Validation of the hospital frailty risk score in a tertiary care hospital in Switzerland: results of a prospective, observational 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.

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.

Frailty and In-Hospital Outcomes for Management of Cardiogenic Shock without Acute Myocardial Infarction

(1) Background: Cardiogenic shock (CS) is associated with high morbidity and mortality. Frailty and cardiovascular diseases are intertwined, commonly sharing risk factors and exhibiting bidirectional relationships. The relationship of frailty and non-acute myocardial infarction with cardiogenic shock (non-AMI-CS) is poorly described. (2) Methods: We retrospectively analyzed the National Inpatient Sample from 2016 to 2020 and identified all hospitalizations for non-AMI-CS. We classified them into frail and non-frail groups according to the hospital frailty risk score cut-off of 5 and compared in-hospital outcomes. (3) Results: A total of 503,780 hospitalizations for non-AMI-CS were identified. Most hospitalizations involved frail adults (80.0%). Those with frailty had higher odds of in-hospital mortality (adjusted odds ratio [aOR] 2.11, 95% confidence interval [CI] 2.03-2.20, p < 0.001), do-not-resuscitate status, and discharge to a skilled nursing facility compared with those without frailty. They also had higher odds of in-hospital adverse events, such as acute kidney injury, delirium, and longer length of stay. Importantly, non-AMI-CS hospitalizations in the frail group had lower use of mechanical circulatory support but not rates of cardiac transplantation. (4) Conclusions: Frailty is highly prevalent among non-AMI-CS hospitalizations. Those accompanied by frailty are often associated with increased rates of morbidity and mortality compared to those without frailty.

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.

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.

The Impact of Frailty on Outcomes of Proximal Aortic Aneurysm Surgery: A Nationwide Analysis

(1) Background: This study examines frailty's impact on proximal aortic surgery outcomes. (2) Methods: All patients with a thoracic aortic aneurysm who underwent aortic root, ascending aorta, or arch surgery from the 2016-2017 National Inpatient Sample were included. Frailty was defined by the Adjusted Clinical Groups Frailty Indicator. Outcomes of interest included in-hospital mortality and a composite of death, stroke, acute kidney injury (AKI), and major bleeding (MACE). (3) Results: Among 5745 patients, 405 (7.0%) met frailty criteria. Frail patients were older, with higher rates of chronic pulmonary disease, diabetes, and chronic kidney disease. There was no difference in in-hospital death (4.9% vs. 2.4%, p = 0.169); however, the frail group exhibited higher rates of stroke and AKI. Frail patients had a longer length of stay (17 vs. 8 days), and higher rates of non-home discharge (74.1% vs. 54.3%) than non-frail patients (both p < 0.001). Sensitivity analysis confirmed increased morbidity and mortality in frail individuals. After adjusting for patient comorbidities and hospital characteristics, frailty independently predicted MACE (OR 4.29 [1.88-9.78], p = 0.001), while age alone did not (OR 1.00 [0.99-1.02], p = 0.568). Urban teaching center status predicted a lower risk of MACE (OR 0.27 [0.08-0.94], p = 0.039). (4) Conclusions: Frailty is associated with increased morbidity in proximal aortic surgery and is a more significant predictor of mortality than age. Coordinated treatment in urban institutions may enhance outcomes for this high-risk group.

Development of a hospital frailty risk score for community-dwelling older adults using data from electronic hospital records in South Korea

PURPOSE

We aimed to develop the Korean Hospital Frailty Risk Score (K-HFRS) by applying the International Classification of Diseases-10 codes to community-dwelling older adults' medical data.

METHODS

We selected data from 2,761 people with no missing main variable values from the Korean Frailty and Aging Cohort Data (KFACD) and National Health Insurance Database (NHID) for analysis. Frailty was determined based on modified Fried's phenotype [MFP] and Korean Frailty Index for Primary Care [KFI-PC] in the KFACD. A previously established method calculated the K-HFRS, verified by the area under the receiver operating characteristic (ROC) curve. The calculated cutoff value predicted the medical use.

RESULTS

The respective K-HFRSs of the frailty group using the MFP and KFI-PC criteria ranged from 3.64 (±3.03) to 8.15 (±5.72) and 4.07 (±3.42) to 9.10 (±6.28), with 7.67 (±5.40) and 8.59 (±6.03) when four diagnoses were included. The K-HFRS of the frailty group using the KFI-PC criteria was higher than that using the MFP criteria. With four diagnoses included using the MFP criteria, the adjusted odds ratio (OR) for medical expenditures in the frailty group compared to the non-frailty group was 3.01 (95% confidence interval [CI] 2.52-3.60, p < .001); for the number of emergency room (ER) visits was 2.19 (95% CI 1.77-2.70, p < .001); for inpatient days was 2.48 (95% CI 2.08-2.96, p < .001). With four diagnoses included using the KFI-PC criteria, the adjusted OR value for medical expenditures was 2.77 (95% CI 2.35-3.27, p < .001); for the number of ER visits was 1.87 (95% CI 1.51-2.32, p < .001); for inpatient days was 2.07 (95% CI 1.75-2.45, p < .001).

CONCLUSION

This study substantiated that the K-HFRS can measure frailty efficiently at a lower cost. Follow-up studies are needed for additional validity.

