External validation of the Hospital Frailty Risk Score and comparison with the Hospital-patient One-year Mortality Risk Score to predict outcomes in elderly hospitalised patients: a retrospective cohort study

Reweighting and validation of the hospital frailty risk score using electronic health records in Germany: a retrospective observational study

BACKGROUND

In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany.

METHODS

The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models.

RESULTS

Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p < 0.001 in the validation cohort). Calibration curves show a good agreement between score-based predictions and actual observed mortality. Additional external validation using inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251) confirms the results regarding discrimination and calibration and underlines the geographic and temporal validity of the reweighted Frailty Score. Decision curve analysis indicates that the clinical usefulness of the reweighted score as a general decision support tool is superior to the initial version of the score. Assessment of the applicability of the reweighted Frailty Score in a non-elderly population (N = 198,819) shows that discrimination is superior to the initial version of the score (AUC = 0.92 vs. AUC = 0.87, p < 0.001). In addition, we observe a fairly age-stable influence of the reweighted Frailty Score on in-hospital mortality, which does not differ substantially for women and men.

CONCLUSIONS

Our data indicate that the reweighted Frailty Score is superior to the original Frailty Score for identification of older, frail patients at risk for in-hospital mortality. Hence, we recommend using the reweighted Frailty Score in the German in-hospital setting.

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.

Impact of frailty on pneumonia outcomes in older patients: a systematic review and meta-analysis

PURPOSE

The ideal method for identifying frailty remains unclear, but the condition is associated with poor prognoses in many illnesses. Despite the availability of studies, the prognostic implications of frailty on older patients with pneumonia remains unexplored. To determine the burden and effect of frailty on selected clinical outcomes among older patients with pneumonia.

METHODS

We searched Medline, Google Scholar, and Science Direct databases for articles published in English following the PRISMA framework to guide our review. We included studies conducted on patients (> 60 years) with frailty and pneumonia, and reporting the effect of frailty on mortality, hospital stay, length readmission, and ICU admission. We performed a meta-analysis using STATA 14.2, calculating pooled odds ratios and 95% confidence intervals.

RESULTS

We analysed data from 16 studies and calculated a pooled frailty prevalence of 49% (95% CI 37-60%) in older patients with pneumonia. Unadjusted analyses revealed an odds ratio (OR) of 2.50 (95% CI 1.88-3.32) for the intermediate risk group, and an OR of 3.51 (95% CI 3.05-4.05) for the high risk group regarding mortality. The high risk frailty group also exhibited significant elevations in the risk of readmissions and extended hospital stay lengths. Substantial heterogeneity was observed in both adjusted and unadjusted analyses.

CONCLUSIONS

Our systematic review and meta-analysis results show that one in every two older individuals with pneumonia present frailty, a condition that significantly influences their rates of mortality and readmission, and their hospital stay length.

Validation of the Hospital Frailty Risk Score in older adults hospitalized with community-acquired pneumonia

AIM

Frailty results from age-associated declines in physiological reserve and function and is prevalent in older people. Our aim is to examine the association of the Hospital Frailty Risk Score (HFRS) with adverse events in older patients hospitalized with community-acquired pneumonia (CAP) and hypothesise that frailty is a comparable predictor of outcomes in CAP versus traditional severity indices such as CURB-65.

METHODS

Retrospective review of electronic medical records in patients ≥65 years with CAP admitted to a tertiary hospital from 1 January to 30 April 2021. Patients were identified using ICD codes for CAP and categorized as high risk (>15), intermediate risk (5-15) and low risk (<5) of frailty using the HFRS.

RESULTS

Of 429 patients with CAP, 53.8% male, mean age of 82.9 years, older patients (85 vs. 79.7 years, P < 0.001) were at higher risk of frailty. Using the HFRS, 47.6% were deemed at high risk, 35.9% at intermediate risk, and 16.6% at low risk of frailty. Multivariate logistic regression shows that HFRS was more strongly associated (≥7 days, OR 1.042, CI 1.017-1.069) than CURB-65 (OR 0.995, CI 0.810-1.222) with long hospital length of stay (LOS), while CURB-65 (Confusion, Urea >7mmol/L, Respiratory rate >30, Blood pressure, age => 65 years old) was more strongly associated with mortality at 30, 90 and 365 days, compared with the HFRS. Comparing the values for the area under the receiver operator characteristic curve, the HFRS was found to be a better predictor of long LOS, while CURB-65 remains a better predictor of mortality.

CONCLUSIONS

Patients with high risk of frailty have higher healthcare utilization and HFRS is a better predictor of long LOS than CURB-65 but CURB-65 was a better predictor of mortality. Geriatr Gerontol Int 2024; 24: 135-141.

Frailty-aware surgical care: Validation of Hospital Frailty Risk Score (HFRS) in older surgical patients

Introduction

Frailty has an important impact on the health outcomes of older patients, and frailty screening is recommended as part of perioperative evaluation. The Hospital Frailty Risk Score (HFRS) is a validated tool that highlights frailty risk using 109 International Classification of Diseases, 10th revision (ICD-10) codes. In this study, we aim to compare HFRS to the Charlson Comorbidity Index (CCI) and validate HFRS as a predictor of adverse outcomes in Asian patients admitted to surgical services.

Method

A retrospective study of electronic health records (EHR) was undertaken in patients aged 65 years and above who were discharged from surgical services between 1 April 2022 to 31 July 2022. Patients were stratified into low (HFRS <5), interme-diate (HFRS 5-15) and high (HFRS >15) risk of frailty.

Results

Those at high risk of frailty were older and more likely to be men. They were also likely to have more comorbidities and a higher CCI than those at low risk of frailty. High HFRS scores were associated with an increased risk of adverse outcomes, such as mortality, hospital length of stay (LOS) and 30-day readmission. When used in combination with CCI, there was better prediction of mortality at 90 and 270 days, and 30-day readmission.

Conclusion

To our knowledge, this is the first validation of HFRS in Singapore in surgical patients and confirms that high-risk HFRS predicts long LOS (≥7days), increased unplanned hospital readmissions (both 30-day and 270-day) and increased mortality (inpatient, 10-day, 30-day, 90-day, 270-day) compared with those at low risk of frailty.

Combining the Hospital Frailty Risk Score With the Charlson and Elixhauser Multimorbidity Indices to Identify Older Patients at Risk of Poor Outcomes in Acute Care

OBJECTIVE

The Hospital Frailty Risk Score (HFRS) can be applied to medico-administrative datasets to determine the risks of 30-day mortality and long length of stay (LOS) in hospitalized older patients. The objective of this study was to compare the HFRS with Charlson and Elixhauser comorbidity indices, used separately or combined.

DESIGN

A retrospective analysis of the French medical information database. The HFRS, Charlson index, and Elixhauser index were calculated for each patient based on the index stay and hospitalizations over the preceding 2 years. Different constructions of the HFRS were considered based on overlapping diagnostic codes with either Charlson or Elixhauser indices. We used mixed logistic regression models to investigate the association between outcomes, different constructions of HFRS, and associations with comorbidity indices.

SETTING

743 hospitals in France.

PARTICIPANTS

All patients aged 75 years or older hospitalized as an emergency in 2017 (n=1,042,234).Main outcome measures: 30-day inpatient mortality and LOS >10 days.

RESULTS

The HFRS, Charlson, and Elixhauser indices were comparably associated with an increased risk of 30-day inpatient mortality and long LOS. The combined model with the highest c-statistic was obtained when associating the HFRS with standard adjustment and Charlson for 30-day inpatient mortality (adjusted c-statistics: HFRS=0.654; HFRS + Charlson = 0.676) and with Elixhauser for long LOS (adjusted c-statistics: HFRS= 0.672; HFRS + Elixhauser =0.698).

CONCLUSIONS

Combining comorbidity indices and HFRS may improve discrimination for predicting long LOS in hospitalized older people, but adds little to Charlson's 30-day inpatient mortality risk.

Understanding the role of frailty in local and systemic complications and healthcare resource utilization in acute pancreatitis: Findings from a national cohort

BACKGROUND

Acute pancreatitis (AP) is a significant gastrointestinal cause of hospitalization with increasing incidence. Risk stratification is crucial for determining AP outcomes, but the association between frailty and AP outcomes is poorly understood. Moreover, age disparities in severity indices for AP complicate risk assessment. This study investigates frailty's impact on local and systemic complications in AP, readmission rates, and healthcare resource utilization.

METHODS

Using the National Readmission Database from 2016 to 2019, we identified adult AP patients and assessed frailty using the Frailty Risk Score. Our analysis included local and systemic complications, resource utilization, readmission rates, procedures performed, and hospitalization outcomes. Multivariate regression was employed, and statistical significance was set at P < 0.05 using Stata version 14.2.

RESULTS

Among 1,134,738 AP patients, 6.94 % (78,750) were classified as frail, with a mean age of 63.42 years and 49.71 % being female. Frail patients experienced higher rates of local complications (e.g., pseudocyst, acute pancreatic necrosis, walled-off necrosis) and systemic complications (e.g., pleural effusion, acute respiratory distress syndrome, sepsis, abdominal compartment syndrome) compared to non-frail patients. Frailty was associated with increased readmission rates and served as an independent predictor of readmission. Frail patients had higher inpatient mortality (7.11 % vs. 1.60 %), longer hospital stays, and greater hospitalization costs.

