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Frailty Before Critical Illness and Mortality for Elderly Medicare Beneficiaries.

ABSTRACT: To estimate the effect of pre-intensive care unit (ICU) health categories on mortality during and after critical illness, focusing specifically on the effect of pre-ICU frailty on short- and long-term mortality.Retrospective cohort study.Medicare claims data from 2004 to 2008.A nationally representative sample of elderly Medicare beneficiaries admitted to an ICU in 2005.Participants were classified into four pre-ICU health categories (robust, cancer, chronic organ failure, frailty) using claims data from the year before admission, allowing for assignment to multiple categories. The association between pre-ICU health category and hospital and 3-year mortality was assessed using multivariable logistic regression and Cox proportional hazards models.Of 47,427 elderly individuals in the ICU, 18.8% were robust, 28.6% had cancer, 68.1% had chronic organ failure, and 34.0% were frail; 41.3% qualified for multiple categories. Overall hospital mortality was 12.6%, with the lowest mortality for robust participants (9.7%). Participants with pre-ICU frailty had higher hospital mortality than those with the same pre-ICU health categories without frailty. (Adjusted odds ratios ranged from 1.27 (95% confidence interval (CI) 1.10-1.47) to 1.52 (95% CI = 1.35-1.63).) Robust hospital survivors had the lowest 3-year mortality (24.6%). Pre-ICU frailty conferred higher 3-year mortality than pre-ICU categories without frailty. (Adjusted hazard ratios ranged from 1.54 (95% CI = 1.45-1.64) to 1.84 (95% CI = 1.70-1.99).)Critically ill elderly adults can be categorized according to pre-ICU health categories. These categories, particularly pre-ICU frailty, may be important for understanding risk of death during and after critical illness.

SUBMITTER: Hope AA

PROVIDER: S-EPMC4752202 | biostudies-other | 2015 Jun

REPOSITORIES: biostudies-other

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Frailty Before Critical Illness and Mortality for Elderly Medicare Beneficiaries.

Hope Aluko A AA Gong Michelle N MN Guerra Carmen C Wunsch Hannah H

Journal of the American Geriatrics Society 20150601 6

<h4>Objectives</h4>To estimate the effect of pre-intensive care unit (ICU) health categories on mortality during and after critical illness, focusing specifically on the effect of pre-ICU frailty on short- and long-term mortality.<h4>Design</h4>Retrospective cohort study.<h4>Setting</h4>Medicare claims data from 2004 to 2008.<h4>Participants</h4>A nationally representative sample of elderly Medicare beneficiaries admitted to an ICU in 2005.<h4>Measurements</h4>Participants were classified into f ...[more]

PMID: 26096386

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Project description:ObjectivesMedicare beneficiaries recovering from a critical illness are increasingly being discharged home instead of to post-acute care facilities. Rehabilitation services are commonly recommended for intensive care unit (ICU) survivors; however, little is known about the frequency and dose of home-based rehabilitation in this population.DesignRetrospective analysis of 2012 Medicare hospital and home health (HH) claims data, linked with assessment data from the Medicare Outcomes and Assessment Information Set.SettingParticipant homes.ParticipantsMedicare beneficiaries recovering from an ICU stay longer than 24 hours, who were discharged directly home with HH services within 7 days of discharge and survived without readmission or hospice transfer for at least 30 days (n = 3,176).MeasurementsCount of rehabilitation visits received during HH care episode.ResultsA total of 19,564 rehabilitation visits were delivered to ICU survivors over 118,145 person-days in HH settings, a rate of 1.16 visits per week. One-third of ICU survivors received no rehabilitation visits during HH care. In adjusted models, those with the highest baseline disability received 30% more visits (rate ratio [RR] = 1.30; 95% confidence interval [CI] = 1.17-1.45) than those with the least disability. Conversely, an inverse relationship was found between multimorbidity (Elixhauser scores) and count of rehabilitation visits received; those with the highest tertile of Elixhauser scores received 11% fewer visits (RR = .89; 95% CI = .81-.99) than those in the lowest tertile. Participants living in a rural setting (vs urban) received 6% fewer visits (RR = .94; 95% CI = .91-.98); those who lived alone received 11% fewer visits (RR = .89; 95% CI = .82-.96) than those who lived with others.ConclusionOn average, Medicare beneficiaries discharged home after a critical illness receive few rehabilitation visits in the early post-hospitalization period. Those who had more comorbidities, who lived alone, or who lived in rural settings received even fewer visits, suggesting a need for their consideration during discharge planning. J Am Geriatr Soc 68:1512-1519, 2020.

