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

Air pollution increases the risk of frailty: China Health and Retirement Longitudinal Study (CHARLS)

The longitudinal association between multiple air pollutants and frailty risk remains unexplored, and it is unclear which factors may modify this relationship. Using data from 10,584 Chinese adults aged 45 years and older in the 2011-2020 waves of the China Health and Retirement Longitudinal Study (CHARLS), we investigated whether exposure to PM1, PM2.5, PM10, O3, and NO2 affects frailty over a median follow-up of seven years. Air pollutant data were obtained from the China High Air Pollutants (CHAP) dataset, and frailty was assessed using a 44-item Frailty Index (FI ≥ 0.25). Time-varying Cox proportional hazards models, adjusted for demographic, socioeconomic, and behavioral factors, indicated that each 10 μg/m³ increase in PM1, PM2.5, PM10, and NO2 corresponded to a 7.8 %, 4.2 %, 3.8 %, and 12.9 % higher risk of frailty, respectively, while O3 showed no significant association. Individuals who were sufficiently active appeared less affected by pollution, whereas those with no formal education were more vulnerable. Implementing future policies and interventions to reduce air pollution can potentially decrease the risk of frailty and promote healthy ageing.

Association between laboratory data-based frailty index and clinical health outcomes in critically ill older patients: A retrospective correlational study

BACKGROUND

Although frailty assessment is crucial for understanding critically ill patients' prognosis, traditional frailty measures require substantial efforts and time from health care professionals. To address this limitation, the laboratory frailty index (FI-LAB) based on laboratory clinical data was developed. However, knowledge regarding its correlation with health outcomes among critically ill older patients is limited.

AIM

To identify the association between the FI-LAB and acute, mid- and long-term outcomes among critically ill older adults.

STUDY DESIGN

This retrospective correlational study used electronic health records of 2106 older patients who were admitted to the intensive care unit at a tertiary hospital in Seoul, Korea. Acute and mid-term outcomes included occurrence of delirium and in-hospital mortality, and the long-term outcome included 1-year mortality. Logistic regression was used to explore the relationships across FI-LAB, delirium, and in-hospital mortality, while Cox proportional hazard regression was used to analyse the relationship between FI-LAB and 1-year mortality.

RESULTS

Frailty assessed by FI-LAB was significantly associated with increased risk of delirium (odds ratio [OR] = 6.21, 95% confidence interval [CI] = 2.31-25.39, p = .009), in-hospital mortality (OR = 2.38, 95% CI = 1.15-5.79, p = .014), and 1-year mortality (hazard ratio = 2.47, 95% CI = 1.16-5.25, p = .019) after controlling for covariates.

CONCLUSIONS

The study highlighted the importance of using FI-LAB for screening frailty in critically ill older adults. Health care providers can improve patients' acute, mid- and long-term outcomes to develop more individualised management plans based on FI-LAB scores.

RELEVANCE TO CLINICAL PRACTICE

The FI-LAB score calculated from routine laboratory data can be used by nurses as a screening tool to identify frail older adults in critical care. Early detection of frailty would allow for closer monitoring and the implementation of interventions to reduce delirium and mortality.

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction

Biological age (BA) and frailty represent two distinct health measures that offer valuable insights into the aging process. Comparing and analyzing blood-based biomarkers from the machine learning (ML) predictors of BA and frailty helps deepen our understanding of aging. This study aimed to develop a novel framework to identify biomarkers of aging by combining BA and frailty ML predictors with eXplainable Artificial Intelligence (XAI) techniques. We utilized data from middle-aged and older Chinese adults (≥ 45 years) in the 2011/2012 wave (n = 9702) and the 2015/2016 wave (n = 9455, as test set validation) of the China Health and Retirement Longitudinal Study (CHARLS). Sixteen blood-based biomarkers were used to predict BA and frailty. Four tree-based ML algorithms were employed in the training and validation, and performance metrics were compared to select the best models. Then, SHapley Additive exPlanations (SHAP) analysis was conducted on the selected models. CatBoost performed the best in the BA predictor, and Gradient Boosting performed the best in the frailty predictor. Traditional ML feature importance identified cystatin C and glycated hemoglobin as the major contributors for their respective models. However, subsequent SHAP analysis demonstrated that only cystatin C was the primary contributor in both models. The proposed framework can easily incorporate additional biomarkers, providing a scalable and comprehensive toolset that offers a quantitative understanding of biomarkers of aging.