CONCLUSION

Frailty in AP patients is linked to elevated rates of local and systemic complications, increased mortality, and higher healthcare costs. Assessing frailty is crucial in AP management as it provides a valuable tool for risk stratification and identifying high-risk patients, thereby improving overall outcomes.

Predicting long-term survival after de novo cardioverter-defibrillator implantation for primary prevention: A population based study

Background

Implantable cardioverter-defibrillators (ICDs) reduce the risk of sudden cardiac death in patients with left ventricular dysfunction. While short-term mortality benefit of ICD insertion has been established in landmark randomized controlled trials, little is known about the long-term outcomes of patients with ICDs in clinical practice. In this paper, we describe the long-term survival of patients following de novo ICD implantation for primary prevention in clinical practice and determine the factors which help predict survival after ICD implant.

Methods

Retrospective population-based study of all patients receiving a de novo ICD for primary prevention in Ontario, Canada from 2007 to 2011 using the Ontario ICD Database housed within ICES. Simple random selection was used to split the population into a derivation and internal validation cohort in a ratio of 2:1. Cox proportional hazards regression was used to determine predictors of interest and predict 10-year survival, model performance was assessed using calibration and validation.

Results

In the derivation cohort (n = 3399), mean age was 65.3 years (standard deviation [SD] = 11.0), 664 patients were female (19.5 %) and 2344 patients (69.0 %) had ischemic cardiomyopathy. Ten year survival was 45.7 % (95 % confidence interval [CI] 44.0 %-47.4 %). The final prediction model included age, sex, disease factors (ischemic vs nonischemic cardiomyopathy, left ventricular ejection fraction) and patient factors (symptoms, comorbidities), and biomarkers at the time of ICD assessment. This model had good discrimination and calibration in derivation (0.79, 95 % CI 0.77, 0.81) and validation samples (0.78, 95 % CI 0.76, 0.79).

Conclusions

A combination of demographic and clinical factors determined at baseline can be used to predict 10-year survival in patients with implantable cardioverter-defibrillators with good accuracy. Our findings help to identify individuals at risk of long-term mortality and may be useful in targeting future prevention strategies to enhance longevity in this high-risk population.

Exploring fraity and sarcopenia in older adults admitted to acute medical unit, looking at prevalence, trajectory, and outcomes: A protocol testing the feasibility and acceptability of the TYSON study

BACKGROUND

Frailty and sarcopenia are common in older people and are associated with adverse outcomes including increased mortality and morbidity. It is unclear whether screening for frailty and sarcopenia would identify specific populations most at risk of poor outcomes during unplanned hospital admissions, which screening tools should be used and what the trajectory of both conditions are over the course of an admission. The TYSON study is an observational cohort study aiming to determine the prevalence, trajectory and outcomes associated with frailty and sarcopenia in different patient cohorts. This protocol tests the feasibility and acceptability of TYSON processes.

OBJECTIVES

To determine in acutely admitted medical patients who are older adults: Primary: The feasibility and acceptability of frailty and sarcopenia assessments; Secondary: (1) Differences in community and hospital frailty assessments, as assessed by the medical team, the patient and elderly care physicians, (2) The dynamic changes in frailty and sarcopenia during a hospital admission, and patient outcomes; Exploratory: Inflammatory and metabolic mediators associated with frailty and sarcopenia.

METHODS

A single centre, prospective observational study including patients aged ≥ 65 years admitted to an acute medical unit. Frailty assessments include the Rockwood clinical frailty and e-frailty index. Sarcopenia assessments include the Bilateral Anterior Thigh Thickness (BATT) measurement. Each participant will be asked to complete 5 visits, at day 0, day 3, day 7, month 3 and month 6. Blood samples will be collected to explore inflammatory and metabolic markers associated with frailty and sarcopenia. The study and protocol have been ethically approved by the Health Research Authority (REC 20/WA/0263).

DISCUSSION

The study will determine the feasibility and acceptability of frailty and sarcopenia assessments in an acute hospital setting, and inform on the prevalence, trajectory and associated outcomes of frailty and sarcopenia in this group of patients. An inflammatory and metabolic profile will be explored in frailty and sarcopenia.

Prognostic Value of Hospital Frailty Risk Score and Clinical Outcomes in Patients Undergoing Revascularization for Critical Limb-Threatening Ischemia

Background The impact of medical record-based frailty assessment on clinical outcomes in patients undergoing revascularization for critical limb-threatening ischemia (CLTI) is unknown. Methods and Results This study included patients with CLTI aged ≥18 years from the nationwide readmissions database 2016 to 2018 who underwent endovascular revascularization (ER) or surgical revascularization (SR). The hospital frailty risk score, a previously validated International Classification of Diseases, Tenth Edition, Clinical Modification (ICD-10-CM) claims-based score, was used to categorize patients into low- (<5), intermediate- (5-15), and high-risk (>15) frailty categories. Primary outcomes were in-hospital mortality and major amputation at 6 months. A total of 64 338 patients were identified who underwent ER (82.3%) or SR (17.7%) for CLTI. The mean (SD) age of the cohort was 69.3 (11.8) years, and 63% of patients were male. This study found a nonlinear association between hospital frailty risk score and in-hospital mortality and 6-month major amputation. In both ER and SR cohorts, the intermediate- and high-risk groups were associated with a significantly higher risk of in-hospital mortality (high-risk group: ER: odds ratio [OR], 7.2 [95% CI, 4.4-11.6], P<0.001; SR: OR, 28.6 [95% CI, 3.4-237.6], P=0.002) and major amputation at 6 months (high-risk group: ER: hazard ratio [HR], 1.6 [95% CI, 1.5-1.7], P<0.001; SR: HR, 1.7 [95% CI, 1.4-2.2], P<0.001) compared with the low-risk group. Conclusions The hospital frailty risk score, generated from the medical record, can identify frailty and predict in-hospital mortality and 6-month major amputation in patients undergoing ER or SR for CLTI. Further studies are needed to assess if this score can be incorporated into clinical decision-making in patients undergoing revascularization for CLTI.

Comparison of a multidomain frailty index from routine health data with the hospital frailty risk score in older patients in an Australian hospital

BACKGROUND

Frailty is an important determinant of health-care needs and outcomes for people in hospital.

OBJECTIVES

To compare characteristics and predictive ability of a multidomain frailty index derived from routine health data (electronic frailty index-acute hospital; eFI-AH) with the hospital frailty risk score (HFRS).

METHODS

This retrospective study included 6771 patients aged ≥75 years admitted to an Australian metropolitan tertiary referral hospital between October 2019 and September 2020. The eFI-AH and the HFRS were calculated for each patient and compared with respect to characteristics, agreement, association with age and ability to predict outcomes.

RESULTS

Median eFI-AH was 0.17 (range 0-0.66) whilst median HFRS was 3.2 (range 0-42.9). Moderate agreement was shown between the tools (Pearson's r 0.61). After adjusting for age and gender, both models had associations with long hospital stay, in-hospital mortality, unplanned all-cause readmission and fall-related readmission. Specifically, the eFI-AH had the strongest association with in-hospital mortality (adjusted odds ratio (aOR) 2.81, 95% confidence intervals (CI) 2.49-3.17), whilst the HFRS was most strongly associated with long hospital stay (aOR 1.20, 95% CI 1.18-1.21). Both tools predicted hospital stay >10 days with good discrimination and calibration.

CONCLUSIONS

Although the eFI-AH and the HFRS did not consistently identify the same inpatients as frail, both were associated with adverse outcomes and they had comparable predictive ability for prolonged hospitalisation. These two constructs of frailty may have different implications for clinical practice and health service provision and planning.

Hospital Frailty Risk Score and Healthcare Resource Utilization After Surgery for Primary Spinal Intradural/Cord Tumors

OBJECTIVE

The Hospital Frailty Risk Score (HFRS) is a metric that measures frailty among patients in large national datasets using ICD-10 codes. While other metrics have been utilized to demonstrate the association between frailty and poor outcomes in spine oncology, none have examined the HFRS. The aim of this study was to investigate the impact of frailty using the HFRS on complications, length of stay, cost of admission, and discharge disposition in patients undergoing surgery for primary tumors of the spinal cord and meninges.

METHODS

A retrospective cohort study was performed using the Nationwide Inpatient Sample database from 2016 to 2018. Adult patients undergoing surgery for primary tumors of the spinal cord and meninges were identified using ICD-10-CM codes. Patients were categorized into 2 cohorts based on HFRS score: Non-Frail (HFRS<5) and Frail (HFRS≥5). Patient characteristics, treatment, perioperative complications, LOS, discharge disposition, and cost of admission were assessed.

RESULTS

Of the 5955 patients identified, 1260 (21.2%) were Frail. On average, the Frail cohort was nearly 8 years older (P < .001) and experienced more postoperative complications (P = .001). The Frail cohort experienced longer LOS (P < .001), a higher rate of non-routine discharge (P = .001), and a greater mean cost of admission (P < .001). Frailty was found to be an independent predictor of extended LOS (P < .001) and non-routine discharge (P < .001).

CONCLUSION

Our study is the first to use the HFRS to assess the impact of frailty on patients with primary spinal tumors. We found that frailty was associated with prolonged LOS, non-routine discharge, and increased hospital costs.

Identifying models of care to improve outcomes for older people with urgent care needs: a mixed methods approach to develop a system dynamics model

Background

We aimed to understand urgent and emergency care pathways for older people and develop a decision support tool using a mixed methods study design.

