Follow the trail: Using insights from the growth of palliative care to propose a roadmap for cancer rehabilitation.

Despite research explicating the benefits of cancer rehabilitation interventions to optimize physical, social, emotional, and vocational functioning, many reports document low rates of referral to and uptake of rehabilitation in oncology. Cancer rehabilitation clinicians, researchers, and policy makers could learn from the multidisciplinary specialty of palliative care, which has benefited from a growth strategy and has garnered national recognition as an important and necessary aspect of oncology care. The purpose of this article is to explore the actions that have increased the uptake and integration of palliative care to yield insights and multimodal strategies for the development and growth of cancer rehabilitation. After examining the history of palliative care and its growth, the authors highlight 5 key strategies that may benefit the field of cancer rehabilitation: 1) stimulating the science in specific gap areas; 2) creating clinical practice guidelines; 3) building clinical capacity; 4) ascertaining and responding to public opinion; and 5) advocating for public policy change. Coordinated and simultaneous advances on these 5 strategies may catalyze the growth, utilization, and effectiveness of patient screening, timely referrals, and delivery of appropriate cancer rehabilitation care that reduces disability and improves quality of life for cancer survivors who need these services.

Risk factors for reduced function in women with a history of breast cancer.

PURPOSE: People with a history of breast cancer are at risk of losing function during and after treatment. Unfortunately, little is known about the individual and additive effects of specific treatment, disease-related, and demographic factors that may contribute to functional decline. This manuscript reports the results of a multi-center study to evaluate the effects of these factors on function. METHODS: In this cross-sectional study, women with a history of breast cancer referred to physical medicine and rehabilitation cancer rehabilitation clinics were administered the PROMIS® Cancer Function Brief 3D Profile to evaluate function in the domains of physical function, fatigue, and social participation. Clinical and demographic information, including treatment history and disease status, was recorded by clinicians. Patients were analyzed in two groups: those with active disease on antineoplastic treatment, and those with no evidence of disease (NED). A multivariable model was constructed to detect associations between clinical and demographic factors. RESULTS: In patients with NED, the presence of chemotherapy-induced peripheral neuropathy (CIPN) was strongly associated with reduced function in all three domains. In those with active disease, having brain metastases was significantly associated with reduced function in all domains and CIPN with reduced physical function. Radiation was associated with improved function in both cohorts. CONCLUSIONS: Among women seeking rehabilitative care, CIPN and the presence of brain metastases were most strongly associated with a decline in function. The effects of radiation on function were unexpected and may be partially explained by the treatment's role in symptom management. Clinicians who treat breast cancer should consider a patient's functional status when providing supportive care.

Responsiveness and interpretation of the PROMIS Cancer Function Brief 3D Profile.

BACKGROUND: Measuring function with valid and responsive tools in patients with cancer is essential for driving clinical decision-making and for the end points of clinical trials. Current patient-reported outcome measurements of function fall short for many reasons. This study evaluates the responsiveness of the Patient-Reported Outcomes Measurement Information System (PROMIS) Cancer Function Brief 3D Profile, a novel measure of function across multiple domains. METHODS: Two hundred nine participants across five geographically distinct tertiary care centers completed the assessment and pain rating at two outpatient cancer rehabilitation clinic visits. Patients and providers completed a global rating of change measure at the second visit to indicate whether the patient was improving or worsening in function. Multiple response indices and linear models measured whether the measure was responsive to self-reported and clinician-rated changes over time. Correlations between changes in function and changes in anchors (pain rating and performance status) were also calculated. RESULTS: Function as measured by the PROMIS Cancer Function Brief 3D Profile changed appropriately as both patients and clinicians rated change. Small to moderate effect sizes supported the tool's responsiveness. Function was moderately correlated with pain and more strongly correlated with performance status, and changes in function corresponded with changes in anchor variables. No floor/ceiling effect was found. CONCLUSIONS: The PROMIS Cancer Function Brief 3D Profile is sensitive to changes over time in patients with cancer. The measure may be useful in clinical practice and as an end point in clinical trials. LAY SUMMARY: We gave patients a questionnaire by which they told their physicians how well they were functioning, including how fatigued they were. This study tested that questionnaire to see whether the scores would change if patients got better or worse.

Psychometric Characteristics and Validity of the PROMIS Cancer Function Brief 3D Profile.

