Development of frailty measurement tools using administrative health data: A systematic review

Assessing causality between inflammatory bowel diseases with frailty index and sarcopenia: a bidirectional Mendelian randomization study

BACKGROUND

Previous studies have found that frailty and sarcopenia are commonly diagnosed in inflammatory bowel disease (IBD) patients, indicating an association between these conditions. Nonetheless, the cause‒effect connection between IBD, frailty, and sarcopenia remains unclear.

METHODS

We sourced the genetic variants for the exposures and outcomes from publicly accessible, extensive genome-wide association studies (GWAS). Specifically, we obtained IBD data from the International IBD Genetics Consortium, frailty index (FI) data from the United Kingdom Biobank and Swedish TwinGene, and sarcopenia data from a recent GWAS meta-analysis. Five methods, including inverse variance weighted (IVW), simple mode, MR-Egger, weighted mode, and the weighted median, were used to proceed with MR estimates. We also performed heterogeneity and horizontal pleiotropy tests.

RESULTS

Our results indicated a positive causal relationship between ulcerative colitis (UC) (IVW: β = 0.014, 95% CI, 0.006 to 0.021, p = 0.001) and Crohn's disease (CD) (IVW: β = 0.012; 95% CI, 0.006 to 0.018, p = 2e-04) with the FI. However, we uncovered no proof of a cause-and-effect relationship between UC (IVW: β = 0.001, 95% CI, -0.015 to 0.017, p = 0.344) or CD (IVW: β = 0.003, 95% CI, -0.009 to 0.015, p = 0.214) and sarcopenia. Additionally, in the inverse order, we also discovered no cause-and-effect connection between FI or sarcopenia on UC or CD in this study.

CONCLUSION

The MR analysis showed a positive causal association between IBD and FI, indicating that IBD patients may exhibit aging-related characteristics. Therefore, frailty assessments should be conducted as early as possible in IBD patients.

Validated frailty measures using electronic primary care records: a review of diagnostic test accuracy

INTRODUCTION

Identification of people who have or are at risk of frailty enables targeted interventions, and the use of tools that screen for frailty using electronic records (which we term as validated electronic frailty measures (VEFMs)) within primary care is incentivised by NHS England. We carried out a systematic review to establish the sensitivity and specificity of available primary care VEFMs when compared to a reference standard in-person assessment.

METHODS

Medline, Pubmed, CENTRAL, CINHAL and Embase searches identified studies comparing a primary care VEFM with in-person assessment. Studies were quality assessed using Quality Assessment of Diagnostic Accuracy Studies revised tool. Sensitivity and specificity values were extracted or were calculated and pooled using StatsDirect.

RESULTS

There were 2,245 titles screened, with 10 studies included. These described three different index tests: electronic frailty index (eFI), claims-based frailty index (cFI) and polypharmacy. Frailty Phenotype was the reference standard in each study. One study of 60 patients examined the eFI, reporting a sensitivity of 0.84 (95% CI = 0.55, 0.98) and a specificity of 0.78 (0.64, 0.89). Two studies of 7,679 patients examined cFI, with a pooled sensitivity of 0.48 (95% CI = 0.23, 0.74) and a specificity of 0.80 (0.53, 0.98). Seven studies of 34,328 patients examined a polypharmacy as a screening tool (defined as more than or equal to five medications) with a pooled sensitivity of 0.61 (95% CI = 0.50, 0.72) and a specificity of 0.66 (0.58, 0.73).

CONCLUSIONS

eFI is the best-performing VEFM; however, based on our analysis of an average UK GP practice, it would return a high number of false-positive results. In conclusion, existing electronic frailty tools may not be appropriate for primary care-based population screening.

Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology

Frailty is an age-related clinical condition characterised by an increased susceptibility to stressors and an elevated risk of adverse outcomes such as mortality. In the light of global population ageing, the prevalence of frailty is expected to soar in coming decades. This narrative review provides critical insights into recent developments and emerging practices in frailty research regarding identification, management, risk factors, and prevention. We searched journals in the top two quartiles of geriatrics and gerontology (from Clarivate Journal Citation Reports) for articles published between 01 January 2018 and 20 December 2022. Several recent developments were identified, including new biomarkers and biomarker panels for frailty screening and diagnosis, using artificial intelligence to identify frailty, and investigating the altered response to medications by older adults with frailty. Other areas with novel developments included exercise (including technology-based exercise), multidimensional interventions, person-centred and integrated care, assistive technologies, analysis of frailty transitions, risk-factors, clinical guidelines, COVID-19, and potential future treatments. This review identified a strong need for the implementation and evaluation of cost-effective, community-based interventions to manage and prevent frailty. Our findings highlight the need to better identify and support older adults with frailty and involve those with frailty in shared decision-making regarding their care.

