Prevalence of frailty according to the Hospital Frailty Risk Score and related factors in older patients with acute coronary syndromes in Vietnam

OBJECTIVES

(1) To investigate the prevalence of frailty defined by the Hospital Frailty Risk Score (HFRS), a new scale for assessing frailty, in older patients with acute coronary syndrome (ACS); (2) To identify associations between frailty and the prescriptions of cardiovascular medications, percutaneous coronary intervention (PCI) and in-hospital adverse outcomes.

METHODS

An observational study was conducted in patients aged older than 60 years with ACS at Thong Nhat Hospital from August to December 2022. The Hospital Frailty Risk Score is retrospectively calculated for all participants based on ICD-10 codes, and those with HFRS scores ≥5 were defined as frail. Logistic regression models were applied to examine the relationship between frailty and the study outcomes.

RESULTS

There were 511 participants in the study. The median age was 72.7, 60% were male and 29% were frail. Frailty was associated with lower odds of beta-blocker use at admission (OR .49 95% CI .25-.94), treatment with PCI during hospitalisation (OR .48, 95% CI .30-.75), but did not show an association with prescriptions of cardiovascular drugs at discharge. Frailty was significantly associated with increased odds of adverse outcomes, including major bleeding (OR 4.07, 95% CI1.73-9.54), hospital-acquired pneumonia (OR 2.55, 95% CI 1.20-5.42), all-cause in-hospital mortality (OR 3.14, 95% CI 1.37-7.20) and non-cardiovascular in-hospital mortality (OR 10.73, 95% CI 1.93-59.55).

CONCLUSIONS

The HFRS was an effective tool for stratifying frailty and predicting adverse health outcomes in older patients with ACS. Further research is needed to compare the HFRS with other frailty assessment tools in this population.

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

INTRODUCTION

This study aimed to evaluate associations between frailty and outcomes in patients with intrahepatic cholangiocarcinoma (ICC) undergoing hepatic lobectomy using a large, nationally representative sample.

METHODS

This population-based, retrospective observational study extracted the data of adults ≥20 years old with ICC undergoing hepatic lobectomy from the US Nationwide Inpatient Sample database between 2005 and 2018. Frailty was assessed by the validated Hospital Frailty Risk Score (HFRS). Associations between frailty and surgical outcomes were analyzed using logistic regression analyses.

RESULTS

After exclusions, 777 patients were enrolled, including 427 frail and 350 non-frail. Patients' mean age was 64.5 (±0.4) years and the majority were males (51.1%) and whites (76.5%). Frailty was significantly associated with increased odds of in-hospital mortality (aOR: 18.51, 95% CI: 6.70, 51.18), non-home discharge (aOR: 3.58, 95% CI: 2.26, 5.66), prolonged LOS (aOR: 5.56, 95% CI: 3.87, 7.99), perioperative cardiac arrest/stroke (aOR: 5.44, 95% CI: 1.62, 18.24), acute respiratory distress syndrome (ARDS)/respiratory failure (aOR: 3.88, 95% CI: 2.40, 6.28), tracheostomy/ventilation (aOR: 3.83, 95% CI: 2.23, 6.58), bleeding/transfusion (aOR: 1.67, 95% CI: 1.24, 2.26), acute kidney injury (AKI) (aOR: 14.37, 95% CI: 7.13, 28.99), postoperative shock (aOR: 4.44, 95% CI: 2.54, 7.74), and sepsis (aOR: 11.94, 95% CI: 6.90, 20.67).

DISCUSSION/CONCLUSION

Among patients with ICC undergoing hepatic lobectomy, HFRS-defined frailty is a strong predictor of worse in-patient outcomes, including in-hospital death, prolonged LOS, unfavorable discharge, and complications (perioperative cardiac arrest/stroke, ARDS/respiratory failure, tracheostomy/ventilation, bleeding/transfusion, AKI, postoperative shock, and sepsis). Study results may help stratify risk in frail patients undergoing hepatic resection for ICC.