OBJECTIVE: To develop an item response theory (IRT)-calibrated, patient-reported outcome measure (the PROMIS Cancer Function Brief 3D Profile) of physical function, including associations with fatigue and social participation, in cancer rehabilitation patients. DESIGN: Large-scale field testing, graded response model IRT analyses, and multivariate regression analysis. SETTING: Six cancer rehabilitation clinics associated with cancer centers across the United States. PARTICIPANTS: Adults (N=616) treated in outpatient cancer rehabilitation medicine clinics. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: The PROMIS(r) Cancer Function 3D Profile (including existing items from PROMIS(r) item banks). RESULTS: A total of 616 patients completed 21 items in the initial item pool. Nine items were removed because of comparatively lower information that they provide according to the IRT item calibrations, low item-total correlations, or bimodal distributions. The remaining items generated a 12-item short form. Regression analyses determined that the items were responsive to and representative of the patient population across trait ranges and multiple domains and subdomains of function. CONCLUSIONS: This psychometric investigation supports the use of the PROMIS Cancer Function Brief 3D Profile for evaluating function in outpatient cancer rehabilitation patients.

Protecting Children From Tobacco Smoke Exposure: A Randomized Controlled Trial of Project Zero Exposure.

INTRODUCTION: Young children are vulnerable to harm from tobacco smoke exposure (TSE). This study assessed the effect of Project Zero Exposure-an intervention program designed to help parents protect children from TSE-on children's exposure. METHODS: Randomized controlled trial of a home-based, theory-driven intervention. Parents of young children (<8 y) in families with a smoking parent were eligible. The intervention included feedback on child TSE (hair nicotine), and home air quality (PM2.5), with motivational interviewing. Families were randomized to: intervention group (IG, N = 69), regular control group (RCG, N = 70), or to a secondary enhanced control group, (ECG, N = 20). Child hair samples were taken at baseline and follow-up. We report on child TSE in the IG versus RCG at six months. RESULTS: Most enrolled families completed the trial (IG: 98.6%[68/69], RCG: 97.1%[68/70]). Log hair nicotine (LHN [ng/mg]) decreased in both the IG (Baseline: -1.78 ± 1.91, Follow-up: -2.82 ± 1.87, p = .003) and RCG (Baseline: -1.79 ± 1.54, Follow-up: -2.85 ± 1.73, p = .002), but did not differ between groups at study end (p = .635). Three of five parentally-reported outcomes showed improvement over time in the IG, and one in the RCG. Among IG participants, 90% found hair nicotine feedback useful. CONCLUSIONS: No difference between the intervention and control groups was found on the objective biomarker, LHN. Child TSE decreased during the trial in intervention and control groups. Trial participation, which included hair nicotine monitoring, may have contributed to decreasing exposure in both groups. Concurrent control group improvements may partially explain lack of proven intervention benefit. Biomarker monitoring warrants further investigation for reduction of child TSE. IMPLICATIONS: Project Zero Exposure is an intervention program designed to help parents protect their children from TSE. Results from the randomized controlled trial of the program showed no difference between groups at study end, but a clear and substantial reduction in child exposure to tobacco smoke from beginning to end of the trial, in both intervention and control groups. Biomarker monitoring, a key element of the trial, was used with all participants. Biomarker monitoring of child exposure to tobacco smoke may help parents become aware of their child's exposure and better protect them, and should be explored as a means to reduce child TSE. Clinical Trial Registration: NCT02867241.

Minimally Invasive Repair of Ascending Aortic Pseudoaneurysms: An Alternative to Open Surgical Repair in High-Risk Patients.

Development of a pseudoaneurysm of the ascending aorta is an uncommon complication of aortic surgery. Several nonsurgical techniques are available for treatment of ascending aortic pseudoaneurysms (AAPs). This report outlines a single-center retrospective experience with 14 nonsurgical procedures for treatment of AAPs in 10 patients. Modified stent grafts, septal defect occlusion devices, coil embolics, and liquid embolics were deployed by transthoracic and endovascular approaches. Complete stasis of the AAP was achieved in 7 of 10 patients (70%). Mean postprocedural recoveries occurred within 3.5 days. Nonsurgical techniques for repair of AAPs offer a comparatively safe and effective alternative to open surgical repair.

Estimation in the Cox survival regression model with covariate measurement error and a changepoint.

The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM10 and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.

A modified partial likelihood score method for Cox regression with covariate error under the internal validation design.

We develop a new method for covariate error correction in the Cox survival regression model, given a modest sample of internal validation data. Unlike most previous methods for this setting, our method can handle covariate error of arbitrary form. Asymptotic properties of the estimator are derived. In a simulation study, the method was found to perform very well in terms of bias reduction and confidence interval coverage. The method is applied to data from the Health Professionals Follow-Up Study (HPFS) on the effect of diet on incidence of Type II diabetes.

