Dataset Information

Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index.

ABSTRACT:

Background

Frailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI).

Methods

We used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculated from the 2006 survey data as a reference standard. We compared CFI to combined comorbidity index (CCI) in the ability to predict death, disability, recurrent falls, and health care utilization in 2007. As validation, we calculated a CFI using the 2011 claims data to predict these outcomes in 2012.

Results

The CFI was correlated with SFI (correlation coefficient: 0.60). In the development sample, CFI was similar to CCI in predicting mortality (C statistic: 0.77 vs. 0.78), but better than CCI for disability, mobility impairment, and recurrent falls (C statistic: 0.62-0.66 vs. 0.56-0.60). Although both indices similarly explained the variation in hospital days, CFI outperformed CCI in explaining the variation in skilled nursing facility days. Adding CFI to age, sex, and CCI improved prediction. In the validation sample, CFI and CCI performed similarly for mortality (C statistic: 0.71 vs. 0.72). Other results were comparable to those from the development sample.

Conclusion

A novel frailty index can measure the risk for adverse health outcomes that is not otherwise quantified using demographic characteristics and traditional comorbidity measures in Medicare data.

SUBMITTER: Kim DH

PROVIDER: S-EPMC6001883 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index.

Kim Dae Hyun DH Schneeweiss Sebastian S Glynn Robert J RJ Lipsitz Lewis A LA Rockwood Kenneth K Avorn Jerry J

The journals of gerontology. Series A, Biological sciences and medical sciences 20180601 7

<h4>Background</h4>Frailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI).<h4>Methods</h4>We used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculate ...[more]

PMID: 29244057

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Project description:BackgroundThe conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors-an approach that creates information loss and depends on the availability of population-derived cut-points. This study proposes an alternative approach to computing the PFP by developing and validating a model that uses PFP components to predict the frailty index (FI) in community-dwelling older adults, without the need for predictor dichotomization.MethodsA sample of 998 community-dwelling older adults (mean [SD], 68 [7] years) participated in this prospective cohort study. Participants completed a multi-domain geriatric screen and a physical fitness assessment from which the count-based PFP and the 36-item FI were computed. One-year prospective falls and hospitalization rates were also measured. Bayesian beta regression analysis, allowing for nonlinear effects of the non-dichotomized PFP criterion predictors, was used to develop a model for FI ("model-based PFP"). Approximate leave-one-out (LOO) cross-validation was used to examine model overfitting.ResultsThe model-based PFP showed good calibration with the FI, and it had better out-of-sample predictive performance than the count-based PFP (LOO-R2, 0.35 vs 0.22). In clinical terms, the improvement in prediction (i) translated to improved classification agreement with the FI (Cohen's kw, 0.47 vs 0.36) and (ii) resulted primarily in a 23% (95%CI, 18-28%) net increase in FI-defined "prefrail/frail" participants correctly classified. The model-based PFP showed stronger prognostic performance for predicting falls and hospitalization than did the count-based PFP.ConclusionThe developed model-based PFP predicted FI and clinical outcomes more strongly than did the count-based PFP in community-dwelling older adults. By not requiring predictor cut-points, the model-based PFP potentially facilitates usage and feasibility. Future validation studies should aim to obtain clear evidence on the benefits of this approach.

Project description:ImportanceGrowing consensus suggests that frailty-associated risks should inform shared surgical decision making. However, it is not clear how best to screen for frailty in preoperative surgical populations.ObjectiveTo develop and validate the Risk Analysis Index (RAI), a 14-item instrument used to measure surgical frailty. It can be calculated prospectively (RAI-C), using a clinical questionnaire, or retrospectively (RAI-A), using variables from the surgical quality improvement databases (Veterans Affairs or American College of Surgeons National Surgical Quality Improvement Projects).Design, setting, and participantsSingle-site, prospective cohort from July 2011 to September 2015 at the Veterans Affairs Nebraska-Western Iowa Heath Care System, a Level 1b Veterans Affairs Medical Center. The study included all patients presenting to the medical center for elective surgery.ExposuresWe assessed the RAI-C for all patients scheduled for surgery, linking these scores to administrative and quality improvement data to calculate the RAI-A and the modified Frailty Index.Main outcomes and measuresReceiver operator characteristics and C statistics for each measure predicting postoperative mortality and morbidity.ResultsOf the participants, the mean (SD) age was 60.7 (13.9) years and 249 participants (3.6%) were women. We assessed the RAI-C 10 698 times, from which we linked 6856 unique patients to mortality data. The C statistic predicting 180-day mortality for the RAI-C was 0.772. Of these 6856 unique patients, we linked 2785 to local Veterans Affairs Surgeons National Surgical Quality Improvement Projects data and calculated the C statistic for both the RAI-A (0.823) and RAI-C (0.824), along with the correlation between the 2 scores (r = 0.478; P < .001). Of these 2785 patients, there were sufficient data to calculate the modified Frailty Index for 1021, in which the C statistics were 0.865 (RAI-A), 0.797 (RAI-C), and 0.811 (modified Frailty Index). The correlation between the RAI-A and RAI-C was 0.547, and the correlations of the modified Frailty Index to the RAI-A and RAI-C were 0.301 and 0.269, respectively (all P < .001). A cutoff of RAI-C of at least 21 classified 18.3% patients as "frail" with a sensitivity of 0.50 and specificity of 0.82, whereas the RAI-A was less sensitive (0.25) and more specific (0.97), classifying only 3.7% as "frail."Conclusions and relevanceThe RAI-C and RAI-A represent effective tools for measuring frailty in surgical populations with predictive ability on par with other frailty tools. Moderate correlation between the measures suggests convergent validity. The RAI-C offers the advantage of prospective, preoperative assessment that is proved feasible for large-scale screening in clinical practice. However, further efforts should be directed at determining the optimal components of preoperative frailty assessment.

