Mobility Device Use and Frailty Progression in Community-Dwelling Older Adults With Mobility Limitations.

Objective: Examine the association between mobility device use and changes in a frailty index (FI) over one year in community-dwelling older adults with mobility limitations. Methods: Analyses utilized 2015-2016 data from the National Health and Aging Trends Study community-dwelling older adults (n = 3934). We calculated a validated 40-item deficit accumulation frailty index (FI) in 2015 and 2016 and compared one year change in FI in older adults with/without canes or walkers using multivariable logistic regression. Analyses were repeated with stratification by baseline frailty. Results: Device use was not associated with worsening frailty in the overall cohort, but was associated with worsening frailty in non-frail individuals when stratified by baseline frailty. Discussion: Device use does not worsen frailty in individuals who are frail at baseline. Device users who were not frail at baseline experienced worsening frailty suggesting additional contributing factors to their frailty aside from mobility limitations.

Claims-Based Frailty Index and Its Relationship With Commonly Used Clinical Frailty Measures.

BACKGROUND: The relationship of claims-based frailty index (CFI), a validated measure to identify frail individuals using Medicare data, and frailty measures used in clinical practice has not yet been fully explored. METHODS: We identified community-dwelling participants of the 2015 National Health and Aging Trends Study (NHATS) whose CFI scores could be calculated using linked Medicare claims. We calculated 9 commonly used clinical frailty measures from their NHATS in-person examination: Study of Osteoporotic Fracture Index (SOF), FRAIL Scale, Frailty Phenotype, Clinical Frailty Scale (CFS), Vulnerable Elder Survey-13 (VES-13), Tilburg Frailty Indicator (TFI), Groningen Frailty Indicator (GFI), Edmonton Frail Scale (EFS), and 40-item Frailty Index (FI). Using equipercentile method, CFI scores were linked to clinical frailty measures. C-statistics and test characteristics of CFI to identify frailty as defined by each clinical frailty measure were calculated. RESULTS: Of the 3 963 older adults, 44.5% were ≥75 years, 59.4% were female, and 82.3% were non-Hispanic White. A CFI of 0.25 was equipercentile to the following clinical frailty measure scores: SOF 1.4, FRAIL 1.8, Phenotype 1.8, CFS 5.4, VES-13 5.7, TFI 4.6, GFI 5.0, EFS 6.0, and FI 0.26. The C-statistics of using CFI to identify frailty as defined by each clinical measure were ≥0.70, except for CFS and VES-13. The optimal CFI cutpoints to identify frailty per clinical frailty measure ranged from 0.212 to 0.242, with sensitivity and specificity of 0.37-0.83 and 0.66-0.84, respectively. CONCLUSIONS: Understanding the relationship of CFI and commonly used clinical frailty measures can enhance the interpretability and potential utility of CFI.

Association between Drug Therapy and Risk of Incident Frailty: A Systematic Review.

Medication is a potential factor influencing frailty. However, the relationship between pharmaceutical treatments and frailty remains unclear. Therefore, we conducted the present systematic review to summarize the association between drug therapy and the risk of incident frailty in older adults. We systematically searched the MEDLINE electronic database for articles indexed between January 1, 2000, and December 31, 2021, for randomized controlled trials (RCTs) and cohort studies reporting frailty changes associated with drug therapy. A total of six RCTs and 13 cohort studies involving 211,948 participants were identified, and their treatments were categorized into six medication classes: analgesics, cardiometabolic medication, chemotherapy, central nervous system (CNS)-active medication, hormonal therapy, and nutritional supplements. While the analysis revealed that only CNS-active medications were associated with an elevated risk of frailty, other medication classes also affected frailty; however, this is not conclusively attributable to a class-wide effect.

Validation of Claims-Based Frailty Index for Identifying Moderate-to-Severe Dementia in Medicare Beneficiaries.

