Frailty Variable Research Articles

The investigation of frailty level and factors affecting frailty in older adults with osteoarthritis; a cross-sectional study on Turkish population

Aims: This study aims to evaluate the fragility level, fall risks and factors affecting both conditions of individuals with OA aged 60 and over living in an underdeveloped province. Methods: The research sample consists of 260 individuals over the age of 60 who were diagnosed with osteoarthritis. The frailty of the participants was measured. Their functional mobility and risk of falling were measured. Results: The prevalence of frailty and pre-frailty was found to be 82%. It was revealed that frailty is not always affected by the severity of osteoarthritis and the progression of age. In addition, it was found that factors such as stage IV osteoarthritis and being frail or pre-frail increase the risk of falling. The stage of osteoartritis, education level, lifestyle, and risk of falling explained 24% of the variance in the frailty variable. Having an unhealthy lifestyle increases frailty 20 times; lowincome level increases frailty eight times; and each increase in education level affects frailty at most four times negatively. Conclusion: Psychological rehabilitation and social support may negatively affect the development of frailty in individuals aged 60 years and older. Frail older women with severe OA are more vulnerable to falls.

A New Two-Parameters Lindley-Frailty Model: Censored and Uncensored Schemes under Different Baseline Models: Applications, Assessments, Censored and Uncensored Validation Testing

Classical survival models assume homogeneity among the population of individuals who are susceptible to the event of interest. However, in many practical circumstances, there is a certain amount of unobserved heterogeneity that can be caused by a variety of sources, such as environmental or genetic factors. If the heterogeneity is ignored, many issues could arise, including an overestimation of the hazard rate and inaccurate estimates of the regression coefficients. Frailty models are usually used to model the heterogeneity among individuals. In this paper, we propose a novel univariate frailty model. The frailty variable is assumed to follow the Two Parameter Lindley distribution. The maximum likelihood method is used to estimate the model parameters. The baseline hazard functions are assumed to follow Weibull, Exponential, Gompertz, and Pareto distributions, and a simulation study is performed under this assumption. We examine the characteristics of the distribution and assess its performance compared to other distributions that are frequently applied in frailty modeling by using both Nikulin-Rao-Robson and Bagdonavicius-Nikulin goodness-of-fit tests to determine the adequacy of the model. We analyze a fresh medical dataset collected from an emergency hospital in Algeria to evaluate the effectiveness and applicability of the proposed model.

Modeling Dependence in Survival Times using Log-Skew Normal Shared Frailties

In survival studies, event times under a common influence are often grouped together in clusters. The association between and within these clusters can be studied using frailty models where randomness in the data or heterogeneity arising due to unknown covariates is described using a frailty variable. In shared frailty models, frailty value is common or shared for all observations within a cluster while it is conditionally independent for different clusters. In this article, we consider a model whose baseline distribution is Weibull and shared frailties follow log skew-normal distribution. This distribution increases flexibility of the model as it allows the frailty term to be positively or negatively skewed and estimation of skewness parameter enables us to comment on dependence structure of the random component. A simulation study is performed and Bayesian estimates of treatment effects, variance and skewness of frailty term are obtained using Metropolis-Hastings algorithm. It is shown that while bias and expected loss for estimates of all parameters reduce as dataset size increases, frailty parameters are more efficiently estimated when the random component is considered to be skewed. The model is also applied to two real-life datasets where positive and negative skewness is observed in the frailty term.

Weighted proportional mean inactivity time model

<abstract><p>In this paper, a mean inactivity time frailty model is considered. Examples are given to calculate the mean inactivity time for several reputable survival models. The dependence structure between the population variable and the frailty variable is characterized. The classical weighted proportional mean inactivity time model is considered as a special case. We prove that several well-known stochastic orderings between two frailties are preserved for the response variables under the weighted proportional mean inactivity time model. We apply this model on a real data set and also perform a simulation study to examine the accuracy of the model.</p></abstract>

A mixed effect model for clustered recurrent event data

For the clustered recurrent event data, a multiplicative intensity model with a mixed effect was proposed. The correlation within the same cluster was specified by a gamma frailty variable. The parameters of the model were estimated by maximum likelihood estimation. The inference procedure was carried out by EM algorithm. Some simulations were conducted to evaluate the proposed method, and a real data was illustrated.