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.

Frailty Among Breast Cancer Survivors: Evidence From Swedish Population Data

Incidence and survival of breast cancer, the most common cancer among women, have been increasing, leaving survivors at risk of aging-related health conditions. In this matched cohort study, we examined frailty risk with the Hospital Frailty Risk Score among breast cancer survivors (n = 34,900) and age-matched comparison subjects (n = 290,063). Women born in 1935-1975, registered in the Swedish Total Population Register (1991-2015), were eligible for inclusion. Survivors had a first breast cancer diagnosis in 1991-2005 and survived ≥5 years after initial diagnosis. Death date was determined by linkage to the National Cause of Death Registry (through 2015). Cancer survivorship was weakly associated with frailty (subdistribution hazard ratio (SHR) = 1.04, 95% confidence interval (CI): 1.00, 1.07). In age-stratified models, those diagnosed at younger ages (<50 years) had higher risk of frailty (SHR = 1.12, 95% CI: 1.00, 1.24) than those diagnosed at ages 50-65 (SHR = 1.03, 95% CI: 0.98, 1.07) or >65 (SHR = 1.09, 95% CI: 1.02, 1.17) years. Additionally, there was increased risk of frailty for diagnoses in 2000 or later (SHR = 1.15, 95% CI: 1.09, 1.21) compared with before 2000 (SHR = 0.97, 95% CI: 0.93, 1.17). This supports work from smaller samples showing that breast cancer survivors have increased frailty risk, particularly when diagnosed at younger ages.

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.

Impact of the COVID pandemic on major abdominal cancer resections in Germany: a retrospective population-based cohort study

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is estimated to have claimed more than 6 million lives globally since it started in 2019. Germany was exposed to two waves of coronavirus disease 2019 in 2020, one starting in April and the other in October. To ensure sufficient capacity for coronavirus disease 2019 patients in intensive care units, elective medical procedures were postponed. The fraction of major abdominal cancer resections affected by these measures remains unknown, and the most affected patient cohort has yet to be identified.

METHODS

This is a register-based, retrospective, nationwide cohort study of anonymized 'diagnosis-related group' billing data provided by the Federal Statistical Office in Germany. Cases were identified using diagnostic and procedural codes for major cancer resections. Population-adjusted cancer resection rates as the primary endpoint were compared at baseline (2012-2019) to those in 2020.

RESULTS

A change in resection rates for all analyzed entities (esophageal, gastric, liver, pancreatic, colon, rectum, and lung cancer) was observed from baseline to 2020. Total monthly oncological resections dropped by 7.4% (8.7% normalized to the annual German population, P =0.011). Changes ranged from +3.7% for pancreatic resections ( P =0.277) to -19.4% for rectal resections ( P <0.001). Reductions were higher during lockdown periods. During the first lockdown period (April-June), the overall drop was 14.3% (8.58 per 100 000 vs. 7.35 per 100 000, P <0.001). There was no catch-up effect during the summer months except for pancreatic cancer resections. In the second lockdown period, there was an overall drop of 17.3%. In subgroup analyses, the elderly were most affected by the reduction in resection rates. There was a significant negative correlation between regional SARS-CoV-2 incidences and resections rates. This correlation was strongest for rectal cancer resections (Spearman's r : -0.425, P <0.001).

CONCLUSIONS

The pandemic lockdowns had a major impact on the oncological surgical caseload in Germany in 2020. The elderly were most affected by the reduction. There was a clear correlation between SARS-CoV-2 incidences regionally and the reduction of surgical resection rates. In future pandemic circumstances, oncological surgery has to be prioritized with an extra focus on the most vulnerable patients.

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.

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.

Disproportionately high failure to rescue rates after resection for colorectal cancer in the geriatric patient population - A nationwide study

BACKGROUND

Colorectal cancer incidence increases with patient age. The aim of this study was to assess, at the nationwide level, in-hospital mortality, and failure to rescue in geriatric patients (≥ 80 years old) with colorectal cancer arising from postoperative complications.

METHODS

All patients receiving surgery for colorectal cancer in Germany between 2012 and 2018 were identified in a nationwide database. Association between age and in-hospital mortality following surgery and failure to rescue, defined as death after complication, were determined in univariate and multivariate analyses.

RESULTS

Three lakh twenty-eight thousands two hundred and ninety patients with colorectal cancer were included of whom 77,287 were 80 years or older. With increasing age, a significant relative increase in right hemicolectomy was observed. In general, these patients had more comorbid conditions and higher frailty. In-hospital mortality following colorectal cancer surgery was 4.9% but geriatric patients displayed a significantly higher postoperative in-hospital mortality of 10.6%. The overall postoperative complication rate as well as failure to rescue increased with age. In contrast, surgical site infection (SSI) and anastomotic leakage (AL) did not increase in geriatric patients, whereas the associated mortality increased disproportionately (13.3% for SSI and 29.9% mortality for patients with AI, both p < 0.001). Logistic regression analysis adjusting for confounders showed that geriatric patients had almost five-times higher odds for death after surgery than the baseline age group below 60 (OR 4.86; 95%CI [4.45-5.53], p < 0.001).

