&lt;p&gt;Identification of Frailty and Its Risk Factors in Elderly Hospitalized Patients from Different Wards: A Cross-Sectional Study in China&lt;/p&gt;

Liang, Yutong; Zhang, Yao-Nan; Liu, Yanming; Chen, Yuhui; Xu, Jingyong; Liu, Ming; Li, Jing; Ma, Zhao; Qiao, Linlin; Wang, Zi; Yang, Jiefu; Wang, Hua

doi:10.2147/cia.s225149

Cited by 52 publications

(70 citation statements)

References 57 publications

(60 reference statements)

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“…Given the importance of laboratory tests for frailty assessment [17,18], we included 5 routine laboratory items for constructing the eFI. Based on our previous studies [8,19], we found that haemoglobin, albumin, sodium, and D-dimer were independently associated with frailty. Meanwhile, qualitative analysis of urine protein was widely tested and was useful to re ect renal function.…”

Section: Validation Cohortmentioning

confidence: 78%

“…These participants were enrolled from September 2018 to February 2019, covering ten departments (i.e., cardiology ward, respiratory ward, geriatric ward, neurology ward, rehabilitation ward, traditional Chinese medicine ward, general surgery ward, orthopaedics ward, urology ward, and cardiac surgery ward) in the same hospital. The crosssectional characteristics of the prospective cohort were analysed and described in detail in our previously published article [8]. Among the 1,000 elderly patients in the cohort study, 703 participants were matched in the EHR database of 49,226 cases by hospitalized ID and admission time.…”

Section: Validation Cohortmentioning

confidence: 99%

“…The CGA-FI contained 48 variables, which included 16 items of chronic diseases, 14 items of activities of daily living (ADLs), 3 items of mood (depression, anxiety, loneliness), 1 item of cognition, 7 items of geriatric syndrome, 2 items of ability of activity, and 5 items of objective measurements (body mass index (BMI), calf circumference, peak ow, grip strength, 4-m walking speed). The CGA-FI was the result of the sum of all variables' scores divided by 48 [8]. The details are described in Appendix Table 1.…”

Section: Validation Cohortmentioning

confidence: 99%

“…Previous studies on frailty in inpatients have mainly focused on the population in a certain department or with a particular disease type, which cannot fully re ect conditions across all departments. Our previous research has found that the prevalence of frailty between medical and surgical departments was signi cantly different [8], but there has been no research that can show in detail the distribution and clinical signi cance of frailty in each department of a general hospital. At this time, the effective utilization of EHR big data is particularly important, which can provide valuable information for clinical management.…”

Section: Introductionmentioning

confidence: 96%

“…The most widely used instruments in clinical research are variations of the frailty phenotype [6] or frailty index (FI) based on the cumulative de cit model [7]. Our previous study showed that the FI based on comprehensive geriatric assessment (CGA-FI) may be an optimal assessment tool among ve prevalent frailty measurements due to the highest sensitivity (94.8%) and good speci city (87.0%) for identifying frailty [8]. Although numerous clinicians have embraced the concept of frailty, we are facing large challenges to make the translation from frailty research to clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of an electronic frailty index using routine electronic health records: an observational study of 49,226 elderly inpatients from a general hospital in China

Liang

Xie

et al. 2020

Preprint

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Background Frailty assessment based on routine electronic health records (EHR) may be a good alternative to time-consuming frailty scales. This study mainly aimed to develop and validate an electronic frailty index (eFI) using routine EHR for elderly inpatients and analyse the correlations between frailty and hospitalized events and costs.Methods Based on the cumulative deficit model, we created an eFI from routine EHR. In a prospective cohort, we validated the effectiveness of the eFI by consistency with the comprehensive geriatric assessment-frailty index (CGA-FI). Then, we analysed the correlations between frailty and hospitalized events by logistic regression and costs by generalized linear regression models.Results During the study period, 49,226 elderly inpatients from the EHR were included in the analysis. There were 42,821 (87.0%) patients with sufficient data to calculate an eFI. The cut-off value for the upper tertile of the eFI for these patients was 0.15. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson’s r = 0.716, P < 0.001). The sensitivity and specificity for an eFI ≥ 0.15 to identify frailty defined as a CGA-FI ≥ 0.25 were 64.8% and 88.7%, respectively. After adjusting for age, gender, and operation, an eFI ≥ 0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889, P < 0.001) and death in hospital (OR = 19.97, P < 0.001) for elderly inpatients from all departments. Moreover, after adjusting for age, gender, and operation, eFI values (per 0.1) were positively associated with total costs (β = 0.453, P < 0.001), examination costs (β = 0.269, P < 0.001), treatment costs (β = 0.414, P < 0.001), nursing costs (β = 0.381, P < 0.001), pharmacy costs (β = 0.524, P < 0.001), and material costs (β = 0.578, P < 0.001) for elderly inpatients from all departments.Conclusions It is feasible to develop an effective eFI from routine EHR for elderly inpatients from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs for elderly inpatients increase accordingly.