Project description:ImportanceThe measured severity of illness of hospitalized Medicare beneficiaries has increased. Whether this change is associated with payment reforms, concentrated among hospitalizations with principal diagnoses targeted by payment reform, and reflective of true increases in severity of illness is unknown.ObjectivesTo assess whether the expansion of secondary diagnosis codes in January 2011 and the incentive payments for health information technology under the US Health Information Technology for Economic and Clinical Health Act were associated with changes in measured severity of illness and whether those changes are reflective of true increases in underlying patient severity.Design, setting, and participantsThis cohort study of Medicare fee-for-service beneficiary discharges (N = 47 951 443) between January 1, 2008, and August 31, 2015, used a regression-discontinuity design to evaluate changes in measured severity of illness after the expansion of secondary diagnoses. Discharge-level linear regression model with hospital fixed effects was used to evaluate changes in measured severity of illness after hospitals' receipt of incentives for health information technology. The change in predictive accuracy of measured severity of illness on 30-day readmissions after the implementation of both policies was evaluated. Data analysis was performed from November 1, 2018, to March 5, 2019.Main outcomes and measuresThe primary outcome was patients' measured severity of illness determined by the number of condition categories from secondary discharge diagnosis codes. Measured severity of illness for diagnoses commonly targeted by Medicare policies and untargeted diagnoses was assessed.ResultsIn total, 47 951 443 discharges at 2850 hospitals were included. In 2008, these beneficiaries included 3 882 672 women (58.5%) with a mean (SD) age of 78.5 (8.4) years. In 2014, the discharges included 3 377 137 women (57.8%) with the mean (SD) age of 78.4 (8.7) years. The Centers for Medicare & Medicaid Services expansion of secondary diagnoses was associated with a 0.348 (95% CI, 0.328-0.367; P < .001) change in condition categories for all diagnoses, 0.445 (95% CI, 0.419-0.470; P < .001) for targeted diagnoses, and 0.321 (95% CI, 0.302-0.341; P < .001) for untargeted diagnoses. Health information technology incentives were associated with a 0.013 (95% CI, 0.004-0.022; P = .005) change in condition categories for all diagnoses, 0.195 (95% CI, 0.184-0.207; P < .001) for targeted diagnoses, and -0.016 (95% CI, -0.025 to -0.007; P < .001) for untargeted diagnoses. Minimal improvements in predictive accuracy were observed.Conclusions and relevanceChanges in Centers for Medicare & Medicaid Services policies appear to be associated with increases in measured severity of illness; these increases do not appear to reflect substantive changes in true patient severity.