Comprehensive geriatric assessment delivered by advanced nursing practitioners within primary care setting: a mixed-methods pilot feasibility randomised controlled trial

BACKGROUND

Comprehensive Geriatric Assessment (CGA)is a widely accepted intervention for frailty and can be cost-effective within a primary care setting.

OBJECTIVE

To explore the feasibility of identifying older adults with frailty and assess the subsequent implementation of a tailored CGA with care and support plan by Advanced Nursing Practitioners (ANPs).

METHODS

A mixed-method parallel randomised controlled trial was conducted. Participants were recruited from two General Practice (GP) centres between January and June 2019. Older adults with confirmed frailty, as assessed by practice nurses, were randomised, using a web service, to the intervention or treatment-as-usual (TAU) groups for six months with an interim and a final review. Data were collected on feasibility, health service usage, function, quality of life, loneliness, and participants' experience and perception of the intervention. Non-parametric tests were used to analyse within and between-group differences. P-values were adjusted to account for type I error. Thematic analysis of qualitative data was conducted.

RESULTS

One hundred sixty four older adults were invited to participate, of which 44.5% (n = 72) were randomised to either the TAU (n = 37) or intervention (n = 35) groups. All participants in the intervention group were given the baseline, interim and final reviews. Eight participants in each group were lost to post-intervention outcome assessment. The health service use (i.e. hospital admissions, GP/emergency calls and GP/Accident Emergency attendance) was slightly higher in the TAU group; however, none of the outcome data showed statistical significance between-group differences. The TAU group showed a deterioration in the total functional independence and its motor and cognition components post-intervention (p < .05), though the role limitation due to physical function and pain outcomes improved (p < .05). The qualitative findings indicate that participants appreciated the consistency of care provided by ANPs, experienced positive therapeutic relationship and were connected to wider services.

DISCUSSION

Frailty identification and intervention delivery in the community by ANPs were feasible. The study shows that older adults with frailty living in the community might benefit from intervention delivered by ANPs. It is suggested to examine the cost-effectiveness of the intervention in sufficiently powered future research.

TRIAL REGISTRATIONS

The protocol is available at clinicaltirals.gov, ID: NCT03394534; 09/01/2018.

Development and validation of an electronic database-based frailty index to predict mortality and hospitalization in a population-based study of adults with SARS-CoV-2

Background

Electronic health databases are used to identify people at risk of poor outcomes. Using electronic regional health databases (e-RHD), we aimed to develop and validate a frailty index (FI), compare it with a clinically based FI, and assess its association with health outcomes in community-dwellers with SARS-CoV-2.

Methods

Data retrieved from the Lombardy e-RHD were used to develop a 40-item FI (e-RHD-FI) in adults (i.e., aged ≥18 years) with a positive nasopharyngeal swab polymerase chain reaction test for SARS-CoV-2 by May 20, 2021. The considered deficits referred to the health status before SARS-CoV-2. The e-RHD-FI was validated against a clinically based FI (c-FI) obtained from a cohort of people hospitalized with COVID-19 and in-hospital mortality was evaluated. e-RHD-FI performance was evaluated to predict 30-day mortality, hospitalization, and 60-day COVID-19 WHO clinical progression scale, in Regional Health System beneficiaries with SARS-CoV-2.

Results

We calculated the e-RHD-FI in 689,197 adults (51.9% females, median age 52 years). On the clinical cohort, e-RHD-FI correlated with c-FI and was significantly associated with in-hospital mortality. In a multivariable Cox model, adjusted for confounders, each 0.1-point increment of e-RHD-FI was associated with increased 30-day mortality (Hazard Ratio, HR 1.45, 99% Confidence Intervals, CI: 1.42-1.47), 30-day hospitalization (HR per 0.1-point increment = 1.47, 99%CI: 1.46-1.49), and WHO clinical progression scale (Odds Ratio = 1.84 of deteriorating by one category, 99%CI 1.80-1.87).

