Cumulative Deficit Model Research Articles

An interRAI-derived frailty index is associated with prior hospitalisations in older adults residing in retirement villages.

To develop and validate a frailty index (FI) from interRAI-Community Health Assessments (CHA) on older adults in retirement villages (RVs). This is a cross-sectional analysis of a current RV research study. A FI was generated using the cumulative deficit model. Health-care utilisation measures were acute, and all, hospitalisations 12months before baseline assessment. Associations between FI and hospitalisations were explored using multivariable logistic regression to estimate odds ratio (OR). Of 577 included residents, mean (SD) age was 81 (7) and 419 (73%) were female. Mean (SD) FI was 0.16 (0.09); 260 (45%) were mildly frail, and 108 (19%) moderate-severely frail. In multivariate-adjusted analysis, odds of acute hospitalisation for mild (OR=3.3, P<.001) and moderate-severely frail (OR=6.4, P<.001) were significantly higher than fit residents. Higher odds were also observed for all hospitalisations. A considerable proportion of RV residents were moderately-severely frail. FI was associated with acute and all hospitalisations.

Construction of the secondary care administrative records frailty (SCARF) index and validation on older women with operable invasive breast cancer in England and Wales: a cohort study

ObjectivesStudies that use national datasets to evaluate the management of older women with breast cancer are often constrained by a lack of information on patient fitness. This study constructed a...

Research advances of perioperative frailty assessment tools

Frailty refers to the state of decreased physiological function reserve caused by the increase of age-related multisystemic accumulated defects. The number of elderly patients with frailty undergoing elective operations is gradually increasing. The stages of frailty have strong correlation with the prognosis and mortality after surgery. By summarizing the frailty assessment scales used in foreign countries, we provide reference to the selection of clinical frailty assessment tools in Chinese population. This article reviewed two frailty models (phenotype model and the cumulative deficit model) and three assessment tools [Simple Frailty Questionnaire (FRAIL), Edmonton Frail Scale (EFS) and Program on Research for Integrating Services for the Maintenance of Autonomy (PRISMA)- 7 Scale]. In addition, three assistant indexes [handgrip strength, gait speed and Timed Up-And-Go Test (TUGT)] are introduced. The present review provides reference for the studies on perioperative complications in patients with frailty. Key words: Aged; Frailty; Risk assessment

Predictive value of an electronic frailty index (FI) in U.S. Veterans with newly diagnosed non-small cell lung cancer (NSCLC).

11545 Background: Frailty is a well-established predictor of survival, but assessing it in a real-world healthcare setting is often time-consuming and impractical. We sought to determine if a quantitative measure of frailty, computed automatically from electronic health records using the cumulative deficit model, also predicts overall survival in patients with NSCLC. Methods: We conducted a retrospective cohort study of Veterans identified to have NSCLC in the VA Precision Oncology Data Repository. Frailty was assessed using a previously validated FI, the Frailty Index for Veterans Affairs (VA-FI), consisting of 31 deficits. We categorized non-frail patients into non-frail (0-0.1) and pre-frail (0.1-0.2) groups, and frail patients into mild (0.2-0.3), moderate (0.3-0.4) and severe ( &gt; 0.4) groups. Associations of frailty and survival were assessed using Kaplan-Meier estimates and Cox models. The added value of frailty status for predictive performance was quantified using the continuous net-reclassification index (NRI). Results: The cohort included 359 patients with NSCLC. The mean age was 66 years, and 96.1% were male, including 76.3% Caucasian and 16.2% African-American. The majority had an advanced stage cancer (15.9% and 44.3% with stages III and IV respectively) with minority falling into the early stage category (15.3% and 6.1% with stages I and II respectively). A substantial proportion of patients were identified to be frail (30%). The frailty categories differentiated survival profiles of patients with 2-year survival ranging from 0.41 for non-frail patients to 0.08 for the severely frail patients (log-rank p &lt; 0.001). Frail patients exhibited significantly shorter survival than non-frail group even after adjusting for age, gender, and stage in a multivariate Cox model (hazard ratio 1.7, p &lt; 0.001). Inclusion of frailty status in the Cox model significantly improved 2-year risk estimates of survival (NRI 0.35; 95% CI 0.27–0.47). Conclusions: Frailty as determined by VA-FI is a significant predictor of survival independent of stage and demographic factors among NSCLC patients in this VA cohort. VA-FI is an automated and a practically feasible tool to better estimate life expectancy and help individualize patient care.

Identifying Data Elements to Measure Frailty in a Dutch Nationwide Electronic Medical Record Database for Use in Postmarketing Safety Evaluation: An Exploratory Study.

