Associations between a laboratory frailty index and adverse health outcomes across age and sex

When in the lifecourse? Socioeconomic position across the lifecourse and biological health score

PURPOSE

Educational attainment is associated with multiphysiological wear and tear. However, associations with measures of socioeconomic position (SEP) across different life-stages are not established.

METHODS

Using regression models and data from 8105 participants from the UK Household Longitudinal Study (Understanding Society), we examined associations of lifecourse SEP with an overall biological health score (BHS). BHS is broader than usual measures of biological 'wear and tear' and is based on six physiological subsystems (endocrine, metabolic, cardiovascular, inflammatory/immune, liver, and kidney), with higher scores indicating worse health. Lifecourse SEP was based on respondents' parental, first, and most recent occupations.

RESULTS

Associations with SEP at all life-stages demonstrated higher BHS with increasing disadvantage (e.g. slope index of inequality (SII) (95 % CI) for most recent SEP: 0.04 (0.02, 0.06)). There was little difference in the magnitude of associations for SEP measured at each life-stage. Cumulative disadvantage across the lifecourse showed a stepped association with increasing BHS (SII (95 % CI): 0.05 (0.04, 0.07)). Associations were largely driven by metabolic, cardiovascular, and inflammatory systems.

CONCLUSION

Our results suggest that disadvantaged SEP across the lifecourse contributes cumulatively to poorer biological health, highlighting that every life-stage should be a target for public health policies and intervention.

Association between frailty index based on laboratory tests and all-cause mortality in critically ill patients with heart failure

BACKGROUND

The frailty index based on laboratory tests (FI-lab) can identify individuals at increased risk for adverse health outcomes. The association between the FI-lab and all-cause mortality in patients with heart failure (HF) in the intensive care unit (ICU) remains unknown. This study aimed to determine the correlation between FI-lab and all-cause mortality to evaluate the impact of FI-lab on the prognosis of critically ill patients with HF.

METHODS

This retrospective observational study utilized data extracted from the Medical Information Mart for Intensive Care IV database. The FI-lab, which consists of 33 laboratory tests, was constructed. Patients were then grouped into quartiles (Q1-Q4) based on their FI-lab scores. Kaplan-Meier analysis was used to compare all-cause mortality among the four groups. A Cox proportional hazard analysis was conducted to examine the association between the FI-lab score and all-cause mortality. The incremental predictive value of adding FI-lab to classical disease severity scores was assessed using Harrell's C statistic, integrated discrimination improvement (IDI) and net reclassification improvement (NRI).

RESULTS

Among 3021 patients, 838 (27.74%) died within 28 days, and 1400 (46.34%) died within a 360 day follow-up period. Kaplan-Meier analysis indicated that patients with higher FI-lab scores had significantly higher risks of all-cause mortality (log-rank P < 0.001). Multivariable Cox regression suggested that FI-lab, evaluated as a continuous variable (for each 0.01 increase), was associated with increased 28 day mortality [hazard ratio (HR) 1.02, 95% confidence interval (CI) (1.01-1.03), P < 0.001] and 360 day mortality [HR 1.02, 95% CI (1.01-1.02), P < 0.001]. When assessed in quartiles, the 28 day mortality risk [HR 1.66, 95% CI (1.28-2.15), P < 0.001] and 360 day mortality risk [HR 1.48, 95% CI (1.23-1.8), P < 0.001] were significantly higher for FI-lab Q4 compared with FI-lab Q1. FI-lab significantly improved the predictive capability of classical disease severity scores for 28 and 360 day mortality.

CONCLUSIONS

In ICU patients diagnosed with HF, the FI-lab is a potent predictor of short-term and long-term mortality in critically ill patients with HF. The active use of FI-lab to identify high-risk groups among critically ill HF patients and initiate timely interventions may have significant value in improving the prognosis of critically ill patients with HF.

The association between physical activity intensity and frailty risk among older adults across different age groups and genders: Evidence from four waves of the China Health and Retirement Longitudinal Survey

"Exercise is the best medicine" is well known, but the optimal dose of physical activity (PA) for males and females across different age groups is still unknown. This study, using data from the four waves of CHARLS, aimed to determine the optimal PA dose that reduces frailty risks among older adults across various age groups and both sexes. We created a frailty index score using 63 health-related variables and used 0.21 as the frailty cut point. Binary logistic regression was used to compare the effect of vigorous, moderate, and light intensity PA under IPAQ criteria on frailty risk. The study found that regardless of whether males or females, the optimal effect of vigorous-intensity PA in reducing the risk of frailty is consistently observed throughout the entire old age career. Moreover, the age groups at which moderate-intensity PA reduces the risk of frailty were from age 70 for males and from age 80 for females. And light-intensity PA had no effect on reducing the risk of frailty. Moderate and vigorous intensity of PA in older adults should be promoted, but guidelines and recommendations must account for optimal associations with PA dose across genders and age groups.