Conclusion

The e-RHD-FI can predict 30-day mortality, 30-day hospitalization, and WHO clinical progression scale in a large population of community-dwellers with SARS-CoV-2 test positivity. Our findings support the need to assess frailty with e-RHD.

Development of Frail RISC-HIV: a Risk Score for Predicting Frailty Risk in the Short-term for Care of People with HIV

OBJECTIVE

Frailty is common among people with HIV (PWH), so we developed frail risk in the short-term for care (RISC)-HIV, a frailty prediction risk score for HIV clinical decision-making.

DESIGN

We followed PWH for up to 2 years to identify short-term predictors of becoming frail.

METHODS

We predicted frailty risk among PWH at seven HIV clinics across the United States. A modified self-reported Fried Phenotype captured frailty, including fatigue, weight loss, inactivity, and poor mobility. PWH without frailty were separated into training and validation sets and followed until becoming frail or 2 years. Bayesian Model Averaging (BMA) and five-fold-cross-validation Lasso regression selected predictors of frailty. Predictors were selected by BMA if they had a greater than 45% probability of being in the best model and by Lasso if they minimized mean squared error. We included age, sex, and variables selected by both BMA and Lasso in Frail RISC-HIV by associating incident frailty with each selected variable in Cox models. Frail RISC-HIV performance was assessed in the validation set by Harrell's C and lift plots.

RESULTS

Among 3170 PWH (training set), 7% developed frailty, whereas among 1510 PWH (validation set), 12% developed frailty. BMA and Lasso selected baseline frailty score, prescribed antidepressants, prescribed antiretroviral therapy, depressive symptomology, and current marijuana and illicit opioid use. Discrimination was acceptable in the validation set, with Harrell's C of 0.76 (95% confidence interval: 0.73-0.79) and sensitivity of 80% and specificity of 61% at a 5% frailty risk cutoff.

CONCLUSIONS

Frail RISC-HIV is a simple, easily implemented tool to assist in classifying PWH at risk for frailty in clinics.

Frailty and Inflammatory Bowel Disease: A Scoping Review of Current Evidence

Frailty is increasingly recognized as an important concept in patients with Inflammatory Bowel Disease (IBD). The aim of this scoping review is to summarize the current literature on frailty in IBD. We will discuss the definition of frailty, frailty assessment methods, the prevalence of frailty, risk factors for frailty and the prognostic value of frailty in IBD. A scoping literature search was performed using the PubMed database. Frailty prevalence varied from 6% to 53.9%, depending on the population and frailty assessment method. Frailty was associated with a range of adverse outcomes, including an increased risk for all-cause hospitalization and readmission, mortality in non-surgical setting, IBD-related hospitalization and readmission. Therefore, frailty assessment should become integrated as part of routine clinical care for older patients with IBD.

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.

Groningen Frailty Indicator-Chinese (GFI-C) for pre-frailty and frailty assessment among older people living in communities: psychometric properties and diagnostic accuracy

Background

The early identification of pre-frailty and frailty among older people is a global priority because of the increasing incidence of frailty and associated adverse health outcomes. This study aimed to validate the Groningen Frailty Indicator-Chinese (GFI-C), a widely used screening instrument, and determine the optimal cut-off value in Chinese communities to facilitate pre-frailty and frailty screening.

Methods

This methodological study employed a cross-sectional and correlational design to examine the psychometric properties of GFI-C, namely, internal consistency, stability, and concurrent and construct validities. The appropriate cut-off values for pre-frailty and frailty screening in the receiver-operating characteristic (ROC) curve were determined through sensitivity and specificity analysis.

Results

A total of 350 community older people had been assessed and interviewed by a nurse. The GFI-C showed satisfactory internal consistency (Cronbach’s α = 0.87) and two-week test-retest reliability (intra-class correlation coefficient = 0.87). Concurrent validity (r = 0.76, p < 0.001) showed a moderate correlation with Fried’s frailty phenotype. The known-groups method, hypothesis testing and confirmatory factory analysis (three-factor model; χ²/df = 2.87, TLI = 0.92, CFI = 0.93, GFI = 0.92, RMR = 0.014; RMSEA = 0.073) were suitable for the establishment of construct validity. Based on the ROC and Youden’s index, the optimal cut-off GFI-C values were 2 (sensitivity, 71.5%; specificity, 84.7%) for pre-frailty and 3 for frailty (sensitivity, 88.2%; specificity, 79.6%).

