Hidden Dangers of Frailty: Higher Mortality, Complications and Costs in Acute Pancreatitis.

BACKGROUND: Frailty is a clinically recognizable state of increased vulnerability due to age-related decline in reserve and function across multiple physiologic systems that compromises the ability to cope with acute stress. As frailty is being identified as an important risk factor in outcomes of gastrointestinal pathologies, we aimed to assess outcomes in patients with acute pancreatitis within this cohort. METHOD: We conducted a retrospective study using the Nationwide Inpatient Sample (NIS) database. ICD-10 codes were used to inquire for patients admitted with acute pancreatitis between September 2015 through 2017. ICD-10 codes corresponding to the Hospital Frailty Risk Score (HFRS) were used to divide the study sample into 2 cohorts: low risk (< 5 points) and intermediate or high risk (> 5 points). To calculate the points, we fitted a logistic regression model that included membership of the frail group as the binary dependent variable (frail vs. non-frail) and the set of ICD-10 codes as binary predictor variables (1 = present, 0 = absent for each code). To simplify the calculation and interpretation, we multiplied regression coefficients by five to create a points system, so that a certain number of points are awarded for each ICD-10 code and added together to create the final frailty risk score. Multivariate regression analysis was performed to find adjusted mortality. RESULTS: Out of a total of 1,267,744 patients admitted with acute pancreatitis, 728,953 (57.5%) were identified as intermediate and high risk (> 5 points) (study cohort) and 538,781 (42.5%) as low risk (< 5 points). The mean age in the study cohort was 64.8 ± 12.6 and that in the low-risk group was 58.6 ± 9.5. Most of the patients in both groups were males and Caucasians; Medicare was the predominant insurance provider. A majority of the admissions in both groups were in an urban teaching hospital and were emergency. (Table 1). The primary outcome was in-hospital mortality which was significantly higher in the study cohort as compared to the low-risk group (4.3% vs. 2.5%, p < 0.0001). The age-adjusted Odds ratio of mortality was 1.72(95% CI (Confidence Interval) 1.65-1.80, p < 0.05). When compared between the two groups; median length of stay (6 vs. 4); hospitalization cost ($14,412 vs. $10,193), disposition to a skilled nursing facility (SNF) (17.1% vs. 8.6%) and need for home health care (HHC) was significantly higher in the study cohort. Complications like septicemia, septic shock, and acute kidney injury were also higher in the study group (Table 2). Table 1 Baseline demographics of the cohort Characteristics Acute pancreatitis with High HES Frailty score (> 5, intermediate + high) Acute pancreatitis with low HES Frailty score (< 5) P-value N = 1,267,744 N = 728,953 (57.5%) N = 538,781 (42.5%) Age  Mean years (Mean ± SD) 64.8 ± 12.6 58.6 ± 9.5 < 0.001 Gender < 0.001  Male 59.1% 52.3%  Female 40.9% 47.7% *Missing-475 Age groups < 0.001  18-44 3.7% 14.3%  45-64 48% 52.9%  65-84 32.2% 28.7%  ≥ 85 16.1% 4.1% Race < 0.001  Caucasians 67.4% 61.9%  African Americans 9.6% 16.8%  Others 23% 21.3% *Missing-10 Insurance type < 0.001  Medicare 40.9% 36.3%  Medicaid 17.2% 24.3%  Private 31.8% 27.9%  Other 9.9% 11.4% *Missing-75 Active smoking 32.7% 37.9% 0.005 Biliary Stone 36.2% 16.7% < 0.001 Admission Type < 0.001  Emergent 93.7% 94.3%  Elective 6.3% 5.7% *Missing-2880 Hospital ownership/control < 0.001  Rural 7.8% 10%  Urban nonteaching 26.3% 26.6%  Urban teaching 65.9% 63.4% Table 2 Outcomes Outcomes Acute pancreatitis with High HES Frailty score (> 5, intermediate + high) Acute pancreatitis with low HES Frailty score (< 5) P-value In-hospital mortality *Missing-920 4.3% 2.5% < .0001 1.72(1.65-1.80) < .0001 Length of stay, days (Median,IQR) 6(3-8) 4(2-6) < .0001 Total hospitalization cost, $ (Median,IQR) 14,412(8843-20,216) 10,193(6840-13,842) < .0001 In-Hospital Complications  ARDS 0.4% 0.3% 0.08  Ventilator dependence respiratory failure 0.23% 0.29% 0.25  Septicemia 15.2% 9.6% < .0001  Septic Shock 6.1% 2.9% < .0001  AKI 24.8% 14.9% < .0001 Disposition < .0001  Discharge to home 58.9% 74.9%  Transfer other: includes  Skilled Nursing Facility (SNF), Intermediate Care Facility (ICF), and another type of facility 17.1% 8.6%  Home health care 11.5% 8.1%  Against medical advice (AMA) 1.6% 3.4% *Missing-920 CONCLUSION: Using frailty as a construct to identify those who are at greater risk for adverse outcomes, can help formulate interventions to target individualized reversible factors to improve outcomes in patients with acute pancreatitis. Future large-scale prospective studies are warranted to understand the dynamic and longitudinal relationship between pancreatitis and frailty.

Impact of Frailty on Short-Term Outcomes of Hepatic Lobectomy in Patients with Intrahepatic Cholangiocarcinoma: Evidence from the US Nationwide Inpatient Sample 2005-2018.