Predictors of adverse outcomes in elders hospitalised for isolated orthopaedic trauma: a multicentre cohort study

BACKGROUND

Patients >64 years of age now represent more than 51% of injury hospitalisations in Canada. The tools used to identify older patients who could benefit the most from an interdisciplinary approach include complex parameters difficult to collect in the ED, which suggests that better tools with higher accuracy and using items that can be derived from routinely collected data are needed. We aimed to identify variables that are associated with adverse outcomes in older patients admitted to a trauma centre for an isolated orthopaedic injury.

METHODS

We conducted a multicentre retrospective cohort study between 1 April 2013 and 31 March 2019 on older patients hospitalised with a primary diagnosis of isolated orthopaedic injury (n=19 928). Data were extracted from the provincial trauma registry (Registre des traumatismes du Québec). We used multilevel logistic regression to estimate the associations between potential predictors and adverse outcomes (extended length of stay, mortality, complications, unplanned readmission and adverse discharge destination).

RESULTS

Increasing age, male sex, specific comorbidities, type of orthopaedic injuries, increasing number of comorbidities, severe orthopaedic injury, head injuries and admission in the year before the injury were all significant predictors of adverse outcomes.

CONCLUSION

We identified eight predictors of adverse outcomes in patients >64 years of age admitted to a trauma centre for orthopaedic injury. These variables could eventually be used to develop a clinical decision rule to identify elders who may benefit the most from interdisciplinary care.

Low HALP (Hemoglobin, Albumin, Lymphocyte, and Platelet) Score Increases the Risk of Post-Stroke Cognitive Impairment: A Multicenter Cohort Study

Objective

The HALP (hemoglobin, albumin, lymphocyte, and platelet) score is a novel indicator that measures systemic inflammation and nutritional status that has not been correlated with the risk of post-stroke cognitive impairment in patients with acute ischemic stroke or transient ischemic attack (TIA).

Methods

Study participants were recruited from 40 stroke centers in China. The HALP score was derived using a weighted sum of hemoglobin, albumin, lymphocytes and platelets, and study participants were categorized into 4 groups of equal sizes based on quartiles cutoffs of the HALP score. The Montreal Cognitive Assessment (MoCA)-Beijing Cognitive Assessment Scale (MoCA-Beijing) was performed at 2 weeks and 12 months following stroke onset. Post-stroke cognitive impairment was considered in patients with MoCA-Beijing≤22. Multiple logistic regression methods were employed to evaluate the relationship between the HALP score and the subsequent risk of developing post-stroke cognitive impairment.

Results

The study population comprised 1022 patients (mean age 61.6±11.0 years, 73% men). The proportion of individuals with MoCA-Beijing≤22 at 2 weeks was 49.2% and 32.4% at one year. Patients in the lowest quartile of HALP score (<36.56) were observed to harbor the highest risk of post-stroke cognitive impairment at 12 months post-stroke/TIA compared to those in the highest quartile (odds ratio=1.59, 95% CI=1.07-2.37, p=0.022), and lower domain scores for executive function, naming, and attention. There were no statistically significant differences between patients in the different quartiles of HALP score and HALP score at 2 weeks post-stroke/TIA.

Conclusion

The HALP score is a simple score that could stratify the risk of post-stroke cognitive impairment in stroke/TIA patients to facilitate early diagnosis and interventions.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

How to construct a frailty index from an existing dataset in 10 steps

BACKGROUND

The frailty index is commonly used in research and clinical practice to quantify health. Using a health deficit accumulation model, a frailty index can be calculated retrospectively from data collected via survey, interview, performance test, laboratory report, clinical or administrative medical record, or any combination of these. Here, we offer a detailed 10-step approach to frailty index creation, with a worked example.

METHODS

We identified 10 steps to guide the creation of a valid and reliable frailty index. We then used data from waves 5 to 12 of the Health and Retirement Study (HRS) to illustrate the steps.

RESULTS

The 10 steps are as follows: (1) select every variable that measures a health problem; (2) exclude variables with more than 5% missing values; (3) recode the responses to 0 (no deficit) through 1 (deficit); (4) exclude variables when coded deficits are too rare (< 1%) or too common (> 80%); (5) screen the variables for association with age; (6) screen the variables for correlation with each other; (7) count the variables retained; (8) calculate the frailty index scores; (9) test the characteristics of the frailty index; (10) use the frailty index in analyses. In our worked example, we created a 61-item frailty index following these 10 steps.