노인의 사회경제적, 심리적, 건강행태 요인이 노쇠에 미치는 영향: 주관적 건강상태와 다중이환을 매개하여

초록·키워드 목차 오류제보하기 Frailty increases the disability and hospitalization rates, reduces individuals" quality of life, and increases dependence on others due to dysfunction. Therefore, preemptive management of factors affecting frailty is required. This study aims to construct a structural equation model by selecting factors that influence frailty through multimorbidity and subjective health status as the mediating factors and to analyze their impact. This study was conducted in 1,958 elderly subjects (males: 922(47.1%), females: 1,036(52.9%) aged 65 years or older who participated in the second year of the 6th Korean National Health and Nutrition Examination Survey(2014~2015). This study"s main variables were the frailty diagnosis variable, subjective health status, multimorbidity, income, physical activity, anxiety/depression, and dietary quality. As a result of this study, according to the frailty criteria, 572 people(29.2%) were normal, 1,105 people(56.4%) were in the pre-frailty state, and 281 people(14.4%) were in the frailty state. The direct and total effects on subjective health status were found to be significant in all observed variables(p<0.05), and the direct and total effects on multimorbidity were significant in the anxiety/depression variables(p<0.05). The total effect on frailty by mediating the subjective health status and multimorbidity was physical activity -0.202, anxiety/depression 0.183, dietary quality -0.137, and income -0.133 in that order. Direct effect, indirect effect, and total effect were significant in all observed variables(p<0.05). The influence of income, anxiety/depression, dietary quality, and physical activity was increased more when subjective health status was mediated than when multimorbidity was mediated. Therefore, to manage frailty, it is necessary to improve the subjective health status. #Frailty #Subjective health status #Multimorbidity #Structural equation model #Korea #노쇠 #주관적 건강상태 #다중이환 #구조방정식 모형 #대한민국 AbstractⅠ. 서론Ⅱ. 연구 방법Ⅲ. 연구결과Ⅳ. 논의 및 결론REFERENCES

Bayesian survival model induced by frailty for lifetime with long‐term survivors

It is introduced the proportional hazards frailty model to allow a discrete distribution for the frailty variable. Frailty zero can be interpreted as being immune or cured. It is defined a class of survival models induced by a discrete frailty having a mixed Poisson distribution, which can account for unobserved dispersion. Further, a new regression to evaluate the effects of covariates in the cure fraction is constructed. Several former cure survival models are special cases of the proposed modeling framework. The inferential approach is based on Bayesian methods. Some simulation results are provided to assess the performance of the new regression. Its importance is illustrated by means of an application to colorectal cancer data.

Gamma frailty model for survival risk estimation: an application to cancer data

Abstract Objectives: In time to event analysis, the risk for an event is usually estimated using Cox proportional hazards (CPH) model. But CPH model has the limitation of biased estimate due to unobserved hidden heterogeneity among the covariates, which can be tackled using frailty models. The best models were usually being identified using Akaike information criteria (AIC). Apart from AIC, the present study aimed to assess predictability of risk models using survival concordance measure. Methods: CPH model and frailty models were used to estimate the risk for breast cancer patient survival, and the frailty variable was assumed to follow gamma distribution. Schoenfeld global test was used to check the proportionality assumption. Survival concordance, AIC and simulation studies were used to identify the significance of frailty. Results: From the univariate analysis it was observed that for the covariate age, the frailty has a significant role (θ = 2.758, p-value: 0.0004) and the corresponding hazard rate was 1.93 compared to that of 1.38 for CPH model (age &gt; 50 vs. ≤ 40). Also the covariates radiotherapy and chemotherapy were found to be significant (θ = 5.944, p-value: &lt;0.001 and θ = 16, p-value: &lt;0.001 respectively). Even though there were only minor differences in hazard rates, the concordance was higher for frailty than CPH model for all the covariates. Further the simulation study showed that the bias and root mean square error (RMSE) obtained for both the methods was almost the same and the concordance measures were higher for frailty model by 12–15%. Conclusions: We conclude that the frailty model is better compared to CPH model as it can account for unobserved random heterogeneity, and if the frailty coefficient doesn’t have an effect it gives exactly the same risk as that of CPH model and this has been established using survival concordance.