INTRODUCTION: This study aimed to evaluate associations between frailty and outcomes in patients with intrahepatic cholangiocarcinoma (ICC) undergoing hepatic lobectomy using a large, nationally representative sample. METHODS: This population-based, retrospective observational study extracted the data of adults ≥20 years old with ICC undergoing hepatic lobectomy from the US Nationwide Inpatient Sample database between 2005 and 2018. Frailty was assessed by the validated Hospital Frailty Risk Score (HFRS). Associations between frailty and surgical outcomes were analyzed using logistic regression analyses. RESULTS: After exclusions, 777 patients were enrolled, including 427 frail and 350 non-frail. Patients' mean age was 64.5 (±0.4) years and the majority were males (51.1%) and whites (76.5%). Frailty was significantly associated with increased odds of in-hospital mortality (aOR: 18.51, 95% CI: 6.70, 51.18), non-home discharge (aOR: 3.58, 95% CI: 2.26, 5.66), prolonged LOS (aOR: 5.56, 95% CI: 3.87, 7.99), perioperative cardiac arrest/stroke (aOR: 5.44, 95% CI: 1.62, 18.24), acute respiratory distress syndrome (ARDS)/respiratory failure (aOR: 3.88, 95% CI: 2.40, 6.28), tracheostomy/ventilation (aOR: 3.83, 95% CI: 2.23, 6.58), bleeding/transfusion (aOR: 1.67, 95% CI: 1.24, 2.26), acute kidney injury (AKI) (aOR: 14.37, 95% CI: 7.13, 28.99), postoperative shock (aOR: 4.44, 95% CI: 2.54, 7.74), and sepsis (aOR: 11.94, 95% CI: 6.90, 20.67). DISCUSSION/CONCLUSION: Among patients with ICC undergoing hepatic lobectomy, HFRS-defined frailty is a strong predictor of worse in-patient outcomes, including in-hospital death, prolonged LOS, unfavorable discharge, and complications (perioperative cardiac arrest/stroke, ARDS/respiratory failure, tracheostomy/ventilation, bleeding/transfusion, AKI, postoperative shock, and sepsis). Study results may help stratify risk in frail patients undergoing hepatic resection for ICC.

Medical costs of Swedish nursing home residents at the end of life: a retrospective observational registry study.

BACKGROUND: There are many studies of medical costs in late life in general, but nursing home residents' needs and the costs of external medical services and interventions outside of nursing home services are less well described. METHODS: We examined the direct medical costs of nursing home residents in their last year of life, as well as limited to the period of stay in the nursing home, adjusted for age, sex, Hospital Frailty Risk Score (HFRS), and diagnosis of dementia or advanced cancer. This was an observational retrospective study of registry data from all diseased nursing home residents during the years 2015-2021 using healthcare consumption data from the Stockholm Regional Council, Sweden. T tests, Wilcoxon rank sum tests and chi-square tests were used for comparisons of groups, and generalized linear models (GLMs) were constructed for univariable and multivariable linear regressions of health cost expenditures to calculate risk ratios (RRs) with 95% confidence intervals (95% CIs). RESULTS: According to the adjusted (multivariable) models for the 38,805 studied nursing home decedents, when studying the actual period of stay in nursing homes, we found significantly greater medical costs associated with male sex (RR 1.29 (1.25-1.33), p < 0.0001) and younger age (65-79 years vs. ≥90 years: RR 1.92 (1.85-2.01), p < 0.0001). Costs were also greater for those at risk of frailty according to the Hospital Frailty Risk Score (HFRS) (intermediate risk: RR 3.63 (3.52-3.75), p < 0.0001; high risk: RR 7.84 (7.53-8.16), p < 0.0001); or with advanced cancer (RR 2.41 (2.26-2.57), p < 0.0001), while dementia was associated with lower medical costs (RR 0.54 (0.52-0.55), p < 0.0001). The figures were similar when calculating the costs for the entire last year of life (regardless of whether they were nursing home residents throughout the year). CONCLUSIONS: Despite any obvious explanatory factors, male and younger residents had higher medical costs at the end of life than women. Having a risk of frailty or a diagnosis of advanced cancer was strongly associated with higher costs, whereas a dementia diagnosis was associated with lower external, medical costs. These findings could lead us to consider reimbursement models that could be differentiated based on the observed differences.

Changes in frailty among patients hospitalized for spine pathologies during the COVID-19 pandemic in Germany-a nationwide observational study.

PURPOSE: In spine care, frailty is associated with poor outcomes. The aim of this study was to describe changes in frailty in spine care during the coronavirus disease 2019 (COVID-19) pandemic and their relation to surgical management and outcomes. METHODS: Patients hospitalized for spine pathologies between January 1, 2019, and May 17, 2022, within a nationwide network of 76 hospitals in Germany were retrospectively included. Patient frailty, types of surgery, and in-hospital mortality rates were compared between pandemic and pre-pandemic periods. RESULTS: Of the 223,418 included patients with spine pathologies, 151,766 were admitted during the pandemic and 71,652 during corresponding pre-pandemic periods in 2019. During the pandemic, the proportion of high-frailty patients increased from a range of 5.1-6.1% to 6.5-8.8% (p < 0.01), while the proportion of low frailty patients decreased from a range of 70.5-71.4% to 65.5-70.1% (p < 0.01). In most phases of the pandemic, the Elixhauser comorbidity index (ECI) showed larger increases among high compared to low frailty patients (by 0.2-1.8 vs. 0.2-0.8 [p < 0.01]). Changes in rates of spine surgery were associated with frailty, most clearly in rates of spine fusion, showing consistent increases among low frailty patients (by 2.2-2.5%) versus decreases (by 0.3-0.8%) among high-frailty patients (p < 0.02). Changes in rates of in-hospital mortality were not associated with frailty. CONCLUSIONS: During the COVID-19 pandemic, the proportion of high-frailty patients increased among those hospitalized for spine pathologies in Germany. Low frailty was associated with a rise in rates of spine surgery and high frailty with comparably larger increases in rates of comorbidities.

