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Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype.

ABSTRACT: BACKGROUND:Fried and colleagues described a frailty phenotype measured in the Cardiovascular Health Study (CHS). This phenotype is manifest when ?3 of the following are present: low grip strength, low energy, slowed waking speed, low physical activity, or unintentional weight loss. We sought to approximate frailty phenotype using only administrative claims data to enable frailty to be assessed without physical performance measures. STUDY DESIGN:We used the CHS cohort data linked to participants Medicare claims. The reference standard was the frailty phenotype measured at visits 5 and 9. With penalized logistic regression, we developed a parsimonious index for predicting the frailty phenotype using a linear combination of diagnoses, operationalized with claims data. We assessed the predictive validity of frailty index by examining how well it predicted common aging-related outcomes including hospitalization, disability, and death. RESULTS:There were 4454 CHS participants from 4 clinical sites. In total, 84% were white, 58% were women and their mean age was 72 years at enrollment. Approximately 11% of the cohort was frail. The model had an area under the receiver operating curve of 0.75 to concurrently predict a frailty phenotype. This Claims-based Frailty Indicator significantly predicted death (odds ratio, 1.84), time to death (hazards ratio, 1.71), number of hospital admissions (incidence rate ratio, 1.74), and nursing home admission (odds ratio, 1.47) in models adjusted for age and sex. CONCLUSIONS:Claims data alone can be used to classify individuals as frail and nonfrail. The Claims-based Frailty Indicator might be used in research with large datasets for confounding adjustment or risk prediction. The indicator might also be used for emergency preparedness for identification of regions enriched with frail individuals.

SUBMITTER: Segal JB

PROVIDER: S-EPMC5471130 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype.

Segal Jodi B JB Chang Hsien-Yen H-Y Du Yu Y Walston Jeremy D JD Carlson Michelle C MC Varadhan Ravi R

Medical care 20170701 7

<h4>Background</h4>Fried and colleagues described a frailty phenotype measured in the Cardiovascular Health Study (CHS). This phenotype is manifest when ≥3 of the following are present: low grip strength, low energy, slowed waking speed, low physical activity, or unintentional weight loss. We sought to approximate frailty phenotype using only administrative claims data to enable frailty to be assessed without physical performance measures.<h4>Study design</h4>We used the CHS cohort data linked t ...[more]

PMID: 28437320

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Project description:Background Prospectively collected frailty markers are associated with an incremental 1-year mortality risk after transcatheter aortic valve replacement (TAVR) compared with comorbidities alone. Whether information on frailty markers captured retrospectively in administrative billing data is similarly predictive of long-term mortality after TAVR is unknown. We sought to characterize the prognostic importance of frailty factors as identified in healthcare billing records in comparison to validated measures of frailty for the prediction of long-term mortality after TAVR. Methods and Results Adult patients undergoing TAVR between August 25, 2011, and September 29, 2015, were identified among Medicare fee-for-service beneficiaries. The Johns Hopkins Claims-based Frailty Indicator was used to identify frail patients. We used nested Cox regression models to identify claims-based predictors of mortality up to 4 years post-procedure. Four groups of variables, including cardiac risk factors, noncardiac risk factors, patient procedural risk factors, and nontraditional markers of frailty, were introduced sequentially, and their integrated discrimination improvement was assessed. A total of 52 338 TAVR patients from 558 clinical sites were identified, with a mean follow-up time period of 16 months. In total, 14 174 (27.1%) patients died within the study period. The mortality rate was 53.9% at 4 years post-TAVR. A total of 34 863 (66.6%) patients were defined as frail. The discrimination of each of the 4 models was 0.60 (95% CI, 0.59-60), 0.65 (95% CI, 0.64-0.65), 0.68 (95% CI, 0.67-0.68), and 0.70 (95% CI, 0.69-0.70), respectively. The addition of nontraditional frailty markers as identified in claims improved mortality prediction above and beyond traditional risk factors (integrated discrimination improvement: 0.019; P<0.001). Conclusions Risk prediction models that include frailty as identified in claims data can be used to predict long-term mortality risk after TAVR. Linkage to claims data may allow enhanced mortality risk prediction for studies that do not collect information on frailty.

Project description:Background In aortic valve disease, the relationship between claims-based frailty indices (CFIs) and validated measures of frailty constructed from in-person assessments is unclear but may be relevant for retrospective ascertainment of frailty status when otherwise unmeasured. Methods and Results We linked adults aged ≥65 years in the US CoreValve Studies (linkage rate, 67%; mean age, 82.7±6.2 years, 43.1% women), to Medicare inpatient claims, 2011 to 2015. The Johns Hopkins CFI, validated on the basis of the Fried index, was generated for each study participant, and the association between CFI tertile and trial outcomes was evaluated as part of the EXTEND-FRAILTY substudy. Among 2357 participants (64.9% frail), higher CFI tertile was associated with greater impairments in nutrition, disability, cognition, and self-rated health. The primary outcome of all-cause mortality at 1 year occurred in 19.3%, 23.1%, and 31.3% of those in tertiles 1 to 3, respectively (tertile 2 versus 1: hazard ratio, 1.22; 95% CI, 0.98-1.51; P=0.07; tertile 3 versus 1: hazard ratio, 1.73; 95% CI, 1.41-2.12; P<0.001). Secondary outcomes (bleeding, major adverse cardiovascular and cerebrovascular events, and hospitalization) were more frequent with increasing CFI tertile and persisted despite adjustment for age, sex, New York Heart Association class, and Society of Thoracic Surgeons risk score. Conclusions In linked Medicare and CoreValve study data, a CFI based on the Fried index consistently identified individuals with worse impairments in frailty, disability, cognitive dysfunction, and nutrition and a higher risk of death, hospitalization, bleeding, and major adverse cardiovascular and cerebrovascular events, independent of age and risk category. While not a surrogate for validated metrics of frailty using in-person assessments, use of this CFI to ascertain frailty status among patients with aortic valve disease may be valid and prognostically relevant information when otherwise not measured.

