External validation of the Hospital Frailty Risk Score in France

Gilbert, Thomas; Cordier, Quentin; Polazzi, Stéphanie; Bonnefoy, Marc; Keeble, Eilìs; Street, Andrew; Conroy, Simon; Duclos, Antoine

doi:10.1093/ageing/afab126

Cited by 34 publications

(52 citation statements)

References 41 publications

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“…This may partly be explained by the potential misclassification of readmission, since we did not have data on patients who were admitted to other clinics than the nine geriatric clinics. Besides, a relatively low AUC or c-statistic for frailty in predicting readmission (around 0.5–0.6) is also frequently reported in the literature (19,22,23,33,42,43). Hospital readmission is often described as a complex outcome not merely related to the health status of patients, but also social factors such as access to care, social support, and drug abuse (44,45), which are factors not captured in the frailty measures.…”

Section: Discussionmentioning

confidence: 83%

“…While these tools are commonly used on data retrieved from primary care, recent studies have shown that such eFIs show good predictive performance for all-cause mortality also in hospital settings (19–21). Another database-derived frailty measure, the Hospital Frailty Risk Score (HFRS), is calculated based on the International Classification of Diseases, Tenth Revision (ICD-10) codes (22), and has been validated for its ability to predict mortality and prolonged length of stay in hospitalized older patients (23–25). However, its composition of ICD-10 diagnoses makes it more similar to a comorbidity measure, possibly missing out other frailty aspects such as functioning (22).…”

Section: Introductionmentioning

confidence: 99%

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Development of an electronic frailty index for hospitalized geriatric patients in Sweden

Mak

Hägg

Eriksdotter

et al. 2021

Preprint

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BackgroundFrailty assessment in the Swedish health system relies on the Clinical Frailty Scale (CFS), but it requires training, in-person evaluation, and is often missing in medical records. We aimed to develop an electronic frailty index (eFI) from routinely collected electronic health records (EHRs) and assess its predictive ability for adverse outcomes in geriatric patients.MethodsEHRs were extracted for 18,225 geriatric patients with unplanned admissions between 1/3/2020 and 17/6/2021 from nine geriatric clinics in Stockholm, Sweden. A 48-item eFI was constructed using diagnostic codes, functioning and other health indicators, and laboratory data. The CFS, Hospital Frailty Risk Score, and Charlson Comorbidity Index were used for comparative assessment of the eFI. We modelled in-hospital mortality and 30-day readmission using logistic regression; 30-day and 6-month mortality using Cox regression; and length of stay using linear regression.Results13,188 patients were included in analyses (mean age 83.1 years). A 10% increment in the eFI was associated with higher risks of in-hospital (odds ratio: 5.34; 95% confidence interval: 4.20-6.82), 30-day (hazard ratio [HR]: 3.28; 2.91-3.69), and 6-month mortality (HR: 2.70; 2.52-2.90) adjusted for age and sex. Of the frailty and comorbidity measures, the eFI had the best predictive accuracy for in-hospital mortality, yielding an area under receiver operating characteristic curve of 0.813. Higher eFI also predicted a longer length of stay, but had a rather poor discrimination for 30-day readmission.ConclusionsAn EHR-based eFI has good predictive accuracy for adverse outcomes, suggesting that it can be used in risk stratification in geriatric patients.

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Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Development of an electronic frailty index for hospitalized geriatric patients in Sweden

Mak

Hägg

Eriksdotter

et al. 2021

Preprint

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“…Prior HFRS studies focused on validating it in general hospitalizations, including non-ICU and ICU patients 17 – 19 , 36 – 39 . Recently, there has been interest in externally validating the HFRS in ICU administrative databases, as interest in big data frailty research increases 21 , 40 , 41 .…”

Section: Discussionmentioning

confidence: 99%

“…We only evaluated in-hospital all-cause mortality instead of 30-day mortality (inpatient or outpatient) because the NRD only records in-hospital deaths. We defined prolonged hospitalization as a hospital length of stay > 10 days and only evaluated 30-day emergency hospital readmissions, similar to Gilbert et al 16 , 17 .…”

Section: Methodsmentioning

confidence: 99%

“…Recently, the Hospital Frailty Risk Score (HFRS) was derived using ridge regression and 109 International Classification of Diseases, Tenth Revision Clinical Modification (ICD-10-CM) codes, from a cohort of > 20,000 hospitalized older adults 16 . The HFRS has been validated to predict the risk of 30-day mortality, prolonged hospitalization, and 30-day emergency hospital readmissions in older hospitalized patients 16 , and it has since been validated in other hospitalized population databases 17 – 20 . However, its validity in critically ill patients has been questioned in a single center study 21 .…”

Section: Introductionmentioning

confidence: 99%

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External validation of the hospital frailty risk score among older adults receiving mechanical ventilation