Impact of the Hospital Frailty Risk Score on Outcomes following Primary Total Elbow Arthroplasty.

BACKGROUND: The Hospital Frailty Risk Score (HFRS) has demonstrated strong correlation with adverse outcomes in various joint replacement surgeries, yet its applicability in total elbow arthroplasty (TEA) remains unexplored. The purpose of this study is to assess the association between HFRS and postoperative complications following elective primary TEA. METHODS: The Nationwide Readmissions Database was queried to identify patients undergoing primary TEA from 2016 to 2020. The HFRS was used to compare medical, surgical, and clinical outcomes of frail vs. non-frail patients. Mean and relative costs, total hospital length of stay (LOS), and discharge disposition for frail and non-frail patients were also compared. RESULTS: We identified 2,049 primary TEA in frail patients and 3,693 in non-frail patients. Frail patients had increased complication rates including acute respiratory failure (13.6% vs. 1.1%; p < 0.001), urinary tract infections (12.3% vs. 0.0%; p < 0.001), transfusions (3.9% vs. 1.1%; p < 0.001), pneumonia (1.1% vs. 0.2%; p < 0.001), acute respiratory distress syndrome (3.2% vs 0.6%; p < 0.001), sepsis (0.7% vs. 0.1%; p < 0.001), and hardware failure (1.2% vs 0.1%; p < 0.001). Frail patients also experienced higher rates of readmission (37% vs. 25%; p < 0.001) and death (1.7% vs. 0.2%; p < 0.001), while being less likely to undergo revision (6.5% vs. 17%; p < 0.001). Frail patients incurred higher healthcare costs ($28,497 vs. $23,377; p < 0.001) and longer LOS (5.3 days vs. 2.6 days; p < 0.001), with reduced likelihood of routine hospital stays (36% vs. 71%; p < 0.001) and increased utilization of short-term hospitalization (p < 0.001), care facilities (p < 0.001), and home health care services (p < 0.001). CONCLUSION: HFRS is a validated indicator of frailty and is strongly associated with increased rates of complications in patients undergoing elective primary TEA. These findings should be considered by orthopedic surgeons when assessing surgical candidacy and discussing treatment options in this at-risk patient population.

Higher Hospital Frailty Risk Score Is Associated With Increased Risk of Stroke: Observational and Genetic Analyses.

BACKGROUND: Frailty is a prevalent state associated with several aging-related traits and conditions. The relationship between frailty and stroke remains understudied. Here we aim to investigate whether the hospital frailty risk score (HFRS) is associated with the risk of stroke and determine whether a significant association between genetically determined frailty and stroke exists. DESIGN: Observational study using data from All of Us research program and Mendelian Randomization analyses. METHODS: Participants from All of Us with available electronic health records were selected for analysis. All of Us began national enrollment in 2018 and is expected to continue for at least 10 years. All of Us is recruiting members of groups that have traditionally been underrepresented in research. All participants provided informed consent at the time of enrollment, and the date of consent was recorded for each participant. Incident stroke was defined as stroke event happening on or after the date of consent to the All of Us study HFRS was measured with a 3-year look-back period before the date of consent for stroke risk. The HFRS was stratified into 4 categories: no-frailty (HFRS=0), low (HFRS ≥1 and <5), intermediate (≥5 and <15), and high (HFRS ≥15). Last, we implemented Mendelian Randomization analyses to evaluate whether genetically determined frailty is associated with stroke risk. RESULTS: Two hundred fifty-three thousand two hundred twenty-six participants were at risk of stroke. In multivariable analyses, frailty status was significantly associated with risk of any (ischemic or hemorrhagic) stroke following a dose-response way: not-frail versus low HFRS (HR, 4.9 [CI, 3.5-6.8]; P<0.001), not-frail versus intermediate HFRS (HR, 11.4 [CI, 8.3-15.7]; P<0.001) and not-frail versus high HFRS (HR, 42.8 [CI, 31.2-58.6]; P<0.001). We found similar associations when evaluating ischemic and hemorrhagic stroke separately (P value for all comparisons <0.05). Mendelian Randomization confirmed this association by indicating that genetically determined frailty was independently associated with risk of any stroke (OR, 1.45 [95% CI, 1.15-1.84]; P=0.002). CONCLUSIONS: Frailty, based on the HFRS was associated with higher risk of any stroke. Mendelian Randomization analyses confirmed this association providing evidence to support a causal relationship.

A comparison of three approaches to measuring frailty to determine adverse health outcomes in critically ill patients.