CONCLUSION

Geriatric patients have higher mortality after colorectal cancer surgery. This may be partly due to higher frailty and disproportionately higher rates of failure to rescue arising from postoperative complications.

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.

The impact of frailty on mortality in older patients admitted to an Intensive Care Unit

OBJECTIVE

Frailty is a relatively new concept for intensivists, and is defined as a status of increased vulnerability to stressors associated with reduced reserve and function of different physiological systems. Supporting the hypothesis that frailty may be an important predictor of poor prognosis among older patients admitted to Intensive Care Unit (ICU), this study seeks to evaluate the association between frailty at ICU admission and short and long-term mortality.

DESIGN

An unmatched case-control study was carried out.

SETTING

Intensive Care Unit.

PATIENTS OR PARTICIPANTS

Patients≥80 years of age admitted to the ICU for medical reasons.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

The primary outcome was 30-day mortality, while secondary outcomes were ICU mortality and mortality at one year.

RESULTS

Most of the patients were classified as frail at ICU admission (55.3%). The prevalence of frailty was higher among those who died than in those who were alive within 30 days from ICU admission (62.3% vs 48.3%, p=0.01). One-year mortality was higher in frail (84.4%) than in non-frail patients (65.2%, p<0.001). In the logistic regression analysis, after adjusting for potential confounders such as chronic diseases, clinical complexity, cause of ICU admission and use of advanced procedures, frailty was seen to be significantly associated to one-year mortality, but not with ICU mortality or 30-day mortality.

DISCUSSION

The admission of geriatric patients to the ICU is increasing. Frailty assessment may play an important role in the clinical evaluation of such individuals for triage, but should not be considered a priori as an exclusion criterion for admission.

Screening, Diagnosis and Management of Sarcopenia and Frailty in Hospitalized Older Adults: Recommendations from the Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR) Expert Working Group

Sarcopenia and frailty are highly prevalent conditions in older hospitalized patients, which are associated with a myriad of adverse clinical outcomes. This paper, prepared by a multidisciplinary expert working group from the Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR), provides an up-to-date overview of current evidence and recommendations based on a narrative review of the literature for the screening, diagnosis, and management of sarcopenia and frailty in older patients within the hospital setting. It also includes suggestions on potential pathways to implement change to encourage widespread adoption of these evidence-informed recommendations within hospital settings. The expert working group concluded there was insufficient evidence to support any specific screening tool for sarcopenia and recommends an assessment of probable sarcopenia/sarcopenia using established criteria for all older (≥65 years) hospitalized patients or in younger patients with conditions (e.g., comorbidities) that may increase their risk of sarcopenia. Diagnosis of probable sarcopenia should be based on an assessment of low muscle strength (grip strength or five times sit-to-stand) with sarcopenia diagnosis including low muscle mass quantified from dual energy X-ray absorptiometry, bioelectrical impedance analysis or in the absence of diagnostic devices, calf circumference as a proxy measure. Severe sarcopenia is represented by the addition of impaired physical performance (slow gait speed). All patients with probable sarcopenia or sarcopenia should be investigated for causes (e.g., chronic/acute disease or malnutrition), and treated accordingly. For frailty, it is recommended that all hospitalized patients aged 70 years and older be screened using a validated tool [Clinical Frailty Scale (CFS), Hospital Frailty Risk Score, the FRAIL scale or the Frailty Index]. Patients screened as positive for frailty should undergo further clinical assessment using the Frailty Phenotype, Frailty Index or information collected from a Comprehensive Geriatric Assessment (CGA). All patients identified as frail should receive follow up by a health practitioner(s) for an individualized care plan. To treat older hospitalized patients with probable sarcopenia, sarcopenia, or frailty, it is recommended that a structured and supervised multi-component exercise program incorporating elements of resistance (muscle strengthening), challenging balance, and functional mobility training be prescribed as early as possible combined with nutritional support to optimize energy and protein intake and correct any deficiencies. There is insufficient evidence to recommend pharmacological agents for the treatment of sarcopenia or frailty. Finally, to facilitate integration of these recommendations into hospital settings organization-wide approaches are needed, with the Spread and Sustain framework recommended to facilitate organizational culture change, with the help of 'champions' to drive these changes. A multidisciplinary team approach incorporating awareness and education initiatives for healthcare professionals is recommended to ensure that screening, diagnosis and management approaches for sarcopenia and frailty are embedded and sustained within hospital settings. Finally, patients and caregivers' education should be integrated into the care pathway to facilitate adherence to prescribed management approaches for sarcopenia and frailty.

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.