CONCLUSIONS

This 10-step procedure can be used as a template to create one continuous health variable. The resulting high-information variable is suitable for use as an exposure, predictor or control variable, or an outcome measure of overall health and ageing.

Comprehensive Geriatric Care in Older Adults: Walking Ability after an Acute Fracture

BACKGROUND/OBJECTIVES

Comprehensive Geriatric Care (CGC) is a specific multimodal treatment for older patients. In the current study, we aimed to investigate walking performance after CGC in medically ill patients versus those with fractures.

METHODS

The timed up and go test (TuG), a 5-grade scale assessment (1 = no walking impairment to 5 = no walking ability at all) for evaluating individual walking ability was performed in all patients who underwent CGC prior to and after treatment. Factors associated with improvement in walking ability were analyzed in the subgroup of patients with fractures.

RESULTS

Out of 1263 hospitalized patients, 1099 underwent CGC (median age: 83.1 years (IQR 79.0-87.8 years); 64.1% were female). Patients with fractures (n = 300) were older than those without (n = 799), (median 85.6 versus 82.4 years, p = 0.001). Improvement in TuG after CGC was found in 54.2% of the fracture patients compared to just 45.9% of those without fractures. In fracture group patients, TuG improved from median 5 on admission to median 3 on discharge (p = 0.001). In fracture patients, improvement in walking ability was associated with higher Barthel index values on admission (median 45 (IQR: 35-55) versus 35 (IQR: 20-50): p = 0.001) and Tinetti assessment scores (median 9 (IQR: 4-14.25) versus 5 (IQR: 0-13); p = 0.001) and was negatively associated with the diagnosis of dementia (21.4% versus 31.5%; p = 0.058).

CONCLUSION

CGC improved walking ability in more than half of all patients examined. Older patients in particular might benefit from undergoing the procedure after an acute fracture. A better initial functional status favors a positive result following the treatment.

Development, validation and performance of laboratory frailty indices: A scoping review

INTRODUCTION

Frailty is a syndrome characterised by decline in functional ability and increasing vulnerability to disease and associated with adverse outcomes. Several established methods exist for assessing frailty. This scoping review aims to characterise the development and validation of frailty indices based on laboratory test results (FI-Lab) and to assess their utility.

METHODS

Studies were included in the review if they included data concerning the development and/or testing an FI-Lab using the deficit accumulation method. Studies were identified using PubMed/MEDLINE, Embase (Elsevier), OpenGrey and Google Scholar from 2010 to 2021. Two reviewers independently screened all abstracts, and those that met the inclusion criteria were reviewed in detail. Data extracted included details about the study characteristics, number, type and coding of laboratory variables included, validation, and outcomes. A narrative synthesis of the available evidence was adopted.

RESULTS

The search yielded 915 articles, of which 29 studies were included. In general, 89% of studies were conducted after 2016 and 51% in a hospital-based setting. The number of variables included in FI-Labs ranged from 13 to 77, and 51% included some non-laboratory variables in their indices, with pulse and blood pressure being the most frequent. The validity of FI-Lab was demonstrated through change with age, correlation with established frailty indices and association with adverse health outcomes. The most frequent outcome studied was mortality (79% of the studies), with FI-Lab associated with increased mortality in all but one. Other outcomes studied included self-reported health, institutionalisation, and activities of daily living. The effect of combining the FI-Lab with a non-laboratory-based FI was assessed in 7 studies with a marginal increase in predictive ability.

CONCLUSION

Frailty indices constructed based on the assessment of laboratory variables, appear to be a valid measure of frailty and robust to the choice of variables included.

A novel easy-to-use index to predict institutionalization and death in older population - a 10-year population-based follow-up study

BACKGROUND

Various indexes have been developed to estimate the risk for mortality, institutionalization, and other adverse outcomes for older people. Most indexes are based on a large number of clinical or laboratory parameters. An index based on only a few parameters would be more practical to use in every-day clinical practice. Our aim was to create an index to predict the risk for mortality and institutionalization with as few parameters as possible without compromising their predictive ability.

METHODS

A prospective study with a 10-year follow-up period. Thirty-six clinical and fourteen laboratory parameters were combined to form an index. Cox regression model was used to analyze the association of the index with institutionalization and mortality. A backward statistical method was used to reduce the number of parameters to form an easy-to-use index for predicting institutionalization and mortality.