Stochastic Comparisons of General Proportional Mean Past Lifetime Frailty Model

In this paper, the general proportional mean past lifetime frailty model is considered, from which the unconditional cumulative distribution and density functions of the lifetime variable are derived. Dependency between the two variables are studied. Stochastic comparisons are made through which it is shown that some well-known stochastic orderings between two frailty variables carry over to the corresponding lifetime variables. The effects of baseline variable and the frailty variable on the proposed frailty model are studied. The relative mean past lifetime ordering is introduced and some relative ordering between two lifetime random variables with different frailty variables are studied. Also a simulation study is given to illustrate some results.

A multivariate survival model induced by discrete frailty

Frailty models are generally used to model heterogeneity and dependence between individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. On the other hand, when a cure rate is present in survival data, these continuous distributions may not be appropriate since individuals with long-term survival times encompass zero frailty. Having zero frailty can be interpreted as being immune or cured (long-term survivors). In this paper, we propose a new multivariate survival model induced by frailty for multivariate lifetime data in the presence of surviving fractions and examine some of its properties. The inferential approach exploits the Bayesian methods. We provide some simulation results to assess the performance of the proposed model. Furthermore, we illustrate the performance of the proposed model by means of a real data set.

VARIABEL-VARIABEL YANG MEMENGARUHI WAKTU HINGGA SESEORANG MENGGUNAKAN NARKOBA PERTAMA KALI MENGGUNAKAN ANALISIS SURVIVAL

Indonesia is currently in a state of emergency of drugs because of its increasing abuse rate and its spread is widespread not only in big cities. The average of first drug use is in adolescence. This study aims to determine the variables that influence the time for someone to use drugs for the first time and the acceleration factor. The data analyzed by using Survival Analysis with the frailty variable is secondary data from BNN and Puslitkes UI survey. The result is, the average of first drug use in eight towns/districts of research locus was 18-year. Smoking, alcohol consumption, the environment, and gender are significant variables that influence the first drug use. A person who has smoked, use alcohol, lived in a drug-exposed environment, and male sex will have a resilient time not to use drugs faster with the acceleration factor of each variable are 0.3184, 0.3985, 0.3501, and 0.6773. The results conclude that regulations on cigarettes and alcohol need to be revisited since both influence drug initiation. In addition, prevention programs need to focus more on adolescents and young children so that they have strong self-defense from the influence of a drug-exposed environment.

Joint analysis of panel count and interval-censored data using distribution-free frailty analysis.

We propose a joint analysis of recurrent and nonrecurrent event data subject to general types of interval censoring. The proposed analysis allows for general semiparametric models, including the Box-Cox transformation and inverse Box-Cox transformation models for the recurrent and nonrecurrent events, respectively. A frailty variable is used to account for the potential dependence between the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty unspecified. We apply the pseudolikelihood for interval-censored recurrent event data, usually termed as panel count data, and the sufficient likelihood for interval-censored nonrecurrent event data by conditioning on the sufficient statistic for the frailty and using the working assumption of independence over examination times. Large sample theory and a computation procedure for the proposed analysis are established. We illustrate the proposed methodology by a joint analysis of the numbers of occurrences of basal cell carcinoma over time and time to the first recurrence of squamous cell carcinoma based on a skin cancer dataset, as well as a joint analysis of the numbers of adverse events and time to premature withdrawal from study medication based on a scleroderma lung diseasedataset.

On comparisons of population and subpopulations in frailty models

This paper studies stochastic comparisons between a population and subpopulations in both multiplicative and additive frailty models. The comparisons between a population and its baseline in stochastic ordering are conducted as a special case. We build equivalent characterizations of some common stochastic orders between a population and a subpopulation, in terms of the frailty of the subpopulation and the first two moments of frailty variable. Some examples and applications are discussed as well.

A new long-term survival model with dispersion induced by discrete frailty.

Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. In this paper, we propose extending the proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured (long-term survivors). Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. A numerical study is carried out under the scenario that the baseline distribution follows an exponential distribution, however this assumption can be easily relaxed and some other distributions can be considered. Moreover, this proposal allows for a more realistic description of the non-risk individuals, since individuals cured due to intrinsic factors (immune) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. Inference is developed by the maximum likelihood method for the estimation of the model parameters. A simulation study is performed in order to evaluate the performance of the proposed inferential method. Finally, the proposed model is applied to a data set on malignant cutaneous melanoma to illustrate the methodology.

Ventral hernia repair outcomes predicted by a 5-item modified frailty index using NSQIP variables.