Project description:BackgroundAlthough elderly population is generally frail, it is important to closely monitor their health deterioration to improve the care and support in residential aged care homes (RACs). Currently, the best identification approach is through time-consuming regular geriatric assessments. This study aimed to develop and validate a retrospective electronic frailty index (reFI) to track the health status of people staying at RACs using the daily routine operational data records.MethodsWe have access to patient records from the Royal Freemasons Benevolent Institution RACs (Australia) over the age of 65, spanning 2010 to 2021. The reFI was developed using the cumulative deficit frailty model whose value was calculated as the ratio of number of present frailty deficits to the total possible frailty indicators (32). Frailty categories were defined using population quartiles. 1, 3 and 5-year mortality were used for validation. Survival analysis was performed using Kaplan-Meier estimate. Hazard ratios (HRs) were estimated using Cox regression analyses and the association was assessed using receiver operating characteristic (ROC) curves.ResultsTwo thousand five hundred eighty-eight residents were assessed, with an average length of stay of 1.2 ± 2.2 years. The RAC cohort was generally frail with an average reFI of 0.21 ± 0.11. According to the Kaplan-Meier estimate, survival varied significantly across different frailty categories (p < 0.01). The estimated hazard ratios (HRs) were 1.12 (95% CI 1.09-1.15), 1.11 (95% CI 1.07-1.14), and 1.1 (95% CI 1.04-1.17) at 1, 3 and 5 years. The ROC analysis of the reFI for mortality outcome showed an area under the curve (AUC) of ≥0.60 for 1, 3 and 5-year mortality.ConclusionA novel reFI was developed using the routine data recorded at RACs. reFI can identify changes in the frailty index over time for elderly people, that could potentially help in creating personalised care plans for addressing their health deterioration.

Project description:BackgroundThe linkage of registries to Medicare claims data can help extend follow-up for patients receiving medical devices. In the present study, we tested and validated an algorithm that does not require patient identifiers to link data from a national vascular registry and Medicare claims data.MethodsWe used data from the Vascular Quality Initiative (VQI), a registry capturing data from >600 centers for several different vascular procedures, and Medicare claims from 2003 to 2018. We restricted the study to patients aged ≥65 years who had fee-for-service entitlement at their procedure. We performed an indirect linkage to combine the VQI and Medicare data at the patient level using a sequential algorithm based on the patient's date of birth, sex, zip code, procedure date, and procedure facility. We compared the indirectly linked cohort against a reference standard of a cohort directly linked using Social Security numbers. We calculated the matching rate and accuracy overall and before and after October 2015 when the International Classification of Diseases, 10th revision (ICD-10) system was adopted in the United States.ResultsA total of 144,045 VQI-Medicare-linked patients were in the reference standard cohort. Using the indirect linking algorithm, we matched 133,966 of the 144,045 VQI patients to their Medicare claims with a matching rate of 93.0%. Of the 133,966 patients, 133,104 were correctly matched (matching accuracy, 99.4%). The matching rate was higher when the indirect linkage was implemented using the ICD-10 coded data than using the ICD-9 coded data (94.0% vs 92.2%). The accuracy of the indirect linkage remained high for all procedure modules after the ICD-10 coding change (overall, 99.4%; range, 99.0%-99.7%).ConclusionsIn the present study, we successfully used indirect identifiers to link the VQI data to Medicare claims with >90% success and >99% accuracy. When direct linkage of the registry claims data using Social Security numbers is not possible because of availability or confidentiality, or both, our algorithm for indirect linkage provides a suitable alternative. The matching rate and accuracy will help ensure the accuracy of long-term follow-up and the completeness and representativeness of linked databases for relevant research and quality improvement initiatives.

Dataset Information

Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index.

Background

Methods

Results

Conclusion

Publications

Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index.

Similar Datasets

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