What can we learn from 68 000 clinical frailty scale scores? Evaluating the utility of frailty assessment in emergency departments

BACKGROUND

Emergency departments (EDs) in England are under significant strain, with increasing attendances and extended wait times, affecting frail older adults. The clinical frailty scale (CFS) has been implemented as a tool to assess frailty in ED settings, but its reliability and predictive accuracy as a screening tool remain debated.

OBJECTIVE

To evaluate the use and variability of the CFS in EDs and its association with patient outcomes, including discharge rates, length of stay, readmission and mortality.

METHODS

A retrospective cohort study of ED attendances at two London (UK) hospitals from 2017 to 2021. Data included CFS scores, demographics, clinical observations and outcomes. Comparative statistics, logistic regression, Cox proportional hazards models and competing risk regression were applied to examine CFS predictive validity.

RESULTS

In a sample of 123 324 ED visits, CFS scores strongly correlated with adverse outcomes: e.g. for long-term mortality (n = 33 475, events = 8871), each CFS single-point increase was associated with a 25% increase in mortality risk (95% CI 1.23-1.26). CFS scores varied significantly between raters and across visits, median difference two levels (interquartile range 1-3). Intraclass correlation coefficient analysis showed that 33.1% of CFS score differences was attributable to between-patient differences, 15.4% to inter-rater differences, with 51.5% residual variance from non-frailty factors, such as acute illness severity.

CONCLUSION

The CFS is associated with crucial patient outcomes in the ED. Inter-rater variability and potentially confounding factors can limit its consistency. Automation to enhance CFS score reliability should be explored as a means to support proactive management.

Accelerated pace of frailty in patients with schizophrenia

BACKGROUND

Schizophrenia is associated with an increased risk of mortality and physical comorbidities, indicating a potentially accelerated frailty process in affected individuals. This study aimed to test association between schizophrenia and frailty using the frailty index based on laboratory markers (FI-Lab).

METHODS

A total of 600 patients with schizophrenia and 518 healthy controls, aged between 20 and 69 years were included in the present study. Frailty was assessed using the FI-Lab, incorporating routine laboratory markers, body mass index, and blood pressure measurements. FI-Lab for patients with schizophrenia and healthy controls was compared, with stratification by age group and sex. In addition, robust was defined as FI-Lab ≤ 0.12, pre-frail as 0.12-0.25, and frail as >0.25. Multiple linear regression analysis was used to test the association between schizophrenia and FI-Lab. Multinomial logistic regression was used to test the association between schizophrenia and frailty status. Spearman correlation analysis was performed to assess the relationship between the Positive and Negative Syndrome Scale (PANSS) scores and FI-Lab in schizophrenia patients.

RESULTS

Schizophrenia patients exhibited significantly higher FI-Lab than healthy controls across all age groups, indicating accelerated pace of frailty in schizophrenia patients. Schizophrenia was significantly associated with FI-Lab (β = 0.044, p = 0.004) in the adjusted model. Schizophrenia was significantly associated with both pre-frail status (OR = 2.26, 95% CI = 1.40-3.68, p = 0.001) and frail status (OR = 10.33, 95% CI = 5.65-19.93, p = 0.007) compared to robust status in the adjusted model. Additionally, a positive correlation between FI-Lab and PANSS scores suggests that more severe schizophrenia symptoms correlate with higher degree of frailty.

CONCLUSION

These findings suggest that schizophrenia contributes to an increased risk of frailty. The FI-Lab provides a quantitative measure of frailty. This underscores the importance of integrating frailty considerations into the treatment and management of schizophrenia.

Application of laboratory frailty index in predicting delirium in elderly patients with community-acquired pneumonia

Background

With the global aging population, community-acquired pneumonia and delirium are increasingly critical health issues among the elderly. The Laboratory Frailty Index provides an objective measure of frailty. This study explores its capacity in predicting delirium and examines the interplay between frailty and nutritional status in elderly patients with community-acquired pneumonia.

