Functional,impulse-based quantification of plantar pressure patterns in typical adult gait

BackgroundDynamic pedobarography is used to measure the change in plantar pressure distribution during gait. Clinical methods of pedobarographic analysis lack, however, a standardized, functional segmentation or require costly motion capture technology and expertise. Furthermore, while commonly used pedobarographic measures are mostly based on peak pressures, progressive foot deformities also depend on the duration the pressure is applied, which can be quantified via impulse measures.Research QuestionOur objectives were to: (1) develop a standardized method for functionally segmenting pedobarographic data during gait without the need for motion capture; (2) compute pedobarographic measures that are based on each segment’s vertical impulse; and (3) obtain a normative set of such pedobarographic measures for non-disabled gait.MethodsPedobarographic data was collected during gait from sixty adults with normal feet. Using the maximum pressure map for each trial, an expert and novice rater independently identified the hallux, heel, medial forefoot, and lateral forefoot and computed nine normalized vertical impulse measures.ResultsFrom the computed impulse measures, the Heel-to-Forefoot Balance was 33.3 ± 5.5%, the Medial-Lateral Forefoot Balance (with hallux) 59.2 ± 8.0%, the Medial-Lateral Forefoot Balance (without hallux) 53.5 ± 7.7%, and the Hallux-to-Medial Forefoot Balance 21.0 ± 8.9% (mean ± standard deviation). The intra- and inter-rater reliability ranged between 0.93 and 1.00 and between 0.89 and 0.99, respectively (ICC(2,1)).SignificanceWe developed a simple, stand-alone method for pedobarographic segmentation that is mechanistically linked to relevant anatomical regions of the foot. The normative impulse measures exhibited excellent reliability. This normative dataset is currently used in the clinical assessment of different foot deformities and gait impairments, and in the evaluation of treatment outcomes.     

Bayesian adaptive lasso for additive hazard regression with current status data

Variable selection is a crucial issue in model building and it has received considerable attention in the literature of survival analysis. However, available approaches in this direction have mainly focused on time-to-event data with right censoring. Moreover, a majority of existing variable selection procedures for survival models are developed in a frequentist framework. In this article, we consider additive hazards model in the presence of current status data. We propose a Bayesian adaptive least absolute shrinkage and selection operator procedure to conduct a simultaneous variable selection and parameter estimation. Efficient Markov chain Monte Carlo methods are developed to implement posterior sampling and inference. The empirical performance of the proposed method is demonstrated by simulation studies. An application to a study on the risk factors of heart failure disease for type 2 diabetes patients is presented.     

基于肥厚型心肌病临床数据管理系统的设计与应用

目的设计一款简单易操作的数据管理系统,解决目前零散的肥厚型心肌病临床数据,将临床信息科学有效地进行整合管理,便于医生及科研人员查询、统计。方法采用Foxtable搭建一个数据库管理软件框架,并建立局域网外部数据源,实现数据的存储、查询、共享、导出。结果基于肥厚型心肌病数据管理系统的设计与应用能够满足临床医生和科研人员的基本工作需求,很大程度上节省了数据整理时间。结论该系统操作简单、移植性高、实用性强,广泛适用于各临床科室有科研项目的小型数据库管理。     

脑卒中个案管理师准入标准和工作职责方案的构建

目的 构建脑卒中个案管理师准入标准和工作职责,为制订符合中国国情的脑卒中个案管理师人才培养方案和工作标准提供参考依据。方法 基于文献研究、脑卒中患者管理现状调查、专家会议讨论和预咨询,构建准入标准和工作职责初稿;采用德尔菲专家咨询法,对全国8个省市的20名专家进行问卷咨询。 结果 2轮专家咨询的问卷有效回收率分别是95.2%和100.0%。第2轮咨询中,专家权威系数为0.910,准入标准和工作职责的一、二、三级指标专家意见的肯德尔和谐系数分别为0.193、0.155、0.141（P<0.05）和0.445、0.156、0.134（P<0.05）。最终形成的脑卒中个案管理师准入标准包括一级指标3项（基础条件、专业能力、综合能力）,二级指标7项,三级指标17项。工作职责包括一级指标4项（评估、计划、实施、评价）,二级指标17项,三级指标79项。 结论 经专家咨询确定的脑卒中个案管理师准入标准和工作职责方案具有较好的科学性、实用性、可行性和创新性,能为脑卒中个案管理护理实践提供理论依据。     

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value <.001), respectively. Results of these different methods applied on real rs‐fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T‐SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC‐based method was unable to detect these dynamic connections.