Trends in Frailty and Use of Evidence-Based Pharmacotherapy for Heart Failure in Australian Hospitalised Patients: An Observational Study

Frailty increases morbidity and mortality in heart failure (HF) patients. Current risk-adjustment models do not include frailty-status and the relationship between frailty and pharmacotherapy is unclear. This study explored trends in frailty over time and its relationship with prescription of heart failure specific pharmacotherapy in hospitalised HF patients. We used the Hospital Frailty Risk Score (HFRS) to determine frailty status of patients ≥18 years admitted between 2015-2019 at two tertiary hospitals in Australia. Patients with an HFRS ≥ 5 were classified as frail. In the 3706 patients with a mean (SD) age of 76.1 (14.4) years, 876 (23.6%) were classified as frail. HFRS was weakly correlated with age (r = 0.16) and Charlson-index (r = 0.35) (both p values < 0.001). Whilst frailty was more common in older HF patients (28.9% of patients ≥80 years), 15.1% of patients ≤65 years of age were also found to be frail. The proportion of frail patients increased from 19.4% in 2015 to 29.2% in 2019 despite no significant change in age during this period. The proportion of patients who received heart failure specific pharmacotherapy decreased from 86.7% in 2015 to 82.9% in 2019 (p value = 0.03) and frail patients were significantly less likely to be prescribed HF specific pharmacotherapy than non-frail patients (77.4% vs. 85.9%, p < 0.001).

Applying Administrative Data-Based Coding Algorithms for Frailty in Patients With Cirrhosis

Frailty is a powerful prognostic tool in cirrhosis. Claims-based frailty scores estimate the presence of frailty without the need for in-person evaluation. These algorithms have not been validated in cirrhosis. Whether they measure true frailty or perform as well as frailty in outcome prediction is unknown. We evaluated 2 claims-based frailty scores-Hospital Frailty Risk Score (HFRS) and Claims-Based Frailty Index (CFI)-in 3 prospective cohorts comprising 1100 patients with cirrhosis. We assessed differences in neuromuscular/neurocognitive capabilities in those classified as frail or nonfrail based on each score. We assessed the ability of the indexes to discriminate frailty based on the Fried Frailty Index (FFI), chair stands, activities of daily living (ADL), and falls. Finally, we compared the performance of claims-based frailty measures and physical frailty measures to predict transplant-free survival using competing risk regression and patient-reported outcomes. The CFI identified neuromuscular deficits (balance, chair stands, hip strength), whereas the HFRS only identified poor chair-stand performance. The CFI had areas under the receiver operating characteristic curve (AUROCs) for identifying frailty as measured by the FFI, ADL, and falls of 0.57, 0.60, and 0.68, respectively; similarly, the AUROCs were 0.66, 0.63, and 0.67, respectively, for the HFRS. Claims-based frailty scores were associated with poor quality of life and sleep but were outperformed by the FFI and chair stands. The HFRS, per 10-point increase (but not the CFI) predicted survival of patients in the liver transplantation (subdistribution hazard ratio [SHR], 1.08; 95% confidence interval [CI], 1.03-1.12) and non-liver transplantation cohorts (SHR, 1.13; 95% CI, 1.05-1.22). Claims-based frailty scores do not adequately associate with physical frailty but are associated with important cirrhosis-related outcomes.

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

BACKGROUND

Despite recent evidence on the effect of frailty on health outcomes among those with heart failure, there is a dearth of knowledge on measuring frailty using administrative health data on a wide range of cardiovascular diseases (CVD).

METHODS

We conducted a retrospective record-linkage cohort study of patients with diverse CVD in Queensland, Australia. We investigated the relationship between the risk of frailty, defined using the hospital frailty risk score (HFRS), and 30-day mortality, 30-day unplanned readmission, non-home discharge, length of hospital stay (LOS) at an emergency department and inpatient units and costs of hospitalisation. Descriptive analysis, bivariate logistic regression and generalised linear models were used to estimate the association between HFRS and CVD outcomes. Smear adjustment was applied to hospital costs and the LOS for each frailty risk groups.

RESULTS

The proportion of low, medium and high risk of frailty was 24.6%, 34.5% and 40.9%, respectively. The odds of frail patients dying or being readmitted within 30 days of discharge was 1.73 and 1.18, respectively. Frail patients also faced higher odds of LOS, and non-home discharge at 3.1 and 2.25, respectively. Frail patients incurred higher hospital costs (by 42.7-55.3%) and stayed in the hospital longer (by 49%).

CONCLUSION

Using the HFRS on a large CVD cohort, this study confirms that frailty was associated with worse health outcomes and higher healthcare costs. Administrative data should be more accessible to research such that the HFRS can be applied to healthcare planning and patient care.

Evaluating the impact of frailty on periprocedural adverse events and mortality among patients with GI bleeding

BACKGROUND AND AIMS

Frailty is a known predictor of mortality and adverse events in the inpatient setting; however, it has not been studied as a modality to assess risk among patients undergoing endoscopy for GI bleeding (GIB). We aimed to determine the association between frailty status and risk of adverse events in hospitalized patients with GIB who underwent endoscopy.

METHODS

We performed a cohort study using the 2016 and 2017 National Inpatient Sample database, using International Classification of Diseases diagnostic codes to identify adult patients with GIB who underwent endoscopic procedures within 2 days of admission and the Hospital Frailty Risk Score to classify patients as frail or nonfrail. Univariable and multivariable logistic regression models were constructed to assess the predictors of periprocedural adverse events, and marginal standardization analysis was performed to assess for possible interaction between age and frailty.