Methods and materials

This retrospective study included 481 elderly patients aged 75 and above diagnosed with community-acquired pneumonia. The Laboratory Frailty Index was calculated by dividing the sum of abnormal indicator scores by the total number of test indicators, resulting in a score ranging from 0 to 1, with higher values indicating greater frailty.

Results

Higher Laboratory Frailty Index scores were associated with an increased risk of delirium. The index's predictive accuracy improved when combined with nutritional assessments. Patients experiencing malnutrition alongside higher frailty scores exhibited a higher risk of adverse outcomes. Nutritional status mediated the relationship between frailty and delirium, underlining the significance of addressing both variables.

Conclusion

The Laboratory Frailty Index is a robust predictor of delirium in elderly patients with community-acquired pneumonia. These findings provide valuable insights for the early identification and intervention of delirium in clinical settings.

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.

FI-lab in the emergency department and adverse outcomes among acutely hospitalized older adults

BACKGROUND

The emergency department is treating a growing number of older patients with frailty, which has been linked to poorer outcomes. Urgency is generally emphasized in the emergency department based on indicators such as triage scores and early warning scores for decision-making. However, this approach may not be sufficient for frail older people. The Frailty Index-laboratory (FI-lab) has been used as a simple assessment tool for frailty, but it may also reflect disease severity and predict adverse outcomes in the emergency care setting. Therefore, we aimed to evaluate the association between FI-lab in the emergency room and adverse outcomes during hospitalization through comparison with assessments using triage and early warning scores.

METHODS

This was a retrospective cohort study conducted in a tertiary hospital. The study included patients aged 65 years or older who were admitted to the general internal medicine ward after being initially evaluated in the emergency department. FI-lab was calculated using 24 laboratory parameters from blood tests. The National Early Warning Score (NEWS), the Japan Triage and Acuity Scale (JTAS), and the modified JTAS were also used as prognostic indicators, and their association with adverse outcomes was compared with that of FI-lab.

RESULTS

In total, 872 patients (mean age, 80.9 years; male, 52.6 %) were analyzed. Patients who died during hospitalization had a higher FI-lab than those who survived. In multiple regression analysis, FI-lab, NEWS, and the modified JTAS were significantly associated with in-hospital death and prolonged length of hospital stay. In contrast, none of these indices were associated with in-hospital falls. The FI-lab was independently associated with the likelihood of discharge to home.

CONCLUSIONS

FI-lab evaluated in the emergency department reflected the severity of illness in acutely hospitalized older adults, similarly to NEWS and JTAS, and was a useful indicator for predicting adverse outcomes. These results may indicate the value of FI-lab for older adults in the acute care setting.

Frailty Index Based on Common Laboratory Tests for Patients Starting Home-Based Medical Care

OBJECTIVES

To determine whether a Frailty Index based on laboratory tests (FI-lab) is associated with clinical outcomes independently of a standard nonlaboratory Frailty Index (FI-clinical) in older patients starting home-based medical care.

DESIGN

Secondary analysis of data from a multicenter prospective cohort study.

SETTING AND PARTICIPANTS

Patients aged ≥65 years who were starting home-based medical care services provided by doctors and nurses at Nagoya, Japan.

METHODS

We calculated FI-lab (proportion of abnormal results out of 25 commonly tested laboratory parameters) and FI-clinical using 42 items based on data obtained at enrollment. The primary outcome was mortality within 2 years after starting home-based medical care. A sensitivity analysis was also conducted with 1-year mortality as the outcome. Other outcomes included hospitalization and nursing home admission within 2 years.

RESULTS

In total, 188 patients (mean age 79.9 ± 10.2 years, 57.5% male) were included. The median FI-lab was 0.40 [interquartile range (IQR) 0.29-0.50] and the median FI-clinical was 0.32 (IQR 0.24-0.43). Sixty-nine patients (36.7%) died within 2 years of starting home-based medical care. A Cox proportional hazards regression analysis including age, sex, FI-lab, and FI-clinical as independent variables revealed that FI-lab was associated with 2-year mortality independently of FI-clinical [FI-lab per 0.1 unit, odds ratio (OR) 1.49, 95% CI 1.25-1.77; FI-clinical per 0.1 unit, OR 1.13, 95% CI 0.90-1.41]. The sensitivity analysis showed similar results for 1-year mortality. Neither FI-lab nor FI-clinical was associated with hospitalization or nursing home admission within 2 years.

