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.     

A KRT1 gene mutation related to epidermolytic ichthyosis in a Chinese family

We report a Chinese family with members affected by epidermolytic ichthyosis (EI), caused by KRT gene mutations. The proband was a 14‐year‐old boy who had simultaneous appearance of nephroblastoma and epidermolytic ichthyosis (EI). Both the patient and his mother exhibited the specific clinical and pathological manifestations of EI. We analysed all exons and flanking sequences of the KRT1 and KRT10 genes using PCR, and found that the proband and his mother had a G>C transition at nucleotide position 1432 in exon 7 of KRT1, resulting in an amino acid substitution of glutamate (GAA) to glutamine (CAA) at codon 478 (E478Q). The KRT10 gene had no mutations.     

Specific alterations in plasma proteins during depressed,manic, and euthymic states of bipolar disorder

Bipolar disorder (BD) is a common psychiatric mood disorder affecting more than 1-2% of the general population of different European countries. Unfortunately, there is no objective laboratory-based test to aid BD diagnosis or monitor its progression, and little is known about the molecular basis of BD. Here, we performed a comparative proteomic study to identify differentially expressed plasma proteins in various BD mood states (depressed BD, manic BD, and euthymic BD) relative to healthy controls. A total of 10 euthymic BD, 20 depressed BD, 15 manic BD, and 20 demographically matched healthy control subjects were recruited. Seven high-abundance proteins were immunodepleted in plasma samples from the 4 experimental groups, which were then subjected to proteome-wide expression profiling by two-dimensional electrophoresis and matrix-assisted laser desorption/ionization-time-of-flight/time-of-flight tandem mass spectrometry. Proteomic results were validated by immunoblotting and bioinformatically analyzed using MetaCore. From a total of 32 proteins identified with 1.5-fold changes in expression compared with healthy controls, 16 proteins were perturbed in BD independent of mood state, while 16 proteins were specifically associated with particular BD mood states. Two mood-independent differential proteins, apolipoprotein (Apo) A1 and Apo L1, suggest that BD pathophysiology may be associated with early perturbations in lipid metabolism. Moreover, down-regulation of one mood-dependent protein, carbonic anhydrase 1 (CA-1), suggests it may be involved in the pathophysiology of depressive episodes in BD. Thus, BD pathophysiology may be associated with early perturbations in lipid metabolism that are independent of mood state, while CA-1 may be involved in the pathophysiology of depressive episodes.     

Patients Requiring Conversion to General Anesthesia during Endovascular Therapy Have Worse Outcomes: A Post Hoc Analysis of Data from the SAGA Collaboration

BACKGROUND AND PURPOSE:Endovascular therapy for acute ischemic stroke is often performed with the patient under conscious sedation. Emergent conversion from conscious sedation to general anesthesia is sometimes necessary. The aim of this study was to assess the functional outcome in converted patients compared with patients who remained in conscious sedation and to identify predictors associated with the risk of conversion.MATERIALS AND METHODS:Data from 368 patients, included in 3 trials randomizing between conscious sedation and general anesthesia before endovascular therapy (SIESTA, ANSTROKE, and GOLIATH) constituted the study cohort. Twenty-one (11%) of 185 patients randomized to conscious sedation were emergently converted to general anesthesia.RESULTS:Absence of hyperlipidemia seemed to be the strongest predictor of conversion to general anesthesia, albeit a weak predictor (area under curve = 0.62). Sex, hypertension, diabetes, smoking status, atrial fibrillation, blood pressure, size of the infarct, and level and side of the occlusion were not significantly associated with conversion to general anesthesia. Neither age (mean age, 71.3   ± 13.8 years for conscious sedation versus 71.6  ± 12.3 years for converters, P = .58) nor severity of stroke (mean NIHSS score, 17 ± 4 versus 18 ± 4, respectively, P = .27) were significantly different between converters and those who tolerated conscious sedation. The converters had significantly worse outcome with a common odds ratio of 2.67 (P = .015) for a shift toward a higher mRS score compared with the patients remaining in the conscious sedation group.CONCLUSIONS:Patients undergoing conversion had significantly worse outcome compared with patients remaining in conscious sedation. No factor was identified that predicted conversion from conscious sedation to general anesthesia.

Five studies published in 2015 proved the efficacy of endovascular therapy (EVT) for acute ischemic stroke caused by a large-vessel occlusion.¹ However, numerous questions remain regarding how to best deliver this treatment, including evaluation of the optimal thrombectomy technique,² the most effective method of patient triage,³ or whether EVT should be performed with the patient under either general anesthesia (GA) or conscious sedation (CS).Observational studies have suggested that EVT with the patient under CS is associated with better neurologic outcome and lower mortality compared with GA.⁴ However, 3 randomized trials reported similar outcomes between CS and GA.^5-7 Proposed benefits of CS include stable hemodynamics, clinical monitoring, and a potentially shorter procedure. The disadvantages are an unprotected airway and patient movement, which sometimes may require emergent conversion to GA. Patients who need conversion might be sicker (larger strokes, more medical complications), but the conversion procedure itself may also have a potentially deleterious influence on outcome due to the emergent anesthetic induction, associated hypotension, and added time delay before reperfusion.Although most patients can be treated under the less complex CS, it is of interest to identify factors that can predict the risk of conversion and hence the requirement for GA. We undertook a detailed analysis of the patients who were converted from CS to GA in our individual patient data base from the 3 randomized trials to examine the outcome of the converted patients compared with patients who remained in CS. We also aimed to identify possible predictors associated with a need for GA with EVT.     

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。