Conclusions

The result indicated that GFI-C is a reliable and valid instrument for pre-frailty and frailty screening among older Chinese people in communities. For optimal diagnostic accuracy, the cut-off values of 3 for frailty and 2 for pre-frailty are recommended.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03437-1.

Development and validation of the electronic screening index of frailty

BACKGROUND

primary care screening for frailty status is recommended in clinical guidelines, but is impeded by doctor and nurse workloads and the lack of valid, easy-to-use and time-saving screening tools.

AIM

to develop and validate a new electronic tool (the electronic screening index of frailty, e-SIF) using routinely available electronic health data to automatically and massively identify frailty status in the population aged ≥65 years.

METHODS

the e-SIF was developed in three steps: selection of clinical conditions; establishment of ICD-10 codes, criteria and algorithms for their definition; and electronic tool design and data extraction, transformation and load processes. The validation phase included an observational cohort study with retrospective data collection from computerised primary care medical records. The study population included inhabitants aged ≥65 years corresponding to three primary care centres (n = 9,315). Evaluated was the relationship between baseline e-SIF categories and mortality, institutionalisation, hospitalisation and health resource consumption after 2 years.

RESULTS

according to the e-SIF, which includes 42 clinical conditions, frailty prevalence increases with age and is slightly greater in women. The 2-year adjusted hazard ratios for pre-frail, frail and very frail subjects, respectively, were as follows: 2.23 (95% CI: 1.74-2.85), 3.34 (2.44-4.56) and 6.49 (4.30-9.78) for mortality; 2.80 (2.39-3.27), 5.53 (4.59-6.65) and 9.14 (7.06-11.8) for hospitalisation; and 1.02 (0.70-1.49), 1.93 (1.21-3.08) and 2.69 (1.34-5.40) for institutionalisation.

CONCLUSIONS

the e-SIF shows good agreement with mortality, institutionalisation, hospitalisation and health resource consumption, indicating satisfactory validity. More studies in larger populations are needed to corroborate our findings.

Defining ICD-10 surrogate variables to estimate the modified frailty index: a Delphi-based approach

BACKGROUND

There are currently no validated globally and freely available tools to estimate the modified frailty index (mFI). The widely available and non-proprietary International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) coding could be used as a surrogate for the mFI. We aimed to establish an appropriate set of the ICD-10 codes for comorbidities to be used to estimate the eleven-variable mFI.

METHODS

A three-stage, web-based, Delphi consensus-building process among a panel of intensivists and geriatricians using iterative rounds of an online survey, was conducted between March and July 2021. The consensus was set a priori at 75% overall agreement. Additionally, we assessed if survey responses differed between intensivists and geriatricians. Finally, we ascertained the level of agreement.

RESULTS

A total of 21 clinicians participated in all 3 Delphi surveys. Most (86%, 18/21) had more than 5-years' experience as specialists. The agreement proportionately increased with every Delphi survey. After the third survey, the panel had reached 75% consensus in 87.5% (112/128) of ICD-10 codes. The initially included 128 ICD-10 variables were narrowed down to 54 at the end of the 3 surveys. The inter-rater agreements between intensivists and geriatricians were moderate for surveys 1 and 3 (κ = 0.728, κ = 0.780) respectively, and strong for survey 2 (κ = 0.811).

CONCLUSIONS

This quantitative Delphi survey of a panel of experienced intensivists and geriatricians achieved consensus for appropriate ICD-10 codes to estimate the mFI. Future studies should focus on validating the mFI estimated from these ICD-10 codes.

TRIAL REGISTRATION

Not applicable.

Comparison of the predictive ability of clinical frailty scale and hospital frailty risk score to determine long-term survival in critically ill patients: a multicentre retrospective cohort study

BACKGROUND

The Clinical Frailty Scale (CFS) is the most commonly used frailty measure in intensive care unit (ICU) patients. The hospital frailty risk score (HFRS) was recently proposed for the quantification of frailty. We aimed to compare the HFRS with the CFS in critically ill patients in predicting long-term survival up to one year following ICU admission.

