External validation of the Hospital Frailty Risk Score in France.
Ontology highlight
ABSTRACT: Background
The Hospital Frailty Risk Score (HFRS) has made it possible internationally to identify subgroups of patients with characteristics of frailty from routinely collected hospital data.Objective
To externally validate the HFRS in France.Design
A retrospective analysis of the French medical information database.Setting
743 hospitals in Metropolitan France.Subjects
All patients aged 75 years or older hospitalised as an emergency in 2017 (n = 1,042,234).Methods
The HFRS was calculated for each patient based on the index stay and hospitalisations over the preceding 2 years. Main outcome measures were 30-day in-patient mortality, length of stay (LOS) >10 days and 30-day readmissions. Mixed logistic regression models were used to investigate the association between outcomes and HFRS score.Results
Patients with high HFRS risk were associated with increased risk of mortality and prolonged LOS (adjusted odds ratio [aOR] = 1.38 [1.35-1.42] and 3.27 [3.22-3.32], c-statistics = 0.676 and 0.684, respectively), while it appeared less predictive of readmissions (aOR = 1.00 [0.98-1.02], c-statistic = 0.600). Model calibration was excellent. Restricting the score to data prior to index admission reduced discrimination of HFRS substantially.Conclusions
HFRS can be used in France to determine risks of 30-day in-patient mortality and prolonged LOS, but not 30-day readmissions. Trial registration: Reference ID on clinicaltrials.gov: ID: NCT03905629.
SUBMITTER: Gilbert T
PROVIDER: S-EPMC8753041 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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.
| S-EPMC9418158 | biostudies-literature
Project description:BackgroundThe Hospital Frailty Risk Score (HFRS) is scored using ICD-10 diagnostic codes in administrative hospital records. Home care clients in Canada are routinely assessed with Resident Assessment Instrument-Home Care (RAI-HC) which can calculate the Clinical Frailty Scale (CFS) and the Frailty Index (FI).ObjectiveMeasure the correlation between the HFRS, CFS and FI and compare prognostic utility for frailty-related outcomes.DesignRetrospective cohort study.SettingAlberta, British Columbia and Ontario, Canada.SubjectsHome care clients aged 65+ admitted to hospital within 180 days (median 65 days) of a RAI-HC assessment (n = 167,316).MethodsCorrelation between the HFRS, CFS and FI was measured using the Spearman correlation coefficient. Prognostic utility of each measure was assessed by comparing measures of association, discrimination and calibration for mortality (30 days), prolonged hospital stay (10+ days), unplanned hospital readmission (30 days) and long-term care admission (1 year).ResultsThe HFRS was weakly correlated with the FI (ρ 0.21) and CFS (ρ 0.28). Unlike the FI and CFS, the HFRS was unable to discriminate for 30-day mortality (area under the receiver operator characteristic curve (AUC) 0.506; confidence interval (CI) 0.502-0.511). It was the only measure that could discriminate for prolonged hospital stay (AUC 0.666; CI 0.661-0.673). The HFRS operated like the FI and CFI when predicting unplanned readmission (AUC 0.530 CI 0.526-0.536) and long-term care admission (AUC 0.600; CI 0.593-0.606).ConclusionsThe HFRS identifies a different subset of older adult home care clients as frail than the CFS and FI. It has prognostic utility for several frailty-related outcomes in this population, except short-term mortality.
| S-EPMC9897298 | biostudies-literature
Project description:Frailty is an established risk factor for adverse outcomes following non-cardiac surgery. The Hospital Frailty Risk Score (HFRS) is a recently described frailty assessment tool that harnesses administrative data and is composed of 109 International Classification of Disease variables. We aimed to examine the incremental prognostic utility of the HFRS in a generalizable surgical population. Using linked administrative databases, a retrospective cohort of patients admitted for non-cardiac surgery between October 1st, 2008 and September 30th, 2019 in Alberta, Canada was created. Our primary outcome was a composite of death, myocardial infarction or cardiac arrest at 30-days. Multivariable logistic regression was undertaken to assess the impact of HFRS on outcomes after adjusting for age, sex, components of the Charlson Comorbidity Index (CCI), Revised Cardiac Risk Index (RCRI) and peri-operative biomarkers. The final cohort consisted of 712,808 non-cardiac surgeries, of which 55·1% were female and the average age was 53·4 +/- 22·4 years. Using the HFRS, 86.3% were considered low risk, 10·7% were considered intermediate risk and 3·1% were considered high risk for frailty. Intermediate and high HFRS scores were associated with increased risk of the primary outcome with an adjusted odds ratio of 1·61 (95% CI 1·50-1.74) and 1·55 (95% CI 1·38-1·73). Intermediate and high HFRS were also associated with increased adjusted odds of prolonged hospital stay, in-hospital mortality, and 1-year mortality. The HFRS is a minimally onerous frailty assessment tool that can complement perioperative risk stratification in identifying patients at high risk of short- and long-term adverse events.
