Comparing the Hospital Frailty Risk Score and the Clinical Frailty Scale Among Older Adults With Chronic Obstructive Pulmonary Disease Exacerbation

Physical frailty, genetic predisposition, and incident dementia: a large prospective cohort study

Physical frailty and genetic factors are both risk factors for increased dementia; nevertheless, the joint effect remains unclear. This study aimed to investigated the long-term relationship between physical frailty, genetic risk, and dementia incidence. A total of 274,194 participants from the UK Biobank were included. We applied Cox proportional hazards regression models to estimate the association between physical frailty and genetic and dementia risks. Among the participants (146,574 females [53.45%]; mean age, 57.24 years), 3,353 (1.22%) new-onset dementia events were recorded. Compared to non-frailty, the hazard ratio (HR) for dementia incidence in prefrailty and frailty was 1.396 (95% confidence interval [CI], 1.294-1.506, P < 0.001) and 2.304 (95% CI, 2.030-2.616, P < 0.001), respectively. Compared to non-frailty and low polygenic risk score (PRS), the HR for dementia risk was 3.908 (95% CI, 3.051-5.006, P < 0.001) for frailty and high PRS. Furthermore, among the participants, slow walking speed (HR, 1.817; 95% CI, 1.640-2.014, P < 0.001), low physical activity (HR, 1.719; 95% CI, 1.545-1.912, P < 0.001), exhaustion (HR, 1.670; 95% CI, 1.502-1.856, P < 0.001), low grip strength (HR, 1.606; 95% CI, 1.479-1.744, P < 0.001), and weight loss (HR, 1.464; 95% CI, 1.328-1.615, P < 0.001) were independently associated with dementia risk compared to non-frailty. Particularly, precise modulation for different dementia genetic risk populations can also be identified due to differences in dementia risk resulting from the constitutive pattern of frailty in different genetic risk populations. In conclusion, both physical frailty and high genetic risk are significantly associated with higher dementia risk. Early intervention to modify frailty is beneficial for achieving primary and precise prevention of dementia, especially in those at high genetic risk.

Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes

Importance

Frailty is associated with adverse outcomes after even minor physiologic stressors. The validated Risk Analysis Index (RAI) quantifies frailty; however, existing methods limit application to in-person interview (clinical RAI) and quality improvement datasets (administrative RAI).

Objective

To expand the utility of the RAI utility to available International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) administrative data, using the National Inpatient Sample (NIS).

Design, Setting, and Participants

RAI parameters were systematically adapted to ICD-10-CM codes (RAI-ICD) and were derived (NIS 2019) and validated (NIS 2020). The primary analysis included survey-weighed discharge data among adults undergoing major surgical procedures. Additional external validation occurred by including all operative and nonoperative hospitalizations in the NIS (2020) and in a multihospital health care system (UPMC, 2021-2022). Data analysis was conducted from January to May 2023.

Exposures

RAI parameters and in-hospital mortality.

Main Outcomes and Measures

The association of RAI parameters with in-hospital mortality was calculated and weighted using logistic regression, generating an integerized RAI-ICD score. After initial validation, thresholds defining categories of frailty were selected by a full complement of test statistics. Rates of elective admission, length of stay, hospital charges, and in-hospital mortality were compared across frailty categories. C statistics estimated model discrimination.

Results

RAI-ICD parameters were weighted in the 9 548 206 patients who were hospitalized (mean [SE] age, 55.4 (0.1) years; 3 742 330 male [weighted percentage, 39.2%] and 5 804 431 female [weighted percentage, 60.8%]), modeling in-hospital mortality (2.1%; 95% CI, 2.1%-2.2%) with excellent derivation discrimination (C statistic, 0.810; 95% CI, 0.808-0.813). The 11 RAI-ICD parameters were adapted to 323 ICD-10-CM codes. The operative validation population of 8 113 950 patients (mean [SE] age, 54.4 (0.1) years; 3 148 273 male [weighted percentage, 38.8%] and 4 965 737 female [weighted percentage, 61.2%]; in-hospital mortality, 2.5% [95% CI, 2.4%-2.5%]) mirrored the derivation population. In validation, the weighted and integerized RAI-ICD yielded good to excellent discrimination in the NIS operative sample (C statistic, 0.784; 95% CI, 0.782-0.786), NIS operative and nonoperative sample (C statistic, 0.778; 95% CI, 0.777-0.779), and the UPMC operative and nonoperative sample (C statistic, 0.860; 95% CI, 0.857-0.862). Thresholds defining robust (RAI-ICD <27), normal (RAI-ICD, 27-35), frail (RAI-ICD, 36-45), and very frail (RAI-ICD >45) strata of frailty maximized precision (F1 = 0.33) and sensitivity and specificity (Matthews correlation coefficient = 0.26). Adverse outcomes increased with increasing frailty.