The role of frailty in postmarketing drug safety is increasingly acknowledged. Few European electronic medical records (EMRs) have been used to explore frailty in observational drug safety research. The aim of this study was to identify data elements, beyond multimorbidity and polypharmacy, that could potentially contribute to measuring frailty among older adults in the Dutch nationwide Integrated Primary Care Information (IPCI) database. Persons aged between 65 and 90years in the IPCI database were identified from 2008 to 2013. Clinical non-disease, non-drug measurements that could potentially contribute to measuring frailty were identified and selected if they were recorded in > 0.005% of patients and could be included in at least one of three definitions of frailty: the frailty phenotype model, the cumulative deficit model, and direct evaluations of frailty through standardized frailty scores. The frequency of these measures was calculated. Overall, 314,191 (17% of the source population) elderly persons were identified. Of these, 7948 (2.53%) had one or more of 12 clinical measurements identified that could potentially contribute to measuring frailty, such as clinical evaluations of cognition, mobility, and cachexia, as well as direct measures of frailty, such as the Groningen Frailty Index. Three of five measurements required for the frailty phenotype were identified in < 0.5% of the population: cachexia, reduced walking speed, and reduced physical activity; weakness and fatigue were not identified. The measurements outlined above may be appropriate for the cumulative deficit definition of frailty, provided that at least 30 deficits, including comorbidities and drug utilization, are evaluated in total. The most commonly recorded item identified that could potentially be used in a cumulative frailty model was the Mini-Mental State Examination score (N= 2850; 0.91%); the only recorded direct measurement of frailty was the Groningen Frailty Index (N = 2382; 0.76%). Non-disease, non-drug clinical data that could potentially contribute to a frailty model was not commonly recorded in the IPCI; less than 3% of a cohort of elderly persons had these data recorded, suggesting that the use of these data in postmarketing drug safety evaluation may be limited.

Defining Frailty in Research Abstracts: A Systematic Review and Recommendations for Standardization.

Multiple definitions of frailty are used. We sought to quantify the frequency that frailty is insufficiently defined in published abstracts. We conducted a systematic review of MEDLINE/PubMed for English abstracts of original research investigating frailty as an exposure or outcome in humans from 2015-2017. A complete definition of frailty included: 1) a named measure of frailty, including "frailty" alone, 2) details on variables included (e.g. grip strength), 3) number of variables included (e.g. 33-item frailty index), and 4) details on cutoffs or levels of frailty unless a definition was used continuously. Our search yielded 1,110 titles; 490 abstracts met review criteria, 348 abstracts had any definition of frailty and were included. Majority reported a single measure of frailty (n=313, 90%). The most commonly used measures were variations of Fried's phenotype (n=167, 48%) and Rockwood's cumulative deficit model (n=101, 29%). Only 56 abstracts had complete definitions (16%). In 123 abstracts (35%), a means of measuring frailty was named, but no additional details were given. When details of the frailty measure were described, they generally referred to cutoffs or levels rather than variables used in the measure. A minority of abstracts of original manuscripts related to frailty research had adequate definitions of frailty. We encourage scientists to adopt a standardized approach to defining the term for all abstracts related to frailty research to facilitate systematic reviews, meta-analysis, and accurate reporting of frailty science.

Raised serum cystatin C can be a potential biomarker of frailty detected by cumulative deficit model.

ObjectiveIdentification of frailty by clinical criteria is often delayed to the advanced stage. A reliable biomarker to identify frailty or its risk does not currently exist. We aimed to determine the association between serum cystatin C and frailty in subjects without renal dysfunction.MethodsWe carried out a cross‐sectional observational study in the Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India. The study involved 125 participants, aged 65 years or older. Frailty status was assessed with Frailty Index criteria (cumulative deficit model). Serum cystatin C was estimated with the nephelometry method and its association with frailty was analyzed.ResultsMean age of the study sample was 76.32 years with 72 (57.6%) male and 53 (42.4%) female participants. Seventy‐three subjects were frail; the mean cystatin C levels in the frail and non‐frail groups were 1.28 mg/L (±0.39) and 1.12 mg/L (±0.27), respectively, and the difference was significant (P < 0.05). A cutoff of 1.12 mg/L was found to be 60.27% sensitive and 57.69% specific in identification of frailty. Multivariate analysis showed that higher cystatin C level was associated with 2.52 (1.05‐6.02) times the risk of being frail.ConclusionHigher levels of cystatin C were found in frail subjects. Cystatin C seems to be a promising marker for identifying frailty in older adults without renal abnormalities.