Frailty is a decrease in physiologic reserve that is separate from the normal aging process. Previously, an 11-item modified frailty index (mFI) using NSQIP variables predicted outcomes for surgical patients. We aim to validate a condensed 5-item mFI in ventral hernia patients and determine outcomes and the relative impact of each frailty variable. The NSQIP database was queried from 2011 to 2016 for patients undergoing VHR. Spearman's rho correlation was used to determine the degree of correlation between 11-item and 5-item mFI raw frailty scores. Chi squared testing was used to determine odds ratios (95% CI) for accumulating frailty variables in both indices with regard to complications vs a baseline of zero variables present on the 11-item scale. Complications were defined by the Clavien-Dindo (CD) classification. Univariate and multivariate analyses were performed on each frailty variable to determine their relative weighted impacts on outcomes. 97,905 patients (99.45%) had all five frailty variables recorded. Only 11,549 patients (11.73%) had all variables from the 11-item mFI. No difference existed between groups for the five mutually shared frailty variables, BMI, emergent vs non-emergent procedures, operative time, or operative approach. For accumulating variables in both indices, the 5-item mFI predicts incidence of any complications, major complications, and discharge not to home similarly to the 11-item mFI. The most significantly weighted variable for complications and discharge not to home is functional status. A 5-item mFI accurately predicts outcomes similar to the validated 11-item mFI and captures more patients for analysis.

Self-reported fatigue as a risk index for dementia diagnosis.

Cognitive impairment and frailty are major problems of older age. This study aims to explore the association between frailty and cognitive impairment in a rural cohort of older subjects in southern Europe (Cretan Aging Cohort). Community-based, primary care, cross-sectional, study in the Heraklion Prefecture, Crete, Greece. Four hundred and two persons aged 60-100years from the Cretan Aging Cohort [100 with dementia, 175 with mild cognitive impairment (MCI) and 127 cognitively non-impaired] were enrolled, mostly rural dwellers (86.2%). Frailty was assessed with the Simple "Frail" Questionnaire Screening Tool. Demographic data, BMI, Mini-Mental State Examination scores (MMSE), severity of dementia according to the Clinical Dementia Rating Scale, and depressive symptoms according to the Geriatric Depression Scale (GDS) were recorded. Frailty was present in 17% of persons with dementia (73.8% of mild severity), in 6.3% of persons with MCI and in 8.7% of cognitively non-impaired persons (P<0.05). Among the various frailty variables, fatigue and difficulty walking were significantly more frequently reported by persons with dementia. Each frailty variable and the frailty score correlated negatively with MMSE score and positively with GDS score and polypharmacy. Multivariate analysis revealed that reported fatigue improved the identification of dementia in addition to MMSE, significantly and independently of symptoms of depression (P=0.04). Frailty rates are significantly higher in persons with dementia. In this predominantly rural cohort of older subjects, reported fatigue could serve as a marker of physical decline and a complementary index for referral for further neuropsychological and neuropsychiatric evaluation.

Assessment of Commonly Used Frailty Markers for High- and Extreme-Risk Patients Undergoing Transcatheter Aortic Valve Replacement

The effect of frailty on outcomes after transcatheter aortic valve replacement (TAVR) remains incompletely understood. The objective of this study was to evaluate the performance of four commonly used frailty markers as predictors of early and late outcomes among patients undergoing TAVR. A review was performed of 361 high- and extreme-risk patients undergoing TAVR from 2011 to 2015. Four frailty variables were assessed: serum albumin (g/dL), 5-m walk (seconds), grip strength (kg), and Katz index of independence in activities of daily living. Logistic regression was used to examine the association between the frailty indicators and 30-day composite of mortality, stroke, new heart block requiring permanent pacemaker, major or life-threatening bleeding, acute renal failure, major vascular complication, and 30-day readmission rate. Minimum distance to the perfect point (0, 1) was performed to delineate a cutoff point for each frailty indicator, and risk models were compared using receiver-operating characteristics curves. The composite of outcomes occurred in 28% of patients. Serum albumin, activities of daily living, and 5-m walk were independent predictors for 30-day composite outcomes, but only albumin was predictive of 30-day mortality. A new frailty model (four frailty indicators, age, and sex) to predict 30-day mortality was created and compared with The Society of Thoracic Surgeons predicted risk of mortality. Better discrimination was found with the new frailty model (area under the curve 0.74 versus 0.58). New individual frailty variable cutoff values were found to predict our composite of events. Among high- and extreme-risk patients undergoing TAVR, our new frailty model was more discriminative of 30-day mortality than The Society of Thoracic Surgeons predicted risk of mortality. New cutoff values for frailty indicators were identified and will require further validation.