Conclusion and Relevance

In this cohort study of hospitalized adults, the RAI-ICD was rigorously adapted, derived, and validated. These findings suggest that the RAI-ICD can extend the quantification of frailty to inpatient adult ICD-10-CM-coded patient care datasets.

Association of Frailty with Patient-Report Outcomes and Major Clinical Determinants in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Purpose

This study aimed to explore the correlation of frailty status with disease characteristics and patient-reported outcomes (PROs) in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and determine the sensitivity and specificity of modified COPD PRO scale (mCOPD-PRO) for detecting frailty.

Patients and Methods

This cross-sectional study surveyed 315 inpatients with AECOPD from a tertiary hospital in China from August 2022 to June 2023. Patient frailty and PROs were assessed using the validated FRAIL scale and mCOPD-PRO, respectively. Spearman's ρ was used to assess the relevance of lung disease indicators commonly used in clinical practice, and ordinal logistic regression analyses were used to identify the variables associated with frailty status. The validity of mCOPD-PRO in discriminating frail or non-frail individuals was determined using the receiver operating characteristic curve.

Results

The participants (N=302, mean age 72.4±9.1 years) were predominantly males (73.2%). Among them, 43 (14.3%) patients were not frail, whereas 123 (40.7%) and 136 (45.0%) patients were pre-frail and frail, respectively. The FRAIL scale was moderately correlated with the mCOPD-PRO scores (Spearman's rank correlation coefficient [Rs]=0.52, P<0.01) for all dimensions (Rs=0.43-0.49, P<0.01). Patients residing in rural areas (odds ratio [OR], 1.67; 95% confidence interval [95% CI], 1.01-2.76) and with higher mCOPD-PRO scores (OR, 4.78; 95% CI, 2.75-8.32) were more likely to be frail. Physically active patients (OR, 0.42; 95% CI, 0.21-0.84) were less likely to be frail. In addition, mCOPD-PRO had good discriminate validity for detecting frailty (area under the curve=0.78), with a sensitivity and specificity of 84.6% and 60.8%, respectively. The optimal probability threshold for mCOPD-PRO was ≥1.52 points.

Conclusion

In patients with AECOPD, frailty is closely related to PROs and disease characteristics. Additionally, the mCOPD-PRO score can distinguish well between frail and non-frail patients. Our findings provide support for interventions targeting frail populations with AECOPD.

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.

Hospitalization-associated disability prediction using Hospital Frailty Risk Score in older patients undergoing pancreatectomy

PURPOSE

Frailty and hospitalization-associated disabilities (HAD) are geriatric conditions that should be managed in older patients undergoing pancreatectomy. The Hospital Frailty Risk Score (HFRS) can evaluate frailty based on medical records and could predict postoperative activities of daily living (ADL) decline. This study aimed to investigate whether HFRS affects the postoperative incidence of HAD in older patients who underwent pancreatectomy.

METHODS

This retrospective, observational study used data from a nationwide hospital-based database. We included consecutive patients aged ≥ 65 years who were diagnosed with pancreatic cancer (International Statistical Classification of Diseases, 10th revision code C25) and underwent curative pancreatectomy between April 2014 and August 2020. We evaluated preoperative frailty using HFRS. The primary outcome was HAD following pancreatectomy. Logistic regression analysis was performed for analyzing the impact of frailty on HAD following pancreatectomy.

RESULTS

We included 671 patients for the analysis (mean age 74.3 ± 5.6, male 54.8%). The prevalence of intermediate and high risk of frailty with HFRS ≥ 5 was 2.7%. The incidence of HAD was significantly higher in the intermediate- and high-risk groups compared to that in the low-risk group (33.3% vs. 8.4%, p < 0.001). Logistic regression analysis demonstrated that intermediate and high risks of frailty increased the likelihood of HAD (odds ratio, 3.55; 95% confidence interval: 1.12-11.2).

CONCLUSIONS

The risk of frailty evaluated using the HFRS increased the likelihood of developing HAD in older patients undergoing pancreatectomy. Future research on effective perioperative interventions for preventing postoperative HAD and enhancing postoperative ADL recovery is warranted.

The role of frailty risk for fracture-related hospital readmission and mortality after a hip fracture

BACKGROUND

Frailty risk estimated using hospital administrative data may provide a useful clinical tool to identify older hip fracture patients at-risk of fracture-related readmissions and mortality. This study examined hip fracture hospitalisation temporal trends and explore the role of frailty risk in fracture-related readmission and mortality.

METHODS

This retrospective cohort study was conducted using linked hospital admission and mortality data in New South Wales, Australia. Patients aged ≥65 years were admitted after a hip fracture between 2014 and 2021 for temporal trends and those admitted and discharged after a hip fracture in 2014-2018 for fracture-related readmission. The Hospital Frailty Risk Score was estimated, and patients were followed for at least 36 months after discharge. A semi-competing risk analysis was used to examine the associations of frailty with fracture-related readmission and/or mortality.