RESULTS

A total of 757,920 patients met inclusion criteria, of which 44.4% (336,895) were identified as frail and 55.6% (421,025) as nonfrail; 49.2% of frail patients had composite periprocedural adverse events compared with 25.5% of nonfrail patients (P < .001). Frail patients notably had more cardiovascular (32.1% vs 17.1%, P < .001), pulmonary (18.5% vs 4.3%, P < .001), GI (10.1% vs 6.1%, P < .001), and infectious (9.9% vs .7%, P < .001) adverse events compared with nonfrail patients. Frail patients also had higher all-cause inpatient mortality rates (4.8% vs .5%, P < .001). On multivariable analysis, positive frailty status was associated with a 2.13 times increased likelihood of having composite periprocedural adverse events.

CONCLUSIONS

In hospitalized patients undergoing endoscopy for GIB, frailty status is associated with increased periprocedural adverse events including all-cause mortality. The use of frailty assessments can thus further guide clinical decision-making when considering endoscopy and risk of adverse events in adult patients with GI hemorrhage.

Validation of the Hospital Frailty Risk Score in older surgical patients: A population-based retrospective cohort study

BACKGROUND

There is a need for standardized and cost-effective identification of frailty risk. The objective was to validate the Hospital Frailty Risk Score which utilizes International Classification Diagnoses in a cohort of older surgical patients, assess the score as an independent risk factor for adverse outcomes and compare discrimination properties of the frailty risk score with other risk stratification scores.

METHODS

Data were analysed from all patients ≥65 years undergoing primary surgical procedures from 2006-2018. Patients were categorized based on the frailty risk score. The primary outcomes were 30-day mortality and 180-day risk of readmission.

RESULTS

Of 16 793 patients evaluated, 7480 (45%), 7605 (45%) and 1708 (10%) had a low, intermediate and high risk of frailty. There was a higher incidence of 30-day mortality for individuals with intermediate (2.9%) and high (8.3%) compared with low (1.4%) risk of frailty (P < .001 for both comparisons). Similarly, the hazard of readmission within the first 180 days was higher for intermediate (HR 1.25; 95% CI: 1.16-1.34) and high (HR 1.84; 95% CI: 1.66-2.03) compared with low (HR 1.00, P < .001 for both comparisons) risk of frailty. The hazard of long-term mortality was higher for intermediate (HR 1.70; 95% CI: 1.61-1.80) and high (HR 4.16; 95% CI: 3.84-4.49) compared with low (HR 1.00, P < .001 for both comparisons) risk of frailty. Finally, long length of primary hospitalization occurred for 9.3%, 15.0% and 27.3% of individuals with low, intermediate and high frailty risk (P < .001 for all comparisons). A model including age and ASA classification had the best discrimination for 30-day mortality (AUC 0.862; 95% CI: 0.847-0.877).

CONCLUSION

Our findings suggest that the Hospital Frailty Risk Score might be used to screen older surgical patients for risk of frailty. While only slightly improving prediction of 30-day mortality using the ASA classification, the Hospital Frailty Risk Score can be used to independently classify older patients for the risk of important outcomes using pre-existing readily available electronic data.

The Hospital Frailty Risk Score is Not an Accurate Predictor of Treatment Costs for Total Joint Replacement Patients in a Medicare Bundled Payment Population

BACKGROUND

Medically complex patients require more resources and experience higher costs within total joint arthroplasty (TJA) bundled payment models. While risk adjustment would be beneficial for such patients, no tool currently exists which can reliably identify these patients preoperatively. The purpose of this study is to determine if the Hospital Frailty Risk Score (HFRS) is a valid predictor of high-TJA treatment costs.

METHODS

Retrospective analysis was performed on patients who underwent primary TJA between 2015 and 2020 from a single large orthopedic practice. ICD-10 codes from an institutional database were used to calculate HFRS. Cost data including inpatient, postacute, and episode of care (EOC) costs were collected. Charlson comorbidity index, demographics, readmissions, and complications were analyzed.

RESULTS

4936 patients had a calculable HFRS and those with intermediate and high scores experienced more frequent readmissions/complications after TJA, as well as higher EOC costs. However, HFRS did not reliably predict EOC costs, yielding a sensitivity of 49% and specificity of 66%. Multivariate analysis revealed that both patient age and sex are superior individual cost predictors when compared with HFRS. Secondary analyses indicated that HFRS more effectively predicts TJA complications and readmissions but is still nonideal for clinical applications.

CONCLUSION

HFRS has poor sensitivity as a predictor of high-EOC costs for TJA patients but has adequate specificity for predicting postoperative readmissions and complications. Further research is needed to develop a scale that can appropriately predict orthopedic cost outcomes.

The degree of frailty as a translational measure of health in aging

Frailty is a multiply determined, age-related state of increased risk for adverse health outcomes. We review how the degree of frailty conditions the development of late-life diseases and modifies their expression. The risks for frailty range from subcellular damage to social determinants. These risks are often synergistic-circumstances that favor damage also make repair less likely. We explore how age-related damage and decline in repair result in cellular and molecular deficits that scale up to tissue, organ and system levels, where they are jointly expressed as frailty. The degree of frailty can help to explain the distinction between carrying damage and expressing its usual clinical manifestations. Studying people-and animals-who live with frailty, including them in clinical trials and measuring the impact of the degree of frailty are ways to better understand the diseases of old age and to establish best practices for the care of older adults.

