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Comparison of simple vs. performance-based fall prediction models: data from the National Health and Aging Trends Study.

ABSTRACT: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data.We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC) across models.National Health and Aging Trends Study (NHATS), which surveyed a nationally-representative sample of Medicare enrollees (age ?65) at baseline (Round 1: 2011-12) and one-year follow-up (Round 2: 2012-3).6056 community-dwelling individuals who participated in Rounds 1 and 2 of NHATS.Primary outcomes were one-year incidence of "any fall" and "recurrent falls". Prediction models were compared and validated in development and validation sets, respectively.A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC=0.69, 95% CI 0.67-0.71) and recurrent falls (AUC=0.77, 95% CI 0.74-0.79) in the development set. Physical performance testing provided marginal additional predictive value.A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

SUBMITTER: Gadkaree SK

PROVIDER: S-EPMC4686273 | biostudies-literature | 2015 Jan-Dec

REPOSITORIES: biostudies-literature

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Comparison of simple vs. performance-based fall prediction models: data from the National Health and Aging Trends Study.

Gadkaree Shekhar K SK Sun Daniel Q DQ Huang Jin J Varadhan Ravi R Agrawal Yuri Y

Gerontology & geriatric medicine 20150101

<h4>Objective</h4>To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data.<h4>Design</h4>We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC) across models.<h4>Setting</h4>National Health and Aging Trends Study (NHATS), which surveyed a nationally-representative sample of Medicar ...[more]

PMID: 26702410

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Project description:AimsPhysical frailty is a commonly encountered geriatric syndrome among older adults without coronary heart disease (CHD). The impact of frailty on the incidence of long-term cardiovascular outcomes is not known.We aimed to evaluate the long-term association of frailty, measured by the Fried frailty phenotype, with all-cause-mortality and MACE among older adults without a history of CHD at baseline in the National Health and Aging Trends Study.Methods and resultsWe used the National Health and Aging Trends Study, a prospective cohort study linked to a Medicare sample. Participants with a prior history of CHD were excluded. Frailty was measured during the baseline visit using the Fried physical frailty phenotype. Cardiovascular outcomes were assessed during a 6-year follow-up.Of the 4656 study participants, 3259 (70%) had no history of CHD 1 year prior to their baseline visit. Compared to those without frailty, subjects with frailty were older (mean age 82.1 vs. 75.1 years, P < 0.001), more likely to be female (68.3% vs. 54.9%, P < 0.001), and belong to an ethnic minority. The prevalence of hypertension, falls, disability, anxiety/depression, and multimorbidity was much higher in the frail and pre-frail than the non-frail participants. In a Cox time-to-event multivariable model and during 6-year follow-up, the incidences of death and of each individual cardiovascular outcomes were all significantly higher in the frail than in the non-frail patients including major adverse cardiovascular event (MACE) [hazard ratio (HR) 1.77, 95% confidence interval (CI) 1.53, 2.06], death (HR 2.70, 95% CI 2.16, 3.38), acute myocardial infarction (HR 1.95, 95% CI 1.31, 2.90), stroke (HR 1.71, 95% CI 1.34, 2.17), peripheral vascular disease (HR 1.80, 95% CI 1.44, 2.27), and coronary artery disease (HR 1.35, 95% CI 1.11, 1.65).ConclusionIn patients without CHD, frailty is a risk factor for the development of MACEs. Efforts to identify frailty in patients without CHD and interventions to limit or reverse frailty status are needed and, if successful, may limit subsequent adverse cardiovascular events.

Project description:ImportanceThe current program-centric algorithm for the National Resident Matching Program (NRMP) primarily uses the program's ranking of students to determine a match. Concerns that the existing algorithm favors programs over students, recent findings that the program's ranking of applicants is not associated with resident performance, and disruptions of existing screening methods and metrics have prompted reevaluation of the current algorithm relative to a student-centric algorithm, in which student ranking of programs is primary and program ranking of students is secondary.ObjectiveTo compare program-centric and student-centric algorithms for the NRMP participants.Design, setting, and participantsThis cross-sectional study used randomized computer-generated data reflecting the NRMP match for 2018, 2019, and 2020, capturing more than 50 000 students and more than 4000 programs in 23 specialties, to compare the 2 algorithms.ExposuresThe same simulated students, programs, and rankings were exposed to the 2 algorithms, running 2300 simulations in the overall analysis and 1000 simulations in each of 23 specialties.Main outcomes and measuresThe percentage of students who did and did not match, the percentage of students who matched to their top-ranked and top-5-ranked programs, and the program's rank of the last student matched per position were examined.ResultsThe 2 algorithms were not different in percentage of students matched overall (eg, for 2020, program-centric: 59% [95% CI, 57%-61%]; student-centric: 58% [95% CI, 56%-60%]; P = .73). The student-centric algorithm, relative to the program-centric algorithm, matched a significantly higher percentage of students to their first-ranked program (eg, for 2020, 50% [95% CI, 48%-52%] vs 14% [95% CI, 13%-15%]; P < .001) and to their top-5-ranked programs (eg, for 2020, 60% [95% CI, 58%-62%] vs 46% [95% CI, 44%-48%]; P < .001). However, the last position was filled with students who had lower program rankings in the student-centric algorithm vs the program-centric algorithm (2 [95% CI, 1-2] vs 8 [95% CI, 6-10]; P < .001).Conclusions and relevanceIn this study, the 2 algorithms were not different in the percentage of students matched overall. However, the student-centric algorithm matched a significantly higher percentage of students to their preferred programs. The program-centric algorithm was associated with a lower program's last matched student rank. Further research is needed on the algorithms' associations with cost and time demands in the match, postmatch resident and program performance, and fit with a changing environment.

Dataset Information

Comparison of simple vs. performance-based fall prediction models: data from the National Health and Aging Trends Study.

Publications

Comparison of simple vs. performance-based fall prediction models: data from the National Health and Aging Trends Study.

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