RESULTS

The mean age of the participants (n = 1172) was 73.1 (SD 6.6, range 64‒97) years. Altogether, 149 (14%) subjects were institutionalized, and 413 (35%) subjects deceased during the follow-up. Institutionalization and mortality rates increased as index scores increased both for the large 50-parameter combined index and for the reduced indexes. After a backward variable selection in the Cox regression model, three clinical parameters remained in the index to predict institutionalization and six clinical and three laboratory parameters in the index to predict mortality. The reduced indexes showed a slightly better predictive value for both institutionalization and mortality compared to the full index.

CONCLUSIONS

A large index with fifty parameters included many unimportant parameters that did not increase its predictive value, and therefore could be replaced with a reduced index with only a few carefully chosen parameters, that were individually associated with institutionalization or death.

The frailty index based on laboratory test data as a tool to investigate the impact of frailty on health outcomes: a systematic review and meta-analysis

The frailty index (FI) quantifies frailty as deficit accumulation. It has been adapted to employ laboratory test data (FI-Lab). Our objective was to systematically review and meta-analyse the FI-Lab's ability to predict mortality. Secondary objectives were to review the FI-Lab's association with adverse health outcomes and whether FI-Lab scores differed between the sexes. A systematic literature search was carried out using six online databases to identify studies that measured the FI-Lab in humans. Hazard ratios (HRs) were combined in a meta-analysis to create a pooled risk estimate for mortality. Of the 1,201 papers identified, spanning January 2010 until 11 July 2022, 38 were included. FI-Lab scores per 0.01 unit increase predicted mortality overall (HR = 1.04; 95% confidence interval (CI) = 1.03-1.05) and for studies with a mean age of 81+ years (HR = 1.04; 95% CI = 1.03-1.05). The quality of evidence for these meta-analyses are moderate and high, respectively. Further, higher FI-Lab scores were associated with more frequent adverse health outcomes. Sex differences in FI-Lab scores varied, with no consistent indication of a sex effect. The FI-Lab is associated with mortality and with a variety of adverse health outcomes. No consistent sex differences in FI-Lab scores were observed, with several studies in disagreement. Notably, these conclusions were most relevant to older (65+ years old) individuals; further evidence in younger people is needed in both clinical and population representative studies.

Frailty index based on laboratory tests improves prediction of short-and long-term mortality in patients with critical acute myocardial infarction

Background

Previous studies have shown that the frailty index based on laboratory tests (FI-Lab) can identify older adults at increased risk of adverse health outcomes. This study aimed to determine whether the FI-Lab is associated with mortality risk and can provide incremental improvements in risk stratification of patients with critical acute myocardial infarction (AMI).

Materials and methods

We conducted a secondary analysis of data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. A 33-item FI-Lab was constructed. Outcomes of interest were in-hospital and 1-year mortality. Logistic regression models were used to investigate the association between the FI-Lab and outcomes. For the assessment of the incremental predictive value, the FI-Lab was added to several risk stratification scoring systems for critically ill patients, and the following indices were calculated: Δ C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

Results

Out of 2,159 patients, 477 died in hospital (22.1%), and 898 died during the 1-year follow-up period. After adjustment for confounders, the FI-Lab was associated with increased in-hospital mortality [odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.05-1.07] and 1-year mortality (OR = 1.05, 95% CI: 1.04-1.06) when assessed as a continuous variable (per 0.01-score increase). When assessed as a categorical variable, the FI-Lab was associated with in-hospital mortality (2nd Quartile: OR = 1.89, 95% CI: 1.18-3.03; 3rd Quartile: OR = 3.46, 95% CI: 2.20-5.46; and 4th Quartile: OR = 5.79, 95% CI: 3.61-9.28 compared to 1st Quartile) as well as 1-year mortality (2nd Quartile: OR = 1.66, 95% CI: 1.23-2.24; 3rd Quartile: OR = 2.40, 95% CI: 1.76-3.26; and 4th Quartile: OR = 3.76, 95% CI: 2.66-5.30 compared to 1st Quartile) after adjustment for confounders. The addition of the FI-Lab to all disease severity scores improved discrimination and significantly reclassified in-hospital and 1-year mortality risk.

Conclusion

The FI-Lab was a strong predictor of short- and long-term mortality in patients with critical AMI. The FI-Lab improved the ability to predict mortality in patients with critical AMI and therefore might be useful in the clinical decision-making process.

