Assessing comorbidity in older adults using prescription claims data

Development and validation of novel multimorbidity indices for older adults

BACKGROUND

Measuring multimorbidity in claims data is used for risk adjustment and identifying populations at high risk for adverse events. Multimorbidity indices such as Charlson and Elixhauser scores have important limitations. We sought to create a better method of measuring multimorbidity using claims data by incorporating geriatric conditions, markers of disease severity, and disease-disease interactions, and by tailoring measures to different outcomes.

METHODS

Health conditions were assessed using Medicare inpatient and outpatient claims from subjects age 67 and older in the Health and Retirement Study. Separate indices were developed for ADL decline, IADL decline, hospitalization, and death, each over 2 years of follow-up. We validated these indices using data from Medicare claims linked to the National Health and Aging Trends Study.

RESULTS

The development cohort included 5012 subjects with median age 76 years; 58% were female. Claims-based markers of disease severity and disease-disease interactions yielded minimal gains in predictive power and were not included in the final indices. In the validation cohort, after adjusting for age and sex, c-statistics for the new multimorbidity indices were 0.72 for ADL decline, 0.69 for IADL decline, 0.72 for hospitalization, and 0.77 for death. These c-statistics were 0.02-0.03 higher than c-statistics from Charlson and Elixhauser indices for predicting ADL decline, IADL decline, and hospitalization, and <0.01 higher for death (p < 0.05 for each outcome except death), and were similar to those from the CMS-HCC model. On decision curve analysis, the new indices provided minimal benefit compared with legacy approaches. C-statistics for both new and legacy indices varied substantially across derivation and validation cohorts.

CONCLUSIONS

A new series of claims-based multimorbidity measures were modestly better at predicting hospitalization and functional decline than several legacy indices, and no better at predicting death. There may be limited opportunity in claims data to measure multimorbidity better than older methods.

Association between cognitive impairment and oral anticancer agent use in older patients with metastatic renal cell carcinoma

BACKGROUND

Kidney cancer is the fastest-growing cancer diagnosis in the developed world. About 16% of new cases are stage IV, which has a low five-year survival rate. Many patients with metastatic renal cell carcinoma (mRCC) are older and may have mild cognitive impairment or dementia (MCI/D). Given prior reports of patients with dementia initiating less cancer therapy and the importance of oral anticancer agents (OAAs) in mRCC treatment, we investigated the prevalence of preexisting MCI/D in patients with mRCC and their OAA use.

METHODS

SEER-Medicare patients were analyzed who were ≥65 years, diagnosed with mRCC between 2007 and 2015, and had Medicare part D coverage. Patterns and predictors of (a) OAA utilization within the 12 months following mRCC diagnosis and (b) adherence (percent of days covered [PDC] ≥ 80%) during the first 90 days following treatment initiation were assessed.

RESULTS

Of the 2792 eligible patients, 268 had preexisting MCI/D, and 907 initiated OAA treatment within 12 months of mRCC diagnosis. Patients with preexisting MCI/D were less likely to begin an OAA than those without MCI/D (fully-adjusted HR 0.53, 95% CI 0.38-0.76). Among OAA initiators, a preexisting MCI/D diagnosis did not alter the likelihood that a person would be adherent (adjusted RR 0.84, 95% CI 0.55-1.28).

CONCLUSIONS

Patients with preexisting MCI/D were half as likely to start an OAA during the year following mRCC diagnosis than patients without comorbid MCI/D. The 90-day adherence of OAA initiators was not significantly different between those with and without preexisting MCI/D. In light of this, clinicians should assess mRCC patients for cognitive impairment and take steps to optimize OAA utilization by those with MCI/D.

Opioid analgesic prescribing for opioid-naïve individuals prior to identification of opioid use disorder in British Columbia, Canada

BACKGROUND AND AIMS

Prescription opioid analgesics have contributed to the development of opioid use disorder (OUD) in many individuals. We aimed to characterize non-cancer opioid prescribing for opioid-naive individuals prior to OUD identification.

DESIGN

Population-based retrospective cohort study using six linked health administrative databases.

SETTING

British Columbia (BC), Canada.

PARTICIPANTS

People with OUD between 1 January 2001 and 30 September 2018 who initiated opioid analgesic therapy for non-cancer pain prior to OUD identification.

MEASUREMENTS

Dose (morphine milligram equivalent per day), days prescribed and clinical guideline non-concordance for initial opioid prescriptions (dose ≥ 90 morphine milligram equivalent per day; ≥ 7 days prescribed; concomitant sedative prescription). We estimated the probability of non-concordant initial prescriptions by source (inpatient post-discharge, non-inpatient acute, non-acute) using logistic regression, adjusting for individual characteristics and comorbidities.

FINDINGS

Among 66 372 individuals identified with OUD from 2001 to 2018, 21 331 (32.1%) received opioid analgesics prior to OUD identification. This proportion increased from 3.0% in 2001 to 41.0% in 2011, before decreasing to 34.2% in 2017. Roughly half of opioid prescriptions were attributed to non-acute care visits, peaking at 56.8% in 2007, while the proportion from inpatient visits increased from 19.7% in 2001 to 28.5% in 2017. The predicted probability of receiving non-guideline concordant prescriptions declined over time-periods across all three measures for inpatient and non-inpatient acute care, while remaining stable for non-acute care. In particular, the predicted probability of receiving ≥ 7-day prescriptions following inpatient visits decreased from 53.3% [95% confidence interval (CI) = 50.9, 55.8%] in 2001-06 to 37.2% (95% CI = 33.9, 40.5%) in 2013-18.