BACKGROUND: studies comparing different frailty measures in intensive care unit settings are lacking. We aimed to compare the frailty index based on physiological and laboratory tests (FI-Lab), modified frailty index (MFI) and hospital frailty risk score (HFRS) to predict short-term outcomes for critically ill patients. METHODS: we conducted a secondary analysis of data from the Medical Information Mart for Intensive Care IV database. Outcomes of interest included in-hospital mortality and discharge with need for nursing care. RESULTS: the primary analysis was conducted with 21,421 eligible critically ill patients. After adjusting for confounding variables, frailty as diagnosed by all three frailty measures was found to be significantly associated with increased in-hospital mortality. In addition, frail patients were more likely to receive further nursing care after being discharged. All three frailty scores could improve the discrimination ability of the initial model generated by baseline characteristics for adverse outcomes. The FI-Lab had the best predictive ability for in-hospital mortality, whereas the HFRS had the best predictive performance for discharge with need for nursing care amongst the three frailty measures. A combination of the FI-Lab with either the HFRS or MFI improved the identification of critically ill patients at increased risk of in-hospital mortality. CONCLUSIONS: frailty, as assessed by the HFRS, MFI and FI-Lab, was associated with short-term survival and discharge with need for nursing care amongst critically ill patients. The FI-Lab was a better predictor of in-hospital mortality than the HFRS and MFI. Future studies focusing on FI-Lab are warranted.

Hospital Frailty Risk Score (HFRS) Predicts Adverse Outcomes Among Hospitalized Patients with Chronic Pancreatitis.

INTRODUCTION: The prevalence of frailty among patients with chronic pancreatitis (CP) and its impact on clinical outcomes is unclear. We report the impact of frailty on mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the United States. METHODS: We extracted data on patients hospitalized with a primary or secondary diagnosis of CP from the Nationwide Readmissions Database 2019. We applied a previously validated hospital frailty risk scoring system to classify CP patients into frail and non-frail on index hospitalization and compared the characteristics of frail and non-frail patients. We studied the impact of frailty on mortality, readmission, and healthcare utilization. RESULTS: Of 56,072 patients with CP, 40.78% of patients were classified as frail. Frail patients experienced a higher rate of unplanned and preventable hospitalizations. Almost two-thirds of frail patients were younger than 65, and one-third had no or only single comorbidity. On multivariate analysis, frailty was independently associated with two times higher mortality risk (adjusted hazard ratio [aHR], 2.05; 95% CI 1.7-2.5). Frailty was also associated with a higher risk of all-cause readmission with an aHR of 1.07; (95% CI 1.03-1.1). Frail patients experienced a longer length of stay, higher hospitalization costs, and hospitalization charges. Infectious causes were the most common cause of readmission among frail patients compared to acute pancreatitis among non-frail patients. CONCLUSIONS: Frailty is independently associated with higher mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the US.

An ensemble machine learning approach to predict postoperative mortality in older patients undergoing emergency surgery.

BACKGROUND: Prediction of preoperative frailty risk in the emergency setting is a challenging issue because preoperative evaluation cannot be done sufficiently. In a previous study, the preoperative frailty risk prediction model used only diagnostic and operation codes for emergency surgery and found poor predictive performance. This study developed a preoperative frailty prediction model using machine learning techniques that can be used in various clinical settings with improved predictive performance. METHODS: This is a national cohort study including 22,448 patients who were older than 75 years and visited the hospital for emergency surgery from the cohort of older patients among the retrieved sample from the Korean National Health Insurance Service. The diagnostic and operation codes were one-hot encoded and entered into the predictive model using the extreme gradient boosting (XGBoost) as a machine learning technique. The predictive performance of the model for postoperative 90-day mortality was compared with those of previous frailty evaluation tools such as Operation Frailty Risk Score (OFRS) and Hospital Frailty Risk Score (HFRS) using the receiver operating characteristic curve analysis. RESULTS: The predictive performance of the XGBoost, OFRS, and HFRS for postoperative 90-day mortality was 0.840, 0.607, and 0.588 on a c-statistics basis, respectively. CONCLUSIONS: Using machine learning techniques, XGBoost to predict postoperative 90-day mortality, using diagnostic and operation codes, the prediction performance was improved significantly over the previous risk assessment models such as OFRS and HFRS.

Performance of administrative database frailty instruments in predicting clinical outcomes and cost for patients undergoing transcatheter aortic valve implantation: a historical cohort study.

PURPOSE: Frailty instruments may improve prognostic estimates for patients undergoing transcatheter aortic valve implantation (TAVI). Few studies have evaluated and compared the performance of administrative database frailty instruments for patients undergoing TAVI. This study aimed to examine the performance of administrative database frailty instruments in predicting clinical outcomes and costs in patients who underwent TAVI. METHODS: We conducted a historical cohort study of 3,848 patients aged 66 yr or older who underwent a TAVI procedure in Ontario, Canada from 1 April 2012 to 31 March 2018. We used the Johns Hopkins Adjusted Clinical Group (ACG) frailty indicator and the Hospital Frailty Risk Score (HFRS) to assign frailty status. Outcomes of interest were in-hospital mortality, one-year mortality, rehospitalization, and healthcare costs. We compared the performance of the two frailty instruments with that of a reference model that adjusted baseline covariates and procedural characteristics. Accuracy measures included c-statistics, Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated discrimination improvement (IDI), net reclassification index (NRI), bias, and accuracy of cost estimates. RESULTS: A total of 863 patients (22.4%) were identified as frail using the Johns Hopkins ACG frailty indicator and 865 (22.5%) were identified as frail using the HFRS. Although agreement between the frailty instruments was fair (Kappa statistic = 0.322), each instrument classified different subgroups as frail. Both the Johns Hopkins ACG frailty indicator (rate ratio [RR], 1.13; 95% confidence interval [CI], 1.06 to 1.20) and the HFRS (RR, 1.14; 95% CI, 1.07 to 1.21) were significantly associated with increased one-year costs. Compared with the reference model, both the Johns Hopkins ACG frailty indicator and HFRS significantly improved NRI for one-year mortality (Johns Hopkins ACG frailty indicator: NRI, 0.160; P < 0.001; HFRS: NRI, 0.146; P = 0.001) and rehospitalization (Johns Hopkins ACG frailty indicator: NRI, 0.201; P < 0.001; HFRS: NRI, 0.141; P = 0.001). These improvements in NRI largely resulted from classification improvement among those who did not experience the event. With one-year mortality, there was a significant improvement in IDI (IDI, 0.003; P < 0.001) with the Johns Hopkins ACG frailty indicator. This improvement in performance resulted from an increase in the mean probability of the event among those with the event. CONCLUSION: Preoperative frailty assessment may add some predictive value for TAVI outcomes. Use of administrative database frailty instruments may provide small but significant improvements in case-mix adjustment when profiling hospitals for certain outcomes.