The impact of cardiovascular health and frailty on mortality for males and females across the life course

BACKGROUND

The effect of frailty and poor cardiovascular health on mortality for males and females is not fully elucidated. We investigated whether the combined burden of frailty and poor cardiovascular health is associated with all-cause and cardiovascular disease (CVD) mortality by sex and age.

METHODS

We analyzed data of 35,207 non-institutionalized US residents aged 20-85 years old (mean age [standard deviation]: 46.6 [16.7 years], 51.4% female, 70.8% White, 10.3% Black, 13.2% Hispanic) from the National Health and Nutrition Examination Survey (1999-2015). Cardiovascular health was measured with the American Heart Association's Life's Simple 7 score (LS7). A 33-item frailty index (FI) was constructed to exclude cardiovascular health deficits. We grouped the FI into 0.1 increments (non-frail: FI &lt; 0.10, very mildly frail: 0.1 ≤ FI &lt; 0.20, mildly frail: 0.20 ≤ FI &lt; 0.30, and moderately/severely frail: FI ≥ 0.30) and LS7 into tertiles (T1[poor] = 0-7, T2[intermediate] = 8-9, T3[ideal] = 10-14). All-cause and CVD mortality data were analyzed up to 16 years. All regression models were stratified by sex.

RESULTS

The average FI was 0.09 (SD 0.10); 29.6% were at least very mildly frail, and the average LS7 was 7.9 (2.3). Mortality from all-causes and CVD were 8.5% (4228/35,207) and 6.1% (2917/35,207), respectively. The median length of follow-up was 8.1 years. The combined burden of frailty and poor cardiovascular health on mortality risk varied according to age in males (FI*age interaction p = 0.01; LS7*age interaction p &lt; 0.001) but not in females. In females, poor FI and LS7 combined to predict all-cause and CVD mortality in a dose-response manner. All-cause and CVD mortality risk was greater for older males (60 and 70 years old) who were at least mildly frail and had intermediate cardiovascular health or worse (hazard ratio [lower/higher confidence interval ranges] range: all-cause mortality = 2.02-5.30 [1.20-4.04, 3.15-6.94]; CVD-related mortality = 2.22-7.16 [1.03-4.46, 4.49-11.50]) but not for younger males (30, 40, and 50 years old).

CONCLUSIONS

The combined burden of frailty and LS7 on mortality is similar across all ages in females. In males, this burden is greater among older people. Adding frailty to assessments of overall cardiovascular health may identify more individuals at risk for mortality and better inform decisions to implement preventative or treatment approaches.

Unsupervised learning of aging principles from longitudinal data

Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the "dynamic frailty indicator" (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.

The association of a frailty index from laboratory tests and vital signs with clinical outcomes in hospitalized older adults

BACKGROUND

Frailty, a state of vulnerability to stressors resulting from loss of physiological reserve due to multisystemic dysfunction, is common among hospitalized older adults. Hospital clinicians need objective and practical instruments that identify older adults with frailty. The FI-LAB is based on laboratory values and vital signs and may capture biological changes of frailty that predispose hospitalized older adults to complications. The study's aim was to assess the association of the FI-LAB versus VA-FI with hospital and post-hospital clinical outcomes in older adults.

METHODS

Retrospective cohort study was conducted on Veterans aged ≥60 admitted to a VA hospital. We identified acute hospitalizations January 2011-December-2014 with 1-year follow-up. A 31-item FI-LAB was created from blood laboratory tests and vital signs collected within the first 48 h of admission and scores were categorized as low (<0.25), moderate (0.25-0.40), and high (>0.40). For each FI-LAB group, we obtained odds ratio (OR) and confidence intervals (CI) for hospital and post-hospital outcomes using multivariate binomial logistic regression. Additionally, we calculated hazard ratios (HR) and CI for all-cause in-hospital mortality comparing the high and moderate FI-LAB group with the low group.

RESULTS

Patients were 1407 Veterans, mean age 72.7 (SD = 9.0), 67.8% Caucasian, 96.1% males, 47.0% (n = 661), 41.0% (n = 577), and 12.0% (n = 169) were in the low, moderate, and high FI-LAB groups, respectively. Moderate and high scores were associated with prolonged LOS, OR:1.62 (95% CI:1.29-2.03); and 3.36 (95% CI:2.27-4.99), ICU admission, OR:1.40 (95% CI:1.03-1.90); and OR:2.00 (95% CI:1.33-3.02), nursing home placement OR:2.36 (95% CI:1.26-4.44); and 5.99 (95% CI:2.83-12.70), 30-day readmissions OR:1.74 (95% CI:1.20-2.52); and 2.20 (95% CI:1.30-3.74), 30-day mortality OR: 2.51 (95% CI:1.01-6.23); and 8.97 (95% CI:3.42-23.53), 6-month mortality OR:3.00 (95% CI:1.90-4.74); and 6.16 (95% CI:3.55-10.71), and 1-year mortality OR: 2.66 (95% CI:1.87-3.79); and 4.76 (95% CI:3.00-7.54) respectively. The high FI-LAB group showed higher risk of in-hospital mortality, HR:18.17 (95% CI:4.01-80.52) with an area-under-the-curve of 0.843 (95% CI:0.75-0.93).