Kassir

Mailman

et al. 2022

Sci Rep

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To externally validate the Hospital Frailty Risk Score (HFRS) in critically ill patients. We selected older adult (≥ 75 years old) hospitalizations receiving mechanical ventilation, using the Nationwide Readmissions Database (January 1, 2016-November 30, 2018). Frailty risk was subcategorized into low-risk (HFRS score < 5), intermediate-risk (score 5–15), and high-risk (score > 15). We evaluated the HFRS to predict in-hospital mortality, prolonged hospitalization, and 30-day readmissions, using multivariable logistic regression, adjusting for patient and hospital characteristics. Model performance was assessed using the c-statistic, Brier score, and calibration plots. Among 649,330 weighted hospitalizations, 9.5%, 68.3%, and 22.2% were subcategorized as low-, intermediate-, and high-risk for frailty, respectively. After adjustment, high-risk patient hospitalizations 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 0.216) and 30-day readmissions (aOR 1.20 [95% CI 1.13–1.27], c-statistic 0.595, Brier 0.162), compared to low-risk hospitalizations. Conversely, high-risk hospitalizations were inversely associated with in-hospital mortality (aOR 0.46 [95% CI 0.45–0.48], c-statistic 0.712, Brier 0.214). The HFRS was not successfully validated to predict in-hospital mortality in critically ill older adults. While it may predict other outcomes, its use should be avoided in the critically ill.

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Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes

Dicpinigaitis,

Khamzina,

Hall

et al. 2024

JAMA Netw Open

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ImportanceFrailty is associated with adverse outcomes after even minor physiologic stressors. The validated Risk Analysis Index (RAI) quantifies frailty; however, existing methods limit application to in-person interview (clinical RAI) and quality improvement datasets (administrative RAI).ObjectiveTo expand the utility of the RAI utility to available International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) administrative data, using the National Inpatient Sample (NIS).Design, Setting, and ParticipantsRAI parameters were systematically adapted to ICD-10-CM codes (RAI-ICD) and were derived (NIS 2019) and validated (NIS 2020). The primary analysis included survey-weighed discharge data among adults undergoing major surgical procedures. Additional external validation occurred by including all operative and nonoperative hospitalizations in the NIS (2020) and in a multihospital health care system (UPMC, 2021-2022). Data analysis was conducted from January to May 2023.ExposuresRAI parameters and in-hospital mortality.Main Outcomes and MeasuresThe association of RAI parameters with in-hospital mortality was calculated and weighted using logistic regression, generating an integerized RAI-ICD score. After initial validation, thresholds defining categories of frailty were selected by a full complement of test statistics. Rates of elective admission, length of stay, hospital charges, and in-hospital mortality were compared across frailty categories. C statistics estimated model discrimination.ResultsRAI-ICD parameters were weighted in the 9 548 206 patients who were hospitalized (mean [SE] age, 55.4 (0.1) years; 3 742 330 male [weighted percentage, 39.2%] and 5 804 431 female [weighted percentage, 60.8%]), modeling in-hospital mortality (2.1%; 95% CI, 2.1%-2.2%) with excellent derivation discrimination (C statistic, 0.810; 95% CI, 0.808-0.813). The 11 RAI-ICD parameters were adapted to 323 ICD-10-CM codes. The operative validation population of 8 113 950 patients (mean [SE] age, 54.4 (0.1) years; 3 148 273 male [weighted percentage, 38.8%] and 4 965 737 female [weighted percentage, 61.2%]; in-hospital mortality, 2.5% [95% CI, 2.4%-2.5%]) mirrored the derivation population. In validation, the weighted and integerized RAI-ICD yielded good to excellent discrimination in the NIS operative sample (C statistic, 0.784; 95% CI, 0.782-0.786), NIS operative and nonoperative sample (C statistic, 0.778; 95% CI, 0.777-0.779), and the UPMC operative and nonoperative sample (C statistic, 0.860; 95% CI, 0.857-0.862). Thresholds defining robust (RAI-ICD &lt;27), normal (RAI-ICD, 27-35), frail (RAI-ICD, 36-45), and very frail (RAI-ICD &gt;45) strata of frailty maximized precision (F1 = 0.33) and sensitivity and specificity (Matthews correlation coefficient = 0.26). Adverse outcomes increased with increasing frailty.Conclusion and RelevanceIn this cohort study of hospitalized adults, the RAI-ICD was rigorously adapted, derived, and validated. These findings suggest that the RAI-ICD can extend the quantification of frailty to inpatient adult ICD-10-CM–coded patient care datasets.

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External validation of the Hospital Frailty Risk Score in France

Cited by 34 publications

References 41 publications

Development of an electronic frailty index for hospitalized geriatric patients in Sweden

Development of an electronic frailty index for hospitalized geriatric patients in Sweden

External validation of the hospital frailty risk score among older adults receiving mechanical ventilation

Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes

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