Dataset Information

The Hospital Frailty Risk Score (HFRS) applied to primary data: protocol for a systematic review.

ABSTRACT:

Introduction

Frailty is characterised by vulnerability to adverse health outcomes and increases with age. Many frailty risk scores have been developed. One important example is the Hospital Frailty Risk Score (HFRS) which has the potential to be widely used and automatically calculated which will provide accurate assessment of frailty in a time/cost-effective manner. This systematic review, therefore, seeks to describe the HFRS use since its publication in 2018.

Methods and analysis

The proposed systematic review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will include published original peer-reviewed articles, preprints, conference proceedings and letters to the editor reporting primary data where there is an English language abstract available from 1 January 2018 to 30 June 2022. Databases to be searched are MEDLINE, EMBASE and Web of Science. Additional studies from, for example, the reference of the included studies will be identified and assessed for potential inclusion. Two independent reviewers will perform and assess the following: (1) eligibility of the included studies, (2) critical appraisal using the Cochrane Risk of Bias in Non-randomized Studies of Interventions tool, and (3) data extraction using a predefined form. Disagreements will be resolved through discussions or by involvement of a third reviewer. It may be possible to undertake a meta-analysis if there are sufficient studies reporting effect measures in homogenous populations and/or settings. Effect sizes will be calculated using meta-analysis methods and expressed as risk ratios or ORs with 95% CIs.

Ethics and dissemination

No ethical approval is required for this systematic review as it will use secondary data only. The results of the systematic review will be submitted for publication in recognised peer-reviewed journals related to frailty and geriatric care and will be widely disseminated through conferences, congresses, seminars, symposia and scientific meetings.

SUBMITTER: Alshibani A

PROVIDER: S-EPMC9582317 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

The Hospital Frailty Risk Score (HFRS) applied to primary data: protocol for a systematic review.

Alshibani Abdullah A Warner Bronwen B Owen Rhiannon K RK Mukherjee Abir A Gilbert Thomas T Conroy Simon S

BMJ open 20221019 10

<h4>Introduction</h4>Frailty is characterised by vulnerability to adverse health outcomes and increases with age. Many frailty risk scores have been developed. One important example is the Hospital Frailty Risk Score (HFRS) which has the potential to be widely used and automatically calculated which will provide accurate assessment of frailty in a time/cost-effective manner. This systematic review, therefore, seeks to describe the HFRS use since its publication in 2018.<h4>Methods and analysis</ ...[more]

PMID: 36261239

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Project description:Background & aimsThe hospital frailty risk score (HFRS) identifies older patients at risk of poor outcomes and may have value in cirrhosis. We compared the Charlson (CCI), Elixhauser (ECI), and cirrhosis (CirCom) comorbidity indices with the HFRS in predicting outcomes for cirrhosis hospitalisations.MethodsUsing the National Inpatient Sample (quarter 4 of 2015-2019), we analysed cirrhosis hospitalisations. For each index, we described the prevalence of comorbid conditions and inpatient mortality. We compared the ability of CCI, ECI, CirCom, and HFRS to predict inpatient mortality. Raw and adjusted models predicting inpatient mortality were compared using the area under the receiver operating characteristic curve and the Akaike information criterion.ResultsThe cohort's (N = 626,553) median age was 61 years (IQR 52-68 years), 60% were male, cirrhosis was caused by alcohol in 43%, and 38% had ascites. The median comorbidity scores are as follows: ECI 4 (IQR 3-6), CCI 5 (IQR 4-8), and HFRS 5.6 (IQR 3.0-8.6). The most common CirCom score was 0 + 0 (44%). Across the range of values of each index, we observed different mortality ranges: CCI 1.9-13.1%, ECI 3.2-8.7%, CirCom 4.9-13.8%, and HFRS 1.0-15.2%. An adjusted model with HFRS had the highest area under the receiver operating characteristic curve in predicting mortality (HFRS 0.782 vs. ECI 0.689, CCI 0.695, and CirCom 0.692). We observed substantial variation in mortality with HFRS within each level of CCI, ECI, and CirCom. For example, for ECI 4, mortality increased from 0.6 to 16.4%, as HFRS increased from 0 to 15.ConclusionsComorbidity indices predict inpatient cirrhosis mortality, but HFRS performs better than CCI, ECI, and CirCom. HFRS is an ideal tool for measuring comorbidity burden and disease severity risk adjustment in cirrhosis-related administrative database studies.Impact and implicationsWe compared commonly used comorbidity indices to a more recently described risk score (hospital frailty risk score [HFRS]) in patients with cirrhosis using a national sample of hospital records. Comorbid conditions are common in hospitalised patients with cirrhosis. There is significant variability in mortality across the range of each index. HFRS outperforms the Charlson comorbidity index, Elixhauser comorbidity index, and CirCom (cirrhosis-specific comorbidity scoring system) in predicting inpatient mortality. HFRS is a valuable index for risk adjustment in inpatient administrative database studies.