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

PURPOSE

In spine care, frailty is associated with poor outcomes. The aim of this study was to describe changes in frailty in spine care during the coronavirus disease 2019 (COVID-19) pandemic and their relation to surgical management and outcomes.

METHODS

Patients hospitalized for spine pathologies between January 1, 2019, and May 17, 2022, within a nationwide network of 76 hospitals in Germany were retrospectively included. Patient frailty, types of surgery, and in-hospital mortality rates were compared between pandemic and pre-pandemic periods.

RESULTS

Of the 223,418 included patients with spine pathologies, 151,766 were admitted during the pandemic and 71,652 during corresponding pre-pandemic periods in 2019. During the pandemic, the proportion of high-frailty patients increased from a range of 5.1-6.1% to 6.5-8.8% (p < 0.01), while the proportion of low frailty patients decreased from a range of 70.5-71.4% to 65.5-70.1% (p < 0.01). In most phases of the pandemic, the Elixhauser comorbidity index (ECI) showed larger increases among high compared to low frailty patients (by 0.2-1.8 vs. 0.2-0.8 [p < 0.01]). Changes in rates of spine surgery were associated with frailty, most clearly in rates of spine fusion, showing consistent increases among low frailty patients (by 2.2-2.5%) versus decreases (by 0.3-0.8%) among high-frailty patients (p < 0.02). Changes in rates of in-hospital mortality were not associated with frailty.

CONCLUSIONS

During the COVID-19 pandemic, the proportion of high-frailty patients increased among those hospitalized for spine pathologies in Germany. Low frailty was associated with a rise in rates of spine surgery and high frailty with comparably larger increases in rates of comorbidities.

Trends in frailty in brain tumor care during the COVID-19 pandemic in a nationwide hospital network in Germany

PURPOSE

Among brain tumor patients, frailty is associated with poor outcomes. The COVID-19 pandemic has led to increased frailty in the general population. To date, evidence on changes in frailty among brain tumor patients during the pandemic is lacking. We aimed to compare frailty among brain tumor patients in Germany during the COVID-19 pandemic to the pre-pandemic era and to assess potential effects on brain tumor care.

METHODS

In this retrospective observational study, we compared frailty among brain tumor patients hospitalized during the COVID-19 pandemic in years 2020 through 2022 to pre-pandemic years 2016 through 2019 based on administrative data from a nationwide network of 78 hospitals in Germany. Using the Hospital Frailty Risk Score (HFRS), frailty was categorized as low, intermediate, or high. We examined changes in frailty, patient demographics, the burden of comorbidity, rates of surgery, and mortality rates for different frailty groups during the pandemic and compared them to pre-pandemic levels.

RESULTS

Of the 20,005 included hospitalizations for brain tumors, 7979 were during the pandemic (mean age 60.0 years (± 18.4); females: 49.8%), and 12,026 in the pre-pandemic period (mean age: 59.0 years [± 18.4]; females: 49.2%). Average daily admissions decreased from 8.2 (± 5.1) during pre-pandemic years to 7.3 (± 4.5) during the pandemic (p < 0.01). The overall median HFRS decreased from 3.1 (IQR: 0.9-7.3) during the pre-pandemic years to 2.6 (IQR: 0.3-6.8) during the pandemic (p < 0.01). At the same time, the Elixhauser Comorbidity Index (ECI) decreased from 17.0 (± 12.4) to 16.1 (± 12.0; p < 0.01), but to a larger degree among high compared to low frailty cases (by 1.8 vs. 0.3 points; p = 0.04). In the entire cohort, the mean length of stay was significantly shorter in the pandemic period (9.5 days [± 10.7]) compared with pre-pandemic levels (10.2 days [± 11.8]; p < 0.01) with similar differences in the three frailty groups. Rates of brain tumor resection increased from 29.9% in pre-pandemic years to 36.6% during the pandemic (p < 0.001) without differences between frailty levels. Rates of in-hospital mortality did not change during the pandemic (6.1% vs. 6.7%, p = 0.07), and there was no interaction with frailty.