Trends in prevalence and outcomes of frailty in a Swiss university hospital: a retrospective observational study

BACKGROUND

Frailty complicates management and worsens outcomes. We assessed the prevalence, determinants and consequences of frailty among elderly patients in a hospital setting.

DESIGN

Retrospective observational study in a Swiss university hospital.

METHODS

22,323 patients aged ≥65 years hospitalized between January 2009 and December 2017 at the internal medicine ward were included. Frailty was defined by the Hospital Frailty Risk Score (HFRS) and patients were categorized as low (HFRS<5), intermediate (HFRS 5-15) and high (HFRS>15) risk.

RESULTS

Overall prevalence of intermediate and high risk of frailty was 43% and 20%, respectively; prevalence was higher in women and increased with age. Prevalence of high risk of frailty increased from 11.4% in 2009 to 31% in 2012, and decreased to 19.2% in 2017. After multivariable adjustment, frailty was associated with increased length of stay: average and (95% confidence interval) 11.9 (11.7-12.1), 15.6 (15.4-15.8) and 19.7 (19.3-20.1) days for low, intermediate and high risk, respectively, and increased likelihood of ICU stay: odds ratio (OR) and (95% CI) 1.57 (1.41-1.75) and 2.10 (1.82-2.42) for intermediate and high risk, respectively, p for trend <0.001. Frailty was associated with increased likelihood of hospital costs >70,000 CHF: OR and (95% CI) 3.46 (2.79-4.29) and 10.7 (8.47-13.6) for intermediate and high risk, respectively, p for trend <0.001, and with a lower likelihood of complete cost coverage: OR and (95% CI) 0.70 (0.65-0.76) and 0.52 (0.47-0.58) for intermediate and high risk, respectively, p for trend<0.001.

CONCLUSIONS

Frailty is a frequent condition among hospitalized patients and is associated with higher costs.

Prognostic association of frailty with post-arrest outcomes following cardiac arrest: A systematic review and meta-analysis

OBJECTIVE

To synthesize the current evidence examining the association between frailty and a series of post-arrest outcomes following the provision of cardiopulmonary resuscitation (CPR).

DATA SOURCES

We searched MEDLINE, PubMed (exclusive of MEDLINE), EMBASE, CINAHL, and Web of Science from inception to August 2020 for observational studies that examined an association between frailty and post-arrest health outcomes, including in-hospital and post-discharge mortality. We conducted citation tracking for all eligible studies.

STUDY SELECTION

Our search yielded 20,480 citations after removing duplicate records. We screened titles, abstracts and full-texts independently and in duplicate.

DATA EXTRACTION

The prognosis research strategy group (PROGRESS) and the critical appraisal and data extraction for systematic review of prediction modelling studies (CHARMS) guidelines were followed. Study and outcome-specific risk of bias were assessed using the Quality in Prognosis Studies (QUIPS) instrument. We rated the certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) recommendations for prognostic factor research.

DATA SYNTHESIS

Four studies were included in this review and three were eligible for statistical pooling. Our sample comprised 1,134 persons who experienced in-hospital cardiac arrest (IHCA). The mean age of the sample was 71 years. The study results were pooled according to the specific frailty instrument. Three studies used the Clinical Frailty Scale (CFS) and adjusted age (our minimum confounder); the presence of frailty was associated with an approximate three-fold increase in the odds of dying in-hospital after IHCA (aOR = 2.93; 95% CI = 2.43-3.53, high certainty). Frailty was also associated with decreased incidence of ROSC (return of spontaneous circulation) and discharge home following IHCA. One study with high risk of bias used the Hospital Frailty Risk Score and reported a 43% decrease in the odds of discharge home for patients with frailty following IHCA.

CONCLUSION

High certainty evidence was found for an association between frailty and in-hospital mortality following IHCA. Frailty is a robust prognostic factor that contributes valuable information and can inform shared-decision making and policies surrounding advance care directives. Registration: PROSPERO Registration # CRD42020212922.

Analysis of Frailty in Geriatric Patients as a Prognostic Factor in Endovascular Treated Patients with Large Vessel Occlusion Strokes

Frailty is associated with an increased risk of adverse health-care outcomes in elderly patients. The Hospital Frailty Risk Score (HFRS) has been developed and proven to be capable of identifying patients which are at high risk of adverse outcomes. We aimed to investigate whether frail patients also face adverse outcomes after experiencing an endovascular treated large vessel occlusion stroke (LVOS). In this retrospective observational cohort study, we analyzed patients ≥ 65 years that were admitted during 2015-2019 with LVOS and endovascular treatment. Primary outcomes were mortality and the modified Rankin Scale (mRS) after three months. Regression models were used to determine the impact of frailty. A total of 318 patients were included in the cohort. The median HFRS was 1.6 (IQR 4.8). A total of 238 (75.1%) patients fulfilled the criteria for a low-frailty risk with a HFRS < 5.72 (22.7%) for moderate-frailty risk with an HFRS from 5-15 and 7 (2.2%) patients for a high-frailty risk. Multivariate regression analyses revealed that the HFRS was associated with an increased mortality after 90 days (CI (95%) 1.001 to 1.236; OR 1.112) and a worse mRS (CI (95%) 1.004 to 1.270; OR 1.129). We identified frailty as an impact factor on functional outcome and mortality in patients undergoing thrombectomy in LVOS.

