William Barcella

Prognostic Value of Late Gadolinium Enhancement Cardiovascular Magnetic Resonance in Cardiac Amyloidosis

Background— The prognosis and treatment of the 2 main types of cardiac amyloidosis, immunoglobulin light chain (AL) and transthyretin (ATTR) amyloidosis, are substantially influenced by cardiac involvement. Cardiovascular magnetic resonance with late gadolinium enhancement (LGE) is a reference standard for the diagnosis of cardiac amyloidosis, but its potential for stratifying risk is unknown. Methods and Results— Two hundred fifty prospectively recruited subjects, 122 patients with ATTR amyloid, 9 asymptomatic mutation carriers, and 119 patients with AL amyloidosis, underwent LGE cardiovascular magnetic resonance. Subjects were followed up for a mean of 24±13 months. LGE was performed with phase-sensitive inversion recovery (PSIR) and without (magnitude only). These were compared with extracellular volume measured with T1 mapping. PSIR was superior to magnitude-only inversion recovery LGE because PSIR always nulled the tissue (blood or myocardium) with the longest T1 (least gadolinium). LGE was classified into 3 patterns: none, subendocardial, and transmural, which were associated with increasing amyloid burden as defined by extracellular volume (P<0.0001), with transitions from none to subendocardial LGE at an extracellular volume of 0.40 to 0.43 (AL) and 0.39 to 0.40 (ATTR) and to transmural at 0.48 to 0.55 (AL) and 0.47 to 0.59 (ATTR). Sixty-seven patients (27%) died. Transmural LGE predicted death (hazard ratio, 5.4; 95% confidence interval, 2.1–13.7; P<0.0001) and remained independent after adjustment for N-terminal pro-brain natriuretic peptide, ejection fraction, stroke volume index, E/E′, and left ventricular mass index (hazard ratio, 4.1; 95% confidence interval, 1.3–13.1; P<0.05). Conclusions— There is a continuum of cardiac involvement in systemic AL and ATTR amyloidosis. Transmural LGE is determined reliably by PSIR and represents advanced cardiac amyloidosis. The PSIR technique provides incremental information on outcome even after adjustment for known prognostic factors.

Variable selection in covariate dependent random partition models: an application to urinary tract infection

Lower urinary tract symptoms can indicate the presence of urinary tract infection (UTI), a condition that if it becomes chronic requires expensive and time consuming care as well as leading to reduced quality of life. Detecting the presence and gravity of an infection from the earliest symptoms is then highly valuable. Typically, white blood cell (WBC) count measured in a sample of urine is used to assess UTI. We consider clinical data from 1341 patients in their first visit in which UTI (i.e. WBC ≥ 1) is diagnosed. In addition, for each patient, a clinical profile of 34 symptoms was recorded. In this paper, we propose a Bayesian nonparametric regression model based on the Dirichlet process prior aimed at providing the clinicians with a meaningful clustering of the patients based on both the WBC (response variable) and possible patterns within the symptoms profiles (covariates). This is achieved by assuming a probability model for the symptoms as well as for the response variable. To identify the symptoms most associated to UTI, we specify a spike and slab base measure for the regression coefficients: this induces dependence of symptoms selection on cluster assignment. Posterior inference is performed through Markov Chain Monte Carlo methods.

Subpopulation Treatment Effect Pattern Plot (STEPP) analysis for continuous, binary, and count outcomes