OBJECTIVE: Previous research using the National Health and Aging Trends Study showed that a claims-based frailty index (CFI) could be useful for identifying moderate-to-severe dementia in Medicare claims data. This study aims to validate the findings in an independent cohort. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: The study included 658 fee-for-service beneficiaries with dementia who participated in the 2016-2020 Medicare Current Beneficiary Survey in the community-dwelling. METHODS: We operationalized the Functional Assessment Staging Test (FAST) scale (range: 1-7, stages 5-7 indicate moderate-to-severe dementia) using survey information. CFI (range: 0-1, higher scores indicate greater frailty) was calculated using Medicare claims 12 months before the participants' interview date. Using the previously proposed cut point of 0.280, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for identifying moderate-to-severe dementia. Survey procedures were used to account for survey design and weighted to reflect national estimates. RESULTS: The population had a mean age (SD) of 80.7 (8.9) years, 58.5% female, and 101 beneficiaries (14.8%) had moderate-to-severe dementia. The CFI cut point of 0.280 demonstrated sensitivity 0.49 (95% CI, 0.38-0.59), specificity 0.80 (0.77-0.84), PPV 0.30 (0.23-0.38), and NPV 0.90 (0.87-0.93). Compared with those with a CFI <0.280, beneficiaries with a CFI ≥0.280 had an elevated risk of mortality (2.9% vs 4.1%) over 1 year. CONCLUSIONS AND IMPLICATIONS: These results confirm our previous findings that CFI among beneficiaries with a dementia diagnosis is a useful measure of moderate-to-severe dementia for Medicare claims data.

Use of chronic care management service among Medicare beneficiaries in 2015-2019.

BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) introduced chronic care management (CCM) services in 2015 for patients with multiple chronic diseases. Few studies examine the utilization of CCM services by geographic region, sociodemographic, and clinical characteristics. METHODS: We used 2014-2019 Medicare claims data from a 5% random sample of fee-for-service beneficiaries aged 65 years or over. We included beneficiaries potentially eligible for CCM services because they had multiple chronic conditions (1,073,729 in 2015 and 1,130,523 in 2019). We calculated the proportion of potentially eligible beneficiaries receiving CCM service each year for the total population and by geographic region, sociodemographic, and clinical characteristics. RESULTS: The proportion of beneficiaries with two or more chronic conditions receiving CCM services increased from 1.1% in 2015 to 3.4% in 2019. The increase in CCM use was higher in the southern region, among dually eligible beneficiaries and beneficiaries with a greater burden of chronic conditions (2-5 conditions vs ≥10 conditions: 0.7% vs 2.0% in 2015; 2.1% vs 7.0% in 2019) and frailty (robust vs severely frail: 0.6% vs 3.3% in 2015; 1.9% vs 9.4% in 2019). Nearly one out of five recipients did not continue CCM service after the initial service. CONCLUSION: We found that CCM service is being used by a very small fraction of eligible patients. Barriers and facilitators to more effective CCM adoption should be identified and incorporated into strategies that encourage more widespread use of this Medicare benefit.

Coordinated-Transitional Care (C-TraC) for Veterans from Subacute Rehabilitation to Home.

OBJECTIVES: To adapt a successful acute care transitional model to meet the needs of veterans transitioning from post-acute care to home. DESIGN: Quality improvement intervention. SETTING AND PARTICIPANTS: Veterans discharged from a subacute care unit in the VA Boston Healthcare System's skilled nursing facility. METHODS: We used the Replicating Effective Programs framework and Plan-Do-Study-Act cycles to adapt the Coordinated-Transitional Care (C-TraC) program to the context of transitions from a VA subacute care unit to home. The major adaptation of this registered nurse-driven, telephone-based intervention was combining the roles of discharge coordinator and transitional care case manager. We report the details of the implementation, its feasibility, and results of process measures, and describe its preliminary impact. RESULTS: Between October 2021 and April 2022, all 35 veterans who met eligibility criteria in the VA Boston Community Living Center (CLC) participated; none were lost to follow-up. The nurse case manager delivered core components of the calls with high fidelity-review of red flags, detailed medication reconciliation, follow-up with primary care physician, and discharge services were discussed and documented in 97.9%, 95.9%, 86.8%, and 95.9%, respectively. CLC C-TraC interventions included care coordination, patient and caregiver education, connecting patients to resources, and addressing medication discrepancies. Nine medication discrepancies were discovered in 8 patients (22.9%; average of 1.1 discrepancies per patient). Compared with a historical cohort of 84 veterans, more CLC C-TraC patients received a post-discharge call within 7 days (82.9% vs 61.9%; P = .03). There was no difference between rates of attendance to appointments and acute care admissions post-discharge. CONCLUSIONS AND IMPLICATIONS: We successfully adapted the C-TraC transitional care protocol to the VA subacute care setting. CLC C-TraC resulted in increased post-discharge follow-up and intensive case management. Evaluation of a larger cohort to determine its impact on clinical outcomes such as readmissions is warranted.