METHODS

In this retrospective multicentre cohort study from 16 public ICUs in the state of Victoria, Australia between 1st January 2017 and 30th June 2018, ICU admission episodes listed in the Australian and New Zealand Intensive Care Society Adult Patient Database registry with a documented CFS, which had been linked with the Victorian Admitted Episode Dataset and the Victorian Death Index were examined. The HFRS was calculated for each patient using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes that represented pre-existing conditions at the time of index hospital admission. Descriptive methods, Cox proportional hazards and area under the receiver operating characteristic (AUROC) were used to investigate the association between each frailty score and long-term survival up to 1 year, after adjusting for confounders including sex and baseline severity of illness on admission to ICU (Australia New Zealand risk-of-death, ANZROD).

RESULTS

7001 ICU patients with both frailty measures were analysed. The overall median (IQR) age was 63.7 (49.1-74.0) years; 59.5% (n = 4166) were male; the median (IQR) APACHE II score 14 (10-20). Almost half (46.7%, n = 3266) were mechanically ventilated. The hospital mortality was 9.5% (n = 642) and 1-year mortality was 14.4% (n = 1005). HFRS correlated weakly with CFS (Spearman's rho 0.13 (95% CI 0.10-0.15) and had a poor agreement (kappa = 0.12, 95% CI 0.10-0.15). Both frailty measures predicted 1-year survival after adjusting for confounders, CFS (HR 1.26, 95% CI 1.21-1.31) and HFRS (HR 1.08, 95% CI 1.02-1.15). The CFS had better discrimination of 1-year mortality than HFRS (AUROC 0.66 vs 0.63 p < 0.0001).

CONCLUSION

Both HFRS and CFS independently predicted up to 1-year survival following an ICU admission with moderate discrimination. The CFS was a better predictor of 1-year survival than the HFRS.

Variation in coded frailty syndromes in secondary care administrative data: an international retrospective exploratory study

OBJECTIVES

Challenges with manual methodologies to identify frailty, have led to enthusiasm for utilising large-scale administrative data, particularly standardised diagnostic codes. However, concerns have been raised regarding coding reliability and variability. We aimed to quantify variation in coding frailty syndromes within standardised diagnostic code fields of an international dataset.

SETTING

Pooled data from 37 hospitals in 10 countries from 2010 to 2014.

PARTICIPANTS

Patients ≥75 years with admission of >24 hours (N=1 404 671 patient episodes).

PRIMARY AND SECONDARY OUTCOME MEASURES

Frailty syndrome groups were coded in all standardised diagnostic fields by creation of a binary flag if the relevant diagnosis was present in the 12 months leading to index admission. Volume and percentages of coded frailty syndrome groups by age, gender, year and country were tabulated, and trend analysis provided in line charts. Descriptive statistics including mean, range, and coefficient of variation (CV) were calculated. Relationship to in-hospital mortality, hospital readmission and length of stay were visualised as bar charts.

RESULTS

The top four contributors were UK, US, Norway and Australia, which accounted for 75.4% of the volume of admissions. There were 553 595 (39.4%) patient episodes with at least one frailty syndrome group coded. The two most frequently coded frailty syndrome groups were 'Falls and Fractures' (N=3 36 087; 23.9%) and 'Delirium and Dementia' (N=221 072; 15.7%), with the lowest CV. Trend analysis revealed some coding instability over the frailty syndrome groups from 2010 to 2014. The four countries with the lowest CV for coded frailty syndrome groups were Belgium, Australia, USA and UK. There was up to twofold, fourfold and twofold variation difference for outcomes of length of stay, 30-day readmission and inpatient mortality, respectively, across the countries.

CONCLUSIONS

Variation in coding frequency for frailty syndromes in standardised diagnostic fields are quantified and described. Recommendations are made to account for this variation when producing risk prediction models.

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.

Review of frailty measurement of older people: Evaluation of the conceptualization, included domains, psychometric properties, and applicability