Project description:BackgroundUnderweight patients are at higher risk of death after acute myocardial infarction (AMI) than normal weight patients; however, it is unclear whether this relationship is explained by confounding due to cachexia or other factors associated with low body mass index (BMI). This study aimed to answer two questions: (1) does comprehensive risk adjustment for comorbid illness and frailty measures explain the higher mortality after AMI in underweight patients, and (2) is the relationship between underweight and mortality also observed in patients with AMI who are otherwise without significant chronic illness and are presumably free of cachexia?Methods and findingsWe analyzed data from the Cooperative Cardiovascular Project, a cohort-based study of Medicare beneficiaries hospitalized for AMI between January 1994 and February 1996 with 17 y of follow-up and detailed clinical information to compare short- and long-term mortality in underweight and normal weight patients (n = 57,574). We used Cox proportional hazards regression to investigate the association of low BMI with 30-d, 1-y, 5-y, and 17-y mortality after AMI while adjusting for patient comorbidities, frailty measures, and laboratory markers of nutritional status. We also repeated the analyses in a subset of patients without significant comorbidity or frailty. Of the 57,574 patients with AMI included in this cohort, 5,678 (9.8%) were underweight and 51,896 (90.2%) were normal weight at baseline. Underweight patients were older, on average, than normal weight patients and had a higher prevalence of most comorbidities and measures of frailty. Crude mortality was significantly higher for underweight patients than normal weight patients at 30 d (25.2% versus 16.4%, p < 0.001), 1 y (51.3% versus 33.8%, p < 0.001), 5 y (79.2% versus 59.4%, p < 0.001), and 17 y (98.3% versus 94.0%, p < 0.001). After adjustment, underweight patients had a 13% higher risk of 30-d death and a 26% higher risk of 17-y death than normal weight patients (30-d hazard ratio [HR] 1.13, 95% CI 1.07-1.20; 17-y HR 1.26, 95% CI 1.23-1.30). Survival curves for underweight and normal weight patients separated early and remained separate over 17 y, suggesting that underweight patients remained at a significant survival disadvantage over time. Similar findings were observed among the subset of patients without comorbidity at baseline. Underweight patients without comorbidity had a 30-d adjusted mortality similar to that of normal weight patients but a 21% higher risk of death over the long term (30-d HR 1.08, 95% CI 0.93-1.26; 17-y HR 1.21, 95% CI 1.14-1.29). The adverse effects of low BMI were greatest in patients with very low BMIs. The major limitation of this study was the use of surrogate markers of frailty and comorbid conditions to identify patients at highest risk for cachexia rather than clear diagnostic criteria for cachexia.ConclusionsUnderweight BMI is an important risk factor for mortality after AMI, independent of confounding by comorbidities, frailty measures, and laboratory markers of nutritional status. Strategies to promote weight gain in underweight patients after AMI are worthy of testing.

Project description:Background Prospectively collected frailty markers are associated with an incremental 1-year mortality risk after transcatheter aortic valve replacement (TAVR) compared with comorbidities alone. Whether information on frailty markers captured retrospectively in administrative billing data is similarly predictive of long-term mortality after TAVR is unknown. We sought to characterize the prognostic importance of frailty factors as identified in healthcare billing records in comparison to validated measures of frailty for the prediction of long-term mortality after TAVR. Methods and Results Adult patients undergoing TAVR between August 25, 2011, and September 29, 2015, were identified among Medicare fee-for-service beneficiaries. The Johns Hopkins Claims-based Frailty Indicator was used to identify frail patients. We used nested Cox regression models to identify claims-based predictors of mortality up to 4 years post-procedure. Four groups of variables, including cardiac risk factors, noncardiac risk factors, patient procedural risk factors, and nontraditional markers of frailty, were introduced sequentially, and their integrated discrimination improvement was assessed. A total of 52 338 TAVR patients from 558 clinical sites were identified, with a mean follow-up time period of 16 months. In total, 14 174 (27.1%) patients died within the study period. The mortality rate was 53.9% at 4 years post-TAVR. A total of 34 863 (66.6%) patients were defined as frail. The discrimination of each of the 4 models was 0.60 (95% CI, 0.59-60), 0.65 (95% CI, 0.64-0.65), 0.68 (95% CI, 0.67-0.68), and 0.70 (95% CI, 0.69-0.70), respectively. The addition of nontraditional frailty markers as identified in claims improved mortality prediction above and beyond traditional risk factors (integrated discrimination improvement: 0.019; P<0.001). Conclusions Risk prediction models that include frailty as identified in claims data can be used to predict long-term mortality risk after TAVR. Linkage to claims data may allow enhanced mortality risk prediction for studies that do not collect information on frailty.

Dataset Information

Frailty Before Critical Illness and Mortality for Elderly Medicare Beneficiaries.

Publications

Frailty Before Critical Illness and Mortality for Elderly Medicare Beneficiaries.

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