CONCLUSIONS

Among the 66 372 individuals in British Columbia, Canada diagnosed with opioid use disorder between 2001 and 2018, more than 32% were earlier prescribed non-cancer opioid analgesics. The proportion who had received an opioid analgesic prescription prior to OUD identification peaked at more than 40% in 2011, before stabilizing between 2011 and 2016 and declining thereafter. Guideline concordance improved over time for high-dose and concomitant sedative prescribing.

More than two-dozen prescription drug-based risk scores are available for risk adjustment: A systematic review

OBJECTIVE

While several prescription drug-based risk indices have been developed, their design, performance, and application has not previously been synthesized.

STUDY DESIGN AND SETTING

We searched Ovid MEDLINE, CINAHL and Embase from inception through March 3, 2020 and included studies that developed or updated a prescription drug-based risk index. Two reviewers independently performed screening and extracted information on data source, study population, cohort sizes, outcomes, study methodology and performance. Predictive performance was evaluated using C statistics for binary outcomes and R² for continuous outcomes. The PROSPERO ID for this review is CRD42020165498.

RESULTS

Of 19,112 articles that were retrieved, 124 were full-text screened and 25 were included, each of which represented a de novo or updated drug-based index. The indices were customized to varied age groups and clinical populations and most commonly evaluated outcomes including mortality (36%), hospitalization (24%) and healthcare costs (24%). C statistics ranged from 0.62 to 0.92 for mortality and 0.59 to 0.72 for hospitalization, while adjusted R² for healthcare costs ranged from 0.06 to 0.62. Seven of the 25 risk indices included used global drug classification algorithms.

CONCLUSIONS

More than two-dozen prescription drug-based risk indices have been developed and they differ significantly in design, performance and application.

Measuring Frailty in Health Care Databases for Clinical Care and Research

Considering the increasing burden and serious consequences of frailty in aging populations, there is increasing interest in measuring frailty in health care databases for clinical care and research. This review synthesizes the latest research on the development and application of 21 frailty measures for health care databases. Frailty measures varied widely in terms of target population (16 ambulatory, 1 long-term care, and 4 inpatient), data source (16 claims-based and 5 electronic health records [EHR]-based measures), assessment period (6 months to 36 months), data types (diagnosis codes required for 17 measures, health service codes for 7 measures, pharmacy data for 4 measures, and other information for 9 measures), and outcomes for validation (clinical frailty for 7 measures, disability for 7 measures, and mortality for 16 measures). These frailty measures may be useful to facilitate frailty screening in clinical care and quantify frailty for large database research in which clinical assessment is not feasible.

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

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.

Dr Foster global frailty score: an international retrospective observational study developing and validating a risk prediction model for hospitalised older persons from administrative data sets

OBJECTIVES

This study aimed to examine the prevalence of frailty coding within the Dr Foster Global Comparators (GC) international database. We then aimed to develop and validate a risk prediction model, based on frailty syndromes, for key outcomes using the GC data set.

DESIGN

A retrospective cohort analysis of data from patients over 75 years of age from the GC international administrative data. A risk prediction model was developed from the initial analysis based on seven frailty syndrome groups and their relationship to outcome metrics. A weighting was then created for each syndrome group and summated to create the Dr Foster Global Frailty Score. Performance of the score for predictive capacity was compared with an established prognostic comorbidity model (Elixhauser) and tested on another administrative database Hospital Episode Statistics (2011-2015), for external validation.

SETTING

34 hospitals from nine countries across Europe, Australia, the UK and USA.

RESULTS

Of 6.7 million patient records in the GC database, 1.4 million (20%) were from patients aged 75 years or more. There was marked variation in coding of frailty syndromes between countries and hospitals. Frailty syndromes were coded in 2% to 24% of patient spells. Falls and fractures was the most common syndrome coded (24%). The Dr Foster Global Frailty Score was significantly associated with in-hospital mortality, 30-day non-elective readmission and long length of hospital stay. The score had significant predictive capacity beyond that of other known predictors of poor outcome in older persons, such as comorbidity and chronological age. The score's predictive capacity was higher in the elective group compared with non-elective, and may reflect improved performance in lower acuity states.

CONCLUSIONS

Frailty syndromes can be coded in international secondary care administrative data sets. The Dr Foster Global Frailty Score significantly predicts key outcomes. This methodology may be feasibly utilised for case-mix adjustment for older persons internationally.

Measuring frailty using claims data for pharmacoepidemiologic studies of mortality in older adults: evidence and recommendations

PURPOSE

Geriatric frailty is a common syndrome of older adults that is characterized by increased vulnerability to adverse health outcomes and influences treatment choice. Pharmacoepidemiologic studies that rely on administrative claims data in older adults are limited by confounding due to unmeasured frailty. A claims-based frailty score may be useful to minimize confounding by frailty in such databases. We provide an overview of definitions and measurement of frailty, evaluated the claims-based models of frailty in literature, and recommend ways to improve frailty adjustment in claims analysis.

METHODS

We searched MEDLINE and EMBASE from inception to April 2014, without language restriction, to identify claims-based multivariable models that predicted frailty or its related outcome, disability. We critically appraised their approach, including population, predictor selection, outcome definition, and model performance.

RESULTS

Of 152 reports, three models were identified. One model that predicted poor functional status using healthcare service claims in a representative sample of community-dwelling and institutionalized older adults showed an excellent discrimination (C statistic, 0.92). The other two models that predicted disability using either diagnosis codes or prescription claims alone in institutionalized or frail adults had limited generalizability and modest model performance. None of the models have been applied to reduce confounding bias in pharmacoepidemiologic studies of drug therapy.

CONCLUSIONS

We found little research conducted on development and application of a claims-based frailty index for confounding adjustment in pharmacoepidemiologic studies in older adults. More research is needed to advance this innovative, potentially useful approach by incorporating the expertise from aging research.