Objective(s), study design, settings and participants

Work package 1 identified best practice through a review of reviews, patient, carer and professional interviews. Work package 2 involved qualitative case studies of selected urgent and emergency care pathways in the Yorkshire and Humber region. Work package 3 analysed linked databases describing urgent and emergency care pathways identifying patient, provider and pathway factors that explain differences in outcomes and costs. Work package 4 developed a system dynamics tool to compare emergency interventions.

Results

A total of 18 reviews summarising 128 primary studies found that integrated social and medical care, screening and assessment, follow-up and monitoring of service outcomes were important. Forty patient/carer participants described emergency department attendances; most reported a reluctance to attend. Participants emphasised the importance of being treated with dignity, timely and accurate information provision and involvement in decision-making. Receiving care in a calm environment with attention to personal comfort and basic physical needs were key. Patient goals included diagnosis and resolution, well-planned discharge home and retaining physical function. Participants perceived many of these goals of care were not attained. A total of 21 professional participants were interviewed and 23 participated in focus groups, largely confirming the review evidence. Implementation challenges identified included the urgent and emergency care environment, organisational approaches to service development, staff skills and resources. Work package 2 involved 45 interviews and 30 hours of observation in four contrasting emergency departments. Key themes relating to implementation included: intervention-related staff: frailty mindset and behaviours resources: workforce, space, and physical environment operational influences: referral criteria, frailty assessment, operating hours, transport. context-related links with community, social and primary care organisation and management support COVID-19 pandemic. approaches to implementation service/quality improvement networks engaging staff and building relationships education about frailty evidence. The linked databases in work package 3 comprised 359,945 older people and 1,035,045 observations. The most powerful predictors of four-hour wait and transfer to hospital were age, previous attendance, out-of-hours attendance and call handler designation of urgency. Drawing upon the previous work packages and working closely with a wide range of patient and professional stakeholders, we developed an system dynamics tool that modelled five evidence-based urgent and emergency care interventions and their impact on the whole system in terms of reducing admissions, readmissions, and hospital related mortality.

Limitations

Across the reviews there was incomplete reporting of interventions. People living with severe frailty and from ethnic minorities were under-represented in the patient/carer interviews. The linked databases did not include patient reported outcomes. The system dynamics model was limited to evidence-based interventions, which could not be modelled conjointly.

Conclusions

We have reaffirmed the poor outcomes frequently experienced by many older people living with urgent care needs. We have identified interventions that could improve patient and service outcomes, as well as implementation tools and strategies to help including clinicians, service managers and commissioners improve emergency care for older people.

Future work

Future work will focus on refining the system dynamics model, specifically including patient-reported outcome measures and pre-hospital services for older people living with frailty who have urgent care needs.

Study registrations

This study is registered as PROSPERO CRD42018111461. WP 1.2: University of Leicester ethics: 17525-spc3-ls:healthsciences, WP 2: IRAS 262143, CAG 19/CAG/0194, WP 3: IRAS 215818, REC 17/YH/0024, CAG 17/CAG/0024.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme [project number 17/05/96 (Emergency Care for Older People)] and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 14. See the NIHR Journals Library website for further project information.

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Background

Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.

Objective

This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.

Method

LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.

Results

Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.

Conclusion

To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

Comparison of Medical and Mental Health Sequelae Following Hospitalization for COVID-19, Influenza, and Sepsis

Importance

People who survive hospitalization for COVID-19 are at risk for developing new cardiovascular, neurological, mental health, and inflammatory autoimmune conditions. It is unclear how posthospitalization risks for COVID-19 compare with those for other serious infectious illnesses.

Objective

To compare risks of incident cardiovascular, neurological, and mental health conditions and rheumatoid arthritis in 1 year following COVID-19 hospitalization against 3 comparator groups: prepandemic hospitalization for influenza and hospitalization for sepsis before and during the COVID-19 pandemic.

Design, Setting, and Participants

This population-based cohort study included all adults hospitalized for COVID-19 between April 1, 2020, and October 31, 2021, historical comparator groups of people hospitalized for influenza or sepsis, and a contemporary comparator group of people hospitalized for sepsis in Ontario, Canada.

Exposure

Hospitalization for COVID-19, influenza, or sepsis.

Main Outcome and Measures

New occurrence of 13 prespecified conditions, including cardiovascular, neurological, and mental health conditions and rheumatoid arthritis, within 1 year of hospitalization.

Results

Of 379 366 included adults (median [IQR] age, 75 [63-85] years; 54% female), there were 26 499 people who survived hospitalization for COVID-19, 299 989 historical controls (17 516 for influenza and 282 473 for sepsis), and 52 878 contemporary controls hospitalized for sepsis. Hospitalization for COVID-19 was associated with an increased 1-year risk of venous thromboembolic disease compared with influenza (adjusted hazard ratio, 1.77; 95% CI, 1.36-2.31) but with no increased risks of developing selected ischemic and nonischemic cerebrovascular and cardiovascular disorders, neurological disorders, rheumatoid arthritis, or mental health conditions compared with influenza or sepsis cohorts.

Conclusions and Relevance

In this cohort study, apart from an elevated risk of venous thromboembolism within 1 year, the burden of postacute medical and mental health conditions among those who survived hospitalization for COVID-19 was comparable with other acute infectious illnesses. This suggests that many of the postacute consequences of COVID-19 may be related to the severity of infectious illness necessitating hospitalization rather than being direct consequences of infection with SARS-CoV-2.

A comparative analysis of the Hospital Frailty Risk Score in predicting postoperative outcomes among intracranial tumor patients

OBJECTIVE

In recent years, frailty indices such as the 11- and 5-factor modified frailty indices (mFI-11 and mFI-5), American Society of Anesthesiologists (ASA) physical status classification, and Charlson Comorbidity Index (CCI) have been shown to be effective predictors of various postoperative outcomes in neurosurgical patients. The Hospital Frailty Risk Score (HFRS) is a well-validated tool for assessing frailty; however, its utility has not been evaluated in intracranial tumor surgery. In the present study, the authors investigated the accuracy of the HFRS in predicting outcomes following intracranial tumor resection and compared its utility to those of other validated frailty indices.

METHODS

A retrospective analysis was conducted using an intracranial tumor patient database at a single institution. Patients eligible for study inclusion were those who had undergone resection for an intracranial tumor between January 1, 2017, and December 31, 2019. ICD-10 codes were used to identify HFRS components and subsequently calculate risk scores. In addition to several postoperative variables, ASA class, CCI, and mFI-11 and mFI-5 scores were determined for each patient. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC), and the DeLong test was used to assess for significant differences between AUROCs. Multivariate models for continuous outcomes were constructed using linear regression, whereas logistic regression models were used for categorical outcomes.

RESULTS

A total of 2518 intracranial tumor patients (mean age 55.3 ± 15.1 years, 53.4% female, 70.4% White) were included in this study. The HFRS had a statistically significant greater AUROC than ASA status, CCI, mFI-11, and mFI-5 for postoperative complications, high hospital charges, nonroutine discharge, and 90-day readmission. In the multivariate analysis, the HFRS was significantly and independently associated with postoperative complications (OR 1.14, p < 0.0001), hospital length of stay (coefficient = 0.50, p < 0.0001), high hospital charges (coefficient = 1917.49, p < 0.0001), nonroutine discharge (OR 1.14, p < 0.0001), and 90-day readmission (OR 1.06, p < 0.0001).

CONCLUSIONS

The study findings suggest that the HFRS is an effective predictor of postoperative outcomes in intracranial tumor patients and more effectively predicts adverse outcomes than other frailty indices. The HFRS may serve as an important tool for reducing patient morbidity and mortality in intracranial tumor surgery.

Using the Hospital Frailty Risk Score to assess mortality risk in older medical patients admitted to the intensive care unit

BACKGROUND

Prognostic information at the time of hospital discharge can help guide goals-of-care discussions for future care. We sought to assess the association between the Hospital Frailty Risk Score (HFRS), which may highlight patients' risk of adverse outcomes at the time of hospital discharge, and in-hospital death among patients admitted to the intensive care unit (ICU) within 12 months of a previous hospital discharge.

METHODS

We conducted a multicentre retrospective cohort study that included patients aged 75 years or older admitted at least twice over a 12-month period to the general medicine service at 7 academic centres and large community-based teaching hospitals in Toronto and Mississauga, Ontario, Canada, from Apr. 1, 2010, to Dec. 31, 2019. The HFRS (categorized as low, moderate or high frailty risk) was calculated at the time of discharge from the first hospital admission. Outcomes included ICU admission and death during the second hospital admission.

RESULTS

The cohort included 22 178 patients, of whom 1767 (8.0%) were categorized as having high frailty risk, 9464 (42.7%) as having moderate frailty risk, and 10 947 (49.4%) as having low frailty risk. One hundred patients (5.7%) with high frailty risk were admitted to the ICU, compared to 566 (6.0%) of those with moderate risk and 790 (7.2%) of those with low risk. After adjustment for age, sex, hospital, day of admission, time of admission and Laboratory-based Acute Physiology Score, the odds of ICU admission were not significantly different for patients with high (adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.78 to 1.23) or moderate (adjusted OR 0.97, 95% CI 0.86 to 1.09) frailty risk compared to those with low frailty risk. Among patients admitted to the ICU, 75 (75.0%) of those with high frailty risk died, compared to 317 (56.0%) of those with moderate risk and 416 (52.7%) of those with low risk. After multivariable adjustment, the risk of death after ICU admission was higher for patients with high frailty risk than for those with low frailty risk (adjusted OR 2.86, 95% CI 1.77 to 4.77).