RéSUMé: OBJECTIF: L'utilisation d'indicateur de fragilité pourrait améliorer l'évaluation pronostique des patients bénéficiant d'un remplacement valvulaire aortique par voie percutanée (procédure TAVI). Peu d'études ont évalué et comparé la performance des instruments d'évaluation de la fragilité développés à partir de données administratives chez les patients bénéficiant d'un TAVI. Nous avions pour objectif d'examiner la performance des instruments d'évaluation de la fragilité développés à partir de données administratives dans la prédiction des issues cliniques et des coûts chez les patients ayant bénéficié d'un TAVI. MéTHODE: Nous avons réalisé une étude de cohorte historique auprès de 3848 patients âgés de 66 ans ou plus qui ont bénéficié d'une procédure TAVI en Ontario, Canada, du 1er avril 2012 au 31 mars 2018. Nous avons utilisé l'indicateur de fragilité ACG (Adjusted Clinical Group) de Johns Hopkins et le score de risque de fragilité à l'hôpital (HFRS) pour définir la fragilité. Les critères d'évaluation étaient la mortalité hospitalière, la mortalité à un an, la réhospitalisation et les coûts des soins de santé. Nous avons comparé la performance des deux instruments d'évaluation de la fragilité à celle d'un modèle de référence qui ajustait les covariables de base et les caractéristiques procédurales. Les mesures d'exactitude comprenaient l'analyse statistique c, le critère d'information d'Akaike (AIC), le critère d'information bayésien (BIC), l'amélioration de la discrimination intégrée (IDI), l'indice NRI (net reclassification index), le biais et l'exactitude des estimations de coûts. RéSULTATS: Au total, 863 patients (22,4 %) ont été identifiés comme fragiles à l'aide de l'indicateur de fragilité ACG de Johns Hopkins, et 865 (22,5 %) ont été identifiés comme fragiles à l'aide du HFRS. Bien que l'agrément entre les instruments d'évaluation de la fragilité ait été acceptable (statistique de Kappa = 0,322), chaque instrument a classé des sous-groupes différents comme étant fragiles. L'indicateur de fragilité ACG de Johns Hopkins (rapport de taux [RR], 1,13; intervalle de confiance à 95 % [IC], 1,06 à 1,20) et le HFRS (RR, 1,14; IC 95 %, 1,07 à 1,21) étaient associés de façon significative à une augmentation des coûts sur un an. Par rapport au modèle de référence, l'indicateur de fragilité ACG de Johns Hopkins améliorent de façon significative le NRI pour la mortalité (l'indicateur de fragilité ACG de Johns Hopkins: NRI, 0.160; P < 0.001; HFRS: NRI, 0.146; P = 0.001) et la réhospitalisation (l'indicateur de fragilité ACG: NRI, 0.201; P < 0.001; HFRS: NRI, 0.141; P = 0.001) à un an. Ces améliorations du NRI résultent en grande partie de l'amélioration de la classification chez ceux qui n'ont pas bénéficié d'un TAVI. En ce qui a trait à la mortalité à un an, il y a eu une amélioration significative de l'IDI (IDI, 0,003; P < 0,001) avec l'indicateur de fragilité ACG de Johns Hopkins. Cette amélioration de la performance résultait d'une augmentation de la probabilité moyenne de TAVI chez les personnes ayant vécu l'événement. CONCLUSION: L'évaluation préopératoire de la fragilité peut ajouter une certaine valeur prédictive aux issues cliniques suivant une procédure de TAVI. L'utilisation d'instruments d'évaluation de la fragilité développés à partir de données administratives peut apporter des améliorations mineures mais significatives pour l'ajustement de risque lors de l'évaluation des hôpitaux en fonction de certaines issues cliniques.

Association of frailty with healthcare resource utilization after open thoracic/thoracolumbar posterior spinal fusion for adult spinal deformity.

