calculated a score using the five strongest predictors in the Fine-Gray model. Model performance was repeated in the validation sample.

RESULTS

Mean (Q1, Q3) age was 85.0 (77.5, 90.6) years and 69.6% were women. Within 2 years of follow-up, 43,976 (6.0%) residents experienced ≥1 FRI. Seventy predictors were included in the model. The discrimination of the 2-year prediction model was good (C-index = 0.70), and the calibration was excellent. Calibration and discrimination of the 6-month model were similar (C-index = 0.71). In the clinical tool to predict 2-year risk, the five characteristics included independence in activities of daily living (ADLs) (HR 2.27; 95% CI 2.14-2.41) and a history of non-hip fracture (HR 2.02; 95% CI 1.94-2.12). Performance results were similar in the validation sample.

CONCLUSIONS

We developed and validated a series of risk prediction models that can identify NH residents at greatest risk for FRI. In NH, these models should help target preventive strategies.

Improving shared decision-making for osteoporosis pharmacologic therapy in nursing homes: a qualitative analysis

Decisions on whether to use pharmacologic osteoporosis therapy in skilled nursing facility residents are complex and require shared decision-making. Residents, proxies, and staff desire individualized fracture risk estimates that consider advanced age, dementia, and mobility. They want options for reducing administration burden, monitoring instructions, and periodic reassessment of risk vs. benefit.

PURPOSE

Decisions about pharmacologic osteoporosis treatment in nursing home (NH) residents with advanced age and multimorbidity are complex and should occur using shared decision-making. Our objective was to identify processes and tools to improve shared decision-making about pharmacologic osteoporosis treatment in NHs.

METHODS

Qualitative analysis of data collected in three NHs from residents at high fracture risk, their proxies, nursing assistants, nurses, and one nurse practitioner (n = 28). Interviews explored participants' stories, attitudes, and experiences with oral osteoporosis medication management. Framework analysis was used to identify barriers to shared decision-making regarding osteoporosis treatment in this setting.

RESULTS

Participants wanted individualized fracture risk estimates that consider immobility, advanced age, and comorbid dementia. Residents and proxies expected nursing staff to be involved in the decision-making; nursing staff wished to be informed on the relative risks vs. benefits of medications and given monitoring instructions. Two important competing demands to address during the shared decision-making process were burdensome administration requirements and polypharmacy. Participants wanted to reassess pharmacologic treatment appropriateness over time as clinical status or goals of care change.

CONCLUSIONS

Shared decision-making using strategies and tools identified in this analysis may move osteoporosis pharmacologic treatment in NHs and for other older adults with multimorbidity from inappropriate inertia to appropriate prescribing or appropriate inaction.

The Effect of Fall Biomechanics on Risk for Hip Fracture in Older Adults: A Cohort Study of Video-Captured Falls in Long-Term Care

Over 95% of hip fractures in older adults are caused by falls, yet only 1% to 2% of falls result in hip fracture. Our current understanding of the types of falls that lead to hip fracture is based on reports by the faller or witness. We analyzed videos of real-life falls in long-term care to provide objective evidence on the factors that separate falls that result in hip fracture from falls that do not. Between 2007 and 2018, we video-captured 2377 falls by 646 residents in two long-term care facilities. Hip fracture was documented in 30 falls. We analyzed each video with a structured questionnaire, and used generalized estimating equations (GEEs) to determine relative risk ratios (RRs) for hip fracture associated with various fall characteristics. All hip fractures involved falls from standing height, and pelvis impact with the ground. After excluding falls from lower than standing height, risk for hip fracture was higher for sideways landing configurations (RR = 5.50; 95% CI, 2.36-12.78) than forward or backward, and for falls causing hip impact (3.38; 95% CI, 1.49-7.67). However, hip fracture risk was just as high in falls initially directed sideways as forward (1.14; 95% CI, 0.49-2.67), due to the tendency for rotation during descent. Falling while using a mobility aid was associated with lower fracture risk (0.30; 95% CI, 0.09-1.00). Seventy percent of hip fractures involved impact to the posterolateral aspect of the pelvis. Hip protectors were worn in 73% of falls, and hip fracture risk was lower in falls where hip protectors were worn (0.45; 95% CI, 0.21-0.99). Age and sex were not associated with fracture risk. There was no evidence of spontaneous fractures. In this first study of video-captured falls causing hip fracture, we show that the biomechanics of falls involving hip fracture were different than nonfracture falls for fall height, fall direction, impact locations, and use of hip protectors. © 2020 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.