Optimal Echocardiographic Parameters to Improve the Diagnostic Yield of Tc-99m-Bone Avid Tracer Cardiac Scintigraphy for Transthyretin Cardiac Amyloidosis.

BACKGROUND: Echocardiographic deformation-based ratios and novel multi-parametric scores have been suggested to discriminate transthyretin cardiac amyloidosis (ATTR-CM) from other causes of increased left ventricular wall thickness among patients referred for ATTR-CM evaluation. Their relative predictive accuracy has not been well studied. We sought to (1) identify echocardiographic parameters predictive of ATTR-CM and (2) compare the diagnostic accuracy of these parameters in patients with suspected ATTR-CM referred for technetium-99m-pyrophosphate scintigraphy. METHODS: Echocardiograms from 598 patients referred to 3 major amyloidosis centers for technetium-99m-pyrophosphate to detect ATTR-CM were analyzed, including longitudinal strain (LS) analysis. Deformation ratios (septal apex to base ratio, relative apical sparing, ejection fraction to global LS), a multi-center European increased wall thickness score, and Mayo Clinic derived ATTR score (transthyretin cardiac amyloidosis score) were calculated. A logistic regression model was used to identify the parameters that best associated with a diagnosis of ATTR-CM. Comparison of the diagnostic capacity of the parameters was performed by receiver operating characteristic curves and the area under the curve (AUC). RESULTS: Over half of the subjects (54.2%) were diagnosed with ATTR-CM (78% were men, median age of 76 years). Age, inferolateral wall thickness, and basal LS were the strongest predictors of ATTR-CM, AUC of 0.87 (95% CI: 0.83, 0.90), superior to the increased wall thickness score AUC of 0.78 (95% CI: 0.73, 0.83; P=0.004). An inferolateral wall thickness of ≥14 mm (AUC: 0.73) was as accurate as the published cut-offs for transthyretin cardiac amyloidosis score and septal apex to base (AUC: 0.72 and 0.69, P=0.8 and P=0.1, respectively), and was superior to ejection fraction to global LS and relative apical sparing (AUC: 0.64 and 0.53, P<0.001, respectively). A cut-off of ≥-8% for average basal LS (AUC: 0.76, CI: 0.72-0.79) had a similar area under the curve to transthyretin cardiac amyloidosis score (TCAS) (P=0.2); outperforming the other indices (P<0.01). CONCLUSION: Inferolateral wall thickness and average basal LS performed as well as or better than more complex echo ratios and multiparametric scores to predict ATTR-CM.

Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data.

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease.

Mesothelioma Risk Score: A New Prognostic Pretreatment, Clinical-Molecular Algorithm for Malignant Pleural Mesothelioma.

INTRODUCTION: Prognostic models for malignant pleural mesothelioma have been limited to demographics, symptoms, and laboratory values. We hypothesize higher accuracy using both tumor and patient characteristics. The mesothelioma prognostic test (MPT) and molecular subtype based on claudin-15-to-vimentin expression ratio are molecular signatures associated with survival. Tumor volume (TV) has improved performance compared with clinical staging, whereas neutrophil-to-lymphocyte ratio (NLR) is prognostic for malignant pleural mesothelioma. METHODS: Tumor specimens and clinical data were collected prospectively from patients who underwent extrapleural pneumonectomy (EPP) or pleurectomy and decortication (PD) during 2007 to 2014. MPT and claudin-15-to-vimentin ratio were determined by real-time quantitative polymerase chain reaction, whereas TV was assessed from preoperative scans. Risk groups were derived from combinations of adverse factors on the basis of the Cox model. Predictive accuracy was assessed using Harrell's c-index. RESULTS: MPT, molecular subtype, TV, and NLR were independently prognostic in patients with EPP (N = 191), suggesting equal weighting in a final three-group model (c = 0.644). In the PD cohort (N = 193), MPT poor risk combined with TV greater than 200 cm3 was associated with triple the risk compared with other subgroups (hazard ratio = 2.94, 95% confidence interval: 1.70-5.09, p < 0.001) persisting when adjusted for molecular subtype, NLR, performance status, and serum albumin to yield a final three-group model (c = 0.641). The EPP and PD models achieved higher accuracy than published models (c ≤ 0.584, c ≤ 0.575) and pathologic staging (c = 0.554, c = 0.571). CONCLUSIONS: The novel models use pretreatment parameters obtained from minimally invasive biopsy, imaging, and blood tests to evaluate the expected outcome of each type of surgery in newly diagnosed patients and improve stratification on clinical trials.

A Longitudinal Case-Based Global Health Curriculum for the Medical Student Clerkship Year.

Introduction: Over 20% of U.S. medical students express interest in global health (GH) and are searching for opportunities within the field. In addition, domestic practice increasingly requires an understanding of the social factors affecting patients' health. Unfortunately, only 39% of medical schools offer formal GH education, and there is a need to incorporate more GH into medical school curricula. Methods: We designed a longitudinal case-based curriculum for the core clerkships. We conducted an institution-wide survey to determine baseline GH interest and developed three case-based sessions to incorporate into medicine, surgery, and pediatrics clerkships. The cases included clinical learning while exploring fundamental GH concepts. Cases were developed with GH faculty, and the pilot was implemented from October to December 2019 with 55 students. We used pre- and postdidactic surveys to assess interest in GH and elicit qualitative feedback. A follow-up survey assessed students' identification of barriers faced by their patients domestically. Results: Students felt that clinical management, physical exam skills, epidemiology, and social determinants of health were strengths of the sessions and that they were able to apply more critical thinking skills and cultural humility to their patients afterwards. Students felt that simulation would be a great addition to the curriculum and wanted both more time per session and more sessions overall. Discussion: Integrating GH didactics into the core clerkships has potential to address gaps in GH education and to help students make connections between clinical learning and GH, enhancing their care of patients both domestically and in future GH work.

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.