PURPOSE: The Hospital Frailty Risk Score (HFRS) is a frailty-identifying metric developed using ICD-10-CM codes. While other studies have examined frailty in adult spinal deformity (ASD), the HFRS has not been assessed in this population. The aim of this study was to utilize the HFRS to investigate the impact of frailty on outcomes in ASD patients undergoing posterior spinal fusion (PSF). METHODS: A retrospective study was performed using the 2016-2019 National Inpatient Sample database. Adults with ASD undergoing elective PSF were identified using ICD-10-CM codes. Patients were categorized into HFRS-based frailty cohorts: Low (HFRS < 5) and Intermediate-High (HFRS ≥ 5). Patient demographics, comorbidities, intraoperative variables, and outcomes were assessed. Multivariate regression analyses were used to determine whether HFRS independently predicted extended length of stay (LOS), non-routine discharge, and increased cost. RESULTS: Of the 7500 patients identified, 4000 (53.3%) were in the Low HFRS cohort and 3500 (46.7%) were in the Intermediate-High HFRS cohort. On average, age increased progressively with increasing HFRS scores (p < 0.001). The frail cohort experienced more postoperative adverse events (p < 0.001), greater LOS (p < 0.001), accrued greater admission costs (p < 0.001), and had a higher rate of non-routine discharge (p < 0.001). On multivariate analysis, Intermediate-High HFRS was independently associated with extended LOS (OR: 2.58, p < 0.001) and non-routine discharge (OR: 1.63, p < 0.001), though not increased admission cost (OR: 1.01, p = 0.929). CONCLUSION: Our study identified HFRS to be significantly associated with prolonged hospitalizations and non-routine discharge. Other factors that were found to be associated with increased healthcare resource utilization include age, Hispanic race, West hospital region, large hospital size, and increasing number of AEs.

Comparison of the predictive ability of clinical frailty scale and hospital frailty risk score to determine long-term survival in critically ill patients: a multicentre retrospective cohort study.

BACKGROUND: The Clinical Frailty Scale (CFS) is the most commonly used frailty measure in intensive care unit (ICU) patients. The hospital frailty risk score (HFRS) was recently proposed for the quantification of frailty. We aimed to compare the HFRS with the CFS in critically ill patients in predicting long-term survival up to one year following ICU admission. METHODS: In this retrospective multicentre cohort study from 16 public ICUs in the state of Victoria, Australia between 1st January 2017 and 30th June 2018, ICU admission episodes listed in the Australian and New Zealand Intensive Care Society Adult Patient Database registry with a documented CFS, which had been linked with the Victorian Admitted Episode Dataset and the Victorian Death Index were examined. The HFRS was calculated for each patient using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes that represented pre-existing conditions at the time of index hospital admission. Descriptive methods, Cox proportional hazards and area under the receiver operating characteristic (AUROC) were used to investigate the association between each frailty score and long-term survival up to 1 year, after adjusting for confounders including sex and baseline severity of illness on admission to ICU (Australia New Zealand risk-of-death, ANZROD). RESULTS: 7001 ICU patients with both frailty measures were analysed. The overall median (IQR) age was 63.7 (49.1-74.0) years; 59.5% (n = 4166) were male; the median (IQR) APACHE II score 14 (10-20). Almost half (46.7%, n = 3266) were mechanically ventilated. The hospital mortality was 9.5% (n = 642) and 1-year mortality was 14.4% (n = 1005). HFRS correlated weakly with CFS (Spearman's rho 0.13 (95% CI 0.10-0.15) and had a poor agreement (kappa = 0.12, 95% CI 0.10-0.15). Both frailty measures predicted 1-year survival after adjusting for confounders, CFS (HR 1.26, 95% CI 1.21-1.31) and HFRS (HR 1.08, 95% CI 1.02-1.15). The CFS had better discrimination of 1-year mortality than HFRS (AUROC 0.66 vs 0.63 p < 0.0001). CONCLUSION: Both HFRS and CFS independently predicted up to 1-year survival following an ICU admission with moderate discrimination. The CFS was a better predictor of 1-year survival than the HFRS.

Assessment of coding-based frailty algorithms for long-term outcome prediction among older people in community settings: a cohort study from the Shizuoka Kokuho Database.

OBJECTIVES: To assess the applicability of Electronic Frailty Index (eFI) and Hospital Frailty Risk Score (HFRS) algorithms to Japanese administrative claims data and to evaluate their association with long-term outcomes. STUDY DESIGN AND SETTING: A cohort study using a regional government administrative healthcare and long-term care (LTC) claims database in Japan 2014-18. PARTICIPANTS: Plan enrollees aged ≥50 years. METHODS: We applied the two algorithms to the cohort and assessed the scores' distributions alongside enrollees' 4-year mortality and initiation of government-supported LTC. Using Cox regression and Fine-Gray models, we evaluated the association between frailty scores and outcomes as well as the models' discriminatory ability. RESULTS: Among 827,744 enrollees, 42.8% were categorised by eFI as fit, 31.2% mild, 17.5% moderate and 8.5% severe. For HFRS, 73.0% were low, 24.3% intermediate and 2.7% high risk; 35 of 36 predictors for eFI, and 92 of 109 codes originally used for HFRS were available in the Japanese system. Relative to the lowest frailty group, the highest frailty group had hazard ratios [95% confidence interval (CI)] of 2.09 (1.98-2.21) for mortality and 2.45 (2.28-2.63) for LTC for eFI; those for HFRS were 3.79 (3.56-4.03) and 3.31 (2.87-3.82), respectively. The area under the receiver operating characteristics curves for the unadjusted model at 48 months was 0.68 for death and 0.68 for LTC for eFI, and 0.73 and 0.70, respectively, for HFRS. CONCLUSIONS: The frailty algorithms were applicable to the Japanese system and could contribute to the identifications of enrollees at risk of long-term mortality or LTC use.

Application of the New Preoperative Frailty Risk Score in Elderly Patients Undergoing Emergency Surgery.