CONCLUSIONS

High and moderate FI-LAB scores were associated with worse in-hospital and post-hospital outcomes. The FI-LAB may identify hospitalized older patients with frailty at higher risk and assist clinicians in implementing strategies to improve outcomes.

Preoperative frailty based on laboratory data and postoperative health outcomes in patients undergoing coronary artery bypass graft surgery

BACKGROUND

Frailty is associated with adverse surgical outcomes. Patients with cardiovascular diseases have many risk factors of frailty; thus, preoperative frailty evaluation is necessary to predict adverse outcomes after coronary artery bypass graft (CABG) surgery. Laboratory data based-frailty assessments are objective and not time-consuming, addressing the need for an accurate but simple frailty screening for patients awaiting CABG surgery.

OBJECTIVES

This retrospective study aimed to determine the association between laboratory based-frailty and patient health outcomes after CABG surgery.

METHODS

We evaluated 896 patients who underwent on-pump or off-pump CABG surgery between August 1, 2015 and July 31, 2020 at a tertiary hospital. The frailty index-laboratory (FI-LAB), which comprises 32 laboratory parameters and vital signs, was used for frailty assessment.

RESULTS

The patients were divided into three groups according to their preoperative FI-LAB level as low (FI-LAB <0.25, 23.0%), moderate (FI-LAB ≥0.25 to ≤0.4, 54.9%), and high (FI-LAB>0.4, 22.1%) frailty groups. In the confounder-adjusted analysis, the lengths of hospital stay and intensive care unit stay were longer by 2.20 days (p=.023) and by 0.89 days (p=.009), respectively, in the high frailty group than those in the low frailty group. The odds ratio for 30-day readmission was also 2.58 times higher in the high frailty group than that in the low frailty group.

CONCLUSION

A high preoperative FI-LAB score indicates increasing risks of adverse postoperative outcomes among CABG surgery patients. FI-LAB has potential strengths to capture the need for a more thorough frailty assessment for cardiac surgery patients.

Frailty indices based on self-report, blood-based biomarkers and examination-based data in the Canadian Longitudinal Study on Aging

BACKGROUND

Frailty can be operationalised using the deficit accumulation approach, which considers health deficits across multiple domains. We aimed to develop, validate and compare three different frailty indices (FI) constructed from self-reported health measures (FI-Self Report), blood-based biomarkers (FI-Blood) and examination-based assessments (FI-Examination).

METHODS

Up to 30,027 participants aged 45-85 years from the baseline (2011-2015) comprehensive cohort of the Canadian Longitudinal Study on Aging were included in the analyses. Following standard criteria, three FIs were created: a 48-item FI-Self Report, a 23-item FI-Blood and a 47-item FI-Examination. In addition a 118-item FI-Combined was constructed. Mortality status was ascertained in July 2019.

RESULTS

FI-Blood and FI-Examination demonstrated broader distributions than FI-Self Report. FI-Self Report and FI-Blood scores were higher in females, whereas FI-Examination scores were higher in males. All FI scores increased nonlinearly with age and were highest at lower education levels. In sex and age-adjusted models, a 0.01 increase in FI score was associated with a 1.08 [95% confidence interval (CI): 1.07,1.10], 1.05 (1.04,1.06), 1.07 (1.05,1.08) and a 1.13 (1.11,1.16) increased odds of mortality for FI-Self Report, FI-Blood, FI-Examination and FI-Combined, respectively. Inclusion of the three distinct FI types in a single model yielded the best prognostic accuracy and model fit, even compared to the FI-Combined, with all FIs remaining independently associated with mortality.

CONCLUSION

Characteristics of all FIs were largely consistent with previously established FIs. To adequately capture frailty levels and to improve our understanding of the heterogeneity of ageing, FIs should consider multiple types of deficits including self-reported, blood and examination-based measures.

Frailty assessment using routine clinical data: An integrative review

BACKGROUND

Frailty is a common but complex problem in older adults. Frailty assessment using routine clinical data has been suggested as a pragmatic approach based on electronic health records from primary care center or hospital settings.