CONCLUSIONS AND IMPLICATIONS

FI-lab was associated with 2-year mortality in patients starting home-based medical care, independently of FI-clinical, and may be useful for risk assessment in this population. Studies with larger sample sizes are needed.

Frailty detection with routine blood tests using data from the english longitudinal study of ageing (ELSA)

PURPOSE

Frailty is a rising global health issue in ageing society. Easily accessible and sensitive tools are needed for frailty monitoring while routine blood factors can be potential candidates.

METHODS

Data from 1907 participants (aged 60 years or above) were collected from the 4th to 9th wave of the English longitudinal study of ageing. 14 blood factors obtained from blood tests were included in the analysis. A 52-item frailty index (FI) was calculated for frailty evaluation. Logistic regression and Cox proportional hazards analysis were used to explore the relationships between baseline blood factors and the incidence of frailty over time respectively. All analyses were controlled for age and sex.

RESULTS

The mean age of participants was 67.3 years and 47.2% of them were male. Our study identified that 8 blood factors (mean corpuscular haemoglobin, HDL, triglyceride, ferritin, hsCRP, dehydroepiandrosterone, haemoglobin, and WBC) involved in inflammatory, nutritional and metabolic processes were associated with frailty. The combined model with these 8 blood factors had an AUC of 0.758 at cross-sectional level. In the Cox proportional hazards analysis, higher triglyceride (HR: 1.30, 95%CI: 1.07 ~ 1.59), WBC (HR: 1.16, 95%CI: 1.05 ~ 1.28), and lower HDL (HR: 0.58, 95%CI: 0.38 ~ 0.90) at baseline were linked to greater risk of developing frailty within 10 years. Compared to adults without abnormal blood factors at baseline, the hazard ratios of participants with two or more abnormal blood factors were almost twofold higher in developing frailty over time.

CONCLUSIONS

Routine blood factors, particularly triglyceride, HDL and WBC, could be used for frailty screening in clinical practice and estimate the development of frailty over time.

Preclinical Studies on the Effects of Frailty in the Aging Heart

Age is a major risk factor for the development of cardiovascular diseases in men and in women. However, not all people age at the same rate and those who are aging rapidly are considered frail, compared with their fit counterparts. Frailty is an important clinical challenge because those who are frail are more likely to develop and die from illnesses, including cardiovascular diseases, than fit people of the same age. This increase in susceptibility to cardiovascular diseases in older individuals might occur as the cellular and molecular mechanisms involved in the aging process facilitate structural and functional damage in the heart. Consistent with this, recent studies in murine frailty models have provided strong evidence that maladaptive cardiac remodelling in older mice is the most pronounced in mice with a high level of frailty. For example, there is evidence that ventricular hypertrophy and contractile dysfunction increase as frailty increases in aging mice. Additionally, fibrosis and slowing of conduction in the sinoatrial node and atria are proportional to the level of frailty. These modifications could predispose frail older adults to diseases like heart failure and atrial fibrillation. This preclinical work also raises the possibility that emerging interventions designed to "treat frailty" might also treat or prevent cardiovascular diseases. These findings might help to explain why frail older people are most likely to develop these disorders as they age.

Sex-specific effects of frailty on cardiac structure and function: insights from preclinical models

Advanced age is an independent risk factor for cardiovascular diseases in both sexes. This is thought to be due, in part, to age-dependent cellular, structural, and functional changes in the heart, a process known as cardiac aging. An emerging view is that cardiac aging leads to the accumulation of cellular and subcellular deficits that increase susceptibility to cardiovascular diseases. Still, people age at different rates, with those aging rapidly considered frail. Evidence suggests that frailty, rather than simply age, is a major risk factor for cardiovascular disease and predicts adverse outcomes in those affected. Recent studies in mouse models of frailty show that many adverse changes associated with cardiac aging are more prominent in mice with a high degree of frailty. This suggests that frailty sets the stage for late life cardiovascular diseases to flourish and raises the possibility that treating frailty may treat cardiovascular diseases. These studies show that ventricular dysfunction increases with frailty in males only, whereas atrial dysfunction increases with frailty in both sexes. These results may shed light on the reasons that men and women can be susceptible to different cardiovascular diseases as they age, and why frail individuals are especially vulnerable to these disorders.

