Frailty risk in hospitalised older adults with and without diabetes mellitus

Lekan, Deborah; McCoy, Thomas P.

doi:10.1111/jocn.14529

Cited by 9 publications

(2 citation statements)

References 45 publications

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“…56) These frailty measures have been applied to define population subgroups by frailty levels, 61) reduce confounding by frailty in examining the association between influenza vaccination and mortality, 62) estimate health care costs attributed to frailty, 63) and improve mortality prediction after transcatheter aortic valve replacement. 64) [65][66][67][68] or the Veterans Affairs EHR database, 69) while the e-Frailty Index was developed for the United Kingdom primary care practices, 35) which was later implemented in a primary care EHR system in Australia. 59) Clinical knowledge-based selection was used for four mea- sures while data-driven selection without a reference standard was used for one measure.…”

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

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

confidence: 99%

Measuring Frailty in Health Care Databases for Clinical Care and Research

Kim

2020

Ann Geriatr Med Res

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“…The frailty risk score. The frailty risk score (FRS) is based on a biopsychosocial conceptual model and operationalized using evidence-based risk factors and blood biomarker lab tests extracted from the EHR (Lekan et al, 2017(Lekan et al, , 2021Lekan & McCoy, 2018). This study creates four definitions of the FRS using combinations of structured nursing flowsheet data, ICD-10 codes, and blood biomarkers.…”

Section: Measurementsmentioning

confidence: 99%

Frailty and In-Hospital Mortality Risk Using EHR Nursing Data

Lekan

McCoy

Jenkins

et al. 2021

Biological Research For Nursing

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Purpose The purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. Methods In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated. Results The 46,645 patients averaged 1.5 hospitalizations (SD = 1.1) over the study period and 63.3% were emergent admissions. The average age was 70.4 years (SD = 11.4), 55.3% were female, 73.0% were non-Hispanic White (73.0%), mean comorbidity score was 3.9 (SD = 2.9), 80.5% were taking 1.5 high risk medications, and 42% recorded polypharmacy. The best performing FRS-NF-26-LABS included nursing flowsheet data and blood biomarkers (Adj. HR = 1.30, 95% CI [1.28, 1.33]), with good accuracy (iAUC = .794); the reduced model with age, sex, and FRS only demonstrated similar accuracy. The poorest performance was the ICD-10 code-based FRS. Conclusion The FRS captures information about the patient that increases risk for in-hospital mortality not accounted for by other factors. Identification of frailty enables providers to enhance various aspects of care, including increased monitoring, applying more intensive, individualized resources, and initiating more informed discussions about treatments and discharge planning.

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