PURPOSE

We aimed to explore the tools and outcome variables used in the published studies on frailty assessment using routine clinical data.

METHODS

An integrative literature review was conducted using the method of Whittemore and Knafl. A literature search was conducted in PubMed, EMBASE, and CINAHL from January 2010 to October 2021.

RESULTS

A total of 45 studies and thirteen frailty assessment tools were analyzed. The assessment items were generally biased toward frailty's risk factors rather than the mechanisms or phenotypes of frailty. Similar to using conventional tools, routine clinical data-based frailty was associated with adverse health outcomes.

CONCLUSIONS

Frailty assessment based on routine clinical data could efficiently evaluate frailty using electronic health records from primary care centers or hospitals. However, they need refinement to consider the risk factors, mechanisms, and frailty phenotypes.

A Simple Risk Score Based on Routine Clinical Parameters Can Predict Frailty in Hospitalized Heart Failure Patients

Frailty syndrome (FS) has recently attracted attention as one of the major predictors of heart failure (HF) course severity. We aimed to develop a simple tool for predicting frailty in hospitalized HF patients using routine clinical parameters. A total of 153 hospitalized patients diagnosed with heart failure with reduced ejection fraction (HFrEF) were included in the study. Presence of FS was assessed with the SHARE-FI questionnaire. Clinical and biochemical parameters were collected. Using ROC curves and logistic regression analysis, a model predicting FS presence was developed and tested. Proposed model includes five variables with following cut-off values (1 point for each variable): age > 50 years, systolic pressure on admission < 110 mmHg, total cholesterol < 4.85 mmol/L, bilirubin ≥ 15.5 mmol/L, and alanine aminotransferase ≤ 34 U/L. Receiving 5 points was considered a high risk of FS with positive and negative predictive values (NPV), 83% and 72%, respectively, and specificity of 97%. Awarding 2 points or less ruled out FS in the studied group with negative predictive value 94%. The presented novel, simple score predicts FS in HFrEF patients with routine clinical parameters and has good positive and negative predictive values.

Number of teeth is associated with all-cause and disease-specific mortality

BACKGROUND

Tooth loss has been shown to correlate with multiple systemic comorbidities. However, the associations between the number of remaining natural teeth (NoT) and all-cause mortality have not been explored extensively. We aimed to investigate whether having fewer NoT imposes a higher risk in mortality. We tested such hypotheses using three groups of NoT (20-28,10-19, and 0-9), edentulism and without functional dentition (NoT < 19).

METHODS

The National Health and Nutrition Examination Survey in the United States (NHANES) (1999-2014) conducted dental examinations and provided linkage of mortality data. NHANES participants aged 20 years and older, without missing information of dental examination, age, gender, race, education, income, body-mass-index, smoking, physical activities, and existing systemic conditions [hypertension, total cardiovascular disease, diabetes, and stroke (N = 33,071; death = 3978), or with femoral neck bone mineral density measurement (N = 13,131; death = 1091)] were analyzed. Cox proportional hazard survival analyses were used to investigate risks of all-cause, heart disease, diabetes and cancer mortality associated with NoT in 3 groups, edentulism, or without functional dentition.

RESULTS

Participants having fewer number of teeth had higher all-cause and disease-specific mortality. In fully-adjusted models, participants with NoT0-9 had the highest hazard ratio (HR) for all-cause mortality [HR(95%CI) = 1.46(1.25-1.71); p < .001], mortality from heart diseases [HR(95%CI) = 1.92(1.33-2.77); p < .001], from diabetes [HR(95%CI) = 1.67(1.05-2.66); p = 0.03], or cancer-related mortality [HR(95%CI) = 1.80(1.34-2.43); p < .001]. Risks for all-cause mortality were also higher among the edentulous [HR(95%CI) = 1.35(1.17-1.57); p < .001] or those without functional dentition [HR(95%CI) = 1.34(1.17-1.55); p < .001].

CONCLUSIONS

Having fewer NoT were associated with higher risks for all-cause mortality. More research is needed to explore possible biological implications and validate our findings.

Elementary Laboratory Assays as Biomarkers of Ageing: Support for Treatment of COVID-19?

