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.     

Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).     

社区“5+1”糖尿病分阶段达标管理对2型糖尿病患者生存质量干预效果分析

目的 分析社区"5+1"糖尿病分阶段达标管理对2型糖尿病患者生存质量的干预效果及其影响因素，为提高患者生存质量提供依据。方法 采用分层整群抽样的方法在山西省、江苏省和宁夏回族自治区选择12个社区卫生服务中心，分别作为干预组（管理方式：社区"5+1"糖尿病分阶段达标管理）、对照组[管理方式：依据《国家基本公共卫生服务规范（2011年版）》的相关要求]，进行为期2年的随访观察。采用面对面问卷调查的方式，收集患者的人口学信息等基本信息；采用健康调查简表（SF-36）对患者在干预前后测量生存质量。采用SAS 9.4软件进行双重差分法以及多重线性回归模型分析。结果 基线时共纳入2 467名研究对象，终末时共1 924人接受了为期2年完整的随访管理。干预后，干预组、对照组患者生理健康维度（PCS）、心理健康维度（MCS）评分变化净差值分别为13.6分、29.8分。多重线性回归分析结果显示，影响患者PCS得分的主要因素有年龄、医保类型、基线PCS得分以及所在地区，影响患者MCS得分的主要因素有年龄、医保类型、基线MCS得分、是否合并高血压以及所在地区。结论 社区"5+1"糖尿病分阶段达标管理对2型糖尿病患者生存质量的干预效果较好。