CONCLUSION

Even though our findings are limited in that the HFRS is validated only for patients ≥ 75 years of age, our study among patients of all ages hospitalized for brain tumors in Germany suggests a marked decrease in levels of frailty and in the burden of comorbidities during the COVID-19 pandemic.

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.

Hearing Impairment and Incident Frailty in Later Life: The Health in Men Study (HIMS)

OBJECTIVES

This study is designed to determine if hearing loss is associated with increased risk of frailty in later life.

DESIGN

A prospective cohort study.

SETTING AND PARTICIPANTS

We retrieved data of a community sample of men aged 70 years and above living in the metropolitan region of Perth, Western Australia. 3,285 participants who were free of frailty at the beginning of the study were followed for up to 17 years. Data were retrieved from the Health in Men Study (HIMS) and the Western Australian Data Linkage System (WADLS).

MEASUREMENTS

Hearing loss was defined by self-report or by diagnosis recorded in the WADLS. Incident frailty was assessed using the Hospital Frailty Risk Score (HFRS).

RESULTS

A total of 2,348 (71.5%) men developed frailty during follow up. The adjusted hazard ratio was 1.03 (95% CI: 0.95-1.12). The majority of the participants became frail by age 90 regardless of hearing condition. The time point where half of the group become frail was delayed by 14.4 months for men without hearing loss compared with hearing impaired men.

CONCLUSIONS

Hearing loss is not associated with incident frailty in men aged 70 years or older when frailty was measured by HFRS. However, this statistically non-significant result could be due to the low sensitivity of study measures. Also, we found a trend that men with hearing loss were more likely to develop frailty compared with their normal-hearing peers, suggesting a potential association between hearing loss and frailty.

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.

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

PURPOSE

Frailty instruments may improve prognostic estimates for patients undergoing transcatheter aortic valve implantation (TAVI). Few studies have evaluated and compared the performance of administrative database frailty instruments for patients undergoing TAVI. This study aimed to examine the performance of administrative database frailty instruments in predicting clinical outcomes and costs in patients who underwent TAVI.

METHODS

We conducted a historical cohort study of 3,848 patients aged 66 yr or older who underwent a TAVI procedure in Ontario, Canada from 1 April 2012 to 31 March 2018. We used the Johns Hopkins Adjusted Clinical Group (ACG) frailty indicator and the Hospital Frailty Risk Score (HFRS) to assign frailty status. Outcomes of interest were in-hospital mortality, one-year mortality, rehospitalization, and healthcare costs. We compared the performance of the two frailty instruments with that of a reference model that adjusted baseline covariates and procedural characteristics. Accuracy measures included c-statistics, Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated discrimination improvement (IDI), net reclassification index (NRI), bias, and accuracy of cost estimates.

RESULTS

A total of 863 patients (22.4%) were identified as frail using the Johns Hopkins ACG frailty indicator and 865 (22.5%) were identified as frail using the HFRS. Although agreement between the frailty instruments was fair (Kappa statistic = 0.322), each instrument classified different subgroups as frail. Both the Johns Hopkins ACG frailty indicator (rate ratio [RR], 1.13; 95% confidence interval [CI], 1.06 to 1.20) and the HFRS (RR, 1.14; 95% CI, 1.07 to 1.21) were significantly associated with increased one-year costs. Compared with the reference model, both the Johns Hopkins ACG frailty indicator and HFRS significantly improved NRI for one-year mortality (Johns Hopkins ACG frailty indicator: NRI, 0.160; P < 0.001; HFRS: NRI, 0.146; P = 0.001) and rehospitalization (Johns Hopkins ACG frailty indicator: NRI, 0.201; P < 0.001; HFRS: NRI, 0.141; P = 0.001). These improvements in NRI largely resulted from classification improvement among those who did not experience the event. With one-year mortality, there was a significant improvement in IDI (IDI, 0.003; P < 0.001) with the Johns Hopkins ACG frailty indicator. This improvement in performance resulted from an increase in the mean probability of the event among those with the event.