Youth, working age and the elderly: On a timeline, chronological age (CA) and biological age (BA) may dissociate; nosological entities manifest themselves at different BAs. In determining which disease corresponds to a given age decade, statistical registries of causes of death are unreliable and this does not change with SARS CoV-2 infection. Beyond adolescence, ageing metrics involve estimations of changes in fitness, including prediction models to estimate the number of remaining years left to live. A substantial disparity in biomarker levels and health status of ageing can be observed: the difference in CA and BA in the large cohorts under consideration is glaring. Here, we focus more closely on ageing and senescence metrics in order to make information available for risk analysis non the least with COVID-19, including the most recent risk factors of ABO blood type and 3p21.31 chromosome cluster impacting on C5a and SC5b-9 plasma levels. From the multitude of routine medical laboratory assays, a potentially meaningful set of assays aimed to best reflect the stage of individual senescence; hence risk factors the observational prospective SENIORLABOR study of 1,467 healthy elderly performed since 2009 and similar approaches since 1958 can be instantiated as a network to combine a set of elementary laboratory assays quantifying senescence.

Sex differences in frailty: Comparisons between humans and preclinical models

Frailty can be viewed as a state of physiological decline that increases susceptibility to adverse health outcomes. This loss of physiological reserve means that even small stressors can lead to disability and death in frail individuals. Frailty can be measured with various clinical tools; the two most popular are the frailty index and the frailty phenotype. Clinical studies have used these tools to show that women are frailer than men even though they have longer lifespans. Still, factors responsible for this frailty-mortality paradox are not well understood. This review highlights evidence for male-female differences in frailty from both the clinical literature and in animal models of frailty. We review evidence for higher frailty levels in female animals as seen in many preclinical models. Mechanisms that may contribute to sex differences in frailty are highlighted. In addition, we review work that suggests frailty may play a role in susceptibility to chronic diseases of aging in a sex-specific fashion. Additional mechanistic studies in preclinical models are needed to understand factors involved in male-female differences in frailty in late life.

Measuring frailty in younger populations: a rapid review of evidence

OBJECTIVES

Frailty is typically assessed in older populations. Identifying frailty in adults aged under 60 years may also have value, if it supports the delivery of timely care. We sought to identify how frailty is measured in younger populations, including evidence of the impact on patient outcomes and care.

DESIGN

A rapid review of primary studies was conducted.

DATA SOURCES

Four databases, three sources of grey literature and reference lists of systematic reviews were searched in March 2020.

ELIGIBILITY CRITERIA

Eligible studies measured frailty in populations aged under 60 years using experimental or observational designs, published after 2000 in English.

DATA EXTRACTION AND SYNTHESIS

Records were screened against review criteria. Study data were extracted with 20% of records checked for accuracy by a second researcher. Data were synthesised using a narrative approach.

RESULTS

We identified 268 studies that measured frailty in samples that included people aged under 60 years. Of these, 85 studies reported evidence about measure validity. No measures were identified that were designed and validated to identify frailty exclusively in younger groups. However, in populations that included people aged over and under 60 years, cumulative deficit frailty indices, phenotype measures, the FRAIL Scale, the Liver Frailty Index and the Short Physical Performance Battery all demonstrated predictive validity for mortality and/or hospital admission. Evidence of criterion validity was rare. The extent to which measures possess validity across the younger adult age (18-59 years) spectrum was unclear. There was no evidence about the impact of measuring frailty in younger populations on patient outcomes and care.

CONCLUSIONS

Limited evidence suggests that frailty measures have predictive validity in younger populations. Further research is needed to clarify the validity of measures across the adult age spectrum, and explore the utility of measuring frailty in younger groups.

A practical laboratory index to predict institutionalization and mortality - an 18-year population-based follow-up study

Background

Previously, several indexes based on a large number of clinical and laboratory tests to predict mortality and frailty have been produced. However, there is still a need for an easily applicable screening tool for every-day clinical practice.

Methods

A prospective study with 10- and 18-year follow-ups. Fourteen common laboratory tests were combined to an index. Cox regression model was used to analyse the association of the laboratory index with institutionalization and mortality.

Results

The mean age of the participants (n = 1153) was 73.6 (SD 6.8, range 64.0–100.0) years. Altogether, 151 (14.8%) and 305 (29.9%) subjects were institutionalized and 422 (36.6%) and 806 (69.9%) subjects deceased during the 10- and 18-year follow-ups, respectively. Higher LI (laboratory index) scores predicted increased mortality. Mortality rates increased as LI scores increased both in unadjusted and in age- and gender-adjusted models during both follow-ups. The LI did not significantly predict institutionalization either during the 10- or 18-year follow-ups.

Conclusions

A practical index based on routine laboratory tests can be used to predict mortality among older people. An LI could be automatically counted from routine laboratory results and thus an easily applicable screening instrument in clinical settings.