INTERPRETATION

Among patients readmitted to hospital within 12 months, patients with high frailty risk were similarly likely as those with lower frailty risk to be admitted to the ICU but were more likely to die if admitted to ICU. The HFRS at hospital discharge can inform prognosis, which can help guide discussions for preferences for ICU care during future hospital stays.

Prevalence and outcomes of frailty in unplanned hospital admissions: a systematic review and meta-analysis of hospital-wide and general (internal) medicine cohorts

Background

Guidelines recommend routine frailty screening for all hospitalised older adults to inform care decisions, based mainly on studies in elective or speciality-specific settings. However, most hospital bed days are accounted for by acute non-elective admissions, in which the prevalence and prognostic value of frailty might differ, and uptake of screening is limited. We therefore did a systematic review and meta-analysis of frailty prevalence and outcomes in unplanned hospital admissions.

Methods

We searched MEDLINE, EMBASE and CINAHL up to 31/01/2023 and included observational studies using validated frailty measures in adult hospital-wide or general medicine admissions. Summary data on the prevalence of frailty and associated outcomes, measurement tools, study setting (hospital-wide vs general medicine), and design (prospective vs retrospective) were extracted and risk of bias assessed (modified Joanna Briggs Institute checklists). Unadjusted relative risks (RR; moderate/severe frailty vs no/mild) for mortality (within one year), length of stay (LOS), discharge destination and readmission were calculated and pooled, where appropriate, using random-effects models. PROSPERO CRD42021235663.

Findings

Among 45 cohorts (median/SD age = 80/5 years; n = 39,041,266 admissions, n = 22 measurement tools) moderate/severe frailty ranged from 14.3% to 79.6% overall (and in the 26 cohorts with low-moderate risk of bias) with considerable heterogeneity between studies (p_het < 0.001) preventing pooling of results but with rates <25% in only 3 cohorts. Moderate/severe vs no/mild frailty was associated with increased mortality (n = 19 cohorts; RR range = 1.08-3.70), more consistently among cohorts using clinically administered tools (n = 11; RR range = 1.63-3.70; p_het = 0.08; pooled RR = 2.53, 95% CI = 2.15-2.97) vs cohorts using (retrospective) administrative coding data (n = 8; RR range = 1.08-3.02; p_het < 0.001). Clinically administered tools also predicted increasing mortality across the full range of frailty severity in each of the six cohorts that allowed ordinal analysis (all p < 0.05). Moderate/severe vs no/mild frailty was also associated with a LOS >8 days (RR range = 2.14-3.04; n = 6) and discharge to a location other than home (RR range = 1.97-2.82; n = 4) but was inconsistently related to 30-day readmission (RR range = 0.83-1.94; n = 12). Associations remained clinically significant after adjustment for age, sex and comorbidity where reported.

Interpretation

Frailty is common in older patients with acute, non-elective hospital admission and remains predictive of mortality, LOS and discharge home with more severe frailty associated with greater risk, justifying more widespread implementation of screening using clinically administered tools.

Funding

None.

Microsurgical clipping and endovascular management of unruptured anterior circulation aneurysms: how age, frailty, and comorbidity indexes influence outcomes

OBJECTIVE

Frailty is one of the important factors in predicting the outcomes of surgery. Many surgical specialties have adopted a frailty assessment in the preoperative period for prognostication; however, there are limited data on the effects of frailty on the outcomes of cerebral aneurysms. The object of this study was to find the effect of frailty on the surgical outcomes of anterior circulation unruptured intracranial aneurysms (UIAs) and compare the frailty index with other comorbidity indexes.

METHODS

A retrospective study was performed utilizing the National Inpatient Sample (NIS) database (2016-2018). The Hospital Frailty Risk Score (HFRS) was used to assess frailty. On the basis of the HFRS, the whole cohort was divided into low-risk (0-5), intermediate-risk (> 5 to 15), and high-risk (> 15) frailty groups. The analyzed outcomes were nonhome discharge, complication rate, extended length of stay, and in-hospital mortality.

RESULTS

In total, 37,685 patients were included in the analysis, 5820 of whom had undergone open surgical clipping and 31,865 of whom had undergone endovascular management. Mean age was higher in the high-risk frailty group than in the low-risk group for both clipping (63 vs 55.4 years) and coiling (64.6 vs 57.9 years). The complication rate for open surgical clipping in the high-risk frailty group was 56.1% compared to 0.8% in the low-risk group. Similarly, for endovascular management, the complication rate was 60.6% in the high-risk group compared to 0.3% in the low-risk group. Nonhome discharges were more common in the high-risk group than in the low-risk group for both open clipping (87.8% vs 19.7%) and endovascular management (73.1% vs 4.4%). Mean hospital charges for clipping were $341,379 in the high-risk group compared to $116,892 in the low-risk group. Mean hospital charges for coiling were $392,861 in the high-risk frailty group and $125,336 in the low-risk group. Extended length of stay occurred more frequently in the high-risk frailty group than in the low-risk group for both clipping (82.9% vs 10.7%) and coiling (94.2% vs 12.7%). Frailty had higher area under the receiver operating characteristic curve values than those for other comorbidity indexes and age in predicting outcomes.

CONCLUSIONS

Frailty affects surgical outcomes significantly and outperforms age and other comorbidity indexes in predicting outcome. It is imperative to include frailty assessment in preoperative planning.

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.

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

AIM

To use the Hospital Frailty Risk Score (HFRS) to investigate the impact of frailty on complication rates and healthcare resource utilization in patients who underwent endovascular treatment of ruptured intracranial aneurysms (IAs).

METHODS

A retrospective cohort study was performed using the 2016-2019 National Inpatient Sample database. All adult patients (≥18 years) undergoing endovascular treatment for IAs after subarachnoid hemorrhage were identified using ICD-10-CM codes. Patients were categorized into frailty cohorts: low (HFRS <5), intermediate (HFRS 5-15) and high (HFRS >15). Patient demographics, adverse events, length of stay (LOS), discharge disposition, and total cost of admission were assessed. Multivariate logistic regression analysis was used to identify independent predictors of prolonged LOS, increased cost, and non-routine discharge.

RESULTS

Of the 33 840 patients identified, 7940 (23.5%) were found to be low, 20 075 (59.3%) intermediate and 5825 (17.2%) high frailty by HFRS criteria. The rate of encountering any adverse event was significantly greater in the higher frailty cohorts (low: 59.9%; intermediate: 92.4%; high: 99.2%, p<0.001). There was a stepwise increase in mean LOS (low: 11.7±8.2 days; intermediate: 18.7±14.1 days; high: 26.6±20.1 days, p<0.001), mean total hospital cost (low: $62 888±37 757; intermediate: $99 670±63 446; high: $134 937±80 331, p<0.001), and non-routine discharge (low: 17.3%; intermediate: 44.4%; high: 69.4%, p<0.001) with increasing frailty. On multivariate regression analysis, a similar stepwise impact was found in prolonged LOS (intermediate: OR 2.38, p<0.001; high: OR 4.49, p<0.001)], total hospital cost (intermediate: OR 2.15, p<0.001; high: OR 3.62, p<0.001), and non-routine discharge (intermediate: OR 2.13, p<0.001; high: OR 4.17, p<0.001).

CONCLUSIONS

Our study found that greater frailty as defined by the HFRS was associated with increased complications, LOS, total costs, and non-routine discharge.

Comparative performance of distinct frailty measures among patients undergoing percutaneous left atrial appendage closure

AIMS

Frailty is associated with increased morbidity and mortality in patients undergoing left atrial appendage closure (LAAC). This study aimed to compare the performance of two claims-based frailty measures in predicting adverse outcomes following LAAC.

METHODS

We identified patients 66 years and older who underwent LAAC between October 1, 2016, and December 31, 2019, in Medicare fee-for-service claims. Frailty was assessed using the previously validated Hospital Frailty Risk Score (HFRS) and Kim Claims-based Frailty Index (CFI). Patients were identified as frail based on HFRS ≥5 and CFI ≥0.25.

RESULTS

Of the 21,787 patients who underwent LAAC, frailty was identified in 45.6% by HFRS and 15.4% by CFI. There was modest agreement between the two frailty measures (kappa 0.25, Pearson's correlation 0.62). After adjusting for age, sex, and comorbidities, frailty was associated with higher risk of 30-day mortality, 1-year mortality, 30-day readmission, long hospital stay, and reduced days at home (p < .01 for all) regardless of the frailty measure used. The addition of frailty to standard comorbidities significantly improved model performance to predict 1-year mortality, long hospital stay, and reduced days at home (Delong p-value < .001).

CONCLUSION

Despite significant variation in frailty detection and modest agreement between the two frailty measures, frailty status remained highly predictive of mortality, readmissions, long hospital stay, and reduced days at home among patients undergoing LAAC. Measuring frailty in clinical practice, regardless of the method used, may provide prognostic information useful for patients being considered for LAAC, and may inform shared decision-making in this population.

Impact of frailty on outcomes in acute ST-elevated myocardial infarctions undergoing percutaneous coronary intervention

AIMS

We analyzed the impact of frailty on readmission rates for ST-elevated myocardial infarctions (STEMIs) and the utilization of percutaneous coronary intervention (PCI) in STEMI admissions.