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Section: Validation Cohortmentioning

confidence: 78%

Section: Validation Cohortmentioning

confidence: 99%

Section: Validation Cohortmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development and validation of an electronic frailty index using routine electronic health records: an observational study of 49,226 elderly inpatients from a general hospital in China

Liang

Xie

et al. 2020

Preprint

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Outcome of older adults at risk of frailty

et al. 2021

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Background The integral part of the definition of frailty is the outcome associated with it. Older adults at risk of frailty are in the process of becoming frail. This study looked at the clinical characteristics and outcomes of older adults at risk of frailty. Methodology The study population was selected from outpatient department of the geriatric medicine department in a tertiary care hospital. Older adults identified as at risk of frailty were assessed at baseline and then followed up after 1 year for the composite primary outcome of death, falls, hospitalization, and self‐rated poor quality of life in the follow‐up period. Results The study included 324 older adults who had completed 1‐year follow up. Mean (SD) age was 74.49 (4.58) years, and males were 241 (74.15%). Frail and pre‐frail at baseline among the study population were 31.17% and 61.11%, respectively. The primary outcome occurred in 43 (13.27%) patients. Poor baseline IADL was significantly associated with primary outcome at the end of 1 year. Conclusion An unfavorable outcome in older adults at risk of frailty was significantly higher and independent of their baseline frailty status. Poor baseline IADL value may be considered as a predictor for primary outcome at 1 year of follow up.

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Prevalence and prognosis of frailty in older patients with stage B heart failure with preserved ejection fraction

Meng

Chai

et al. 2023

ESC Heart Failure

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Aims Frailty in older patients with stage B heart failure with preserved ejection fraction (HFpEF) has not been fully explored. We evaluated the prevalence and prognostic significance of frailty in older patients diagnosed with stage B HFpEF. Methods Our prospective cohort study included inpatients aged ≥65 years who were followed up for 3 years. Stage B HFpEF was defined as cardiac structural or functional abnormalities with a left ventricular ejection fraction (LVEF) ≥ 50% without signs or symptoms. Frailty was assessed using the Fried phenotype. The primary outcome was 3‐year all‐cause mortality or readmission. Results Overall, 520 older inpatients diagnosed with stage B HFpEF [mean ± standard deviation age: 75.5 ± 6.25 years, male: 222 (42.7%)] were included in the study. Of these, 145 (27.9%) were frail. Frail patients were older (78.5 ± 6.23 vs. 74.3 ± 6.22 years, P < 0.001), with a lower body mass index (24.6 ± 3.60 vs. 25.7 ± 3.27 kg/m 2 , P = 0.001), higher level of N‐terminal pro‐B‐type natriuretic peptide [279 (interquartile range: 112.4, 596) vs. 140 (67.1, 266) pg/mL, P < 0.001], longer timed up‐and‐go test result (19.9 ± 9.71 vs. 13.3 ± 5.08 s, P < 0.001), and poorer performance in the short physical performance battery (4.1 ± 3.26 vs. 8.2 ± 2.62, P < 0.001), basic activities of daily living (BADL, 4.7 ± 1.71 vs. 5.7 ± 0.57, P < 0.001), and instrumental activities of daily living (IADL, 4.4 ± 2.73 vs. 7.4 ± 1.33, P < 0.001). Frail patients were more likely to have a Mini‐Mental State Examination (MMSE) score <24 (55.9% vs. 28.8%, P < 0.001) and take more than five medications (64.1% vs. 47.2%, P = 0.001). Frail patients had a higher incidence of all‐cause mortality or readmission (62.8% vs. 47.7%, P = 0.002), all‐cause readmission (56.6% vs. 45.9%, P = 0.029), and readmission for non‐heart failure (55.2% vs. 41.3%, P = 0.004) during the 3‐year follow‐up, with a 1.53‐fold (95%CI 1.11–2.11, P = 0.009) higher risk of all‐cause mortality or readmission, a 1.52‐fold (95%CI 1.09–2.11, P = 0.014) higher risk of all‐cause readmission, and a 1.70‐fold (95%CI 1.21–2.38, P = 0.002) higher risk of readmission for non‐clinical heart failure, adjusted for sex, age, polypharmacy, Athens Insomnia Scale, MMSE, LVEF, BADL, and IADL. Conclusions Frailty is common in elderly patients with stage B HFpEF. Physical frailty, particularly low physical activity, can independently predict the long‐term prognosis in these patients.

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<p>Identification of Frailty and Its Risk Factors in Elderly Hospitalized Patients from Different Wards: A Cross-Sectional Study in China</p>

Cited by 52 publications

References 57 publications

Development and validation of an electronic frailty index using routine electronic health records: an observational study of 49,226 elderly inpatients from a general hospital in China

Development and validation of an electronic frailty index using routine electronic health records: an observational study of 49,226 elderly inpatients from a general hospital in China

Outcome of older adults at risk of frailty

Prevalence and prognosis of frailty in older patients with stage B heart failure with preserved ejection fraction

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