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

Prevalence of frailty in Indonesia: a systematic review and meta-analysis

BACKGROUND

Frailty increases the risks of hospitalization, injury, fall, psychological disorders, and death in older adults. Accurate estimation of the prevalence of frailty is crucial for promoting health in these individuals. Therefore, this study was conducted to estimate the prevalence of frailty and prefrailty in older adults residing in Indonesia.

METHODS

In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, six electronic databases were searched (without any language restriction) for relevant articles from inception to February 2023. Studies on the prevalence of frailty and prefrailty in older adults (age ≥ 60 years) residing in Indonesia were included in the analysis. A random-effects model was selected a priori because of the expected high degree of heterogeneity in the study, followed by sensitivity analysis, subgroup analysis, and meta-regression. The protocol of this review study was registered in the PROSPERO database (CRD42022381132).

RESULTS

A total of 79 studies were identified, of which 20 were finally included in the analysis. The pooled prevalence of frailty and prefrailty in older adults in Indonesia was 26.8% and 55.5%, respectively. The pooled prevalence of frailty and prefrailty was 37.9% and 44.8% in nursing homes, 26.3% and 61.4% in hospitals, and 21.1% and 59.6% in community settings, respectively. Furthermore, the pooled prevalence of frailty and prefrailty was 21.6% and 64.3%, 18.7% and 62%, and 27.8% and 59.8% in studies using the Frailty Index-40, FRAIL, and Fried Frailty Phenotype questionnaires, respectively. However, the parameters did not vary significantly across measurement tools or study settings. Publication bias was not detected while the year of data collection influenced the heterogeneity between the studies.

CONCLUSIONS

To the best of our knowledge, this study is the first meta-analysis to report the prevalence of frailty and prefrailty in older adults residing in Indonesia. The gradual increase in the number of older adults with frailty or prefrailty in Indonesia is concerning. Therefore, the government, private sectors, health-care professionals, and the community must jointly design effective strategies and policies to address this problem.

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 (phet < 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; phet = 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; phet < 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.

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.

Consistency in the prevalence and associated factors of frailty determined by two instruments among hospitalised older adults: A cross-sectional study

AIMS AND OBJECTIVES

To investigate the consistency in the prevalence and associated factors of frailty determined by the physical-originated Fatigue, Resistance, Ambulation, Illnesses and Loss of weight (FRAIL) scale and the multidimensional Tilburg Frailty Indicators (TFI) scale.

BACKGROUND

Accurate assessment of frailty and the identification of its associated factors could guide the development and implementation of holistic and individualised treatment plan. However, recommendations regarding the selection of frailty assessment tools are inconclusive.

DESIGN

This is a cross-sectional study, the reporting of which followed the STROBE guidelines.

METHODS

A total of 1220 older adults were recruited from a university affiliated tertiary hospital in Xi'an City, Northwest China, and administrated with a social-demographic and health-related information sheet, the FRAIL, the TFI, the Short-Form Mini-Nutritional Assessment, the Pittsburgh Sleep Quality Index and the 5-level EuroQol 5 dimensions questionnaire. Descriptive statistics and binary logistic regression analysis were used to investigate the prevalence of frailty and its associated factors.

RESULTS

The prevalence of physical-originated and multidimensional frailty was 55.2% and 77.6%, respectively. The consistency between the two scales was low. Taking the combined use of the two instruments as the reference, the TFI and FRAIL could identify 89.99% and 64.02% of the participants with frailty. Polypharmacy, health-related quality of life and sleep quality were found to be associated with both physical-originated and multidimensional frailty. Nutritional status and level of physical activity were additionally identified as the independent associated factors of multidimensional frailty.

CONCLUSIONS

The prevalence of frailty among hospitalised older adults is high. There is low consistency between the FRAIL and TFI in detecting frailty. The TFI exhibited higher sensitivity in detecting individuals with frailty and its associated factors.

RELEVANCE TO CLINICAL PRACTICE

The findings of this study supported a single use of the TFI for the assessment of frailty in the hospital setting.