Project description:BackgroundMost claims-based frailty instruments have been designed for group stratification of older populations according to the risk of adverse health outcomes and not frailty itself. We aimed to develop and validate a tool based on one-year hospital discharge data for stratification on Fried's frailty phenotype (FP).MethodsWe used a three-stage development/validation approach. First, we created a clinical knowledge-driven electronic frailty score (eFS) calculated as the number of deficient organs/systems among 18 critical ones identified from the International Statistical Classification of Diseases and Related Problems, 10th Revision (ICD-10) diagnoses coded in the year before FP assessment. Second, for eFS development and internal validation, we linked individual records from the Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV) for the period 2004-2015. The development/internal validation sample included community-dwelling, non-institutionalised residents of Lausanne (Switzerland) recruited in the Lc65+ cohort in three waves (2004, 2009, and 2014), aged 65-70 years at enrolment, and hospitalised at the CHUV at least once in the year preceding the FP assessment. Using this sample, we selected the best performing model for predicting the dichotomised FP, with the eFS or ICD-10-based variables as predictors. Third, we conducted an external validation using 2016 Swiss nationwide hospital discharge data and compared the performance of the eFS model in predicting 13 adverse outcomes to three models relying on well-designed and validated claims-based scores (Claims-based Frailty Index, Hospital Frailty Risk Score, Dr Foster Global Frailty Score).FindingsIn the development/internal validation sample (n = 469), 14·3% of participants (n = 67) were frail. Among 34 models tested, the best-subsets logistic regression model with four predictors (age and sex at FP assessment, time since last hospital discharge, eFS) performed best in predicting the dichotomised FP (area under the curve=0·71; F1 score=0·39) and one-year adverse health outcomes. On the external validation sample (n = 54,815; 153 acute care hospitals), the eFS model demonstrated a similar performance to the three other claims-based scoring models. According to the eFS model, the external validation sample showed an estimated prevalence of 56·8% (n = 31,135) of frail older inpatients at admission.InterpretationThe eFS model is an inexpensive, transportable and valid tool allowing reliable group stratification and individual prioritisation for comprehensive frailty assessment and may be applied to both hospitalised and community-dwelling older adults.FundingThe study received no external funding.

Project description:BackgroundAdministrative healthcare claims data provide a mechanism for assessing and monitoring multiple sclerosis (MS) disease status across large, clinically representative "real-world" populations. The estimation of MS disease status using administrative claims can be a challenge, however, due to a lack of detailed clinical information. Retrospective claims analyses in MS have traditionally used rates of MS relapses to approximate disease status. Healthcare costs may be alternate, broader claims-based indicators of disease activity because costs reflect multiple facets of care of patients with MS, and there is a strong correlation between quality of life of patients with MS and costs of the disease. This study developed, tested, and validated a healthcare cost-based measure to serve as an indicator of overall disease status in patients with MS treated with disease-modifying drugs (DMDs) utilizing administrative claims.MethodsUsing IMS Health Real World Data Adjudicated Claims - US data (January 2006-June 2013), a negative binomial regression predicted annual all-cause medical costs. Coefficients reaching statistical significance (p < 0.05) and increasing costs by ?5% were selected for inclusion into an MS-specific severity score (scale of 0 to 100). Components of the score included rehabilitation services, altered mental state, pain, disability, stiffness, balance disorder, urinary incontinence, numbness, malaise/fatigue, and infections. Coefficient weights represented each predictor's contribution. The predictive model was derived using 50% of a random sample and tested/validated using the remaining 50%.ResultsAverage overall predicted annual total medical cost was $11,134 (development sample, n = 11,384, vs. $10,528 actual) and $11,303 (validation sample, n = 11,385, vs. $10,620 actual). The model had consistent bias (approximately +$600 or +6% of actual costs) for both samples. In the validation sample, mean MS disease status scores were 0.24, 8.95, and 21.77 for low, medium, and high tertiles, respectively. Mean costs were most accurately predicted among less severe patients ($5243 predicted vs. $5233 actual cost for lowest tertile).ConclusionThe algorithm developed in this study provides an initial step to helping understand and potentially predict cost changes for a commercially insured MS population.

Dataset Information

Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype.

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Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype.

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