| S-EPMC8769314 | biostudies-literature
Project description:BackgroundIdentification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients.MethodsA retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration.ResultsCompared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)).ConclusionsThe COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.
| S-EPMC7877986 | biostudies-literature
Project description:BackgroundReadmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort.MethodsA cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression.ResultsThe final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575-0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33-5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort.ConclusionThis external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.
| S-EPMC8610256 | biostudies-literature
Project description:ObjectivesRecently, the Hospital Frailty Risk Score based on a derivation and validation study in the UK has been proposed as a low-cost, systematic screening tool to identify older, frail patients who are at a greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful. We aimed to validate this Score in an independent cohort in Switzerland.DesignSecondary analysis of a prospective, observational study (TRIAGE study).SettingOne 600-bed tertiary care hospital in Aarau, Switzerland.ParticipantsConsecutive medical inpatients aged ≥75 years that presented to the emergency department or were electively admitted between October 2015 and April 2018.Primary and secondary outcome measuresThe primary endpoint was all-cause 30-day mortality. Secondary endpoints were length of hospital stay, hospital readmission, functional impairment and quality of life measures. We used multivariate regression analyses.ResultsOf 4957 included patients, 3150 (63.5%) were classified as low risk, 1663 (33.5%) intermediate risk, and 144 (2.9%) high risk for frailty. Compared with the low-risk group, patients in the moderate risk and high-risk groups had increased risk for 30-day mortality (OR (OR) 2.53, 95% CI 2.09 to 3.06, p<0.001 and OR 4.40, 95% CI 2.94 to 6.57, p<0.001) with overall moderate discrimination (area under the ROC curve 0.66). The results remained robust after adjustment for important confounders. Similarly, we found longer length of hospital stay, more severe functional impairment and a lower quality of life in higher risk group patients.ConclusionOur data confirm the prognostic value of the Hospital Frailty Risk Score to identify older, frail people at risk for mortality and adverse outcomes in an independent patient population.Trial registration numberNCT01768494; Post-results.
| S-EPMC6340447 | biostudies-literature
Project description:BackgroundChronic pancreatitis (CP) is a pathological fibroinflammatory response to persistent inflammation or stress to the pancreas. The effect of frailty on outcomes in patients with CP has not been previously examined. In this study, we examined the effect of frailty on outcomes in hospitalized patients with CP.MethodsRecords of patients with a primary or secondary discharge diagnosis of CP (ICD10-CM codes K86.0, K86.1) between January 2016 and December 2019 were obtained from the National Inpatient Sample database. Data were collected on patient demographics, hospital characteristics, comorbidities, and etiology of CP. The relationship between frailty and outcomes, including mortality, intensive care unit (ICU) admission, sepsis, shock, length of stay (LOS), and total hospitalization charges (THC), were analyzed using multivariate analysis.Results722,160 patients were included in the analysis. Patients with a high hospital frailty risk score had a higher mortality risk (adjusted odds ratio [aOR] 12.57, 95% confidence interval [CI] 10.42-15.16; P<0.001) compared to patients with low frailty scores. Patients with high frailty scores also had a higher risk of sepsis (aOR 5.75, 95%CI 4.97-6.66; P<0.001), shock (aOR- 26.25, 95%CI-22.83-30.19; P<0.001), ICU admission (aOR 25.86, 95% CI-22.58-29.62; P<0.001), and acute kidney injury (aOR 24.4, 95%CI 22.39-26.66; P<0.001). They also had a longer LOS (7.04 days, 95%CI 6.57-7.52; P<0.001) and higher THC ($72,200, 95%CI 65,904.52-78,496.66; P<0.001).ConclusionsFrail patients, as determined by their hospital frailty risk score, are at high risk of worse outcomes. This data suggests opportunities for physicians to risk-stratify patients and predict outcomes.