Frailty Is a Risk Factor for Postoperative Mortality in Patients With Cirrhosis Undergoing Diverse Major Surgeries

With a rising burden of cirrhosis surgeries, understanding risk factors for postoperative mortality is more salient than ever. The role of baseline frailty has not been assessed in this context. We evaluated the association between patient frailty and postoperative risk among diverse patients with cirrhosis and determined if frailty improves prognostication of cirrhosis surgical risk scores. This was a retrospective cohort study of U.S. veterans with cirrhosis identified between 2008 and 2016 who underwent nontransplant major surgery. Frailty was ascertained using the Hospital Frailty Risk Score (HFRS). Cox regression analysis was used to investigate the impact of patient frailty on postoperative mortality. Logistic regression was used to identify incremental changes in discrimination for postoperative mortality when frailty was added to the risk prediction models, including the Model for End-Stage Liver Disease (MELD), MELD-sodium (MELD-Na), Child-Turcotte-Pugh (CTP), Mayo Risk Score (MRS), and Veterans Outcomes and Costs Associated With Liver Disease (VOCAL)-Penn. A total of 804 cirrhosis surgeries were identified. The majority of patients (48.5%) had high-risk frailty at baseline (HFRS >15). In adjusted Cox regression models, categories of increasing frailty scores were associated with poorer postoperative survival. For example, intermediate-risk frailty (HFRS 5-15) conferred a 1.77-fold increased hazard relative to low-risk frailty (HFRS, <5; 95% confidence interval [CI], 1.06-2.95; P = 0.03). High-risk frailty demonstrated a similarly increased hazard (hazard ratio, 1.74; 95% CI, 1.05-2.88; P = 0.03), suggesting a threshold effect of frailty on postoperative mortality. The incorporation of frailty improved discrimination of MELD, MELD-Na, and CTP for postoperative mortality, but did not do so for the MRS or VOCAL-Penn score. Patient frailty was an additional important predictor of cirrhosis surgical risk. The incorporation of preoperative frailty assessments may help to risk stratify patients, especially in settings where the MELD-Na and CTP are commonly applied.

Patient Frailty Is Independently Associated With the Risk of Hospitalization for Acute-on-Chronic Liver Failure

There is significant interest in identifying risk factors associated with acute-on-chronic liver failure (ACLF). In transplant candidates, frailty predicts wait-list mortality and posttransplant outcomes. However, the impact of frailty on ACLF development and mortality is unknown. This was a retrospective study of US veterans with cirrhosis identified between 2008 and 2016. First hospitalizations were characterized as ACLF or non-ACLF admissions. Prehospitalization patient frailty was ascertained using a validated score based on administrative coding data. We used logistic regression to investigate the impact of an increasing frailty score on the odds of ACLF hospitalization and short-term ACLF mortality. Cox regression was used to analyze the association between frailty and longterm survival from hospitalization. We identified 16,561 cirrhosis hospitalizations over a median follow-up of 4.19 years (interquartile range, 2.47-6.34 years). In adjusted models, increasing frailty score was associated with significantly increased odds of ACLF hospitalization versus non-ACLF hospitalization (odds ratio, 1.03 per point; 95% CI 1.02-1.03; P < 0.001). By contrast, frailty score was not associated with ACLF 28- or 90-day mortality (P = 0.13 and P = 0.33, respectively). In an adjusted Cox analysis of all hospitalizations, increasing frailty scores were associated with poorer longterm survival from the time of hospitalization (hazard ratio, 1.02 per 5 points; 95% confidence interval, 1.01-1.03; P = 0.004). Frailty increases the likelihood of ACLF hospitalization among patients with cirrhosis, but it does not impact short-term ACLF mortality. These findings have implications for clinicians caring for frail outpatients with cirrhosis, including tailored follow-up, risk mitigation strategies, and possible expedited transplant evaluation.

Development and validation of a frailty index based on data routinely collected across multiple domains in NSW hospitals

OBJECTIVE(S)

To develop and validate a frailty index (FI) that covers multiple domains, using routine hospital data. To investigate the FI's validity, after excluding medication-related items (FI-ExMeds), for studies of frailty and polypharmacy.

METHODS

A FI was derived from routine NSW hospital data following standard published guidance. In a development cohort (151 inpatients ≥ 70 years), the FI was correlated with the Reported Edmonton Frail Scale (REFS) using Pearson's R. Validity and distribution of FI and FI-ExMeds, and correlation with each other, were evaluated in a validation cohort (999 inpatients ≥ 75 years).

RESULTS

The mean FI for the development cohort was 0.27 (SD 0.09). The FI showed moderate linear correlation with the REFS (n = 148, R = 0.52, P < .001). In the validation cohort, mean FI (n = 993) and FI-ExMeds (n = 990) were both 0.28 (SD 0.11). FI-ExMeds showed high linear correlation with the FI (n = 990, R = 0.99, P < .001).

CONCLUSION

This multi-domain FI is comparable to REFS, with adequate redundancy to exclude deficits for specific analyses.