Joint Scale-Change Models for Recurrent Events and Failure Time

ABSTRACTRecurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change model for the recurrent event process and the failure time, where a shared frailty variable is used to model the association between the two types of outcomes. In contrast to the popular Cox-type joint modeling approaches, the regression parameters in the proposed joint scale-change model have marginal interpretations. The proposed approach is robust in the sense that no parametric assumption is imposed on the distribution of the unobserved frailty and that we do not need the strong Poisson-type assumption for the recurrent event process. We establish consistency and asymptotic normality of the proposed semiparametric estimators under suitable regularity conditions. To estimate the corresponding variances of the estimators, we develop a computationally efficient resampling-based procedure. Simulation studies and an analysis of hospitalization data from the Danish Psychiatric Central Register illustrate the performance of the proposed method. Supplementary materials for this article are available online.

SEMIPARAMETRIC EFFICIENT ESTIMATION FOR SHARED-FRAILTY MODELS WITH DOUBLY-CENSORED CLUSTERED DATA.

In this paper, we investigate frailty models for clustered survival data that are subject to both left- and right-censoring, termed "doubly-censored data". This model extends current survival literature by broadening the application of frailty models from right-censoring to a more complicated situation with additional left censoring. Our approach is motivated by a recent Hepatitis B study where the sample consists of families. We adopt a likelihood approach that aims at the nonparametric maximum likelihood estimators (NPMLE). A new algorithm is proposed, which not only works well for clustered data but also improve over existing algorithm for independent and doubly-censored data, a special case when the frailty variable is a constant equal to one. This special case is well known to be a computational challenge due to the left censoring feature of the data. The new algorithm not only resolves this challenge but also accommodate the additional frailty variable effectively. Asymptotic properties of the NPMLE are established along with semi-parametric efficiency of the NPMLE for the finite-dimensional parameters. The consistency of Bootstrap estimators for the standard errors of the NPMLE is also discussed. We conducted some simulations to illustrate the numerical performance and robustness of the proposed algorithm, which is also applied to the Hepatitis B data.

Frailty and sarcopenia in Bogotá: results from the SABE Bogotá Study.

Latin American countries like Colombia are experiencing a unique aging process due to a mixed epidemiological regime of communicable and non-communicable diseases. To estimate the prevalence of frailty and sarcopenia among older adults in Colombia and identify variables associated with these conditions. Data come from the "Salud Bienestar y Envejecimiento" (SABE) Bogotá Study, a cross-sectional study conducted in 2012 in Bogotá, Colombia. Sociodemographic, health, cognitive and anthropometric measures were collected from 2000 community-dwelling adults aged 60years and older. Frailty variable was created using the Fried phenotype and sarcopenia following the European Working Group on Sarcopenia in Older People algorithm. Logistic regression analyses were used to identify factors associated with frailty and sarcopenia. A total of 135 older adults are frail (9.4%), while 166 have sarcopenia (11.5%). Older age and female gender have a significant association with both conditions (Frailty: Age OR 1.05, 95% CI 1.03-1.06, Gender OR 1.44, 95% CI 1.12-1.84; Sarcopenia: Age 1.04, 95% CI 1.02-1.07, Gender OR 1.51, 95% CI 1.05-2.17). Depression was also significantly associated with frailty (OR 1.17, 95% CI 1.12-1.22), while smoking was significantly associated with sarcopenia (OR 2.38, 95% CI 1.29-4.37). Finally, higher function, measured by independence in IADL (Instrumental Activities of Daily Living) was significantly associated with less frailty (OR 0.74, 95% CI 0.64-0.86). Education, higher number of comorbidities, better MMSE score, activities of daily living disability and alcohol consumption were not significantly associated with frailty or sarcopenia. Frailty, sarcopenia and multimorbidity are overlapping, yet distinct conditions in this sample. There are potentially reversible factors that are associated with frailty and sarcopenia in this sample. Future studies need to analyze the best way to prevent these conditions, and examine individuals that have frailty, sarcopenia and comorbidities to design interventions to improve their quality of life.