Frailty and Early Hospital Readmission After Kidney Transplantation
TL;DR: In this article, a measure of physiologic reserve, called frailty, was proposed as a predictor of early hospital readmission after kidney transplantation (EHR) in kidney transplant patients.
About: This article is published in American Journal of Transplantation.The article was published on 2013-08-01 and is currently open access. It has received 260 citations till now. The article focuses on the topics: Transplantation.
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TL;DR: The findings support the importance of frailty in late-life health etiology and potential value of frailt as a marker of risk for adverse health outcomes and as a means of identifying opportunities for intervention in clinical practice and public health policy.
Abstract: Background Frailty assessment provides a means of identifying older adults most vulnerable to adverse outcomes. Attention to frailty in clinical practice is more likely with better understanding of its prevalence and associations with patient characteristics. We sought to provide national estimates of frailty in older people. Methods A popular, validated frailty phenotype proposed by Fried and colleagues was applied to 7,439 participants in the 2011 baseline of the National Health and Aging Trends Study, a national longitudinal study of persons aged 65 and older. All measures drew on a 2-hour in-person interview. Weighted estimates of frailty prevalence were obtained. Results Fifteen percent (95% CI: 14%, 16%) of the older non-nursing home population is frail, and 45% is prefrail (95% CI: 44%, 47%). Frailty is more prevalent at older ages, among women, racial and ethnic minorities, those in supportive residential settings, and persons of lower income. Independently of these characteristics, frailty prevalence varies substantially across geographic regions. Chronic disease and disability prevalence increase steeply with frailty. Among the frail, 42% were hospitalized in the previous year, compared to 22% of the prefrail and 11% of persons considered robust. Hip, back, and heart surgery in the last year were associated with frailty. Over half of frail persons had a fall in the previous year. Conclusions Our findings support the importance of frailty in late-life health etiology and potential value of frailty as a marker of risk for adverse health outcomes and as a means of identifying opportunities for intervention in clinical practice and public health policy.
482 citations
Cites background from "Frailty and Early Hospital Readmiss..."
...Clinical applications of frailty assessment are emerging, for example that frailty can improve screening for risk of adverse postsurgical events (18,32)....
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TL;DR: Frailty strongly predicts waitlist mortality, even after adjustment for liver disease severity, demonstrating the applicability and importance of the frailty construct in this population of liver transplant candidates.
343 citations
Cites background from "Frailty and Early Hospital Readmiss..."
...Frailty has already been applied and found to be highly prevalent in other solid organ transplant candidates including kidney (16,22) and lung (28,29) and predictive of transplant-related outcomes, supporting the utility of this geriatric concept in transplant hepatology....
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...surgical and kidney transplant patients (12,16), whom we felt were most...
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TL;DR: The concept of frailty has become increasingly recognized as one of the most important issues in health care and health outcomes and is of particular importance in patients with cancer who are receiving treatment with surgery, chemotherapy, and radiotherapy as discussed by the authors.
Abstract: Answer questions and earn CME/CNE The concept of frailty has become increasingly recognized as one of the most important issues in health care and health outcomes and is of particular importance in patients with cancer who are receiving treatment with surgery, chemotherapy, and radiotherapy. Because both cancer itself, as well as the therapies offered, can be significant additional stressors that challenge a patient's physiologic reserve, the incidence of frailty in older patients with cancer is especially high-it is estimated that over one-half of older patients with cancer have frailty or prefrailty. Defining frailty can be challenging, however. Put simply, frailty is a state of extreme vulnerability to stressors that leads to adverse health outcomes. In reality, frailty is a complex, multidimensional, and cyclical state of diminished physiologic reserve that results in decreased resiliency and adaptive capacity and increased vulnerability to stressors. In addition, over 70 different measures of frailty have been proposed. Still, it has been demonstrated that frail patients are at increased risk of postoperative complications, chemotherapy intolerance, disease progression, and death. Although international standardization of frailty cutoff points are needed, continued efforts by oncology physicians and surgeons to identify frailty and promote multidisciplinary decision making will help to develop more individualized management strategies and optimize care for patients with cancer. CA Cancer J Clin 2017;67:362-377. © 2017 American Cancer Society.
338 citations
Cites background from "Frailty and Early Hospital Readmiss..."