METHODS AND RESULTS

The 2016-2019 Nationwide Readmission Database was analyzed for patients admitted with an acute STEMI. Patients were categorized by frailty risk and analyzed for 30-day readmission risk after acute STEMIs, PCI utilization and outcomes, and healthcare resource utilization. Qualifying index admissions were found in 584,918 visits. Low risk frailty was noted in 78.20%, intermediate risk in 20.67%, and high risk in 1.14% of admissions. Thirty-day readmissions occurred in 7.74% of index admissions, increasing with frailty (p < 0.001). Readmission risk increased with frailty, 1.37 times with intermediate and 1.21 times with high-risk frailty. PCI was performed in 86.40% of low-risk, 66.03% of intermediate-risk, and 58.90% of high-risk patients (p < 0.001). Intermediate patients were 55.02% less likely and high-risk patients were 61.26% less likely to undergo PCI (p < 0.001). Length of stay means for index admissions were 2.96, 7.83, and 16.32 days for low, intermediate, and high-risk groups. Intermediate and high-risk frailty had longer length of stay, higher total cost, and were more likely to be discharged to a skilled facility (p < 0.001).

CONCLUSION

Among adult, all-payer inpatient visits, frailty discerned by the hospital frailty risk score was associated with increased readmissions, increased healthcare resource utilization, and lower PCI administration.

External validation of the hospital frailty risk score among hospitalised home care clients in Canada: a retrospective cohort study

BACKGROUND

The 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).

OBJECTIVE

Measure the correlation between the HFRS, CFS and FI and compare prognostic utility for frailty-related outcomes.

DESIGN

Retrospective cohort study.

SETTING

Alberta, British Columbia and Ontario, Canada.

SUBJECTS

Home care clients aged 65+ admitted to hospital within 180 days (median 65 days) of a RAI-HC assessment (n = 167,316).

METHODS

Correlation 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).

RESULTS

The 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).

CONCLUSIONS

The 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.

Impact of frailty on early rhythm control outcomes in older adults with atrial fibrillation: A nationwide cohort study

Purpose

Rhythm-control therapy administered early following the initial diagnosis of atrial fibrillation (AF) has superior cardiovascular outcomes compared to rate-control therapy. Frailty is a key factor in identifying older patients' potential for improvement after rhythm-control therapy. This study evaluated whether frailty affects the outcome of early rhythm-control therapy in older patients with AF.

Methods

From the Korean National Health Insurance Service database (2005-2015), we collected 20,611 populations aged ≥65 years undergoing rhythm- or rate-control therapy initiated within 1 year of AF diagnosis. Participants were emulated by the EAST-AFNET4 trial, and stratified into non-frail, moderately frail, and highly frail groups based on the hospital frailty risk score (HFRS). A composite outcome of cardiovascular-related mortality, myocardial infarction, hospitalization for heart failure, and ischemic stroke was compared between rhythm- and rate-control.

Results

Early rhythm-control strategy showed a 14% lower risk of the primary composite outcome in the non-frail group [weighted incidence 7.3 vs. 8.6 per 100 person-years; hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.79-0.93, p < 0.001] than rate-control strategy. A consistent trend toward a lower risk of early rhythm-control was observed in the moderately frail (HR 0.91, 95% CI 0.81-1.02, p = 0.09) and highly frail (HR 0.93, 95% CI 0.75-1.17, p = 0.55) groups.

Conclusion

Although the degree attenuated with increasing frailty, the superiority of cardiovascular outcomes of early rhythm-control in AF treatment was maintained without increased risk for safety outcomes. An individualized approach is required on the benefits of early rhythm-control therapy in older patients with AF, regardless of their frailty status.

Leveraging HFRS to assess how frailty affects healthcare resource utilization after elective ACDF for CSM

BACKGROUND CONTEXT

Frailty is a common comorbidity associated with worsening outcomes in various medical and surgical fields. The Hospital Frailty Risk Score (HFRS) is a recently developed tool which assesses frailty using 109 International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) comorbidity codes to assess severity of frailty. However, there is a paucity of studies utilizing the HFRS with patients undergoing anterior cervical discectomy and fusion (ACDF) for cervical spondylotic myelopathy (CSM).

PURPOSE

The aim of this study was to investigate the impact of HFRS on health care resource utilization following ACDF for CSM.

STUDY DESIGN

A retrospective cohort study was performed using the Nationwide Inpatient Sample (NIS) database from 2016-2019.

PATIENT SAMPLE

All adult (≥18 years old) patients undergoing primary, ACDF for CSM were identified using the ICD-10 CM codes.

OUTCOME MEASURES

Weighted patient demographics, comorbidities, perioperative complications, LOS, discharge disposition, and total admission costs were assessed.

METHODS

The 109 ICD-10 codes with pre-assigned values from 0.1 to 7.1 pertaining to frailty were queried in each patient, with a cumulative HFRS ≥5 indicating a frail patient. Patients were then categorized as either Low HFRS (HFRS<5) or Moderate to High HFRS (HFRS≥5). A multivariate stepwise logistic regression was used to determine the odds ratio for risk-adjusted extended LOS, non-routine discharge disposition, and increased hospital cost.

RESULTS

A total of 29,305 patients were identified, of which 3,135 (10.7%) had a Moderate to High HFRS. Patients with a Moderate to High HFRS had higher rates of 1 or more postoperative complications (Low HFRS: 9.5% vs. Moderate-High HFRS: 38.6%, p≤.001), significantly longer hospital stays (Low HFRS: 1.8±1.7 days vs. Moderate-High HFRS: 4.4 ± 6.0, p≤.001), higher rates of non-routine discharge (Low HFRS: 5.8% vs. Moderate-High HFRS: 28.2%, p≤.001), and increased total cost of admission (Low HFRS: $19,691±9,740 vs. Moderate-High HFRS: $26,935±22,824, p≤.001) than patients in the Low HFRS cohort. On multivariate analysis, Moderate to High HFRS was found to be a significant independent predictor for extended LOS [OR: 3.19, 95% CI: (2.60, 3.91), p≤.001] and non-routine discharge disposition [OR: 3.88, 95% CI: (3.05, 4.95), p≤.001] but not increased cost [OR: 1.10, 95% CI: (0.87, 1.40), p=.418].

CONCLUSIONS

Our study suggests that patients with a higher HFRS have increased total hospital costs, a longer LOS, higher complication rates, and more frequent nonroutine discharge compared with patients with a low HFRS following elective ACDF for CSM. Although frail patients should not be precluded from surgical management of cervical spine pathology, these findings highlight the need for peri-operative protocols to medically optimize patients to improve health care quality and decrease costs.

Association between Frailty Assessed Using Two Electronic Medical Record-Based Frailty Assessment Tools and Long-Term Adverse Prognosis in Older Critically Ill Survivors

OBJECTIVES

Frailty has become an independent risk factor for adverse outcomes in critically ill patients. This study aimed to explore the predictive ability of two electronic medical record-based frailty assessment tools, the Hospital Frailty Risk Score (HFRS) and Frailty Index based on physiological and laboratory tests (FI-lab), for long-term adverse prognosis in older critically ill survivors.

DESIGN

Retrospective observational study.

SETTING AND PARTICIPANTS

9,082 critically ill survivors aged ≥ 65 years.

MEASUREMENTS

The HFRS and the 33-item FI-lab were constructed based on the published literature. Cox and logistic regression models assessed the association between frailty and 1-year mortality and post-discharge care needs.

RESULTS

2,586 patients died within 1 year of follow-up. In fully adjusted models, frailty assessed using both the HFRS (per point, hazard ratio [HR] 1.06, 95% confidential interval [CI] 1.05-1.06; intermediate frailty risk, HR 2.00, 95% CI 1.78-2.25; high frailty risk, HR 3.06, 95% CI 2.68-3.50) and FI-lab (per 0.01 points, HR 1.03, 95% CI 1.03-1.03; intermediate frailty risk, HR 1.59, 95% CI 1.44-1.76; high frailty risk, HR 2.30, 95% CI 2.06-2.57) was associated with mortality. Addition of frailty indicators improved the predictive validity of the Sequential Organ Failure Assessment score for mortality (HFRS alone ∆ C-index 0.034; FI-lab alone ∆ C-index 0.016; HFRS and FI-lab combined ∆ C-index 0.042). The HFRS but not the FI-lab was associated with higher probability of post-discharge care needs.

CONCLUSION

Both the HFRS and FI-lab could independently predict 1-year mortality in older critically ill survivors. Adding the HFRS to the SOFA score model improved it more than adding the FI-lab. The greatest improvement was achieved when both frailty indicators were used together. These findings suggest that electronic medical record-based frailty assessment methods can be useful tools for predicting long-term outcomes in older critically ill patients.

Frailty, length of stay and cost in hip fracture patients

A hip fracture causes high morbidity and mortality. Frailty is associated with adverse outcomes and increased costs. Frailty measured using the Hospital Frailty Risk Score (HFRS) is associated with higher costs and adverse outcomes. HFRS is useful as a fuss-free frailty measurement in the management of older adults with hip fractures.

INTRODUCTION

Hip fractures account for an increasing number of hospital admissions around the world and are associated with high rates of morbidity and mortality. Frailty is increasingly recognized to be associated with adverse outcomes and increased costs. The purpose of this study is to determine the association of the Hospital Frailty Risk Score (HFRS) with the healthcare cost and outcomes in older adults who present with a hip fracture.

METHODS

A retrospective analysis was performed on 1014 patients ≥ 60 years who presented with a hip fracture between January 2016 to June 2020. Each patient was classified into HFRS low, intermediate or high frailty cohorts. Demographics, hip fracture type, comorbidities, Charlson Comorbidity Index (CCI), American Society of Anesthesiologist score (ASA), costs, length of stay, time to surgery, complications, readmission rate and mortality were compared between the cohorts.