The impact of frailty on short-term prognosis in discharged adult stroke patients: A multicenter prospective cohort study

BACKGROUND

Frailty is commonly observed in stroke patients and it is associated with adverse outcomes. However, there remains a gap in longitudinal studies investigating the causal relationship between baseline frailty and short-term prognosis in discharged adult stroke patients.

OBJECTIVE

To examine the causal impact of frailty on non-elective readmission and major adverse cardiac and cerebral events, and investigate its associations with cognitive impairment and post-stroke disability.

DESIGN

A multicenter prospective cohort study.

SETTING

Two tertiary hospitals in Central and Northwest China.

PARTICIPANTS

667 adult stroke patients in stroke units were included from January 2022 to June 2022.

METHODS

Baseline frailty was assessed by the Frailty Scale. Custom-designed questions were utilized to assess non-elective readmission and major adverse cardiac and cerebral events as primary outcomes. Cognitive impairment, assessed using the Mini-Mental State Examination Scale (MMSE), and post-stroke disability, measured with the Modified Rankin Scale (mRS), were considered secondary outcomes at a 3-month follow-up. The impact of baseline frailty on non-elective readmission and major adverse cardiac and cerebral events was examined using bivariate and multiple Cox regression analyses. Furthermore, associations between baseline frailty and cognitive impairment, or post-stroke disability, were investigated through generalized linear models.

RESULTS

A total of 5 participants died, 12 had major adverse cardiac and cerebral events, and 57 had non-selective readmission among 667 adult stroke patients. Frailty was an independent risk factor for non-selective readmission (hazard ratio [HR]: 2.71, 95 % confidence interval [CI]: 1.59, 4.62) and major adverse cardiac and cerebral events (HR: 3.77, 95 % CI: 1.07, 13.22) for stroke patients. Baseline frailty was correlated with cognitive impairment (regression coefficient [β]: -2.68, 95 % CI: -3.78, -1.58) adjusting for socio-demographic and clinical factors and follow-up interval. However, the relationship between frailty and cognitive impairment did not reach statistical significance when further adjusting for baseline MMSE (β: -0.39, 95 % CI: -1.43, 0.64). Moreover, baseline frailty was associated with post-stroke disability (β: 0.36, 95 % CI: 0.08, 0.65) adjusting for socio-demographic and clinical variables, follow-up interval, and baseline mRS.

CONCLUSIONS

The finding highlights the importance of assessing baseline frailty in discharged adult stroke patients, as it is significantly associated with non-elective readmission, major adverse cardiac and cerebral events, and post-stroke disability at 3 months. These results highlight the crucial role of screening and evaluating frailty status in improving short-term prognosis for adult stroke patients. Interventions should be developed to address baseline frailty and mitigate the short-term prognosis of stroke.

TWEETABLE ABSTRACT

Baseline frailty predicts non-elective readmission, major adverse cardiac and cerebral events, and post-stroke disability in adult stroke patients. @haiyanhexyyy.

Development and validation of a frailty index for use in the osteoarthritis initiative

BACKGROUND

The Osteoarthritis Initiative (OAI) evaluates the development and progression of osteoarthritis. Frailty captures the heterogeneity in aging. Use of this resource-intensive dataset to answer aging-related research questions could be enhanced by a frailty measure.

OBJECTIVE

To: (i) develop a deficit accumulation frailty index (FI) for the OAI; (ii) examine its relationship with age and compare between sexes, (iii) validate the FI versus all-cause mortality and (iv) compare this association with mortality with a modified frailty phenotype.

DESIGN

OAI cohort study.

SETTING

North America.

SUBJECTS

An FI was determined for 4,755/4,796 and 4,149/4,796 who had a valid FI and frailty phenotype.