Further Development and Validation of the electronic Frailty Index using the Secure Anonymised Information Linkage Databank

IntroductionAging populations with increasing frailty have major implications for health services internationally, and evidence-based treatment becomes increasingly important. The development of an electronic Frailty Index (eFI) using routine primary care data facilitates implementation of evidence-based interventions. However, the eFI does not account for time restrictions regarding when information was recorded.&#x0D; Objectives and ApproachOur aim is to implement and further validate the eFI using the Secure Anonymised Information Linkage (SAIL) databank, introducing refinements based on time restrictions.&#x0D; Our implementation of the eFI identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on the internationally established cumulative deficit model, and each deficit contributes equally to the eFI value.&#x0D; However, although each deficit is equally weighted, only one of them is currently time dependent. We therefore analyse the time at which each deficit is identified, and propose time dependent cut-points based on our findings.&#x0D; ResultsWe were able to successfully implement the eFI using data from over 400,000 individuals from the Welsh population using data held in the SAIL databank. Our results agree with the baseline characteristics and distributions of frailty found in the original development of the eFI.&#x0D; We also found that the percentage of individuals identified as frail increased as the number of years of records included was increased. Furthermore, the increase in percentage year by year was almost linear for a number of the deficits. This led to the identification of time bounds for particular deficits, which could help to refine future implementations of the eFI.&#x0D; Conclusion/ImplicationsOur work validates and refines the eFI, which is a particularly useful resource as it uses existing primary care data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily reproducible and achievable by others.

Cross-Sectional Association of Frailty and Arterial Stiffness in Community-Dwelling Older Adults: The Framingham Heart Study.

Frailty is a risk factor for cardiovascular disease (CVD). Underlying mechanisms to explain the connection between frailty and CVD are unclear. We sought to examine the association between frailty and arterial stiffness, a precursor of hypertension and CVD. We conducted a cross-sectional analysis of community-dwelling Framingham Heart Study Offspring and Omni participants ≥60 years of age examined in 2005-2008. Frailty was defined primarily according to the Fried physical phenotype definition, which identifies nonfrail, prefrail, and frail individuals. Arterial stiffness was assessed using carotid-femoral pulse wave velocity (CFPWV). Generalized linear regression was used to examine the association between frailty level and CFPWV (modeled as -1000/CFPWV in msec/m, then transformed back to the original scale, m/s), adjusted for age, sex, cohort, mean arterial pressure, heart rate, height, and smoking. Of 2,171 participants (55% women, 91% white), 45% were prefrail and 7% were frail. Mean ages were 67, 70, and 73 years, and adjusted CFPWV least squares means were 10.0 (95% CI, 9.9-10.1), 10.3 (10.2-10.5), and 10.5 m/s (10.1-11.0); p = .0002 for nonfrail, prefrail, and frail groups, respectively. Results were similar using the Rockwood cumulative deficit model of frailty, and in a sensitivity analysis adjusting for prevalent coronary heart disease and diabetes. Prefrailty and frailty were associated with higher arterial stiffness in a cohort of community-dwelling older adults. Arterial stiffness may help explain the relationship between frailty and CVD.

A Comparative Review of Frailty Models and a description of the European-wide FRAILOMIC Initiative

Background Frailty in aging populations is associated with increasing evidence of ill health, functional loss, increasing dependency and premature death. The FRAILOMIC Initiative was designed to define, predict and prevent frailty by means of identifying omic markers associated with the diagnosis, risk, and prognosis of frailty. The project hopes to build the foundations for a consensus towards a single operative, clinical definition of frailty to guide the development of ready-to-use kits measuring biomarkers to facilitate frailty risk prediction and improve diagnostic accuracy of frailty in clinical practice. Aims We review current models of frailty, describe the design of the FRAILOMIC study, and discuss the advent of the concept of intrinsic capacity as influencing the science of frailty and how this can be clinically interpreted. Methods Two main frailty models (Fried Phenotype model; Cumulative Deficit Model) are described and evaluated by detailed literature review. A description of the exploratory phase of the FRAILOMIC initiative is given which resulted in the identification of 13 omic markers related to frailty: these are currently being validated in a validation phase. We then discuss how combining the characteristics of frailty with omic-based biomarkers will enable the development of predictive, diagnostic and prognostic models. Conclusions Relating biomarkers associated with frailty to a continuous measure of intrinsic capacity will help in identifying and monitoring at-risk populations. The FRAILOMIC Initiative will provide key insights into the prevention, early detection, and treatment of frailty to reduce the impact of disability in our aging populations.