RESULTS

Median total hospitalization costs were significantly higher in the highest HFRS (SGD$22,432) patients as compared to intermediate (SGD$18,759) and low HFRS (SGD$15,671) patients. The difference between the high and low groups remains significant after adjusting for covariates using quantile regression. Similar results were shown for median length of stay (14 vs 10 vs 8 days), total number of complications (2 vs 1 vs 0) and adjusted time to surgery (p < 0.05). HFRS was not associated with 30-day readmission or 30-day or 1-year mortality.

CONCLUSION

Frailty is associated with a marked increase in total costs in hip fracture patients. HFRS proved useful in estimating LOS and outcomes for older patients with hip fractures.

Racial and socioeconomic disparities in cost and postoperative complications following sacrocolpopexy in a US National Inpatient Database

PURPOSE

We sought to determine the association between socioeconomic factors, procedural costs, and postoperative complications among patients who underwent sacrocolpopexy.

METHODS

The 2016-2017 US National Inpatient Sample from the Healthcare Cost and Utilization Project was used to identify females > 18 years of age with an ICD10 diagnosis code of apical prolapse who received open or laparoscopic/robotic sacrocolpopexy. We analyzed relationships between socioeconomic factors, procedural costs, and postoperative complications in these patients. Multivariate logistic and linear regressions were used to identify variables associated with increased complications and costs, respectively.

RESULTS

We identified 4439 women who underwent sacrocolpopexy, of which 10.7% had complications. 34.6% of whites, 29.1% of Blacks, 29% of Hispanics, and 34% of Others underwent a laparoscopic/robotic procedure. Hispanic patients had the highest median charge associated with surgical admission for sacrocolpopexy at $51,768, followed by Other ($44,522), White ($43,471), and Black ($40,634) patients. Procedure being within an urban teaching hospital (+ $2602), laparoscopic/robotic (+ $6790), or in the West (+ $9729) were associated with a significantly higher median cost of surgical management.

CONCLUSIONS

In women undergoing sacrocolpopexy, the protective factors against postoperative complications included private insurance status, a laparoscopic approach, and concurrent hysterectomy. Procedures held within an urban teaching hospital, conducted laparoscopically/robotically or in the West are associated with significantly higher costs of surgical management. Hispanic patients observe significantly higher procedure charges and costs, possibly resulting from the large number of this ethnic group living in the Western United States.

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

BACKGROUND

Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes.

OBJECTIVE

We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms.

METHODS

This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy.

RESULTS

In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease.

CONCLUSIONS

The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.

Prevalence of Frailty Among Hospitalized Older Adults in New Brunswick, Canada: an Administrative Data Population-Based Study

Background

Characterizing the prevalence and distribution of frailty within a population can help guide decision-making and policy development by identifying health service resource needs. Here we describe the prevalence of frailty among hospitalized older adults in New Brunswick (NB), Canada.

Methods

NB administrative hospital claims data were used to identify hospitalized older adults aged 65 or older between April 1, 2017 and March 31, 2019. Frailty was quantified using the Hospital Frailty Risk Score (HFRS), a validated frailty tool derived from claims data. Individuals with a HFRS ranked as intermediate or high were categorized as frail. The distribution of frailty across sex and age are described. Crude prevalence estimates and corresponding 95% confidence intervals are presented.

Results

A total of 55,675 older adults (52% females) were hospitalized. The overall prevalence of frailty was 21.2% (95%CI: 20.9-21.6). Prevalence increased with age: 12.7% (95%CI: 12.3-13.1) in the 65-74 age group, 24.7% (95%CI: 24.1-25.3) in the 75-84 age group and 41.6% (95%CI: 40.6-42.7) for those aged 85 and over (p<.001).

Discussion/Conclusion

The distribution of frailty is in line with that reported in other jurisdictions. We demonstrate the feasibility of the HFRS to identify and characterize frailty in a large sample of older adults who were hospitalized, using administrative data.

Impact of Frailty Risk on Adverse Outcomes after Traumatic Brain Injury: A Historical Cohort Study

We evaluated the utility of the Hospital Frailty Risk Score (HFRS) as a predictor of adverse events after hospitalization in a retrospective analysis of traumatic brain injury (TBI). This historical cohort study analyzed the data of patients hospitalized with TBI between April 2014 and August 2020 who were registered in the JMDC database. We used HFRS to classify the patients into the low- (HFRS < 5), intermediate- (HFRS5-15), and high- (HFRS > 15)-frailty risk groups. Outcomes were the length of hospital stay, the number of patients with Barthel Index score ≥ 95 on, Barthel Index gain, and in-hospital death. We used logistic and linear regression analyses to estimate the association between HFRS and outcome in TBI. We included 18,065 patients with TBI (mean age: 71.8 years). Among these patients, 10,139 (56.1%) were in the low-frailty risk group, 7388 (40.9%) were in the intermediate-frailty risk group, and 538 (3.0%) were in the high-frailty risk group. The intermediate- and high-frailty risk groups were characterized by longer hospital stays than the low-frailty risk group (intermediate-frailty risk group: coefficient 1.952, 95%; confidence interval (CI): 1.117−2.786; high-frailty risk group: coefficient 5.770; 95% CI: 3.160−8.379). The intermediate- and high-frailty risk groups were negatively associated with a Barthel Index score ≥ 95 on discharge (intermediate-frailty risk group: odds ratio 0.645; 95% CI: 0.595−0.699; high-frailty risk group: odds ratio 0.221; 95% CI: 0.157−0.311) and Barthel Index gain (intermediate-frailty risk group: coefficient −4.868, 95% CI: −5.599−−3.773; high-frailty risk group: coefficient −19.596, 95% CI: −22.242−−16.714). The intermediate- and high-frailty risk groups were not associated with in-hospital deaths (intermediate-frailty risk group: odds ratio 0.901; 95% CI: 0.766−1.061; high-frailty risk group: odds ratio 0.707; 95% CI: 0.459−1.091). We found that HFRS could predict adverse outcomes during hospitalization in TBI patients.

Elderly Outcomes After Hospitalization: The Hospital Frailty Risk Score Applied on the French Health Data Hub

Objectives: To demonstrate the association between the Hospital Frailty Risk Score (HFRS) and 30-day mortality, 30-day hospital readmission and length of stay (LOS) in France. Methods: Logistic regressions were performed using data recorded in the French national health data system ( SNDS) for elderly patients (≥75 years old) hospitalized in France in 2017. Results: Over the 1,111,090 patients included, 30-day mortality was associated with the HFRS: adjusted OR (aOR) for an intermediate HFRS (5–15 points) was 1.91 [95% confidence interval (95% IC); 1.87–1.95] and aOR 2.57 [95% IC; 2.50–2.64] for high HFRS (>15 points), as compared to low HFRS (<5 points). LOS >10 days increased with the HFRS (aOR = 1.36 [95% IC; 1.34–1.38] for an intermediate HFRS and aOR 1.51 [95% IC; 1.48–1.54] for a high HFRS). A high HFRS was associated with 30-day hospital readmission (aOR = 1.06 [95% IC; 1.04–1.08]). Discussion: This real-life analysis of in- and out-patient healthcare pathways confirmed the HFRS’s ability to predict adverse outcomes, after adjustment on social deprivation.

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

BACKGROUND

Older adults living with frailty who require treatment in hospitals are increasingly seen in the Emergency Departments (EDs). One quick and simple frailty assessment tool-the Clinical Frailty Scale (CFS)-has been embedded in many EDs in the United Kingdom (UK). However, it carries time/training and cost burden and has significant missing data. The Hospital Frailty Risk Score (HFRS) can be automated and has the potential to reduce costs and increase data availability, but has not been tested for predictive accuracy in the ED. The aim of this study is to assess the correlation between and the ability of the CFS at the ED and HFRS to predict hospital-related outcomes.

METHODS

This is a retrospective cohort study using data from Leicester Royal Infirmary hospital during the period from 01/10/2017 to 30/09/2019. We included individuals aged + 75 years as the HFRS has been only validated for this population. We assessed the correlation between the CFS and HFRS using Pearson's correlation coefficient for the continuous scores and weighted kappa scores for the categorised scores. We developed logistic regression models (unadjusted and adjusted) to estimate Odds Ratios (ORs) and Confidence Intervals (CIs), so we can assess the ability of the CFS and HFRS to predict 30-day mortality, Length of Stay (LOS) > 10 days, and 30-day readmission.

RESULTS

Twelve thousand two hundred thirty seven individuals met the inclusion criteria. The mean age was 84.6 years (SD 5.9) and 7,074 (57.8%) were females. Between the CFS and HFRS, the Pearson correlation coefficient was 0.36 and weighted kappa score was 0.15. When comparing the highest frailty categories to the lowest frailty category within each frailty score, the ORs for 30-day mortality, LOS > 10 days, and 30-day readmission using the CFS were 2.26, 1.36, and 1.64 and for the HFRS 2.16, 7.68, and 1.19.

CONCLUSION

The CFS collected at the ED and the HFRS had low/slight agreement. Both frailty scores were shown to be predictors of adverse outcomes. More research is needed to assess the use of historic HFRS in the ED.

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

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.