Hospital Frailty Risk Score Predicts Adverse Events in Primary Total Hip and Knee Arthroplasty

BACKGROUND

The Hospital Frailty Risk Score (HFRS) is a validated geriatric comorbidity measure derived from routinely collected administrative data. The purpose of this study is to evaluate the utility of the HFRS as a predictor for postoperative adverse events after primary total hip (THA) and knee (TKA) arthroplasty.

METHODS

In a retrospective analysis of 8250 patients who had undergone THA or TKA between 2011 and 2019, the HFRS was calculated for each patient. Reoperation rates, readmission rates, complication rates, and transfusion rates were compared between patients with low and intermediate or high frailty risk. Multivariate logistic regression models were used to assess the relationship between the HFRS and postoperative adverse events.

RESULTS

Patients with intermediate or high frailty risk showed a higher rate of reoperation (10.6% vs 4.1%, P < .001), readmission (9.6% vs 4.3%, P < .001), surgical complications (9.1% vs 1.8%, P < .001), internal complications (7.3% vs 1.1%, P < .001), other complications (24.4% vs 2.0%, P < .001), Clavien-Dindo grade IV complications (4.1% vs 1.5%, P < .001), and transfusion (10.4% vs 1.3%, P < .001). Multivariate logistic regression analyses revealed a high HFRS as independent risk factor for reoperation (odds ratio [OR] = 2.1; 95% confidence interval [CI], 1.46-3.09; P < .001), readmission (OR = 1.78; 95% CI, 1.21-2.61; P = .003), internal complications (OR = 3.72; 95% CI, 2.28-6.08; P < .001), surgical complications (OR = 3.74; 95% CI, 2.41-5.82; P < .001), and other complications (OR = 9.00; 95% CI, 6.58-12.32; P < .001).

CONCLUSION

The HFRS predicts adverse events after THA and TKA. As it derives from routinely collected data, the HFRS enables hospitals to identify at-risk patients without extra effort or expense.

LEVEL OF EVIDENCE

Level III-retrospective cohort study.

Utility of the Hospital Frailty Risk Score for Predicting Adverse Outcomes in Degenerative Spine Surgery Cohorts

BACKGROUND

As spine surgery becomes increasingly common in the elderly, frailty has been used to risk stratify these patients. The Hospital Frailty Risk Score (HFRS) is a novel method of assessing frailty using International Classification of Diseases, Tenth Revision (ICD-10) codes. However, HFRS utility has not been evaluated in spinal surgery.

OBJECTIVE

To assess the accuracy of HFRS in predicting adverse outcomes of surgical spine patients.

METHODS

Patients undergoing elective spine surgery at a single institution from 2008 to 2016 were reviewed, and those undergoing surgery for tumors, traumas, and infections were excluded. The HFRS was calculated for each patient, and rates of adverse events were calculated for low, medium, and high frailty cohorts. Predictive ability of the HFRS in a model containing other relevant variables for various outcomes was also calculated.

RESULTS

Intensive care unit (ICU) stays were more prevalent in high HFRS patients (66%) than medium (31%) or low (7%) HFRS patients. Similar results were found for nonhome discharges and 30-d readmission rates. Logistic regressions showed HFRS improved the accuracy of predicting ICU stays (area under the curve [AUC] = 0.87), nonhome discharges (AUC = 0.84), and total complications (AUC = 0.84). HFRS was less effective at improving predictions of 30-d readmission rates (AUC = 0.65) and emergency department visits (AUC = 0.60).

CONCLUSION

HFRS is a better predictor of length of stay (LOS), ICU stays, and nonhome discharges than readmission and may improve on modified frailty index in predicting LOS. Since ICU stays and nonhome discharges are the main drivers of cost variability in spine surgery, HFRS may be a valuable tool for cost prediction in this specialty.

The impact of frailty on mortality in older patients admitted to an Intensive Care Unit

OBJECTIVE

Frailty is a relatively new concept for intensivists, and is defined as a status of increased vulnerability to stressors associated with reduced reserve and function of different physiological systems. Supporting the hypothesis that frailty may be an important predictor of poor prognosis among older patients admitted to Intensive Care Unit (ICU), this study seeks to evaluate the association between frailty at ICU admission and short and long-term mortality.

DESIGN

An unmatched case-control study was carried out.

SETTING

Intensive Care Unit.

PATIENTS OR PARTICIPANTS

Patients≥80 years of age admitted to the ICU for medical reasons.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

The primary outcome was 30-day mortality, while secondary outcomes were ICU mortality and mortality at one year.

RESULTS

Most of the patients were classified as frail at ICU admission (55.3%). The prevalence of frailty was higher among those who died than in those who were alive within 30 days from ICU admission (62.3% vs 48.3%, p=0.01). One-year mortality was higher in frail (84.4%) than in non-frail patients (65.2%, p<0.001). In the logistic regression analysis, after adjusting for potential confounders such as chronic diseases, clinical complexity, cause of ICU admission and use of advanced procedures, frailty was seen to be significantly associated to one-year mortality, but not with ICU mortality or 30-day mortality.

DISCUSSION

The admission of geriatric patients to the ICU is increasing. Frailty assessment may play an important role in the clinical evaluation of such individuals for triage, but should not be considered a priori as an exclusion criterion for admission.