Routinely Identifying frailty: Implementing the electronic Frailty Index in the Secure Anonymised Information Linkage Databank

BackgroundAging populations with increasing frailty have major implications for health services, and evidence-based treatment becomes increasingly important. The development of the electronic Frailty Index (eFI) using routine primary care data facilitatesthe implementation of evidence-based interventions and care.&#x0D; MethodOur implementation of the eFI in the Secure Anonymised Information Linkage (SAIL) databank identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on a cumulative deficitmodel, and each deficit contributes equally to the eFI value.&#x0D; FindingsAlthough each deficit is equally weighted, only one is currently time dependent. We therefore analysed the cumulative prevalence of each deficit on a year-by-year basis. This led to the identification of time bounds for particular deficits, which willhelp to refine future implementations of the eFI. We also further validated the eFI using data from over 400,000 individuals held in SAIL.&#x0D; ConclusionThe eFI is particularly useful as it uses existing data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily achievable by others.

Prevalence of Cognitive Impairment and Association With Survival Among Older Patients With Hematologic Cancers

As the population ages, cognitive impairment has promised to become increasingly common among patients with cancer. Little is known about how specific domains of cognitive impairment may be associated with survival among older patients with hematologic cancers. To determine the prevalence of domain-specific cognitive impairment and its association with overall survival among older patients with blood cancer. This prospective observational cohort study included all patients 75 years and older who presented for initial consultation in the leukemia, myeloma, or lymphoma clinics of a large tertiary hospital in Boston, Massachusetts, from February 1, 2015, to March 31, 2017. Patients underwent screening for frailty and cognitive dysfunction and were followed up for survival. The Clock-in-the-Box (CIB) test was used to screen for executive dysfunction. A 5-word delayed recall test was used to screen for impairment in working memory. The Fried frailty phenotype and Rockwood cumulative deficit model of frailty were also assessed to characterize participants as robust, prefrail, or frail. Among 420 consecutive patients approached, 360 (85.7%) agreed to undergo frailty assessment (232 men [64.4%] and 128 women [35.6%]; mean [SD] age, 79.8 [3.9] years), and 341 of those (94.7%) completed both cognitive screening tests. One hundred twenty-seven patients (35.3%) had probable executive dysfunction on the CIB, and 62 (17.2%) had probable impairment in working memory on the 5-word delayed recall. Impairment in either domain was modestly correlated with the Fried frailty phenotype (CIB, ρ = 0.177; delayed recall, ρ = 0.170; P = .01 for both), and many phenotypically robust patients also had probable cognitive impairment (24 of 104 [23.1%] on CIB and 9 of 104 [8.7%] on delayed recall). Patients with impaired working memory had worse median survival (10.9 [SD, 12.9] vs 12.2 [SD, 14.7] months; log-rank P < .001), including when stratified by indolent cancer (log-rank P = .01) and aggressive cancer (P < .001) and in multivariate analysis when adjusting for age, comorbidities, and disease aggressiveness (odds ratio, 0.26; 95% CI, 0.13-0.50). Impaired working memory was also associated with worse survival for those undergoing intensive treatment (log-rank P < .001). Executive dysfunction was associated with worse survival only among patients who underwent intensive treatment (log-rank P = .03). These data suggest that domains of cognitive dysfunction may be prevalent in older patients with blood cancer and may have differential predictive value for survival. Targeted interventions are needed for this vulnerable patient population.

Frailty and chronic kidney disease: current evidence and continuing uncertainties.

Frailty, the state of increased vulnerability to physical stressors as a result of progressive and sustained degeneration in multiple physiological systems, is common in those with chronic kidney disease (CKD). In fact, the prevalence of frailty in the older adult population is reported to be 11%, whereas the prevalence of frailty has been reported to be greater than 60% in dialysis-dependent CKD patients. Frailty is independently linked with adverse clinical outcomes in all stages of CKD and has been repeatedly shown to be associated with an increased risk of mortality and hospitalization. In recent years there have been efforts to create an operationalized definition of frailty to aid its diagnosis and to categorize its severity. Two principal concepts are described, namely the Fried Phenotype Model of Physical Frailty and the Cumulative Deficit Model of Frailty. There is no agreement on which frailty assessment approach is superior, therefore, for the time being, emphasis should be placed on any efforts to identify frailty. Recognizing frailty should prompt a holistic assessment of the patient to address risk factors that may exacerbate its progression and to ensure that the patient has appropriate psychological and social support. Adequate nutritional intake is essential and individualized exercise programmes should be offered. The acknowledgement of frailty should prompt discussions that explore the future care wishes of these vulnerable patients. With further study, nephrologists may be able to use frailty assessments to inform discussions with patients about the initiation of renal replacement therapy.