Background: For the past few decades, randomized clinical trials have provided evidence for effective treatments by comparing several competing therapies. Their successes have led to numerous new therapies to combat many diseases. However, since their conclusions are based on the entire cohort in the trial, the treatment recommendation is for everyone, and may not be the best option for an individual. Medical research is now focusing more on providing personalized care for patients, which requires investigating how patient characteristics, including novel biomarkers, modify the effect of current treatment modalities. This is known as heterogeneity of treatment effects. A better understanding of the interaction between treatment and patient-specific prognostic factors will enable practitioners to expand the availability of tailored therapies, with the ultimate goal of improving patient outcomes. The Subpopulation Treatment Effect Pattern Plot (STEPP) approach was developed to allow researchers to investigate the heterogeneity of treatment effects on survival outcomes across values of a (continuously measured) covariate, such as a biomarker measurement. Methods: Here, we extend the Subpopulation Treatment Effect Pattern Plot approach to continuous, binary, and count outcomes, which can be easily modeled using generalized linear models. With this extension of Subpopulation Treatment Effect Pattern Plot, these additional types of treatment effects within subpopulations defined with respect to a covariate of interest can be estimated, and the statistical significance of any observed heterogeneity of treatment effect can be assessed using permutation tests. The desirable feature that commonly used models are applied to well-defined patient subgroups to estimate treatment effects is retained in this extension. Results: We describe a simulation study to confirm that the proper Type I error rate is maintained when there is no treatment heterogeneity, and a power study to show that the statistics have power to detect treatment heterogeneity under alternative scenarios. As an illustration, we apply the methods to data from the Aspirin/Folate Polyp Prevention Study, a clinical trial evaluating the effect of oral aspirin, folic acid, or both as a chemoprevention agent against colorectal adenomas. The pre-existing R software package stepp has been extended to handle continuous, binary, and count data using Gaussian, Bernoulli, and Poisson models, and it is available on the Comprehensive R Archive Network. Conclusion: The extension of the method and the availability of new software now permit STEPP to be applied to the full range of clinical trial end points.

Response to Letters Regarding Article, "Prognostic Value of Late Gadolinium Enhancement Cardiovascular Magnetic Resonance in Cardiac Amyloidosis"

We thank Aquaro, Cohen, and colleagues for their interest in our article.1 Cardiac involvement is a chief driver of prognosis in systemic amyloidosis, and the stratification of patients is essential for prognosis and choosing management strategies. Cardiovascular magnetic resonance with late gadolinium enhancement has good diagnostic accuracy for cardiac amyloidosis, but its prognostic impact was uncertain.2–6 This study confirms incremental prognostic information after adjusting for known prognosis factors. We note the comments of Aquaro and colleagues regarding cardiac biopsy, but point out that microscopic histological analyses of these tiny samples is not only open to sampling error, but, crucially, the presence of amyloid in heart muscle is not actually proof of cardiac amyloidosis. It is essential to make the distinction between the presence of amyloid deposits and the clinical syndromes of amyloidosis. Amyloid deposits occur widely throughout the tissues in patients with systemic amyloidosis, often without any clinical consequences, providing the basis for rectal, salivary gland, skin, and fat biopsies to support diagnosis. It is likely …

Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia

We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete distributions. This process is defined by including a suitable Beta regression framework within a generalized Dirichlet process to induce dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations. Some advantages of the proposed approach include wide applicability, ease of interpretation, and availability of efficient MCMC algorithms. The motivation for this work is the study of the impact of asparginage metabolism on lipid levels in a group of pediatric patients treated for acute lymphoblastic leukemia.

A Bayesian nonparametric model for white blood cells in patients with lower urinary tract symptoms

Lower Urinary Tract Symptoms (LUTS) affect a significant proportion of the population and often lead to a reduced quality of life. LUTS overlap across a wide variety of diseases, which makes the diagnostic process extremely complicated. In this work we focus on the relation between LUTS and Urinary Tract Infection (UTI). The latter is detected through the number of White Blood Cells (WBC) in a sample of urine: WBC≥ 1 indicates UTI and high levels may indicate complications. The objective of this work is to provide the clinicians with a tool for supporting the diagnostic process, deepening the available knowledge about LUTS and UTI. We analyze data recording both LUTS profile and WBC count for each patient. We propose to model the WBC using a random partition model in which we specify a prior distribution over the partition of the patients which includes the clustering information contained in the LUTS profile. Then, within each cluster, the WBC counts are assumed to be generated by a zero-inflated Poisson distribution. The results of the predictive distribution allows to identify the symptoms configuration most associated with the presence of UTI as well as with severe infections.