Validation of hospital frailty risk score to predict hospital use in older people: Evidence from the Australian Longitudinal Study on Women’s Health

Shebeshi, Dinberu S.; Dolja‐Gore, Xenia; Byles, Julie

doi:10.1016/j.archger.2020.104282

Cited by 20 publications

(18 citation statements)

References 27 publications

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Section: Hospital Frailty Risk Scorementioning

confidence: 99%

“…Since its introduction in 2018, the HFRS has been validated in several Western countries [13,[19][20][21][22][23][24][25][26][27][28][29][30][31] (supplementary table 7). Our findings showed that the HFRS is a predictor for mortality and LOS which are consistent with the findings of previous studies [13,[19][20][21][22][23][24][25][26][27][28][29][30][31] (supplementary table 7). However, we found that the HFRS is not a predictor for 30-day readmission and the evidence around the risk of unplanned readmission from previous studies is by far less consistent [13,[19][20][21][22][23][24][25][26][27][28][29][30][31] (supplementary table 7).…”

Section: Hospital Frailty Risk Scorementioning

confidence: 99%

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A comparison between the clinical frailty scale and the hospital frailty risk score to risk stratify older people with emergency care needs

Alshibani

Coats²,

Maynou³

et al. 2022

BMC Emerg Med

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Background Older adults living with frailty who require treatment in hospitals are increasingly seen in the Emergency Departments (EDs). One quick and simple frailty assessment tool—the Clinical Frailty Scale (CFS)—has been embedded in many EDs in the United Kingdom (UK). However, it carries time/training and cost burden and has significant missing data. The Hospital Frailty Risk Score (HFRS) can be automated and has the potential to reduce costs and increase data availability, but has not been tested for predictive accuracy in the ED. The aim of this study is to assess the correlation between and the ability of the CFS at the ED and HFRS to predict hospital-related outcomes. Methods This is a retrospective cohort study using data from Leicester Royal Infirmary hospital during the period from 01/10/2017 to 30/09/2019. We included individuals aged + 75 years as the HFRS has been only validated for this population. We assessed the correlation between the CFS and HFRS using Pearson’s correlation coefficient for the continuous scores and weighted kappa scores for the categorised scores. We developed logistic regression models (unadjusted and adjusted) to estimate Odds Ratios (ORs) and Confidence Intervals (CIs), so we can assess the ability of the CFS and HFRS to predict 30-day mortality, Length of Stay (LOS) > 10 days, and 30-day readmission. Results Twelve thousand two hundred thirty seven individuals met the inclusion criteria. The mean age was 84.6 years (SD 5.9) and 7,074 (57.8%) were females. Between the CFS and HFRS, the Pearson correlation coefficient was 0.36 and weighted kappa score was 0.15. When comparing the highest frailty categories to the lowest frailty category within each frailty score, the ORs for 30-day mortality, LOS > 10 days, and 30-day readmission using the CFS were 2.26, 1.36, and 1.64 and for the HFRS 2.16, 7.68, and 1.19. Conclusion The CFS collected at the ED and the HFRS had low/slight agreement. Both frailty scores were shown to be predictors of adverse outcomes. More research is needed to assess the use of historic HFRS in the ED.

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Section: Hospital Frailty Risk Scorementioning

confidence: 99%

Section: Hospital Frailty Risk Scorementioning

confidence: 99%

A comparison between the clinical frailty scale and the hospital frailty risk score to risk stratify older people with emergency care needs

Alshibani

Coats²,

Maynou³

et al. 2022

BMC Emerg Med

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“…It has been validated for use in a number of settings. [19,[24][25][26] The GFS defines 7 domains of frailty (dementia and delirium; mobility problems; falls and fractures; pressure ulcers and weight loss; incontinence; and anxiety and depression) based on ICD-10 codes for hospital admissions during the previous year [20]. The GFS is closely aligned with the key clinical subdomains of frailty and considers the impact of manifestations of frailty on functional ability.…”

Section: Frailty/comorbidity Featuresmentioning

confidence: 99%

Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data

Heyl¹,

Hardy²,

Tucker³

et al. 2022

Interact J Med Res

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Background Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes. Objective We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms. Methods This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy. Results In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease. Conclusions The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.

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“…Operationally, there are various ways to define frailty without widespread consensus on a ‘best’ methodology. Two commonly used tools are the modified 5-item Frailty Index (mFI-5) and modified 11-item Frailty Index (mFI-11) 15 16…”

Section: Introductionmentioning

confidence: 99%

Higher Hospital Frailty Risk Score is associated with increased complications and healthcare resource utilization after endovascular treatment of ruptured intracranial aneurysms

Koo

Elsamadicy

Renedo

et al. 2022

J NeuroIntervent Surg

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AimTo use the Hospital Frailty Risk Score (HFRS) to investigate the impact of frailty on complication rates and healthcare resource utilization in patients who underwent endovascular treatment of ruptured intracranial aneurysms (IAs).MethodsA retrospective cohort study was performed using the 2016–2019 National Inpatient Sample database. All adult patients (≥18 years) undergoing endovascular treatment for IAs after subarachnoid hemorrhage were identified using ICD-10-CM codes. Patients were categorized into frailty cohorts: low (HFRS <5), intermediate (HFRS 5–15) and high (HFRS >15). Patient demographics, adverse events, length of stay (LOS), discharge disposition, and total cost of admission were assessed. Multivariate logistic regression analysis was used to identify independent predictors of prolonged LOS, increased cost, and non-routine discharge.ResultsOf the 33 840 patients identified, 7940 (23.5%) were found to be low, 20 075 (59.3%) intermediate and 5825 (17.2%) high frailty by HFRS criteria. The rate of encountering any adverse event was significantly greater in the higher frailty cohorts (low: 59.9%; intermediate: 92.4%; high: 99.2%, p<0.001). There was a stepwise increase in mean LOS (low: 11.7±8.2 days; intermediate: 18.7±14.1 days; high: 26.6±20.1 days, p<0.001), mean total hospital cost (low: $62 888±37 757; intermediate: $99 670±63 446; high: $134 937±80 331, p<0.001), and non-routine discharge (low: 17.3%; intermediate: 44.4%; high: 69.4%, p<0.001) with increasing frailty. On multivariate regression analysis, a similar stepwise impact was found in prolonged LOS (intermediate: OR 2.38, p<0.001; high: OR 4.49, p<0.001)], total hospital cost (intermediate: OR 2.15, p<0.001; high: OR 3.62, p<0.001), and non-routine discharge (intermediate: OR 2.13, p<0.001; high: OR 4.17, p<0.001).ConclusionsOur study found that greater frailty as defined by the HFRS was associated with increased complications, LOS, total costs, and non-routine discharge.

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Validation of hospital frailty risk score to predict hospital use in older people: Evidence from the Australian Longitudinal Study on Women’s Health

Cited by 20 publications

References 27 publications

A comparison between the clinical frailty scale and the hospital frailty risk score to risk stratify older people with emergency care needs

A comparison between the clinical frailty scale and the hospital frailty risk score to risk stratify older people with emergency care needs

Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data

Higher Hospital Frailty Risk Score is associated with increased complications and healthcare resource utilization after endovascular treatment of ruptured intracranial aneurysms

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