Project description:BackgroundOlder 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.MethodsThis 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.ResultsTwelve 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.ConclusionThe 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.

Project description:ObjectiveTo develop and externally validate a risk algorithm (QAdmissions) to estimate the risk of emergency hospital admission for patients aged 18-100 years in primary care.DesignProspective open cohort study using routinely collected data from general practice linked to hospital episode data during the 2-year study period 1 January 2010 to 31 December 2011.Setting405 general practices in England contributing to the national QResearch database to develop the algorithm. Two validation cohorts to validate the algorithm (1) 202 different QResearch practices and (2) 343 practices in England contributing to the Clinical Practice Research DataLink (CPRD). All general practices had data linked to hospital episode statistics at the individual patient level.ParticipantsWe studied 2 849 381 patients aged 18-100 years in the derivation cohort with over 4.6 million person-years of follow-up. 265 573 of these patients had one or more emergency admissions during follow-up. For the QResearch validation cohort, we identified 1 340 622 patients aged 18-100 years with over 2.2 million person-years of follow-up. Of these patients, 132 723 had one or more emergency admissions during follow-up. The CPRD cohort included 2 475 360 patients aged 18-100 years with over 3.8 million person-years of follow-up. 234 204 of these patients had one or more emergency admissions during follow-up. We excluded patients without a valid NHS number and a valid Townsend score.EndpointFirst (ie, incident) emergency admission to hospital in the next 2 years as recorded on the linked hospital episodes records.Risk factorsCandidate variables recorded on the general practitioner computer system including (1) demographic variables (age, sex, strategic health authority, Townsend deprivation score, ethnicity); (2) lifestyle variables (smoking, alcohol intake); (3) chronic diseases; (4) prescribed medication; (5) clinical values (body mass index, systolic blood pressure); (6) laboratory test results (haemoglobin, platelets, erythrocyte sedimentation rate, ratio of total serum cholesterol to high density lipoprotein cholesterol concentrations, liver function tests). We also included the number of emergency admissions in the preceding year based on information recorded on the linked hospital episodes records.ResultsThe final QAdmissions algorithm incorporated 30 variables. When applied to the QResearch validation cohort, it explained 41% of the variation in women and 43% of that in men. The D statistic for QAdmissions was 1.7 in women and 1.8 in men. The receiver operating curve statistic was 0.78 for men and 0.77 for women. QAdmissions had good performance on all measures of discrimination and calibration. The positive predictive value for emergency admissions for the top tenth of patients at highest risk was 42% and the sensitivity was 39%. The results for the CPRD validation cohort were similar.ConclusionsThe QAdmissions model provided a valid measure of absolute risk of emergency admission to hospital in the general population as shown by its performance in a separate validation cohort. Further research is needed to evaluate the cost-effectiveness of using these algorithms in primary care.

Project description:IntroductionFrailty is a common and clinically significant condition in geriatric populations, associated with adverse health outcomes such as hospitalisation, disability and mortality. Although there are systematic reviews/meta-analyses assessing the prevalence of frailty in community-dwelling older adults, nursing home residents, and cancer and general surgery patients, there are none assessing the overall prevalence of frailty in geriatric hospital inpatients.Methods and analysisThis review will systematically search and analyse the prevalence of frailty within geriatric hospital inpatients within the literature. A search will be employed on the platforms of Ovid, Web of Science and databases of Cumulative Index to Nursing and Allied Health Literature (CINAHL) Plus, SCOPUS and the Cochrane Library. Any observational or experimental study design which utilises a validated operational definition of frailty, reports the prevalence of frailty, has a minimum age ≥65 years, attempts to assess the whole ward/clinical population and occurs in hospital inpatients, will be included. Title and abstract and full-text screenings will be conducted by three reviewers. Methodological quality of eligible studies will be assessed using the Joanna Briggs Institute critical appraisal tool. Data extraction will be performed by two reviewers. If sufficient data are available, a meta-analysis synthesising pooled estimates of the prevalence of frailty and pre-frailty, as well as the prevalence of frailty stratified by age, sex, operational frailty definition, prevalent morbidities, ward type and location, among older hospitalised inpatients will be conducted. Clinical heterogeneity will be assessed by two reviewers. Statistical heterogeneity will be assessed through a Cochran Q test, and an I2 test performed to assess its magnitude.Ethics and disseminationEthical approval was not required as primary data will not be collected. Findings will be disseminated through publication in peer reviewed open access scientific journals, public engagement events, conference presentations and social media.Prospero registration number79202.

Dataset Information

The Hospital Frailty Risk Score (HFRS) applied to primary data: protocol for a systematic review.

Introduction

Methods and analysis

Ethics and dissemination

Publications

The Hospital Frailty Risk Score (HFRS) applied to primary data: protocol for a systematic review.

Similar Datasets

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