Parental Perceptions and Misconceptions of Child Tobacco Smoke Exposure.

Introduction: Forty percent of young children worldwide are exposed to the harmful effects of tobacco smoke, predominantly by parental smoking. Little is known about why parents regularly expose their children to these risks; perhaps parents underestimate the degree of exposure. Qualitative methods were used to investigate parental perceptions of tobacco smoke exposure. Methods: Sixty-five in-depth interviews were conducted with parents of young children in smoking families in central Israel. Parents were asked to explain what "exposure to smoking" meant. Thematic analysis was performed, a conceptual model of perceptions was built, and misconceptions were identified. Results: Parents reported that exposure occurs when smoke or smokers are visible, when smoke can be smelled, felt, or inhaled, or when it "reaches" an individual. Conversely, some believed that exposure does not occur in the absence of odor, visible smoke, or smokers or if smoking occurs outdoors or in indoor ventilated environments. Proximity in space and time affected perceptions of exposure; some parents believed that smoke does not spread far but dissipates rapidly. There was some uncertainty regarding whether or not exposure was occurring. Conclusions: Awareness of child exposure to tobacco smoke among parents in this study was based on sensory perceptions in the context of the physical environment. The limited capacity of humans to perceive tobacco smoke can lead to misconceptions about exposure. In order to protect children, parents must be convinced that exposure can occur even in situations where they are unable to sense it. Implications: Parents use sensory perceptions (sight, smell, and feel) in the context of the physical environment to assess whether or not their children are exposed to tobacco smoke. Because 85% of smoke is invisible and the sense of smell is unreliable, assessments based on sensory perceptions cannot provide accurate information about the presence of tobacco smoke. In order to protect children, parents must be convinced that exposure can occur even in situations where they are unable to sense it. The scientific information summarized here about exposure in common situations should be useful in persuading parents to protect their children. Clinical Trial Registration: This study is registered as a Phase I study which is part of a larger research endeavor entitled: A program to protect young children from tobacco smoke exposure. Registration number: NCT01335178.

Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study.

Amyloid-binding compounds maintain protein homeostasis during ageing and extend lifespan.

Genetic studies indicate that protein homeostasis is a major contributor to metazoan longevity. Collapse of protein homeostasis results in protein misfolding cascades and the accumulation of insoluble protein fibrils and aggregates, such as amyloids. A group of small molecules, traditionally used in histopathology to stain amyloid in tissues, bind protein fibrils and slow aggregation in vitro and in cell culture. We proposed that treating animals with such compounds would promote protein homeostasis in vivo and increase longevity. Here we show that exposure of adult Caenorhabditis elegans to the amyloid-binding dye Thioflavin T (ThT) resulted in a profoundly extended lifespan and slowed ageing. ThT also suppressed pathological features of mutant metastable proteins and human ß-amyloid-associated toxicity. These beneficial effects of ThT depend on the protein homeostasis network regulator heat shock factor 1 (HSF-1), the stress resistance and longevity transcription factor SKN-1, molecular chaperones, autophagy and proteosomal functions. Our results demonstrate that pharmacological maintenance of the protein homeostatic network has a profound impact on ageing rates, prompting the development of novel therapeutic interventions against ageing and age-related diseases.

Accounting for measurement error in biomarker data and misclassification of subtypes in the analysis of tumor data.

A common paradigm in dealing with heterogeneity across tumors in cancer analysis is to cluster the tumors into subtypes using marker data on the tumor, and then to analyze each of the clusters separately. A more specific target is to investigate the association between risk factors and specific subtypes and to use the results for personalized preventive treatment. This task is usually carried out in two steps-clustering and risk factor assessment. However, two sources of measurement error arise in these problems. The first is the measurement error in the biomarker values. The second is the misclassification error when assigning observations to clusters. We consider the case with a specified set of relevant markers and propose a unified single-likelihood approach for normally distributed biomarkers. As an alternative, we consider a two-step procedure with the tumor type misclassification error taken into account in the second-step risk factor analysis. We describe our method for binary data and also for survival analysis data using a modified version of the Cox model. We present asymptotic theory for the proposed estimators. Simulation results indicate that our methods significantly lower the bias with a small price being paid in terms of variance. We present an analysis of breast cancer data from the Nurses' Health Study to demonstrate the utility of our method. Copyright © 2016 John Wiley & Sons, Ltd.