METHODS

Fifty-nine-variables were screened for inclusion. Multivariate Cox regression evaluated the impact of FI or phenotype on all-cause mortality at follow-up (up to 146 months), controlling for age and sex.

RESULTS

Thirty-one items were included. FI scores (0.16 ± 0.09) were higher in older adults and among females (both, P < 0.001). By follow-up, 264 people had died (6.4%). Older age, being male, and greater FI were associated with a higher risk of all-cause mortality (all, P < 0.001). The model including FI was a better fit than the model including the phenotype (AIC: 4,167 vs. 4,178) and was a better predictor of all-cause mortality than the phenotype with an area under receiver operating characteristic curve: 0.652 vs. 0.581.

CONCLUSION

We developed an FI using the OAI and validated it in relation to all-cause mortality. The FI may be used to study aging on clinical, functional and structural aspects of osteoarthritis included in the OAI.

Association between dietary supplement use and mortality in cancer survivors with different body mass index and frailty status: a cohort study

BACKGROUND

The association between Body Mass Index (BMI), frailty index (FI), and dietary supplement in cancer survivors has been a subject of growing interest. This study investigates the relationship of BMI and FI with mortality in American cancer survivors and explores the impact of dietary supplement usage on different BMI and FI groups.

METHODS

Three thousand nine hundred and thirty-two cancer patients from the National Health and Nutrition Examination Survey (NHANES) database were included in the analyses. BMI, FI, and supplement usage were obtained through the NHANES structured survey and the 49-item FI tool. Weighted logistic and Cox proportional hazards models, Kaplan-Meier survival analyses, and propensity score matching (PSM) were used to elucidate the relationships between BMI, FI, dietary supplement, and mortality outcomes.

RESULTS

The study found significant associations between higher BMI and increased frailty (Odds ratio [OR] = 1.04, 95% confidence interval [95% CI], 1.02-1.06). BMI < 25 kg/m2 and FI > 0.2 are associated with an increased mortality rate. Dietary supplement use can reduce all-cause and cancer mortality in cancer patients with BMI < 25 kg/m2 (Hazard ratio [HR] = 0.63, 95% CI, 0.47-0.84; HR = 0.48, 95% CI, 0.29-0.80) or FI ≤ 0.2 (HR = 0.77, 95% CI, 0.60-0.99; HR = 0.59, 95% CI, 0.39-0.89). In cancer patients with BMI < 25 kg/m2 and FI ≤ 0.2, dietary supplement users had lower all-cause and cancer mortality (HR = 0.49, 95% CI, 0.30-0.79; HR = 0.25, 95% CI, 0.10-0.60).

CONCLUSION

The study revealed a negative correlation between BMI and the FI among the cancer patient cohort as well as their complex impact on mortality and highlighted the role of dietary supplement in cancer prognosis, indicating benefits for non-frail patients with BMI < 25 kg/m2.

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.

Prognostic Value of a Laboratory Index of Frailty in Older Patients Hospitalized for COVID-19: The COMEPA Study

BACKGROUND

Several indexes based on clinical and laboratory tests to identify frailty and to predict mortality have been produced. Only two studies, mixing clinical and laboratory parameters were made about a frailty index made of laboratory tests (FI-Lab) and mortality in older patients hospitalized for COVID-19. The aim of this study was to explore the accuracy and precision of an FI-Lab constructed with some common bio-humoral tests and mortality in a cohort of patients hospitalized for COVID-19.

METHODS

The FI-Lab was constructed using 40 different bio-humoral tests during the first four days of hospitalization, with a score from 0 to 1. The association between FI-Lab and mortality was assessed using a multivariate Cox's regression analysis, reported as hazard ratios (HRs) and 95% confidence intervals (CIs). The accuracy of the FI-Lab was reported as area under the curve (AUC) and the precision with the C-Index.

RESULTS

376 patients (mean age: 65 years; 53.7% males) were initially included. During the follow-up period, 41 deceased. After adjusting for five different factors, an FI-Lab value >0.54, the median value of our cohort, was associated with a relative risk about five times greater than lower values. Modeling FI-LAB as a continous variable, each increase in 0.01 points was associated with an increased risk in mortality of 8.4% (HR=1.084; 95%CI: 1.039-2.044). The FI-Lab was highly accurate (AUC=0.91; 95%CI: 0.87-0.95) and precise (C-Index=0.81) in predicting death.