| S-EPMC9756028 | biostudies-literature
Project description:BackgroundOlder people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data.MethodsA three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools.FindingsIn the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68-1·75), long hospital stay (6·03, 5·92-6·10), and 30-day readmission (1·48, 1·46-1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15-0·30 and 0·30, 0·22-0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38-0·47).InterpretationThe Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful.FundingNational Institute for Health Research.
| S-EPMC5946808 | biostudies-literature
Project description:BackgroundPediatric heart transplant (PHT) patients have the highest waitlist mortality of solid organ transplants, yet more than 40% of viable hearts are unutilized. A tool for risk prediction could impact these outcomes. This study aimed to compare and validate the PHT risk score models (RSMs) in the literature.MethodsThe literature was reviewed to identify RSMs published. The United Network for Organ Sharing (UNOS) registry was used to validate the published models identified in a pediatric cohort (<18 years) transplanted between 2017 and 2019 and compared against the Scientific Registry of Transplant Recipients (SRTR) 2021 model. Primary outcome was post-transplant 1-year mortality. Odds ratios were obtained to evaluate the association between risk score groups and 1-year mortality. Area under the curve (AUC) was used to compare the RSM scores on their goodness-of-fit, using Delong's test.ResultsSix recipient and one donor RSMs published between 2008 and 2021 were included in the analysis. The validation cohort included 1,003 PHT. Low-risk groups had a significantly better survival than high-risk groups as predicted by Choudhry (OR = 4.59, 95% CI [2.36-8.93]) and Fraser III (3.17 [1.43-7.05]) models. Choudhry's and SRTR models achieved the best overall performance (AUC = 0.69 and 0.68, respectively). When adjusted for CHD and ventricular assist device support, all models reported better predictability [AUC > 0.6]. Choudhry (AUC = 0.69) and SRTR (AUC = 0.71) remained the best predicting RSMs even after adjustment.ConclusionAlthough the RSMs by SRTR and Choudhry provided the best prediction for 1-year mortality, none demonstrated a strong (AUC ≥ 0.8) concordance statistic. All published studies lacked advanced analytical approaches and were derived from an inherently limited dataset.
| S-EPMC9157612 | biostudies-literature
Project description:BackgroundMost claims-based frailty instruments have been designed for group stratification of older populations according to the risk of adverse health outcomes and not frailty itself. We aimed to develop and validate a tool based on one-year hospital discharge data for stratification on Fried's frailty phenotype (FP).MethodsWe used a three-stage development/validation approach. First, we created a clinical knowledge-driven electronic frailty score (eFS) calculated as the number of deficient organs/systems among 18 critical ones identified from the International Statistical Classification of Diseases and Related Problems, 10th Revision (ICD-10) diagnoses coded in the year before FP assessment. Second, for eFS development and internal validation, we linked individual records from the Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV) for the period 2004-2015. The development/internal validation sample included community-dwelling, non-institutionalised residents of Lausanne (Switzerland) recruited in the Lc65+ cohort in three waves (2004, 2009, and 2014), aged 65-70 years at enrolment, and hospitalised at the CHUV at least once in the year preceding the FP assessment. Using this sample, we selected the best performing model for predicting the dichotomised FP, with the eFS or ICD-10-based variables as predictors. Third, we conducted an external validation using 2016 Swiss nationwide hospital discharge data and compared the performance of the eFS model in predicting 13 adverse outcomes to three models relying on well-designed and validated claims-based scores (Claims-based Frailty Index, Hospital Frailty Risk Score, Dr Foster Global Frailty Score).FindingsIn the development/internal validation sample (n = 469), 14·3% of participants (n = 67) were frail. Among 34 models tested, the best-subsets logistic regression model with four predictors (age and sex at FP assessment, time since last hospital discharge, eFS) performed best in predicting the dichotomised FP (area under the curve=0·71; F1 score=0·39) and one-year adverse health outcomes. On the external validation sample (n = 54,815; 153 acute care hospitals), the eFS model demonstrated a similar performance to the three other claims-based scoring models. According to the eFS model, the external validation sample showed an estimated prevalence of 56·8% (n = 31,135) of frail older inpatients at admission.InterpretationThe eFS model is an inexpensive, transportable and valid tool allowing reliable group stratification and individual prioritisation for comprehensive frailty assessment and may be applied to both hospitalised and community-dwelling older adults.FundingThe study received no external funding.
| S-EPMC8760435 | biostudies-literature