RESULTS

Hip fracture hospitalisation rate was 472 per 100,000 and declined by 2.9 % (95 % confidence intervals (CI): -3.7 to -2.1) annually. Amongst 28,567 patients, 9.8 % were identified with low frailty risk, 39.4 %, intermediate frailty risk, and 50.6 % with high frailty risk. Patients with intermediate or high frailty risk had a higher chance of fracture-related readmission (Hazard ratios (HR): 1.33, 95 %CI: 1.21-1.47, HR: 1.65, 95 %CI: 1.49-1.83), death (HR: 1.50, 95 %CI: 1.38-1.63, HR: 1.80, 95 %CI: 1.65-1.96) and death post fracture-related readmission (HR: 1.32, 95 %CI: 1.12-1.56, HR: 1.56, 95 %CI: 1.32-1.84) than those with low frailty risk.

CONCLUSIONS

It appears that frailty risk estimated using hospital administrative data can contribute to identify patients who could benefit from targeted interventions to prevent further fractures.

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.

Effects of virtual reality-based pulmonary rehabilitation in patients with chronic obstructive pulmonary disease: A meta-analysis

BACKGROUND

Virtual reality (VR)-based pulmonary rehabilitation has been used in the management of chronic obstructive pulmonary disease (COPD). The efficacy of VR-based pulmonary rehabilitation for improving lung function in patients with COPD is controversial. Therefore, the aim of this meta-analysis was to evaluate the efficacy of VR combined with pulmonary rehabilitation for lung function in patients with COPD.

METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was performed in the Cochrane Library, EMBASE, Web of Science, PubMed, and China National Knowledge Infrastructure databases from inception to February 1, 2023. All included studies were randomized controlled trials that assessed VR combined with pulmonary rehabilitation for COPD patients. The effect size was calculated with standardized mean difference (SMD) and its 95% confidence interval (CI). The Cochrane Collaboration tool was used to assess the risk of bias. Publication bias was assessed by Egger test.

RESULTS

A total of 11 studies met the inclusion criteria and were included in this study. The combined effect size showed that VR combined with pulmonary rehabilitation was more effective than pulmonary rehabilitation alone at improving forced expiratory volume in 1 second% (SMD: 0.51; 95% CI 0.19,0.82; P = .002), forced expiratory volume in 1 second/forced vital capacity (SMD: 0.71; 95% CI 0.49,0.93; P < .001), dyspnea (SMD: -0.44; 95% CI -0.66, -0.22; P < .001), and 6-minute walking test (SMD: 059; 95% CI 0.39, 0.79; P < .001). In addition, the VR combined with pulmonary rehabilitation improved depression (SMD: -0.34; 95% CI -0.05, -0.03; P = .033) and anxiety mood (SMD: -0.57; 95% CI -1.11, -0.04; P = .036) compared with the pulmonary rehabilitation group.

CONCLUSION

This meta-analysis indicated that VR regimens could be used to enhance the therapeutic effect of pulmonary rehabilitation in patients with COPD. However, as a rapidly evolving field, more well-designed randomized controlled trials are needed to determine the impact of VR-based pulmonary rehabilitation on COPD patients.

Differences in Characteristics Between Physical Frailty Assessments in Chronic Obstructive Pulmonary Disease: A Multicenter Cross-Sectional Observational Study

Purpose

Assessment for frailty is important as it enables timely intervention to prevent or delay poor prognosis in chronic obstructive pulmonary disease (COPD). The aims of this study, in a sample of outpatients with COPD, were to (i) assess the prevalence of physical frailty using the Japanese version of the Cardiovascular Health Study (J-CHS) criteria and the Short Physical Performance Battery (SPPB) and the degree of agreement between the findings of the two assessments and (ii) identify factors associated with the disparity in the results obtained with these instruments.

Patients and Methods

This was a multicenter cross-sectional study of individuals with stable COPD enrolled in four institutions. Frailty was assessed using the J-CHS criteria and the SPPB. Weighted Cohen's kappa (k) statistic was performed to investigate the magnitude of agreement between the instruments. We divided participants into two groups depending on whether there was agreement or non-agreement between the results of the two frailty assessments. The two groups were then compared with respect to their clinical data.

Results

A total of 103 participants (81 male) were included in the analysis. The median age and FEV₁ (%predicted) were 77 years and 62%, respectively. The prevalence of frailty and pre-frail was 21% and 56% with the J-CHS criteria and 10% and 17% with the SPPB. The degree of agreement was fair (k = 0.36 [95% CI: 0.22-0.50], P<0.001). There were no significant differences in the clinical characteristics between the agreement group (n = 44) and the non-agreement group (n = 59).

Conclusion

We showed that the degree of agreement was fair with the J-CHS criteria detecting a higher prevalence than the SPPB. Our findings suggest that the J-CHS criteria may be useful in people with COPD with the aim of providing interventions to reverse frailty in the early stages.