Interaction effects of multimorbidity and frailty on adverse health outcomes in elderly hospitalised patients

We quantified the interaction of multimorbidity and frailty and their impact on adverse health outcomes in the hospital setting. Using aretrospective cohort study of persons aged ≥ 75 years, admitted to hospital during 2010–2012 in New South Wales, Australia, and linked with mortality data, we constructed multimorbidity, frailty risk and outcomes: prolonged length of stay (LOS), 30-day mortality and 30-day unplanned readmissions. Relative risks (RR) of outcomes were obtained using Poisson models with random intercept for hospital. Among 257,535 elderly inpatients, 33.6% had multimorbidity and elevated frailty risk, 14.7% had multimorbidity only, 19.9% had elevated frailty risk only and 31.8% had neither. Additive interactions were present for all outcomes, with a further multiplicative interaction for mortality and LOS. Mortality risk was 4.2 (95% CI 4.1–4.4), prolonged LOS 3.3 (95% CI 3.3–3.4) and readmission 1.8 (95% CI 1.7–1.9) times higher in patients with both factors present compared with patients with neither. In conclusion, multimorbidity and frailty coexist in older hospitalized patients and interact to increase the risk of adverse outcomes beyond the sum of their individual effects. Their joint effect should be considered in health outcomes research and when administering hospital resources.

The association of a frailty index from laboratory tests and vital signs with clinical outcomes in hospitalized older adults

BACKGROUND

Frailty, a state of vulnerability to stressors resulting from loss of physiological reserve due to multisystemic dysfunction, is common among hospitalized older adults. Hospital clinicians need objective and practical instruments that identify older adults with frailty. The FI-LAB is based on laboratory values and vital signs and may capture biological changes of frailty that predispose hospitalized older adults to complications. The study's aim was to assess the association of the FI-LAB versus VA-FI with hospital and post-hospital clinical outcomes in older adults.

METHODS

Retrospective cohort study was conducted on Veterans aged ≥60 admitted to a VA hospital. We identified acute hospitalizations January 2011-December-2014 with 1-year follow-up. A 31-item FI-LAB was created from blood laboratory tests and vital signs collected within the first 48 h of admission and scores were categorized as low (<0.25), moderate (0.25-0.40), and high (>0.40). For each FI-LAB group, we obtained odds ratio (OR) and confidence intervals (CI) for hospital and post-hospital outcomes using multivariate binomial logistic regression. Additionally, we calculated hazard ratios (HR) and CI for all-cause in-hospital mortality comparing the high and moderate FI-LAB group with the low group.

RESULTS

Patients were 1407 Veterans, mean age 72.7 (SD = 9.0), 67.8% Caucasian, 96.1% males, 47.0% (n = 661), 41.0% (n = 577), and 12.0% (n = 169) were in the low, moderate, and high FI-LAB groups, respectively. Moderate and high scores were associated with prolonged LOS, OR:1.62 (95% CI:1.29-2.03); and 3.36 (95% CI:2.27-4.99), ICU admission, OR:1.40 (95% CI:1.03-1.90); and OR:2.00 (95% CI:1.33-3.02), nursing home placement OR:2.36 (95% CI:1.26-4.44); and 5.99 (95% CI:2.83-12.70), 30-day readmissions OR:1.74 (95% CI:1.20-2.52); and 2.20 (95% CI:1.30-3.74), 30-day mortality OR: 2.51 (95% CI:1.01-6.23); and 8.97 (95% CI:3.42-23.53), 6-month mortality OR:3.00 (95% CI:1.90-4.74); and 6.16 (95% CI:3.55-10.71), and 1-year mortality OR: 2.66 (95% CI:1.87-3.79); and 4.76 (95% CI:3.00-7.54) respectively. The high FI-LAB group showed higher risk of in-hospital mortality, HR:18.17 (95% CI:4.01-80.52) with an area-under-the-curve of 0.843 (95% CI:0.75-0.93).

CONCLUSIONS

High and moderate FI-LAB scores were associated with worse in-hospital and post-hospital outcomes. The FI-LAB may identify hospitalized older patients with frailty at higher risk and assist clinicians in implementing strategies to improve outcomes.

The Hospital Frailty Risk Score Identifies Fewer Cases of Frailty in a Community-Based Cohort of Older Men Than the FRAIL Scale and Frailty Index

OBJECTIVES

The recently developed Hospital Frailty Risk Score (HFRS) allows ascertainment of frailty from administrative data. We aimed to compare the HFRS against the widely used FRAIL Scale and Frailty Index.

DESIGN

Population-based cohort study linked to Western Australian Hospital Morbidity Data Collection and Death Registrations.

SETTING AND PARTICIPANTS

The Health in Men Study with frailty determined at Wave 2 (2001/2004), mortality in the 1-year period following Wave 2, and disability at Wave 3 (2008). Participants were 4228 community-based men aged ≥75 years, followed until Wave 3.

MEASUREMENTS

We used multivariable regression to determine the association between each frailty measure and outcomes of length of stay (LOS), death, and disability. We also determined if the additional cases of frailty identified by one measure over the other was associated with these outcomes.

RESULTS

Of 4228 men studied, the HFRS (n = 689) identified fewer men as frail than the FRAIL Scale (n = 1648) and Frailty Index (n = 1820). In the fully adjusted models, all 3 frailty measures were associated with longer LOS and mortality, whereas only the FRAIL Scale and Frailty Index were significantly associated with disability. The additional cases of frailty identified by the FRAIL Scale and Frailty Index had longer LOS and greater risks of death and disability. The fully adjusted hazard ratio for death among the additional cases of frailty identified by the FRAIL Scale (compared to being not frail on both HFRS and FRAIL Scale) was 2.14 (95% CI 1.48-3.08).

CONCLUSIONS AND IMPLICATIONS

The HFRS is associated with adverse outcomes. However, it identified approximately 60% fewer men who were frail than the FRAIL Scale and Frailty Index, and the additional cases identified were also at high risks of adverse outcomes. Users of the HFRS should be aware of the differences with other frailty measures.

Hospital Frailty Risk Score and healthcare resource utilization after surgery for metastatic spinal column tumors

OBJECTIVE

The Hospital Frailty Risk Score (HFRS) was developed utilizing ICD-10 diagnostic codes to identify frailty and predict adverse outcomes in large national databases. While other studies have examined frailty in spine oncology, the HFRS has not been assessed in this patient population. The aim of this study was to examine the association of HFRS-defined frailty with complication rates, length of stay (LOS), total cost of hospital admission, and discharge disposition in patients undergoing spine surgery for metastatic spinal column tumors.

METHODS

A retrospective cohort study was performed using the years 2016 to 2019 of the National Inpatient Sample (NIS) database. All adult patients (≥ 18 years old) undergoing surgical intervention for metastatic spinal column tumors were identified using the ICD-10-CM diagnostic codes and Procedural Coding System. Patients were categorized into the following three cohorts based on their HFRS: low frailty (HFRS < 5), intermediate frailty (HFRS 5-15), and high frailty (HFRS > 15). Patient demographics, comorbidities, treatment modality, perioperative complications, LOS, discharge disposition, and total cost of hospital admission were assessed. A multivariate logistic regression analysis was used to identify independent predictors of prolonged LOS, nonroutine discharge, and increased cost.

RESULTS

Of the 11,480 patients identified, 7085 (61.7%) were found to have low frailty, 4160 (36.2%) had intermediate frailty, and 235 (2.0%) had high frailty according to HFRS criteria. On average, age increased along with progressively worsening frailty scores (p ≤ 0.001). The proportion of patients in each cohort who experienced ≥ 1 postoperative complication significantly increased along with increasing frailty (low frailty: 29.2%; intermediate frailty: 53.8%; high frailty: 76.6%; p < 0.001). In addition, the mean LOS (low frailty: 7.9 ± 5.0 days; intermediate frailty: 14.4 ± 13.4 days; high frailty: 24.1 ± 18.6 days; p < 0.001), rate of nonroutine discharge (low frailty: 40.4%; intermediate frailty: 60.6%; high frailty: 70.2%; p < 0.001), and mean total cost of hospital admission (low frailty: $48,603 ± $29,979; intermediate frailty: $65,271 ± $43,110; high frailty: $96,116 ± $60,815; p < 0.001) each increased along with progressing frailty. On multivariate regression analysis, intermediate and high frailty were each found to be significant predictors of both prolonged LOS (intermediate: OR 3.75 [95% CI 2.96-4.75], p < 0.001; high: OR 7.33 [95% CI 3.47-15.51]; p < 0.001) and nonroutine discharge (intermediate: OR 2.05 [95% CI 1.68-2.51], p < 0.001; high: OR 5.06 [95% CI 1.93-13.30], p = 0.001).

CONCLUSIONS

This study is the first to use the HFRS to assess the impact of frailty on perioperative outcomes in patients with metastatic bony spinal tumors. Among patients with metastatic bony spinal tumors, frailty assessed using the HFRS was associated with longer hospitalizations, more nonroutine discharges, and higher total hospital costs.

Development of an age- and comorbidity adjusted- optimal radiotherapy utilisation rate for women with breast cancer

INTRODUCTION

Optimal radiotherapy utilisation (RTU) modelling estimates the proportion of people with cancer who would benefit from radiotherapy. Older adults with cancer may have comorbidities that can impact physiological reserve and affect radiotherapy recommendations. These have not been considered in previous models. We aimed to develop an age- and comorbidity-adjusted optimal RTU model for breast cancer.