Claims-Based Frailty Index as a Measure of Dementia Severity in Medicare Claims Data.

BACKGROUND: Dementia severity is unavailable in administrative claims data. We examined whether a claims-based frailty index (CFI) can measure dementia severity in Medicare claims. METHODS: This cross-sectional study included the National Health and Aging Trends Study Round 5 participants with possible or probable dementia whose Medicare claims were available. We estimated the Functional Assessment Staging Test (FAST) scale (range: 3 [mild cognitive impairment] to 7 [severe dementia]) using information from the survey. We calculated CFI (range: 0-1, higher scores indicating greater frailty) using Medicare claims 12 months prior to the participants' interview date. We examined C-statistics to evaluate the ability of the CFI in identifying moderate-to-severe dementia (FAST stage 5-7) and determined the optimal CFI cut-point that maximized both sensitivity and specificity. RESULTS: Of the 814 participants with possible or probable dementia and measurable CFI, 686 (72.2%) patients were ≥75 years old, 448 (50.8%) were female, and 244 (25.9%) had FAST stage 5-7. The C-statistic of CFI to identify FAST stage 5-7 was 0.78 (95% confidence interval: 0.72-0.83), with a CFI cut-point of 0.280, achieving the maximum sensitivity of 76.9% and specificity of 62.8%. Participants with CFI ≥0.280 had a higher prevalence of disability (19.4% vs 58.3%) and dementia medication use (6.0% vs 22.8%) and higher risk of mortality (10.7% vs 26.3%) and nursing home admission (4.5% vs 10.6%) over 2 years than those with CFI <0.280. CONCLUSIONS: Our study suggests that CFI can be useful in identifying moderate-to-severe dementia from administrative claims among older adults with dementia.

Common non-cardiovascular multimorbidity groupings and clinical outcomes in older adults with major cardiovascular disease.

BACKGROUND: Among older adults, non-cardiovascular multimorbidity often coexists with cardiovascular disease (CVD) but their clinical significance is uncertain. We identified common non-cardiovascular comorbidity patterns and their association with clinical outcomes in Medicare fee-for-service beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF), or atrial fibrillation (AF). METHODS: Using 2015-2016 Medicare data, we took 1% random sample to create 3 cohorts of beneficiaries diagnosed with AMI (n = 24,808), CHF (n = 57,285), and AF (n = 36,277) prior to 1/1/2016. Within each cohort, we applied latent class analysis to classify beneficiaries based on 9 non-cardiovascular comorbidities (anemia, cancer, chronic kidney disease, chronic lung disease, dementia, depression, diabetes, hypothyroidism, and musculoskeletal disease). Mortality, cardiovascular and non-cardiovascular hospitalizations, and home time lost over a 1-year follow-up period were compared across non-cardiovascular multimorbidity classes. RESULTS: Similar non-cardiovascular multimorbidity classes emerged from the 3 CVD cohorts: (1) minimal, (2) depression-lung, (3) chronic kidney disease (CKD)-diabetes, and (4) multi-system class. Across CVD cohorts, multi-system class had the highest risk of mortality (hazard ratio [HR], 2.7-3.9), cardiovascular hospitalization (HR, 1.6-3.3), non-cardiovascular hospitalization (HR, 3.1-7.2), and home time lost (rate ratio, 2.7-5.4). Among those with AMI, the CKD-diabetes class was more strongly associated with all the adverse outcomes than the depression-lung class. In CHF and AF, differences in risk between the depression-lung and CKD-diabetes classes varied per outcome; and the depression-lung and multi-system classes had double the rates of non-cardiovascular hospitalizations than cardiovascular hospitalizations. CONCLUSION: Four non-cardiovascular multimorbidity patterns were found among Medicare beneficiaries with CHF, AMI, or AF. Compared to the minimal class, the multi-system, CKD-diabetes, and depression-lung classes were associated with worse outcomes. Identification of these classes offers insight into specific segments of the population that may benefit from more than the usual cardiovascular care.

