Dataset Information

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

ABSTRACT: OBJECTIVE:Tools were unavailable to assess fracture risk in nursing homes (NH); therefore, we developed the Fracture Risk Assessment in Long term care (FRAiL) model. The objective of this validation study was to assess the performance of the FRAiL model to predict 2-year risk of non-vertebral and hip fractures in a separate large cohort of NH residents. METHODS:This retrospective cohort study included most long-stay NH residents in the United States (N?=?896,840). Hip and non-vertebral fractures were identified using Medicare claims. The Minimum Data Set (MDS) was used to identify characteristics from the original FRAiL model. Multivariable competing risk regression was used to model risk of fracture. RESULTS:Mean age was 83.8?years (±8.2?years) and 70.7% were women. Over a mean follow-up of 1.52?years (SD 0.65), 41,531 residents (4.6%) were hospitalized with non-vertebral fracture (n?=?30,356 hip fractures). In the fully adjusted model, 14/15 model characteristics remained significant predictors of non-vertebral fracture. Female sex (HR?=?1.55, 95% CI 1.52, 1.59), wandering (HR?=?1.30, 95% CI 1.26, 1.34), and falls (HR?=?1.28, 95% CI 1.26, 1.31) were strongly associated with non-vertebral fracture rate. Total dependence in ADLs (versus independence) was associated with a decrease in non-vertebral fracture rate (HR?=?0.57, 95% CI 0.52, 0.64). Discrimination was moderate in men (C-index?=?0.68 for hip, 0.66 for non-vertebral) and women (C-index?=?0.68 for hip, 0.65 for non-vertebral), and calibration was excellent. CONCLUSIONS:Our model comprised entirely from routinely collected data was able to identify NH residents at greatest risk for non-vertebral fracture.

SUBMITTER: Berry SD

PROVIDER: S-EPMC6823926 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Berry Sarah D SD Zullo Andrew R AR Zhang Tingting T Lee Yoojin Y McConeghy Kevin W KW Kiel Douglas P DP

Bone 20190828

<h4>Objective</h4>Tools were unavailable to assess fracture risk in nursing homes (NH); therefore, we developed the Fracture Risk Assessment in Long term care (FRAiL) model. The objective of this validation study was to assess the performance of the FRAiL model to predict 2-year risk of non-vertebral and hip fractures in a separate large cohort of NH residents.<h4>Methods</h4>This retrospective cohort study included most long-stay NH residents in the United States (N = 896,840). Hip and non-vert ...[more]

PMID: 31472301

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Project description:Little is known regarding outcomes after hip fracture among long-term nursing home residents.To describe patterns and predictors of mortality and functional decline in activities of daily living (ADLs) among nursing home residents after hip fracture.Retrospective cohort study of 60,111 Medicare beneficiaries residing in nursing homes who were hospitalized with hip fractures between July 1, 2005, and June 30, 2009.Data sources included Medicare claims and the Nursing Home Minimum Data Set. Main outcomes included death from any cause at 180 days after fracture and a composite outcome of death or new total dependence in locomotion at the latest available assessment within 180 days. Additional analyses described within-residents changes in function in 7 ADLs before and after fracture.Of 60,111 patients, 21,766 (36.2%) died by 180 days after fracture; among patients not totally dependent in locomotion at baseline, 53.5% died or developed new total dependence within 180 days. Within individual patients, function declined substantially after fracture across all ADL domains assessed. In adjusted analyses, the greatest decreases in survival after fracture occurred with age older than 90 years (vs ?75 years: hazard ratio [HR], 2.17; 95% CI, 2.09-2.26 [P?<?.001]), nonoperative fracture management (vs internal fixation: HR for death, 2.08; 95% CI, 2.01-2.15 [P?<?.001]), and advanced comorbidity (Charlson score of ?5 vs 0: HR, 1.66; 95% CI, 1.58-1.73 [P?<?.001]). The combined risk of death or new total dependence in locomotion within 180 days was greatest among patients with very severe cognitive impairment (vs intact cognition: relative risk [RR], 1.66; 95% CI, 1.56-1.77 [P?<?.001]), patients receiving nonoperative management (vs internal fixation: RR, 1.48; 95% CI, 1.45-1.51 [P?<?.001]), and patients older than 90 years (vs ?75 years: RR, 1.42; 95% CI, 1.37-1.46 [P?<?.001]).Survival and functional outcomes are poor after hip fracture among nursing home residents, particularly for patients receiving nonoperative management, the oldest old, and patients with multiple comorbidities and advanced cognitive impairment. Care planning should incorporate appropriate prognostic information related to outcomes in this population.

Project description:BackgroundAccurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation.MethodsA total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models.ResultsThe two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model.ConclusionsThe integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.

Project description:The aim of the present study was to identify predictors for initial uptake and adherence with the use of hip protectors when offering hip protectors free of charge to nursing-home residents. An 18 months prospective follow up study was carried out in 18 Norwegian nursing homes. One thousand two hundred and thirty-six residents were included in the study of which 604 started to use a hip protector. A multivariate logistic regression model was used to identify predictors for the initial uptake. A Cox proportional hazard model was used to identify predictors for adherence. A stepwise backward strategy was used in both the logistic and in the Cox regression. The effect of nursing homes as clusters was adjusted for in the analysis. The uptake rate among all residents was 46% and the adherence was approximately 75% after 3 months, and approximately 60% after 18 months. Female gender [odds ratio (OR): 1.54, 95% CI: 1.06-2.24, P = 0.022], previous fractures (OR: 1.67, 95% CI: 1.02-2.75, P = 0.043), previous falls (OR: 2.08, 95% CI: 1.35-3.19, P < 0.001) and memory (not able to memorise: OR: 3.71, 95% CI: 2.09-6.59, P < 0.001, large problems with memorising: OR: 2.85, 95% CI: 1.81-4.49, P < 0.001, medium problems with memorising: OR: 2.45, 95% CI: 1.39-4.33, P = 0.002, some problems with memorising: OR: 1.99, 95% CI: 1.14-3.48, P = 0.016) seemed to be important predictors for uptake. Among those who took up the offer male gender (HR: 1.71, 95% CI: 1.00-2.91, P = 0.049), memory (not able to memorise: HR: 0.26, 95% CI: 0.14-0.50, P < 0.001, large problems with memorising: HR: 0.32, 95% CI: 0.22-0.45, P < 0.001, medium problems with memorising: HR: 0.46, 95% CI: 0.30-0.73, P < 0.001, some problems with memorising: HR: 0.49, 95% CI: 0.32-0.73, P = 0.001) and bowel incontinence (HR: 0.41, 95% CI: 0.25-0.66, P < 0.001) were predictors for a lower probability of ending hip protector use. Factors related to a high risk of falling were important predictors for both uptake and adherence. The fact that neither memory impairments nor incontinence (bowel) seemed to be barriers to hip protector use is important since these characteristics are common among nursing-home residents and tertiary prevention such as the use of hip protectors is probably the most feasible intervention to prevent hip fractures in this group.

Dataset Information

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

Publications

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

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