...Similarly, it has been demonstrated that phenotypic frailty is predictive of postoperative outcomes in patients presenting for elective surgery, including major abdominal and transplantation surgeries.(10,45,70,79,80) In a study by Revenig et al, frailty was even predictive of postoperative complications among patients undergoing minimally invasive abdominal surgery....
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TL;DR: Regardless of age, frailty is a strong, independent risk factor for post‐KT mortality, even after carefully adjusting for many confounders using a novel, efficient statistical approach.
258 citations
Cites background or result from "Frailty and Early Hospital Readmiss..."
...Effect heterogeneity Consistent with our previous findings in KT (11), the association of frailty and mortality did not differ between older and younger KT recipients (interaction p1⁄40....
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...Consistent with previous findings of frailty as an independent domain (11), no recipient factors were statistically significantly associated with frailty except age (Table 1)....
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...In KT recipients, frailty is associated with 94% increased risk of delayed graft function (10) and 61% increased risk of early hospital readmission (11)....
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...older adults (2,12–21) and by our group in ESRD and KT populations (10,11,22,23)....
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TL;DR: The RAi-C and RAI-A represent effective tools for measuring frailty in surgical populations with predictive ability on par with other frailty tools and Moderate correlation between the measures suggests convergent validity.
Abstract: Importance Growing consensus suggests that frailty-associated risks should inform shared surgical decision making. However, it is not clear how best to screen for frailty in preoperative surgical populations. Objective To develop and validate the Risk Analysis Index (RAI), a 14-item instrument used to measure surgical frailty. It can be calculated prospectively (RAI-C), using a clinical questionnaire, or retrospectively (RAI-A), using variables from the surgical quality improvement databases (Veterans Affairs or American College of Surgeons National Surgical Quality Improvement Projects). Design, Setting, and Participants Single-site, prospective cohort from July 2011 to September 2015 at the Veterans Affairs Nebraska–Western Iowa Heath Care System, a Level 1b Veterans Affairs Medical Center. The study included all patients presenting to the medical center for elective surgery. Exposures We assessed the RAI-C for all patients scheduled for surgery, linking these scores to administrative and quality improvement data to calculate the RAI-A and the modified Frailty Index. Main Outcomes and Measures Receiver operator characteristics and C statistics for each measure predicting postoperative mortality and morbidity. Results Of the participants, the mean (SD) age was 60.7 (13.9) years and 249 participants (3.6%) were women. We assessed the RAI-C 10 698 times, from which we linked 6856 unique patients to mortality data. The C statistic predicting 180-day mortality for the RAI-C was 0.772. Of these 6856 unique patients, we linked 2785 to local Veterans Affairs Surgeons National Surgical Quality Improvement Projects data and calculated the C statistic for both the RAI-A (0.823) and RAI-C (0.824), along with the correlation between the 2 scores ( r = 0.478; P P Conclusions and Relevance The RAI-C and RAI-A represent effective tools for measuring frailty in surgical populations with predictive ability on par with other frailty tools. Moderate correlation between the measures suggests convergent validity. The RAI-C offers the advantage of prospective, preoperative assessment that is proved feasible for large-scale screening in clinical practice. However, further efforts should be directed at determining the optimal components of preoperative frailty assessment.
249 citations
References
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TL;DR: This study provides a potential standardized definition for frailty in community-dwelling older adults and offers concurrent and predictive validity for the definition, and finds that there is an intermediate stage identifying those at high risk of frailty.
Abstract: Background: Frailty is considered highly prevalent in old age and to confer high risk for falls, disability, hospitalization, and mortality. Frailty has been considered synonymous with disability, comorbidity, and other characteristics, but it is recognized that it may have a biologic basis and be a distinct clinical syndrome. A standardized definition has not yet been established.
Methods: To develop and operationalize a phenotype of frailty in older adults and assess concurrent and predictive validity, the study used data from the Cardiovascular Health Study. Participants were 5,317 men and women 65 years and older (4,735 from an original cohort recruited in 1989-90 and 582 from an African American cohort recruited in 1992-93). Both cohorts received almost identical baseline evaluations and 7 and 4 years of follow-up, respectively, with annual examinations and surveillance for outcomes including incident disease, hospitalization, falls, disability, and mortality.