The impact of covariate measurement error on risk prediction.

In the development of risk prediction models, predictors are often measured with error. In this paper, we investigate the impact of covariate measurement error on risk prediction. We compare the prediction performance using a costly variable measured without error, along with error-free covariates, to that of a model based on an inexpensive surrogate along with the error-free covariates. We consider continuous error-prone covariates with homoscedastic and heteroscedastic errors, and also a discrete misclassified covariate. Prediction performance is evaluated by the area under the receiver operating characteristic curve (AUC), the Brier score (BS), and the ratio of the observed to the expected number of events (calibration). In an extensive numerical study, we show that (i) the prediction model with the error-prone covariate is very well calibrated, even when it is mis-specified; (ii) using the error-prone covariate instead of the true covariate can reduce the AUC and increase the BS dramatically; (iii) adding an auxiliary variable, which is correlated with the error-prone covariate but conditionally independent of the outcome given all covariates in the true model, can improve the AUC and BS substantially. We conclude that reducing measurement error in covariates will improve the ensuing risk prediction, unless the association between the error-free and error-prone covariates is very high. Finally, we demonstrate how a validation study can be used to assess the effect of mismeasured covariates on risk prediction. These concepts are illustrated in a breast cancer risk prediction model developed in the Nurses' Health Study.

Calibrated predictions for multivariate competing risks models.

Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

The killifish germline regulates longevity and somatic repair in a sex-specific manner.

Classical evolutionary theories propose tradeoffs between reproduction, damage repair, and lifespan. However, the specific role of the germline in shaping vertebrate aging remains largely unknown. Here, we use the turquoise killifish ( N. furzeri ) to genetically arrest germline development at discrete stages, and examine how different modes of infertility impact life-history. We first construct a comprehensive single-cell gonadal atlas, providing cell-type-specific markers for downstream phenotypic analysis. Next, we show that germline depletion - but not arresting germline differentiation - enhances damage repair in female killifish. Conversely, germline-depleted males instead showed an extension in lifespan and rejuvenated metabolic functions. Through further transcriptomic analysis, we highlight enrichment of pro-longevity pathways and genes in germline-depleted male killifish and demonstrate functional conservation of how these factors may regulate longevity in germline-depleted C. elegans . Our results therefore demonstrate that different germline manipulation paradigms can yield pronounced sexually dimorphic phenotypes, implying alternative responses to classical evolutionary tradeoffs.

The killifish germline regulates longevity and somatic repair in a sex-specific manner.

Classical evolutionary theories propose tradeoffs among reproduction, damage repair and lifespan. However, the specific role of the germline in shaping vertebrate aging remains largely unknown. In this study, we used the turquoise killifish (Nothobranchius furzeri) to genetically arrest germline development at discrete stages and examine how different modes of infertility impact life history. We first constructed a comprehensive single-cell gonadal atlas, providing cell-type-specific markers for downstream phenotypic analysis. We show here that germline depletion-but not arresting germline differentiation-enhances damage repair in female killifish. Conversely, germline-depleted males instead showed an extension in lifespan and rejuvenated metabolic functions. Through further transcriptomic analysis, we highlight enrichment of pro-longevity pathways and genes in germline-depleted male killifish and demonstrate functional conservation of how these factors may regulate longevity in germline-depleted Caenorhabditis elegans. Our results, therefore, demonstrate that different germline manipulation paradigms can yield pronounced sexually dimorphic phenotypes, implying alternative responses to classical evolutionary tradeoffs.

A regularization corrected score method for nonlinear regression models with covariate error.

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer.

Power of testing for exposure effects under incomplete mediation.

Mediation analysis studies situations where an exposure may affect an outcome both directly and indirectly through intervening variables called mediators. It is frequently of interest to test for the effect of the exposure on the outcome, and the standard approach is simply to regress the latter on the former. However, it seems plausible that a more powerful test statistic could be achieved by also incorporating the mediators. This would be useful in cases where the exposure effect size might be small, which for example is common in genomics applications. Previous work has shown that this is indeed possible under complete mediation, where there is no direct effect. In most applications, however, the direct effect is likely nonzero. In this paper we study linear mediation models and find that under certain conditions, power gain is still possible under this incomplete mediation setting for testing the null hypothesis that there is neither a direct nor an indirect effect. We study a class of procedures that can achieve this performance and develop their application to both low- and high-dimensional mediators. We then illustrate their performances in simulations as well as in an analysis using DNA methylation mediators to study the effect of cigarette smoking on gene expression.