The purposes of this review are to describe the existing research on frailty measurement of older people and to understand their characteristics, with a focus on conceptual definitions, psychometric properties, and diagnostic accuracies. We reviewed the published literature to explore if cross-cultural studies of different types of frailty measurements have been conducted and to determine their applicability in the community setting. Narrative review with limited electronic database search and cross reference searching of included studies was performed. Studies published after year 2001 were searched for using MEDLINE and CINAHL Plus databases with keywords. A total of 5144 search results were obtained, but only 42 frailty measurements were identified in 68 studies. For the type, three different measurements were indicated, namely, self-report instrument (n = 17), clinical observation assessment (n = 19), and mixed frailty assessment instrument (n = 6). Only 12 (29%) measurements examined reliability and validity. Nevertheless, over 35% did not perform any psychometric testing before applying. For diagnosis accuracies, 35 (83%) frailty measurements reported the cut-off value(s) for determining level of the frailty. However, the sensitivity (56%-89.5%) and specificity (52%-91.3%) varied. The applicability was also diverse and some frailty instruments should be only used in some specific population and mode of administration. This review provides an overview of three major types of frailty measurements used in different settings with different purposes. For estimating the prevalence of frailty of older people in a community, the self-report type may be appropriate. The psychometric properties of many reviewed instruments are reported insufficiently. The cut-off value(s) are usually suggested with diverse sensitivity and specificity. Self-report instruments, such as Groningen Frailty Indicator (GFI) and Tilburg Frailty Indicator (TFI), are the most extensively examined in terms of satisfactory psychometric properties. Thus, GFI and TFI, with the current evidence, are recommended to be used in the community setting for frailty screening tools.

How blood pressure predicts frailty transitions in older adults in a population-based cohort study: a multi-state transition model

Background

Low blood pressure (BP) is associated with frailty in older adults. Our aim was to explore how BP predicts transitions between frailty states.

Methods

We used data from the Lausanne cohort Lc65+, a population-based cohort of older adults randomly drawn from a population registry in Switzerland, in 2004, 2009 and 2014. BP was measured using a clinically validated oscillometric automated device and frailty was defined using Fried’s phenotype, every 3 years. We used an illness-death discrete multi-state Markov model to estimate hazard ratios of forward and backward transitions between frailty states (outcome) in relation to BP categories (predictor of interest) with adjustment for sex, age and antihypertensive medication (other predictors).

Results

Among 4200 participants aged 65–70 years (58% female) at baseline, 70% were non-frail, 27% pre-frail and 2.0% frail. Over an average follow-up of 5.8 years, 2422 transitions were observed, with 1575 (65%) forward and 847 (35%) backward. Compared with systolic BP (SBP) <130 mmHg, the hazard ratio (95% confidence interval) of the transition from non-frail to pre-frail was 0.86 (0.74 to 1.00) for SBP 130–150 mmHg, and 0.89 (0.74 to 1.06) for SBP ≥150 mmHg. Compared with SBP <130 mmHg, the hazard ratio of the transition from pre-frail to frail was 0.71 (0.50 to 1.01) for SBP 130–150 mmHg, and 0.90 (0.62 to 1.32) for SBP ≥150 mmHg. Diastolic BP was a weaker predictor of forward transitions.

Conclusions

BP categories had no strong relationship with either forward transitions or backward transitions in frailty states. If our findings are confirmed with greater precision and assuming a causal relationship, they would suggest that there is no well-defined optimal BP level to prevent frailty among older adults.

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.

Potential value of electronic health records in capturing data on geriatric frailty for population health

OBJECTIVES

Despite the availability of many frailty measures to identify older adults at risk, frailty instruments are not routinely used for risk assessment in population health management. Here, we assessed the potential value of electronic health records (EHRs) and administrative claims in providing the necessary data for variables used across various frailty instruments.

SETTING AND PARTICIPANTS

The review focused on studies conducted worldwide. Participants included older people aged 50 and older.

DESIGN

We identified frailty instruments published between 2011 and 2018. Frailty variables used in each of the frailty instruments were extracted, grouped, and categorized across health determinants and various clinical factors.

MEASURES

The availability of the extracted frailty variables across various data sources (e.g., EHRs, administrative claims, and surveys) was evaluated by experts.

RESULTS

We identified 135 frailty instruments, which contained 593 unique variables. Clinical determinants of health were the best represented variables across frailty instruments (n = 516; 87 %), unlike social and health services factors (n = 33; ∼5% and n = 32; ∼5%). Most frailty instruments require at least one variable that is not routinely available in EHRs or claims (n = 113; ∼83 %). Only 22 frailty instruments have the potential to completely rely on EHR (structured or free-text data) and/or claims data, and possibly be operationalized on a population-level.

CONCLUSIONS AND IMPLICATIONS

Frailty instruments continue to be highly survey-based. More research is therefore needed to develop EHR-based frailty instruments for population health management. This will permit organizations and societies to stratify risk and better allocate resources among different older adult populations.