CONCLUSIONS

A simple index based on common laboratory tests can be used to predict mortality among older people hospitalized for COVID-19.

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.

Seeing the forest (plot) for the trees-the importance of evidence synthesis in older adult care

Systematically reviewing all the available evidence and then creating summary analyses of the pooled data is the foundation of evidence-based practice. Indeed, this evidence synthesis approach informs much of the care of older adults in hospital and community. It is perhaps no surprise that the journal Age and Ageing is a frequent platform for publishing research papers based on systematic review and synthesis. This research has evolved substantially from the early days of evidence-based medicine and the Cochrane Collaboration. The traditional approach would be a quantitative summary, calculated using pair-wise meta-analysis of randomised controlled trials of drug versus placebo, or a synthesis of observational studies to create summaries of prevalence, associations and outcomes. Methods have evolved and newer techniques such as scoping reviews, test accuracy meta-analysis and qualitative synthesis are all now available. The sophistication of these methods is driven in part by the increasingly complex decisions that need be made in contemporary older adult care. Age and Ageing continues to champion established and novel evidence synthesis approaches, and in the accompanying Collection exemplars of these differing methods are presented and described. Whilst there is marked heterogeneity in the techniques used, the consistent and defining feature of all these papers is the desire to comprehensively, and critically summarise the evidence in order to answer the most pertinent questions regarding older adult care.

The influence of inflammation and frailty in the aging continuum

Inflammaging is a low-grade inflammatory state that can be considered an adaptive process aimed at stimulating appropriate anti-inflammatory response. Frailty is determined by the accumulation of molecular and cellular defects accumulated throughout life; therefore, an appropriate frailty computation could be a valuable tool for measuring biological age. This study aims to analyse the association between inflammatory markers and both chronological age "per se" and frailty. We studied 452 persons aged 43-114 years. A Frailty Index (FI) was computed considering a wide range of age-related signs, symptoms, disabilities, and diseases. Plasma concentrations of inflammatory cytokines and peripheral markers of neuroinflammation were analysed by next-generation ELISA. The mean age of the cohort was 79.7 (from 43 to 114) years and the median FI was 0.19 (from 0.00 to 0.75). The concentrations of most inflammatory markers increased significantly with chronological age, after adjustment for sex and FI. Interferon-γ was significantly affected only by FI, while interleukin (IL)-10 and IL-1β were associated only with chronological age. In conclusion, we described different associations between inflammatory components and chronological vs. biological age. A better characterization of the molecular signature of aging could help to understand the complexity of this process.

Combined use of the Clinical Frailty Scale and laboratory tests in acutely hospitalized older patients

AIMS

To evaluate the Clinical Frailty Scale (CFS) and a Frailty Index based on laboratory tests (FI-lab) in terms of what each assesses about frailty and to determine the appropriateness of combined use of these two frailty scales.

METHODS

This was a prospective observational cohort study in an acute geriatric ward of a university hospital. The FI-lab is the proportion of laboratory parameters that yield abnormal results from a total of 23. The FI-lab and CFS were assessed at admission. Data on activities of daily living (ADL), cognition, geriatric syndromes, and comorbidities were also collected. Main outcomes were in-hospital mortality and 90-day mortality after admission.

RESULTS

In total, 378 inpatients (mean age 85.2 ± 5.8 years, 59.3% female) were enrolled. ADL and cognition correlated strongly with the CFS (Spearman's |r|> 0.60) but weakly with the FI-lab (|r|< 0.30). Both the CFS and FI-lab correlated weakly with geriatric syndromes and comorbidities (|r|< 0.40). The correlation between the CFS and FI-lab was also weak (r = 0.28). The CFS and FI-lab were independently associated with in-hospital mortality and 90-day mortality after admission. The Akaike information criterion was lower for models using both the CFS and FI-lab than for models using either tool alone.

CONCLUSIONS

The CFS and FI-lab each reflected only some of the aspects of frailty in acutely hospitalized older patients. The model fit was better when the two frailty scales were used together to assess the mortality risk than when either was used alone.