CONCLUSION

Preoperative frailty assessment may add some predictive value for TAVI outcomes. Use of administrative database frailty instruments may provide small but significant improvements in case-mix adjustment when profiling hospitals for certain outcomes.

Association between frailty and short- and long-term mortality in patients with critical acute myocardial infarction: Results from MIMIC-IV

Background

Frailty has been recognized as an important prognostic indicator in patients with acute myocardial infarction (AMI). However, no study has focused on critical AMI patients. We aimed to determine the impact of frailty on short- and long-term mortality risk in critical AMI patients.

Methods

Data from the Medical Information Mart for Intensive Care (MIMIC)-IV database was used. Frailty was assessed using the Hospital Frailty Risk Score (HFRS). Outcomes were in-hospital mortality and 1-year mortality. Logistic regression and Cox proportional-hazards models were used to investigate the association between frailty and outcomes.

Results

Among 5,003 critical AMI patients, 2,176 were non-frail (43.5%), 2,355 were pre-frail (47.1%), and 472 were frail (9.4%). The in-hospital mortality rate was 13.8%, and the 1-year mortality rate was 29.5%. In our multivariable model, frailty was significantly associated with in-hospital mortality [odds ratio (OR) = 1.30, 95% confidence interval (CI): 1.20-1.41] and 1-year mortality [hazard ratio (HR) = 1.29, 95% CI: 1.24-1.35] as a continuous variable (per five-score increase). When assessed as categorical variables, pre-frailty and frailty were both associated with in-hospital mortality (OR = 2.80, 95% CI: 2.19-3.59 and OR = 2.69, 95% CI: 1.93-3.73, respectively) and 1-year mortality (HR = 2.32, 95% CI: 2.00-2.69 and HR = 2.81, 95% CI: 2.33-3.39, respectively) after adjustment for confounders. Subgroup analysis showed that frailty was only associated with in-hospital mortality in critically ill patients with non-ST-segment elevation myocardial infarction (STEMI) but not STEMI (p for interaction = 0.012). In addition, frailty was associated with 1-year mortality in both STEMI and non-STEMI patients (p for interaction = 0.447). The addition of frailty produced the incremental value over the initial model generated by baseline characteristics for both in-hospital and 1-year mortality.

Conclusion

Frailty, as assessed by the HFRS, was associated with both in-hospital and 1-year mortality in critical AMI patients. Frailty improves the prediction of short- and long-term mortality in critical AMI patients and may have potential clinical applications.

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.

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.

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

BACKGROUND

Predicting preoperative frailty risk in emergency surgery is difficult with limited information because preoperative evaluation is not commonly performed properly. A recent study attempted to predict preoperative frailty risk using only diagnostic and surgical codes that can be extracted from the electronic medical records system.

OBJECTIVE

This study aimed to validate whether the prediction model of preoperative frailty risk presented in the previous study is well applied to other medical hospitals' data.

METHODS

This is a retrospective cohort study including 1,557 patients (≥75 years old) who were admitted to a single institution for emergency operations between January 1, 2010, and December 31, 2019, for study analysis. The Charlson comorbidity index, Hospital Frailty Risk Score, and the recently developed Operation Frailty Risk Score (OFRS) were calculated using the patient's diagnostic and operation codes. The predictive performances of these calculated risk scores and the American Society of Anesthesiologists-Physical Status classification for postoperative 90-day mortality were compared by using the receiver operating characteristic curve analysis.

FINDINGS

The predictive performance of the OFRS, Charlson comorbidity index, American Society of Anesthesiologists-Physical Status, and Hospital Frailty Risk Score for postoperative 90-day mortality was 0.81, 0.630, 0.699, and 0.549 on a c-statistics basis, respectively.

