Derivation and Validation of a Generalizable Preoperative Frailty Index Using Population-based Health Administrative Data

McIsaac, Daniel I.; Wong, Coralie A; Huang, Allen; Moloo, Husein; Walraven, Carl van

doi:10.1097/sla.0000000000002769

Cited by 78 publications

(79 citation statements)

References 49 publications

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“…Such robust clinical information is typically not available in claims data, thus precluding our ability in the present study to measure objectively either of these baseline risk factors. Future research should consider using newly proposed techniques to measure frailty using claims data to study larger cohorts of elderly EGS patients . However, the impact of frailty and sarcopenia are ideally prospectively studied on a large scale across many older patients.…”

Section: Discussionmentioning

confidence: 99%

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Loss of Community‐Dwelling Status Among Survivors of High‐Acuity Emergency General Surgery Disease

Smith

Davis

Quatman‐Yates

et al. 2019

J American Geriatrics Society

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OBJECTIVES To examine loss of community‐dwelling status 9 months after hospitalization for high‐acuity emergency general surgery (HA‐EGS) disease among older Americans. DESIGN Retrospective analysis of claims data. SETTING US communities with Medicare beneficiaries. PARTICIPANTS Medicare beneficiaries age 65 years or older hospitalized urgently/emergently between January 1, 2015, and March 31, 2015, with a principal diagnosis representing potential life or organ threat (necrotizing soft tissue infections, hernias with gangrene, ischemic enteritis, perforated viscus, toxic colitis or gastroenteritis, peritonitis, intra‐abdominal hemorrhage) and an operation of interest on hospital days 1 or 2 (N = 3319). MEASUREMENTS Demographic characteristics (age, race, and sex), comorbidities, principal diagnosis, complications, and index hospitalization disposition (died; discharged to skilled nursing facility [SNF], long‐term acute care [LTAC], rehabilitation, hospice, home (with or without services), or acute care hospital; other) were measured. Survivors of index hospitalization were followed until December 31, 2015, on mortality and community‐dwelling status (SNF/LTAC vs not). Descriptive statistics, Kaplan‐Meier plots, and χ2 tests were used to describe and compare the cohort based on disposition. A multivariable logistic regression model, adjusted for age, sex, comorbidities, complications, and discharge disposition, determined independent predictors of loss of community‐dwelling status at 9 months. RESULTS A total of 2922 (88%) survived index hospitalization. Likelihood of discharge to home decreased with increasing age, baseline comorbidities, and in‐hospital complications. Overall, 418 (14.3%) HA‐EGS survivors died during the follow‐up period. Among those alive at 9 months, 10.3% were no longer community dwelling. Initial discharge disposition to any location other than home and three or more surgical complications during index hospitalization were independent predictors of residing in a SNF/LTAC 9 months after surviving HA‐EGS. CONCLUSION Older Americans, known to prioritize living in the community, will experience substantial loss of independence due to HA‐EGS. Long‐term expectations after surviving HA‐EGS must be framed from the perspective of the outcomes that older patients value the most. Further research is needed to examine the quality‐of‐life burden of EGS survivorship prospectively. J Am Geriatr Soc 67:2289–2297, 2019

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Section: Discussionmentioning

confidence: 99%

“…Future research should consider using newly proposed techniques to measure frailty using claims data to study larger cohorts of elderly EGS patients. [45][46][47] However, the impact of frailty and sarcopenia are ideally prospectively studied on a large scale across many older patients.…”

Section: Discussionmentioning

confidence: 99%

Loss of Community‐Dwelling Status Among Survivors of High‐Acuity Emergency General Surgery Disease

Smith

Davis

Quatman‐Yates

et al. 2019

J American Geriatrics Society

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“…It provides better predictive performance of frailty and adverse health outcomes than counting the number of codes or calculating a deficit-accumulation frailty index directly from the codes. 41) FRAILTY MEASUREMENT IN ADMINISTRATIVE CLAIMS DATA 8,[39][40][41][42][43][44][45][46][47][48] or Veterans Affairs 49,50) claims databases, including two proprietary measures; namely, the Johns Hopkins Adjusted Clinical Groups Frailty Indicators 43) and JEN-Frailty Index, 45,46) two were developed for the Canadian claims databases, 51,52) and two were developed for the United Kingdom hospital claims data-…”

