A systematic review of the methodological quality of economic evaluations in genetic screening and testing for monogenic disorders

PurposeUnderstanding the value of genetic screening and testing for monogenic disorders requires high-quality, methodologically robust economic evaluations. This systematic review sought to assess the methodological quality among such studies and examined opportunities for improvement.MethodsWe searched PubMed, Cochrane, Embase, and Web of Science for economic evaluations of genetic screening/testing (2013-2019). Methodological rigor and adherence to best practices were systematically assessed using the British Medical Journal checklist.ResultsAcross the 47 identified studies, there were substantial variations in modeling approaches, reporting detail, and sophistication. Models ranged from simple decision trees to individual-level microsimulations that compared between 2 and >20 alternative interventions. Many studies failed to report sufficient detail to enable replication or did not justify modeling assumptions, especially for costing methods and utility values. Meta-analyses, systematic reviews, or calibration were rarely used to derive parameter estimates. Nearly all studies conducted some sensitivity analysis, and more sophisticated studies implemented probabilistic sensitivity/uncertainty analysis, threshold analysis, and value of information analysis.ConclusionWe describe a heterogeneous body of work and present recommendations and exemplar studies across the methodological domains of (1) perspective, scope, and parameter selection; (2) use of uncertainty/sensitivity analyses; and (3) reporting transparency for improvement in the economic evaluation of genetic screening/testing.     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Prolyl and lysyl hydroxylases in collagen synthesis

Collagens are the most abundant proteins in the extracellular matrix. They provide a framework to build organs and tissues and give structural support to make them resistant to mechanical load and forces. Several intra‐ and extracellular modifications are needed to make functional collagen molecules, intracellular post‐translational modifications of proline and lysine residues having key roles in this. In this article, we provide a review on the enzymes responsible for the proline and lysine modifications, that is collagen prolyl 4‐hydroxylases, 3‐hydroxylases and lysyl hydroxylases, and discuss their biological functions and involvement in diseases.     

Patient navigation across the cancer care continuum: An overview of systematic reviews and emerging literature

Patient navigation is a strategy for overcoming barriers to reduce disparities and to improve access and outcomes. The aim of this umbrella review was to identify, critically appraise, synthesize, and present the best available evidence to inform policy and planning regarding patient navigation across the cancer continuum. Systematic reviews examining navigation in cancer care were identified in the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Embase, Cumulative Index of Nursing and Allied Health (CINAHL), Epistemonikos, and Prospective Register of Systematic Reviews (PROSPERO) databases and in the gray literature from January 1, 2012, to April 19, 2022. Data were screened, extracted, and appraised independently by two authors. The JBI Critical Appraisal Checklist for Systematic Review and Research Syntheses was used for quality appraisal. Emerging literature up to May 25, 2022, was also explored to capture primary research published beyond the coverage of included systematic reviews. Of the 2062 unique records identified, 61 systematic reviews were included. Fifty-four reviews were quantitative or mixed-methods reviews, reporting on the effectiveness of cancer patient navigation, including 12 reviews reporting costs or cost-effectiveness outcomes. Seven qualitative reviews explored navigation needs, barriers, and experiences. In addition, 53 primary studies published since 2021 were included. Patient navigation is effective in improving participation in cancer screening and reducing the time from screening to diagnosis and from diagnosis to treatment initiation. Emerging evidence suggests that patient navigation improves quality of life and patient satisfaction with care in the survivorship phase and reduces hospital readmission in the active treatment and survivorship care phases. Palliative care data were extremely limited. Economic evaluations from the United States suggest the potential cost-effectiveness of navigation in screening programs.     

Cribado de cáncer de pulmón: Supervivencia en un amplio programa de detección precoz en España (I-ELCAP)

IntroductionLung cancer (LC) is usually diagnosed at advanced stages with only a 12% 5-year survival. Trials as NLST and NELSON show a mortality decrease, which justifies implementation of lung cancer screening in risk population. Our objective was to show survival results of the largest LC screening program in Spain with low dosage computed tomography (LDCT).MethodsClinical records from International Early Lung Cancer Detection Program (IELCAP) at Valencia, Spain were analysed. This program recruited volunteers, ever-smokers aged 40-80 years, since 2008. Results are compared to those from other similar sizeable programs.ResultsA total of 8278 participants were screened with at least two-rounds until November 2020. A mean of 6 annual screening rounds were performed. We detected 239 tumours along 12-year follow-up. Adenocarcinoma was the most common histology, being 61.3% at stage I. The lung cancer prevalence and incidence proportion was 1.5% and 1.4%, respectively with an annual detection rate of 0.17. One-year survival and 10-year survival were 90% and 80.1%, respectively. Adherence was 96.84%.ConclusionLargest lung cancer screening in Spain shows that survival is improved when is performed in multidisciplinary team experienced in management of LC, and is comparable to similar screening programs.     

Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

ObjectivesSurvival extrapolation for chimeric antigen receptor T-cell therapies is challenging, owing to their unique mechanistic properties that translate to complex hazard functions. Axicabtagene ciloleucel is indicated for the treatment of relapse or refractory diffuse large B-cell lymphoma after 2 or more lines of therapy based on the ZUMA-1 trial. Four data snapshots are available, with minimum follow-up of 12, 24, 36, and 48 months. This analysis explores how survival extrapolations for axicabtagene ciloleucel using ZUMA-1 data can be validated and compared.MethodsThree different parametric modeling approaches were applied: standard parametric, spline-based, and cure-based models. Models were compared using a range of metrics, across the 4 data snapshot, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates.ResultsStandard and spline-based parametric extrapolations were generally incapable of fitting the ZUMA-1 data well. Cure-based models provided the best fit based on the earliest data snapshot, with extrapolations remaining consistent as data matured. At 48 months, the maximum survival overestimate was 8.3% (Gompertz mixture-cure model) versus the maximum underestimate of 33.5% (Weibull standard parametric model).ConclusionsWhere a plateau in the survival curve is clinically plausible, cure-based models may be helpful in making accurate predictions based on immature data. The ability to reliably extrapolate from maturing data may reduce delays in patient access to potentially lifesaving treatments. Additional research is required to understand how models compare in broader contexts, including different treatments and therapeutic areas.     

Improving State-Mandated Breast Density Notifications

PurposeEffective written communication directly affects health care outcomes. Since 2016, the complex language of state-mandated breast density notifications (BDNs) has been challenged, because it is perceived to be beyond the comprehension of most patients. The aim of this study was to assess whether a revised BDN written at a lower reading grade level improves understanding compared with the current state-mandated BDN.MethodsA revised notification with similar content to the current state-mandated BDN was developed. Both notifications were presented to patients for direct comparison, using a paper survey asking questions that evaluated patients’ perceptions and convictions associated with breast density. Surveys were distributed at four outpatient imaging centers to screening mammography patients.ResultsThe current BDN’s mean readability metric was 13.4, and that of the revised BDN was 6.6. Five hundred surveys were analyzed. Survey data demonstrated that 56.6% of all women perceived that dense breast tissue results indicated a “high” associated lifetime breast cancer risk from the current state-mandated BDN compared with only 2.2% with the revised notification (P < .001). Nearly all women were more likely to initiate discussions with their providers regarding their breast tissue density after reading the revised notification (96.0%) as opposed to the current state-mandated BDN (32.8%; P < .001).ConclusionsA significant portion of women misinterpret the intended messages of the current state-mandated BDN. Thus, a revised notification at a lower reading grade level may improve understanding of breast density, leading to improved individualized breast cancer screening for women with dense breasts.