Results: Frailty was defined as a clinical syndrome in which three or more of the following criteria were present: unintentional weight loss (10 lbs in past year), self-reported exhaustion, weakness (grip strength), slow walking speed, and low physical activity. The overall prevalence of frailty in this community-dwelling population was 6.9%; it increased with age and was greater in women than men. Four-year incidence was 7.2%. Frailty was associated with being African American, having lower education and income, poorer health, and having higher rates of comorbid chronic diseases and disability. There was overlap, but not concordance, in the cooccurrence of frailty, comorbidity, and disability. This frailty phenotype was independently predictive (over 3 years) of incident falls, worsening mobility or ADL disability, hospitalization, and death, with hazard ratios ranging from 1.82 to 4.46, unadjusted, and 1.29-2.24, adjusted for a number of health, disease, and social characteristics predictive of 5-year mortality. Intermediate frailty status, as indicated by the presence of one or two criteria, showed intermediate risk of these outcomes as well as increased risk of becoming frail over 3-4 years of follow-up (odds ratios for incident frailty = 4.51 unadjusted and 2.63 adjusted for covariates, compared to those with no frailty criteria at baseline).
Conclusions: This study provides a potential standardized definition for frailty in community-dwelling older adults and offers concurrent and predictive validity for the definition. It also finds that there is an intermediate stage identifying those at high risk of frailty. Finally, it provides evidence that frailty is not synonymous with either comorbidity or disability, but comorbidity is an etiologic risk factor for, and disability is an outcome of, frailty. This provides a potential basis for clinical assessment for those who are frail or at risk, and for future research to develop interventions for frailty based on a standardized ascertainment of frailty.
16,255 citations
"Frailty and Early Hospital Readmiss..." refers background in this paper
...We hypothesized that frailty, a measure of physiologic reserve initially described and validated in geriatric populations (8), is not only applicable to patientsof all ageswith end stage renal disease (ESRD) but alsomay capture the type of risk in this population that leads to EHR....
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...below an established cutoff by gender and height) (8)....
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TL;DR: Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100, and the method is illustrated with two data sets.
Abstract: Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
7,045 citations
"Frailty and Early Hospital Readmiss..." refers methods in this paper
...The association between frailty and EHR was evaluated using modified Poisson regression (24) adjusted for recipient and transplant factors based on our previously published registry-based model (1)....
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TL;DR: Two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables, are introduced that offer incremental information over the AUC and are proposed to be considered in addition to the A UC when assessing the performance of newer biomarkers.
Abstract: Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.
5,651 citations
Additional excerpts
...In other words, the NRI was used to quantify the relative ability of the two models (registry-based alone versus registry-based plus frailty) in classifying the patients as low, intermediate, or high risk for EHR as selected a priori (<20%, 20–50%, and >50%)....
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...Assessing clinically relevant prediction improvement: Net reclassification index Additionally, we tested the clinically relevant improvement in prediction using the net reclassification index (NRI) (26)....
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...Key words: Frailty, readmission, transplantation Abbreviations: AUC, area under the receiver operating characteristic curve; DGF, delayed graft function; EHR, early hospital readmission; ESRD, end stage renal disease; KT, kidney transplantation; NRI, net reclassification index....
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...NRI was calculated using the NRI package in Stata12, based on methods described by Pencina et al. (26)....
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...Finally, the NRI quantified ‘‘correct movement’’ in risk classification: for participants who experienced EHR, correct movementwas an upgrade in classification (low to intermediate, low to high, or intermediate to high); for participantswhodid not experienceEHR, correct movement was a downgrade in classification (high to intermediate, high to low, or intermediate to low)....
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Book•
13 Mar 2003
TL;DR: A comparison of Binary Tests and Regression Analysis and the Receiver Operating Characteristic Curve shows that Binary Tests are more accurate than Ordinal Tests when the Receiver operating characteristic curve is considered.
Abstract: 1. Introduction 2. Measures of Accuracy for Binary Tests 3. Comparing Binary Tests and Regression Analysis 4. The Receiver Operating Characteristic Curve 5. Estimating the ROC Curve 6. Covariate Effects on Continuous and Ordinal Tests 7. Incomplete Data and Imperfect Reference Tests 8. Study Design and Hypothesis Testing 9. More Topics and Conclusions References/Bibliography Index
2,289 citations