Section: Approach 3: Data-driven Selection With a Reference Standardmentioning

confidence: 99%

“…Table 1 summarizes 16 frailty measures for administrative claims data. Of these, 12 measures were developed for the United States Medicare 8 , 39 - 48 ) or Veterans Affairs 49 , 50 ) claims databases, including two proprietary measures; namely, the Johns Hopkins Adjusted Clinical Groups Frailty Indicators 43 ) and JEN-Frailty Index, 45 , 46 ) two were developed for the Canadian claims databases, 51 , 52 ) and two were developed for the United Kingdom hospital claims database. 37 , 53 ) Database-derived frailty measures varied widely in terms of development approaches (clinical knowledge in nine measures, cluster analysis in one measure, and reference standard measures in six measures), number of variables included (nine to 109 variables), target populations (general vs. specific disease populations), and validation outcomes (clinical frailty assessment, functional status, mortality, health care utilization, or costs).…”

Section: Frailty Measurement In Administrative Claims Datamentioning

confidence: 99%

Measuring Frailty in Health Care Databases for Clinical Care and Research

Kim

2020

Ann Geriatr Med Res

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Frailty is a clinical state resulting from age-related changes in multiple physiologic systems and accumulation of diseases that reduces patient ability to maintain homeostasis in response to stressors. 1) Frailty is common in older adults, affecting one in every 10 community-dwellers 2,3) and one in every two nursing home residents, 4) and is associated with increased risks of death (relative risk [RR], 1.6-6.0), disability (RR, 1.8-2.8), institutionalization (RR, 2.6-24.0), and falls (RR, 1.2-2.4). 1) Health care costs for older adults with frailty increase by up to 2-fold compared to those in their non-frail counterparts, 5,6) mainly due to inpatient care, post-acute care, and care for potentially preventable conditions. 7,8) Given the considerable clinical and societal consequences of frailty in the ever-growing aging population, assessment of frailty in clinical and population settings offers valuable opportunities for prevention and treatment through efficient use of evidence-based interventions and resources. 9-11) 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.

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“…Due to the accumulation of deficits, people with frailty are vulnerable to stressors, and are at increased risk of adverse health outcomes. While data detailing the prevalence of frailty in lung surgery patients is limited, and dependent upon criteria and cut-offs employed, over 30% of older people having major inpatient surgery live with frailty (22). Many frailty instruments have been described and studied in the perioperative period (23)(24)(25), and almost all are associated with a 2-fold or greater increase in risk for mortality, POCs, and loss of independence after surgery.…”

Section: Frailtymentioning

confidence: 99%

Modifiable risk factors for patients undergoing lung cancer surgery and their optimization: a review

Gagne

McIsaac

2018

J. Thorac. Dis

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Enhanced recovery after surgery (ERAS) programs include a variety of pre-, intra-and postoperative interventions that seek to decrease the stress response to surgery and facilitate the recovery of surgical patients. There are a number of patient factors that may increase the risk of postoperative complications (POC) after lung cancer surgery and delay recovery. Some of these factors may be amenable to optimization preoperatively and potentially decrease the incidence of these complications. We have chosen to discuss the incidence and complications associated with factors we felt were both relatively common and significant contributors to POCs. For each of these factors we discuss potential avenues for optimization and the evidence for the resulting decrease in complications.

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Derivation and Validation of a Generalizable Preoperative Frailty Index Using Population-based Health Administrative Data

Abstract: A preoperative FI derived from HA data is a robust method to measure frailty in elective and emergency patients. Generalizable FIs should be considered a standard approach to population-level study of surgical frailty.

Cited by 78 publications

References 49 publications

Loss of Community‐Dwelling Status Among Survivors of High‐Acuity Emergency General Surgery Disease

Loss of Community‐Dwelling Status Among Survivors of High‐Acuity Emergency General Surgery Disease

Measuring Frailty in Health Care Databases for Clinical Care and Research

Modifiable risk factors for patients undergoing lung cancer surgery and their optimization: a review

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