CONCLUSIONS

The OFRS using diagnostic and operation codes may show the best predictive performance for 90-day mortality compared to other risk scores, and it can be the clinically applicable model to evaluate the preoperative frailty risk in elderly patients undergoing emergency surgery.

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.

Clinical Frailty Scale (CFS) indicated frailty is associated with increased in-hospital and 30-day mortality in COVID-19 patients: a systematic review and meta-analysis

BACKGROUND

The concept of frailty provides an age-independent, easy-to-use tool for risk stratification. We aimed to summarize the evidence on the efficacy of frailty tools in risk assessment in COVID-19 patients.

METHODS

The protocol was registered (CRD42021241544). Studies reporting on frailty in COVID-19 patients were eligible. The main outcomes were mortality, length of hospital stay (LOH) and intensive care unit (ICU) admission in frail and non-frail COVID-19 patients. Frailty was also compared in survivors and non-survivors. Five databases were searched up to 24th September 2021. The QUIPS tool was used for the risk of bias assessment. Odds ratios (OR) and weighted mean differences (WMD) were calculated with 95% confidence intervals (CI) using a random effect model. Heterogeneity was assessed using the I² and χ² tests.

RESULTS

From 3640 records identified, 54 were included in the qualitative and 42 in the quantitative synthesis. Clinical Frailty Scale (CFS) was used in 46 studies, the Hospital Frailty Risk Score (HFRS) by 4, the Multidimensional Prognostic Index (MPI) by 3 and three studies used other scores. We found that patients with frailty (CFS 4-9 or HFRS ≥ 5) have a higher risk of mortality (CFS: OR: 3.12; CI 2.56-3.81; HFRS OR: 1.98; CI 1.89-2.07). Patients with frailty (CFS 4-9) were less likely to be admitted to ICU (OR 0.28, CI 0.12-0.64). Quantitative synthesis for LOH was not feasible. Most studies carried a high risk of bias.

CONCLUSIONS

As determined by CFS, frailty is strongly associated with mortality; hence, frailty-based patient management should be included in international COVID-19 treatment guidelines. Future studies investigating the role of frailty assessment on deciding ICU admission are strongly warranted.

Hospital Frailty Risk Score predicts adverse events in older patients with vertebral compression fractures: Analysis of data in a nationwide in-patient database in Japan

AIMS

This study investigated the usefulness of frailty for predicting adverse events in patients with vertebral compression fractures (VCFs) during hospitalization using data obtained from the Japanese health insurance system.

METHODS

This retrospective cohort study of patients with VCFs aged ≥65 years was conducted using a nationwide database in Japan. We examined the relationships between frailty risk, classified using the Hospital Frailty Risk Score (HFRS), in-hospital mortality, and complications such as pressure ulcers and pneumonia. Multivariate logistic regression analysis was used to estimate the association between the HFRS and the outcomes of patients with VCFs.

RESULTS

In this study, the data of 30 980 in-patients with VCFs were analyzed. Of these patients, 76.8%, 21.3%, and 1.9% had low, intermediate, and high risks of frailty, respectively. The higher the risk of frailty, the higher the rate of in-hospital mortality and the occurrence of all complications (P < 0.001 for trend). An intermediate risk of frailty was independently associated with in-hospital mortality (odds ratio [OR], 1.421; P < 0.001), whereas a high risk of frailty did not show statistical significance (OR, 1.385; P = 0.150). Each frailty risk was independently associated with the occurrence of all complications during hospitalization.

CONCLUSIONS

The HFRS, which can assess the risk of frailty based on routinely collected medical records, was predictive of adverse events in older patients with VCFs based on a nationwide database in Japan. Future studies need to assess approaches to preventing adverse events in frail VCF patients. Geriatr Gerontol Int 2022; ••: ••-••.

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.