A crosswalk of commonly used frailty scales.

BACKGROUND: Several validated scales have been developed to measure frailty, yet the direct relationship between these measures and their scores remains unknown. To bridge this gap, we created a crosswalk of the most commonly used frailty scales. METHODS: We used data from 7070 community-dwelling older adults who participated in National Health and Aging Trends Study (NHATS) Round 5 to construct a crosswalk among frailty scales. We operationalized the Study of Osteoporotic Fracture Index (SOF), FRAIL Scale, Frailty Phenotype, Clinical Frailty Scale (CFS), Vulnerable Elder Survey-13 (VES-13), Tilburg Frailty Indictor (TFI), Groningen Frailty Indicator (GFI), Edmonton Frailty Scale (EFS), and 40-item Frailty Index (FI). A crosswalk between FI and the frailty scales was created using the equipercentile linking method, a statistical procedure that produces equivalent scoring between scales according to percentile distributions. To demonstrate its validity, we determined the 4-year mortality risk across all scales for low-risk (equivalent to FI <0.20), moderate-risk (FI 0.20 to <0.40), and high-risk (FI ≥0.40) categories. RESULTS: Using NHATS, the feasibility of calculating frailty scores was at least 90% for all nine scales, with the FI having the highest number of calculable scores. Participants considered frail on FI (cutpoint of 0.25) corresponded to the following scores on each frailty measure: SOF 1.3, FRAIL 1.7, Phenotype 1.7, CFS 5.3, VES-13 5.5, TFI 4.4, GFI 4.8, and EFS 5.8. Conversely, individuals considered frail according to the cutpoint of each frailty measure corresponded to the following FI scores: 0.37 for SOF, 0.40 for FRAIL, 0.42 for Phenotype, 0.21 for CFS, 0.16 for VES-13, 0.28 for TFI, 0.21 for GFI, and 0.37 for EFS. Across frailty scales, the 4-year mortality risks between the same categories were similar in magnitude. CONCLUSION: Our results provide clinicians and researchers with a useful tool to directly compare and interpret frailty scores across scales.

Feasibility of implementing a telephone-based frailty assessment.

BACKGROUND: Despite the growing literature on the importance of identifying and managing frailty, its assessment has been limited in clinical settings. With the goal of integrating frailty assessment into routine clinical practice, this quality improvement project aimed to determine the feasibility, acceptability, and utility of administering a telephone-based frailty assessment. METHODS: Between 9/2020 and 6/2021, we identified 169 established patients with serious illnesses in an academic primary care-geriatric clinic. Patients were contacted via telephone, and their current medical, functional, nutritional, cognitive, and mood statuses were assessed using validated screening tools. A deficit-accumulation frailty score was then calculated using an electronic medical record-based frailty index calculator and standardized documentation with recommendations was generated for providers. The primary outcome was feasibility, measured as the proportion of patients successfully assessed. Secondary outcomes included completion rates of each domain, administration time, providers' perception, and clinical utility of the assessment. RESULTS: A total of 139 (82.2%) patients, mean age of 82 years, 63.3% frail were successfully assessed. Of the 139 assessments, medical and functional domains were completed for all, while nutrition, mood, and cognition were completed by 88.5% (n = 123), 68.3% (n = 95), and 59.7% (n = 83) of the time, respectively. Conducting the full assessment took an average (standard deviation) time of 26.1 (7.3) minutes. Without the cognitive and mood domain, assessment took an average of 15.7 (7.5) minutes. Patients' providers found the information from the assessment helpful in evaluating and managing their patients. Care plans of 51.8% and 65.0% of patients who had mobility and mind issues, respectively, addressed these domains within 30 days after the assessment. CONCLUSION: Implementation of the telephone-based frailty assessment is feasible, acceptable, and has the potential to influence the care plans of older adults. This work demonstrated how frailty assessment can be integrated with the outpatient setting.