BACKGROUND: Predicting preoperative frailty risk in emergency surgery is difficult with limited information because preoperative evaluation is not commonly performed properly. A recent study attempted to predict preoperative frailty risk using only diagnostic and surgical codes that can be extracted from the electronic medical records system. OBJECTIVE: This study aimed to validate whether the prediction model of preoperative frailty risk presented in the previous study is well applied to other medical hospitals' data. METHODS: This is a retrospective cohort study including 1,557 patients (≥75 years old) who were admitted to a single institution for emergency operations between January 1, 2010, and December 31, 2019, for study analysis. The Charlson comorbidity index, Hospital Frailty Risk Score, and the recently developed Operation Frailty Risk Score (OFRS) were calculated using the patient's diagnostic and operation codes. The predictive performances of these calculated risk scores and the American Society of Anesthesiologists-Physical Status classification for postoperative 90-day mortality were compared by using the receiver operating characteristic curve analysis. FINDINGS: The predictive performance of the OFRS, Charlson comorbidity index, American Society of Anesthesiologists-Physical Status, and Hospital Frailty Risk Score for postoperative 90-day mortality was 0.81, 0.630, 0.699, and 0.549 on a c-statistics basis, respectively. CONCLUSIONS: The OFRS using diagnostic and operation codes may show the best predictive performance for 90-day mortality compared to other risk scores, and it can be the clinically applicable model to evaluate the preoperative frailty risk in elderly patients undergoing emergency surgery.

Timely goals of care documentation in patients with frailty in the COVID-19 era: a retrospective multi-site study.

BACKGROUND: Older frail patients are more likely to have timely goals of care (GOC) documentation than non-frail patients. AIMS: To investigate whether timely documentation of GOC within 72 h differed in the context of the COVID-19 pandemic (2020), compared with the pre-COVID-19 era (2019) for older frail patients. METHODS: Multi-site retrospective cohort study was conducted in two public hospitals where all consecutive frail adult patients aged ≥65 years were admitted under medical units for at least 24 h between 1 March 31 and October in 2019 and between 1 March and 31 October 2020 were included. The GOC was derived from electronic records. Frailty status was derived from hospital coding data using hospital frailty risk score (frail ≥5). The primary outcome was the documentation of GOC within 72 h of hospital admission. Secondary outcomes included hospital mortality, rapid response call, intensive care unit admission, prolonged hospital length of stay (≥10 days) and time to the documentation of GOC. RESULTS: The study population comprised 2021 frail patients admitted in 2019 and 1849 admitted in 2020, aged 81.2 and 90.9 years respectively. The proportion of patients with timely GOC was lower in 2020, than 2019 (48.3% (893/1849) vs 54.9% (1109/2021); P = 0.021). After adjusting for confounding factors, patients in 2020 were less likely to receive timely GOC (odds ratio = 0.77; 95% confidence interval (CI) 0.68-0.88). Overall time to GOC documentation was longer in 2020 (hazard ratio = 0.86; 95% CI 0.80-0.93). CONCLUSION: Timely GOC documentation occurred less frequently in frail patients during the COVID-19 pandemic than in the pre-COVID-19 era.

Hospital frailty risk score and adverse health outcomes: evidence from longitudinal record linkage cardiac data.

BACKGROUND: Despite recent evidence on the effect of frailty on health outcomes among those with heart failure, there is a dearth of knowledge on measuring frailty using administrative health data on a wide range of cardiovascular diseases (CVD). METHODS: We conducted a retrospective record-linkage cohort study of patients with diverse CVD in Queensland, Australia. We investigated the relationship between the risk of frailty, defined using the hospital frailty risk score (HFRS), and 30-day mortality, 30-day unplanned readmission, non-home discharge, length of hospital stay (LOS) at an emergency department and inpatient units and costs of hospitalisation. Descriptive analysis, bivariate logistic regression and generalised linear models were used to estimate the association between HFRS and CVD outcomes. Smear adjustment was applied to hospital costs and the LOS for each frailty risk groups. RESULTS: The proportion of low, medium and high risk of frailty was 24.6%, 34.5% and 40.9%, respectively. The odds of frail patients dying or being readmitted within 30 days of discharge was 1.73 and 1.18, respectively. Frail patients also faced higher odds of LOS, and non-home discharge at 3.1 and 2.25, respectively. Frail patients incurred higher hospital costs (by 42.7-55.3%) and stayed in the hospital longer (by 49%). CONCLUSION: Using the HFRS on a large CVD cohort, this study confirms that frailty was associated with worse health outcomes and higher healthcare costs. Administrative data should be more accessible to research such that the HFRS can be applied to healthcare planning and patient care.

Frailty status, timely goals of care documentation and clinical outcomes in older hospitalised medical patients.

BACKGROUND: Hospitalised frail older patients are at risk of clinical deterioration. Early goals of care (GOC) documentation is vital to avoid futile/unwarranted interventions in the event of deterioration. AIMS: To investigate the impact of frailty on timely GOC and its association with clinical outcomes in hospitalised older patients. METHODS: This was a single-centre retrospective study of all medical patients aged ≥80 years admitted to the acute medical unit between 1/3/2015 and 31/8/2015, with GOC derived from electronic records. Frailty was measured using the Hospital Frailty Risk Score (HFRS) derived from hospital coding data. Primary outcome compared proportions of timely GOC within 72-h between frail (HFRS ≥ 5) and non-frail (HFRS < 5) patients. Exploratory secondary outcomes included in-hospital mortality, rapid response calls (RRC), prolonged length of stay (LOS) and 28-day readmission rates. RESULTS: Of the 1118 admitted patients, 529 (47.3%) were frail. Timely GOC occurred in 50% (559/1118), more commonly in frail patients (283/529, 53.5%) than non-frail patients (276/589, 46.9%), P = 0.027. Frailty was positively associated with timely GOC independent of age and gender (odds ratio = 1.28; 95% confidence interval = 1.01-163; P = 0.041). In univariable analyses, timely GOC was associated with greater in-hospital mortality, RRC, and hospital LOS in both frail and non-frail patients (all P < 0.05) and greater 28-day readmissions only among frail patients (P = 0.028). Multivariable regression demonstrated that timely GOC was associated only with in-hospital mortality in both frail and non-frail patients, independent of age and gender. CONCLUSION: Older frail hospitalised patients were more likely to have timely GOC than older non-frail patients. Timely GOC in such patients may avoid burdensome treatments.