METHODS

New South Wales (NSW) Cancer Registry data (2010-2014) linked to radiotherapy data (2010-2015) and hospitalisation data (2008-2015) was used to determine the number of women diagnosed with invasive breast cancer in four pre-specified age groups. The Charlson Comorbidity Index (CCI), Cancer-Specific C3 'all sites' index and the Hospital Frailty Risk Score (HFRS) were derived for each woman from diagnostic codes in hospital records. Women were deemed unfit, and thus unsuitable candidates for radiotherapy, if the comorbidity indices were as follows: CCI ≥2; C3 score ≥ 3; and HFRS ≥5. The proportions of women suitable for radiotherapy in each age group were then incorporated into a breast cancer decision tree model. The actual RTU was also calculated using the linked datasets.

RESULTS

23,601 women were diagnosed with breast cancer in NSW from 2010 to 2014 and 2526 were aged 80+ years. The overall comorbidity adjusted- RTU for women of all ages was 85·9% (CCI), 83·7% (C3) and 81·9% (HFRS). The optimal comorbidity adjusted- RTU for women aged 80+ was 76·1% (CCI), 70·1% (C3) and 61·8% (HFRS). The actual RTU for women aged 80+ years was 24.7%.

CONCLUSION

The vast majority of older Australian women with breast cancer are fit for radiotherapy. The overall optimal RTU is only slightly reduced when adjusted for age and comorbidities and was similar using each of the three indices examined. Our data suggest radiotherapy is markedly underutilised for older women with breast cancer.

Assessment of Frailty Indices and Charlson Comorbidity Index for Predicting Adverse Outcomes in Patients Undergoing Surgery for Spine Metastases: A National Database Analysis

OBJECTIVE

The aim of this study was to assess the predictive ability of Metastatic Spinal Tumor Frailty Index (MSTFI) and the Modified 5-Item Frailty Index (mFI-5) on adverse outcomes, compared with the known Charlson Comorbidity Index (CCI).

METHODS

A retrospective cohort study was performed using National Surgical Quality Improvement Program database from 2011 to 2019. All adult patients undergoing various procedures for extradural spinal metastases were identified. Patients were stratified into frail and nonfrail cohorts based on CCI, mFI-5, and MSTFI scores. A multivariate logistic regression analysis was used to identify independent predictors of prolonged length of stay, nonroutine discharge, adverse events, and unplanned readmission.

RESULTS

Of the 1613 patients included in this study, 21.4% had a CCI >0, 56.6% had an mFI-5 >0, and 76.7% of patients had an MSTFI >0. On multivariate analysis, all 3 indices were found to be predictive of nonroutine discharge (CCI: adjusted odds ratio [aOR], 1.41 vs. mFI-5: aOR, 1.37 vs. MSTFI: aOR, 1.5) and adverse events (CCI: aOR, 1.53 vs. mFI-5: aOR, 1.23 vs. MSTFI: aOR, 1.43). High CCI (adjusted relative risk, 1.67) and MSTFI (adjusted relative risk, 1.14), but not mFI-5, were also associated with a prolonged length of stay, whereas MSTFI was found to be the only significant predictor of unplanned readmission (aOR, 1.22).

CONCLUSIONS

Our study suggests that MSTFI frailty index may be more sensitive than both CCI and mFI-5 in identifying adverse outcomes after spine surgery for metastases.

Frailty in patients undergoing percutaneous left atrial appendage closure

BACKGROUND

Frailty is associated with significant morbidity and mortality in older adults. Whether frailty predicts adverse outcomes after percutaneous left atrial appendage closure (LAAC) remains uncertain.

OBJECTIVE

The purpose of this study was to examine the association between frailty and clinical outcomes after percutaneous LAAC.

METHODS

We identified patients 65 years and older in Medicare fee-for-service claims who underwent LAAC between October 1, 2016, and December 31, 2019. Patients were identified as frail on the basis of the Hospital Frailty Risk Score (HFRS), a validated frailty measure centered on health resource utilization, with the cohort stratified into low (<5), intermediate (5-15), and high (>15) risk groups.

RESULTS

Of the 21,787 patients who underwent LAAC, 10,740 (49.3%) were considered frail (HFRS >5), including 3441 (15.8%) in the high-risk group. The mortality rate (up to 1095 days) were 16.1% in the low-risk group, 26.7% in the intermediate-risk group, and 41.1% in the high-risk group (P < .001). After adjusting for age, sex, and comorbidities, HFRS >15 (compared with HFRS <5) was associated with a higher risk of long hospital stay (odds ratio [OR] 8.29; 95% confidence interval [CI] 5.94-11.57), 30-day readmission (OR 1.80, 95% CI 1.58-2.05), 30-day mortality (OR 5.68, 95% CI 3.40-9.40), and 1-year mortality (OR 2.83, 95% CI 2.39-3.35). In restricted cubic spline models, the adjusted OR for all outcomes monotonically increased with increasing HFRS.

CONCLUSION

Frailty is common in patients undergoing LAAC and is associated with increased risks of long hospital stay, readmissions, and short-term mortality.

External Validation of the Hospital Frailty-Risk Score in Predicting Clinical Outcomes in Older Heart-Failure Patients in Australia

Frailty is common in older hospitalised heart-failure (HF) patients but is not routinely assessed. The hospital frailty-risk score (HFRS) can be generated from administrative data, but it needs validation in Australian health-care settings. This study determined the HFRS scores at presentation to hospital in 5735 HF patients ≥ 75 years old, admitted over a period of 7 years, at two tertiary hospitals in Australia. Patients were classified into 3 frailty categories: HFRS < 5 (low risk), 5−15 (intermediate risk) and >15 (high risk). Multilevel multivariable regression analysis determined whether the HFRS predicts the following clinical outcomes: 30-day mortality, length of hospital stay (LOS) > 7 days, and 30-day readmissions; this was determined after adjustment for age, sex, Charlson index and socioeconomic status. The mean (SD) age was 76.1 (14.0) years, and 51.9% were female. When compared to the low-risk HFRS group, patients in the high-risk HFRS group had an increased risk of 30-day mortality and prolonged LOS (adjusted OR (aOR) 2.09; 95% CI 1.21−3.60) for 30-day mortality, and an aOR of 1.56 (95% CI 1.01−2.43) for prolonged LOS (c-statistics 0.730 and 0.682, respectively). Similarly, the 30-day readmission rate was significantly higher in the high-risk HFRS group when compared to the low-risk group (aOR 1.69; 95% CI 1.06−2.69; c-statistic = 0.643). The HFRS, derived at admission, can be used to predict ensuing clinical outcomes among older hospitalised HF patients.

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.

External validation of the Hospital Frailty Risk Score in France

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.

Beyond the revised cardiac risk index: Validation of the hospital frailty risk score in non-cardiac surgery

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.

Frailty and In-Hospital Mortality Risk Using EHR Nursing Data

PurposeThe purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. Methods In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated. Results The 46,645 patients averaged 1.5 hospitalizations (SD = 1.1) over the study period and 63.3% were emergent admissions. The average age was 70.4 years (SD = 11.4), 55.3% were female, 73.0% were non-Hispanic White (73.0%), mean comorbidity score was 3.9 (SD = 2.9), 80.5% were taking 1.5 high risk medications, and 42% recorded polypharmacy. The best performing FRS-NF-26-LABS included nursing flowsheet data and blood biomarkers (Adj. HR = 1.30, 95% CI [1.28, 1.33]), with good accuracy (iAUC = .794); the reduced model with age, sex, and FRS only demonstrated similar accuracy. The poorest performance was the ICD-10 code-based FRS. Conclusion The FRS captures information about the patient that increases risk for in-hospital mortality not accounted for by other factors. Identification of frailty enables providers to enhance various aspects of care, including increased monitoring, applying more intensive, individualized resources, and initiating more informed discussions about treatments and discharge planning.

Association of Frailty With Treatment Selection and Long-Term Outcomes Among Patients With Chronic Limb-Threatening Ischemia

Background The optimal treatment strategy for patients with chronic limb-threatening ischemia (CLTI) is often unclear. Frailty has emerged as an important factor that can identify patients at greater risk of poor outcomes and guide treatment selection, but few studies have explored its utility among the CLTI population. We examine the association of a health record-based frailty measure with treatment choice and long-term outcomes among patients hospitalized with CLTI. Methods and Results We included patients aged >65 years hospitalized with CLTI in the Medicare Provider Analysis and Review data set between October 1, 2009 and September 30, 2015. The primary exposure was frailty, defined by the Claims-based Frailty Indicator. Baseline frailty status and revascularization choice were examined using logistic regression. Cox proportional hazards regression was used to determine the association between frailty and death or amputation, stratifying by treatment strategy. Of 85 060 patients, 35 484 (42%) were classified as frail. Frail patients had lower likelihood of revascularization (adjusted odds ratio [OR], 0.78; 95% CI, 0.75‒0.82). Among those revascularized, frailty was associated with lower likelihood of surgical versus endovascular treatment (adjusted OR, 0.76; CI, 0.72‒0.81). Frail patients experienced increased risk of amputation or death, regardless of revascularization status (revascularized: adjusted hazard ratio [HR], 1.34; CI, 1.30‒1.38; non-revascularized: adjusted HR, 1.22; CI, 1.17‒1.27). Among those revascularized, frailty was independently associated with amputation or death irrespective of revascularization strategy (surgical: adjusted HR, 1.36; CI, 1.31‒1.42; endovascular: aHR, 1.29; CI, 1.243‒1.35). Conclusions Among patients hospitalized with CLTI, frailty is an important independent predictor of revascularization strategy and longitudinal adverse outcomes.