Frailty in Pulmonary and Critical Care Medicine.

Conceptualized first in the field of geriatrics, frailty is a syndrome characterized by a generalized vulnerability to stressors resulting from an accumulation of physiologic deficits across multiple interrelated systems. This accumulation of deficits results in poorer functional status and disability. Frailty is a "state of risk" for subsequent disproportionate declines in health status following new exposure to a physiologic stressor. Two predominant models have emerged to operationalize the measurement of frailty. The phenotype model defines frailty as a distinct clinical syndrome that includes conceptual domains such as strength, activity, wasting, and mobility. The cumulative deficit model defines frailty by enumerating the number of age-related things wrong with a person. The biological pathways driving frailty include chronic systemic inflammation, sarcopenia, and neuroendocrine dysregulation, among others. In adults with chronic lung disease, frailty is independently associated with more frequent exacerbations of lung disease, all-cause hospitalization, declines in functional status, and all-cause mortality. In addition, frail adults who become critically ill are more likely develop chronic critical illness or severe disability and have higher in-hospital and long-term mortality rates. The evaluation of frailty appears to provide important prognostic information above and beyond routinely collected measures in adults with chronic lung disease and the critically ill. The study of frailty in these populations, however, requires multipronged efforts aimed at refining clinical assessments, understanding the mechanisms, and developing therapeutic interventions.

Cumulative deficit model of geriatric assessment to predict the postoperative outcomes of older patients with solid abdominal cancer.

The geriatric assessment (GA) has proven to be of great value for clinicians treating older patients. However, a clear consensus on the optimal set of GA instruments is lacking, particularly for surgical patients. Therefore, the aim of this prospective study was to compare the prevalence of frailty, depending on the number of incorporated GA domains, and to evaluate its accuracy in predicting postoperative outcome. Seventy-five patients aged 65 years and older, qualified for abdominal surgery due to solid cancer, were enrolled. The GA included a wide variety of validated tools that evaluate functional, mobility, nutritional, co-morbidity, polypharmacy, and psychosocial domains. Depending on the number of incorporated GA domains the frequency of frailty was 23-97%. The cumulative score rather than individual components of the GA, turned out to be an independent risk factor of 30-day postoperative morbidity. In predicting 30-day "any" and "major" morbidities, the area under the curve was 0.67-0.72 and 0.70-0.82 (model including the severity of the surgery) vs. 0.57-0.66 and 0.50-0.65 (model not including the severity of the surgery), respectively. The number of incorporated GA domains has a great influence on the prevalence of frailty and on adequate surgical risk assessment. The summary deficit score based on Pre-operative Assessment of Cancer in the Elderly (PACE) or the GA consisting of functional, mobility, cognitive, depression, nutritional, co-morbidity, polypharmacy, and social support assessment domains can predict 30-day postoperative morbidity. However, only models with addition of the severity of surgery show moderate to good predictive value.

Aile Hekimliğinde Düşkünlük: Tanısı ve Yönetimi

The frequency of frailty is increasing with the aging world population. Frailty is a long-term process and physiopathological theory describes six different dysfunction systems to be considered (i.e. hemoglobin, IL-6, IGF-1, DHEA-S, HgbA1c, triceps skinfold, fine motor movement, micronutrients). The definition of frailty is an important task. Two different models have been described: the phenotype of frailty and cumulative deficit model. Symptoms and findings of frailty are commonly non-specific. Additionally extreme fatique, unexplained weight loss and infections are observed. Frailty is a leading cause of mortality and the prevalence is 4-59% depending on the definition. It is frequent in women and the prevalence increases with age (65–69: %4;>84:%26). Although the number of studies has increased in family medicine, further studies are needed. This field needs to be prioritized in family practice.

Applying a cumulative deficit model of frailty to dementia: progress and future challenges.

The article by Song and colleagues presents findings from the Canadian Study of Health and Aging showing that the accumulation of health deficits, defined dichotomously and unqualified by severity or domain, predicted late-life dementia independent of chronological age. We identify strengths of this model, and also areas for future research. Importantly, this article broadens the perspective of research into measuring risk of dementia from focusing on specific neuropathological markers of dementia subtypes, to mechanisms underlying more general bodily vitality and health, as well as dysfunctions in repair. This work places late-life dementia in a new context, influenced more broadly by health maintenance, and less by specific neurological disease. While useful at a global level, the lack of specificity of this approach may ultimately limit its application to individual patients because without linking risk to etiology, assessment does not indicate an intervention. Ultimately, the article has value for stimulating debate about approaches to risk identification and risk reduction, suggesting that the current focus on cardiometabolic risk factors may be too limited.