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.     

Morphological,Functional, and Tissue Characterization of Silent Myocardial Involvement in Patients With Primary Biliary Cholangitis

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Discordant Electrocardiogram Left Ventricular Wall Thickness and Strain Findings in Influenza Myocarditis

A 42‐year‐old man presented with a viral prodrome and tested positive for influenza A. He rapidly deteriorated developing cardiogenic shock, rhabdomyolysis, and acute kidney injury. Patient improved 1 week later with supportive measures including vasopressors, inotropes, and an intraaortic balloon pump. We report this case as it highlights the discordance between echocardiographic ventricular wall thickening as a result of myocardial edema, and electrocardiographic findings at presentation, with a reversal in findings at time of resolution. Additionally, there was some suggestion of a regional pattern to the reduced longitudinal strain.     

母亲龋易感性与婴儿口腔微生物多样性关系的纵向探究

目的通过检查有龋及无龋母亲的口腔卫生状况,并通过随访收集其婴儿1个月(1月龄)及6个月(6月龄)的唾液样本测序分析,观察母亲患龋情况对其婴儿口腔微生物多样性的影响。方法通过筛查收集1月龄婴儿的唾液样本;于首次采样时记录母亲的口腔卫生状况,根据母亲患龋情况将婴儿分为母亲有龋组(简称有龋组)和母亲无龋组(简称无龋组),跟踪随访至婴儿6个月时再次收集唾液样本。通过高通量测序的方法,分析婴儿不同月龄微生物多样性的变化。结果本研究随访受试者10例(男6例,女4例),其中有龋组7例,无龋组3例,各组间的微生物群落多样性Shannon指数均无显著性差异(P>0.05)。无龋组婴儿1月龄至6月龄时微生物群落的物种组成有较大变化;有龋组婴儿在1月龄和6月龄时组内各样本间物种组成均差异较大;1月龄时两组微生物群落较相似,而至6月龄时两组婴儿唾液的物种组成已开始发生变化。结论有龋组婴儿口腔内菌群多样性总体高于无龋组,在1月龄至6月龄间婴儿口腔内微生物物种的多样性及丰度均有了不同程度的提高。     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Tissue tracking of segmental strain as a predictor of provoked dynamic left ventricular outflow tract obstruction

BackgroundLeft ventricular outflow tract obstruction (LVOTO) is commonly observed in patients with hypertrophic cardiomyopathy (HCM) or left ventricular hypertrophy (LVH). Some patients develop LVOTO provoked by physical exertion, and hence termed dynamic LVOTO (DLVOTO). However, its precise prevalence and mechanism are still unclear.AimTwo-dimensional speckle tracking echocardiography (2D STE) seems to be helpful for the detection of early LV structural abnormalities. This study aimed to examine the possible role of segmental as well as global longitudinal strain in identifying DLVOTO non-HCM patients as detected by dobutamine stress echocardiography (DSE).Methods and resultsTwo hundred and fifty patients without structural heart disease had undergone conventional transthoracic echocardiography, 2D STE, and DSE. All patients with non-ischemic evidence were divided into two groups according to the DSE results; DLVOTO (+) and DLVOTO (?).Among 250 patients, 50 patients (36%) had shown DLVOTO after DSE (15 males, 35 females; mean age 55±7years). They were compared with 90 non -LVOTO obstruction patients (43 males, 47 females; mean age 57±6years). Based on multivariate logistic regression analysis, the independent predictors of provoked DLVOTO during DSE were resting basal septal longitudinal strain BS-LS _average (p < 0.001), resting LA reservoir strain (p < 0.001), and systolic LVOT diameter (p = 0.03). Resting BS-LS _average with cut-off - 17.5% was recognized as a critical indicator of DLVOTO, with sensitivity 78%, and specificity 95% (better than systolic LVOT diameter of sensitivity 76%, and specificity 15% and resting LA reservoir strain which showed poor AUC at ROC curve 0.007).ConclusionWe demonstrate that provoked LVOTO during DSE in non HCM symptomatic patients is directly correlated to resting regional LS, where the increased BS-LS of ≥ ?17.5% was a key determinant of LVOT gradient provocation. Assessment of baseline BS-LS _average might be a bedside simple tool for detection of patients with DLVOTO not able to do DSE.