Comparing Survey-Based Frailty Assessment to Medicare Claims in Predicting Health Outcomes and Utilization in Medicare Beneficiaries

Claims-based Frailty Index in Japanese Older Adults: A Cohort Study Using LIFE Study Data

BACKGROUND

We aimed to assess whether the United States-developed Claims-based Frailty Index (CFI) can be implemented in Japanese older adults using claims data.

METHODS

We used the monthly claims data and certification of long-term care (LTC) insurance data of residents from 12 municipalities from April 2014 to March 2019. The 12 months from first recording was defined as the "baseline period," and the time thereafter as the "follow-up period". Participants aged ≥65 years were included, and those with no certified LTC insurance or who died at baseline were excluded. New certification of LTC insurance and all-cause mortality during the follow-up period were defined as outcome events. CFI categorization consisted of three steps including: 1) using 12 months deficit-accumulation approach that assigned different weights to each of the 52 items; 2) the accumulated score to derive the CFI; and 3) categorizing the CFI as "robust" (<0.15), "prefrail" (0.15-0.24), and "frail" (≥0.25). Kaplan-Meier survival curves and Cox proportional hazard models were used to determine the association between CFI and outcomes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated.

RESULTS

There were 519,941 participants in total. After adjusting for covariates, the severe CFI category had a high risk of certification of LTC insurance (prefrail: HR 1.33; 95% CI, 1.27-1.39 and frail: HR 1.60; 95% CI, 1.53-1.68) and all-cause mortality (prefrail: HR 1.44; 95% CI, 1.29-1.60 and frail: HR 1.84; 95% CI, 1.66-2.05).

CONCLUSION

This study suggests that CFI can be implemented in Japanese claims data to predict the certification of LTC insurance and mortality.

Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review

Background

Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults

Methods and findings

We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain.

Conclusions

Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines.