Corrigendum to: Updating and Validating the U.S. Veterans Affairs Frailty Index: Transitioning From ICD-9 to ICD-10

Cheng, David; DuMontier, Clark; Yildirim, Cenk; Charest, Brian; Hawley, Chelsea E.; Zhuo, Min; Paik, Julie M.; Yaksic, Enzo; Gaziano, J. Michael; Do, Nhan; Brophy, Mary; Cho, Kelly; Kim, Dae; Driver, Jane A.; Fillmore, Nathanael; Orkaby, Ariela R.

doi:10.1093/gerona/glab159

Cited by 5 publications

(4 citation statements)

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“…Missing data are described in the ESM eTable 5. Of all hospitalizations of older adults receiving mechanical ventilation, 50.0% were female, the median (IQR) age was 81 (78-86) years old, the median (IQR) Elixhauser-van Walraven comorbidity index score was 18 (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), and the median (IQR) costs of hospitalization were $28,212 USD ($14,947-$51,521). Infection-related diagnoses (30.5%) were the most common primary diagnoses for hospitalization.…”

Section: Resultsmentioning

confidence: 99%

Validation of the Hospital Frailty Risk Score among older adults receiving mechanical ventilation

Kassir

Mailman

et al. 2021

Preprint

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Background:Older adults are increasingly being admitted to intensive care units, with frailty recognized as a risk factor for worse outcomes. The Hospital Frailty Risk Score (HFRS) was developed for use in administrative databases of older adults, but it has not yet been well-validated for critically ill patients. The objective of this study was to validate the HFRS to predict prolonged hospitalization, in-hospital mortality, and 30-day emergency hospital readmissions in critically ill patients.Methods:We selected index hospitalizations of older adults (≥75 years old) receiving mechanical ventilation, using the United States Nationwide Readmissions Database from January 1, 2016 to November 30, 2018. Frailty risk was determined by the HFRS using International Classification of Diseases, Tenth Revision Clinical Modification (ICD-10-CM) codes, and further subcategorized into low (score <5), intermediate (score 5-15), and high (score >15) risk for frailty. We evaluated the HFRS to predict prolonged hospitalization, in-hospital mortality, and 30-day emergency hospital readmissions, using multivariable logistic regression after adjustment for patient and hospital characteristics. Model performance was assessed using the c-statistic, Brier score, and calibration plots.Results:Among the 649,330 weighted index hospitalizations in the cohort, 50.0% were female, the median (interquartile range [IQR]) age was 81 (78-86) years old, and the median (IQR) HFRS was 10.8 (7.7-14.5). Among the cohort, 9.5%, 68.3%, and 22.2% were subcategorized as low, intermediate, and high risk for frailty, respectively. After adjustment, patient hospitalizations with high frailty risk were associated with increased risks of prolonged hospitalization (adjusted odds ratio [aOR] 5.59 [95% confidence interval [CI] 5.24-5.97], c-statistic 0.694, Brier score 0.216) and 30-day emergency hospital readmissions (aOR 1.20 [95% CI 1.13-1.27], c-statistic 0.595, Brier score 0.162), compared to low frailty risks. Conversely, high frailty risk using the HFRS was inversely associated with in-hospital mortality (aOR 0.46 [95% CI 0.45-0.48], c-statistic 0.712, Brier score 0.214). Calibration plots demonstrated good calibration for the adjusted analyses.Conclusions:The HFRS is associated with prolonged hospitalization and 30-day readmission in older adults receiving mechanical ventilation. Further research is necessary to develop frailty scores that accurately and intuitively predict mortality in critically ill patients.

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Section: Resultsmentioning

confidence: 99%

Validation of the Hospital Frailty Risk Score among older adults receiving mechanical ventilation

Kassir

Mailman

et al. 2021

Preprint

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“…The VA-FI was generated using data elements two weeks before the SARS-CoV-2 testing to minimize changes in frailty status due to COVID-19 infection. The 31-item VA-FI was validated in over 2 million veterans ( 27 ), and includes variables from medical, psychological, functional, and social domains (Supplementary Appendix). This deficit accumulation framework does not rely on predetermined variables, is based on multimorbidity and disability, and is better suited to the veteran population ( 28 ).…”

Section: Methodsmentioning

confidence: 99%

Frailty Reduces Vaccine Effectiveness Against SARS-CoV-2 Infection: A Test-Negative Case Control Study Using National VA Data

Tang

Hammel²,

Andrew³

et al. 2023

The Journal of nutrition, health and aging

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Objectives To assess the variation of vaccine effectiveness against SARS-CoV-2 infection during the Delta wave according to frailty status among U.S. veterans. Design Test-negative case-control study of SARS-CoV-2 mRNA vaccine effectiveness. Setting Veterans Health Administration (VHA) medical centers. Participants Veterans 19 years and older who had at least one COVID-19/Flu like symptoms and received a SARS-CoV-2 PCR or antigen test at VHA medical centers between July 25 to September 30, 2021. Intervention mRNA vaccination. Measurements New SARS-CoV-2 infection. Vaccine effectiveness was defined as 1-odds of vaccination in cases/odds of vaccination in controls, where cases were patients who had a COVID-19 test and tested positive for SARS-CoV-2, and controls were those who tested negative. Frailty was measured using the VA frailty index, categorized as robust (0–<0.1), pre-frail (≥0.1–<0.21) and frail (≥0.21). Results A total of 58,604 patients (age:58.9±17.0, median:61, IQR:45–72; 87.5%men; 68.1%white; 1.3%African American, 8.3%Hispanic) were included in the study. Of these, 27,733 (47.3%) were robust, 16,276 (27.8%) were prefrail, and 14,595 (24.9%) were frail. mRNA vaccine effectiveness against the Delta variant symptomatic infection was lower in patients with frailty, 62.8 %(95%CI:59.8–65.7), versus prefrail 73.9%(95%CI:72.0–75.7), and robust, 77.0 %(95%CI:75.7–78.3). Conclusions This test-negative case control study showed that mRNA vaccine effectiveness against infection declined in veterans with frailty. Frailty status is a factor to consider when designing, developing, and evaluating COVID-19 vaccines. Electronic Supplementary Material Supplementary material is available for this article at 10.1007/s12603-023-1885-1 and is accessible for authorized users.

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“…Passive versions of a CGA-FI that extract clinical data from an electronic health record have been developed. 29 , 60 , 61 These are best used for screening and should be followed with an in-person assessment during follow up for formal cognitive evaluation. All other features should take <5 minutes.…”

Section: Putting It Together: Cga-fimentioning

confidence: 99%

“… 92 In the context of this review, automated electronic frailty indices have been developed that can be integrated into the electronic health records to capture existing data to calculate a frailty index. 29 , 60 , 61 eFIs are excellent screening tools that correlate with in-person frailty assessments. Where data are available, a semiautomated version of the CGA-FI can similarly be integrated.…”

Section: Technology In the Care Of Older Tavr Patientsmentioning

confidence: 99%

TAVR in Older Adults

Damluji,

Bernacki,

Afilalo

et al. 2024

JACC: Advances

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Corrigendum to: Updating and Validating the U.S. Veterans Affairs Frailty Index: Transitioning From ICD-9 to ICD-10

Cited by 5 publications

References 0 publications

Validation of the Hospital Frailty Risk Score among older adults receiving mechanical ventilation

Validation of the Hospital Frailty Risk Score among older adults receiving mechanical ventilation

Frailty Reduces Vaccine Effectiveness Against SARS-CoV-2 Infection: A Test-Negative Case Control Study Using National VA Data

TAVR in Older Adults

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