Hospital Frailty Risk Score Outperforms Current Risk Stratification Models in Primary Total Hip and Knee Arthroplasty.

BACKGROUND: Models for risk stratification and prediction of outcome, such as the Charlson Comorbidity Index (CCI), the Elixhauser Comorbidity Method (ECM), the 5-factor modified Frailty Index (mFI-5), and the Hospital Frailty Risk Score (HFRS) have been validated in orthopedic surgery. The aim of this study is to compare the predictive power of these models in total hip and knee replacement. METHODS: In a retrospective analysis of 8250 patients who had undergone total joint replacement between 2011 and 2019, CCI, ECM, mFI-5, and HFRS were calculated for each patient. Receiver operating characteristic curve plots were generated and the area under the curve (AUC) was compared between each score with regard to adverse events such as transfusion, surgical, medical, and other complications. Multivariate logistic regression models were used to assess the relationship among risk stratification models, demographic factors, and postoperative adverse events. RESULTS: In prediction of surgical complications, HFRS performed best (AUC: 0.719, P < .001), followed by ECM (AUC: 0.578, P < .001), mFI-5 (AUC: 0.564, P = .003), and CCI (AUC: 0.555, P = .012). With regard to medical complications, other complications, and transfusion, HFRS also was superior to ECM, mFI-5, and CCI. Multivariate logistic regression analyses revealed HFRS as an independent risk stratification model associated with all captured adverse events (P ≤ .001). CONCLUSION: The HFRS is superior to current risk stratification models in the context of total joint replacement. As the HRFS derives from routinely collected administrative data, healthcare providers can identify at-risk patients without additional effort or expense.

The hospital frailty risk score effectively predicts adverse outcomes in patients with atrial fibrillation in the intensive care unit.

Background: Atrial fibrillation (AF) and frailty are significant global public health problems associated with advancing age. However, the relationship between frailty and older patients with AF in the intensive care unit (ICU) has not been thoroughly investigated. This study aimed to investigate whether the hospital frailty risk score (HFRS) is associated with adverse outcomes in older patients with AF in the ICU. Methods: This was the first retrospective analysis of older patients with AF admitted to the ICU between 2008 and 2019 at a tertiary academic medical center in Boston. The HFRS was used to measure frailty severity. The outcomes of interest were in-hospital and 30-day mortality and the incidence of sepsis and ischemic stroke. Results: There were 7,792 participants aged approximately 80 years, almost half (44.9%) of whom were female. Among this group, 2,876 individuals were identified as non-frail, while 4,916 were classified as frail. The analysis revealed a significantly greater incidence of in-hospital (18.8% compared to 7.6%) and 30-day mortality (24.5% versus 12.3%) in the frail group. After accounting for potential confounding factors, a multivariate Cox proportional hazards regression analysis revealed that frail participants had a 1.56-fold greater risk of mortality within 30 days (95% CI = 1.38-1.76, p < 0.001). Conclusions: Frailty is an independent risk factor for adverse outcomes in older patients with AF admitted to the ICU. Therefore, prioritizing frailty assessment and implementing specific intervention strategies to improve prognostic outcomes are recommended.

Frailty is a predictor for worse outcomes in patients hospitalized with Clostridioides difficile infection.

Background: Frailty has major health implications for affected patients and is widely used in the perioperative risk assessment. The Hospital Frailty Risk Score (HFRS) is a validated score that utilizes administrative billing data to identify patients at higher risk because of frailty. We investigated the utility of the HFRS in patients with Clostridioides difficile infection (CDI) to determine whether they were at risk for worse outcomes and higher healthcare resource utilization. Methods: Using the 2017 National Inpatient Sample, we identified all adults with a primary diagnosis of CDI. We classified patients into 2 groups: those who had an HFRS <5 (NonFrailCDI) and those with a score ≥5 (FrailCDI). We assessed differences in hospital outcomes and healthcare resource utilization based on frailty status. Results: We identified 93,810 hospitalizations, of which 54,300 (57.88%) were FrailCDI. FrailCDI patients were at higher risk for fulminant CDI (odds ratio [OR] 1.9, 95% confidence interval [CI] 1.6-2.3), requiring colectomy (OR 4.1, 95%CI 1.5-11.2), and inpatient mortality (OR 4.5, 95%CI 2.8-7.1). Furthermore, FrailCDI patients had higher odds of requiring Intensive Care Unit admission (OR 13.7, 95%CI 6.3-29.9) or transfer to another facility on discharge (OR 2.2, 95%CI 2.0-2.4), and had longer hospital stays and higher total charges when compared with NonFrailCDI. Conclusions: Frailty as defined by the HFRS is an independent factor for worse outcomes and higher healthcare utilization in adults admitted for CDI. Risk stratifying patients by frailty may improve outcomes.