临床路径下病例组合方法研究

目的：探讨临床路径下病例组合方法和人工神经网络在病例组合中的应用。方法：利用某综合性医院的一个临床路径流程（腰椎间盘突出行椎板切除术或髓核摘除术）下的523份出院病历资料，采用K-MEANS聚类方法进行组合，用神经网络对预测病例的病例组合进行判断。结果：523份病历聚为4组，各组间费用95％可信区间互不重合；神经网络的训练误差为0．0029，病例组合预测和判断符合率为98．91％。结论：以临床路径下产生的病例为单元样本进行病例组合，结果更科学、客观。神经网络用于病例组合判断，不用确定单个节点变量的分割值，更符合病例组合由多变量共同作用的实情。     

Fatty Liver Disease Index: A Simple Screening Tool to Facilitate Diagnosis of Nonalcoholic Fatty Liver Disease in the Chinese Population

Background and Objectives

A simple screening tool is needed as an aid to optimize management of nonalcoholic fatty liver disease (NAFLD). The purpose of this study was to design a simple index that could be used to predict the presence of NAFLD.

Methods

A cross-sectional case–control study design was used for this study with 6,926 subjects who had received health assessments.

Results

Multivariate analysis indicated that body mass index (BMI), triglyceride (TG), alanine aminotransferase (ALT) to aspartate aminotransferase (AST) ratio (ALT/AST ratio), and hyperglycemia (HG) were independent risk factors for NAFLD. These variables were used to construct a fatty liver disease (FLD) index: FLD index = BMI + TG + 3 × (ALT/AST ratio) + 2 × HG (presence of HG, HG = 1; absence of HG, HG = 0). The FLD index had an area under the receiver-operating characteristic (AUROC) curve of 0.819 in the training set and 0.817 in the validation set. Values of the FLD index <28.0 or >37.0 excluded the possibility of NAFLD with sensitivity of 94.9 % or detected NAFLD with specificity of 96.0 %, respectively.

Conclusions

The FLD index is a simple, efficient NAFLD screening tool for the Chinese population that may be used to select people for further analysis and/or treatment, and/or for lifestyle modification.     

Impacto en niños menores de un año del programa de vacunación con dTpa en embarazadas en Castelló

IntroductionThe objective was to compare incidence rates of pertussis in children under the age of one in Castelló, before and after the introduction of vaccination of pregnant women in January 2015.MethodsThe incidence of the post-vaccine period (2015-2018) was compared with the pre-vaccine period (2011-2014) in all ages, in children from 3 to 11 months and under 3 months. The relative risks and their 95% confidence intervals (95% CI) were calculated.ResultsThe overall rate of pertussis in all ages was higher in the post-vaccine period than in the pre-vaccine period (0.23 vs. 0.15 per 1.000 person-years), but decreased in those under 3 months. The relative risks were: 1.56 (95% CI 1.34-1.82) in all ages; 1.73 (95% CI 0.87-3.57) for children aged 3 to 11 months, and 0.35 (95% CI 0.16-0.69) for children under 3 months. A similar pattern was observed for hospitalised children.ConclusionsThe incidence rate in children under 3 months was reduced by 65% in the period after the intervention, and the hospitalisation risk rate by 71%, suggesting that the measure has been effective and specific for this age group.     

Artificial Intelligence–Enabled Electrocardiogram for Atrial Fibrillation Identifies Cognitive Decline Risk and Cerebral Infarcts

ObjectiveTo investigate whether artificial intelligence–enabled electrocardiogram (AI-ECG) assessment of atrial fibrillation (AF) risk predicts cognitive decline and cerebral infarcts.Patients and MethodsThis population-based study included sinus-rhythm ECG participants seen from November 29, 2004 through July 13, 2020, and a subset with brain magnetic resonance imaging (MRI) (October 10, 2011, through November 2, 2017). The AI-ECG score of AF risk calculated for participants was 0-1. To determine the AI-ECG-AF relationship with baseline cognitive dysfunction, we compared linear mixed-effects models with global and domain-specific cognitive z-scores from longitudinal neuropsychological assessments. The AI-ECG-AF score was logit transformed and modeled with cubic splines. For the brain-MRI subset, logistic regression evaluated correlation of the AI-ECG-AF score and the high-threshold, dichotomized AI-ECG-AF score with infarcts.ResultsParticipants (N=3729; median age, 74.1 years) underwent cognitive analysis. Adjusting for age, sex, education, and APOE ?4-carrier status, the AI-ECG-AF score correlated with lower baseline and faster decline in global-cognitive z-scores (P=.009 and P=.01, respectively, non–linear-based spline-models tests) and attention z-scores (P<.001 and P=.01, respectively). Sinus-rhythm-ECG participants (n=1373) underwent MRI. As a continuous measure, the AI-ECG-AF score correlated with infarcts but not after age and sex adjustment (P=.52). For dichotomized analysis, an AI-ECG-AF score greater than 0.5 correlated with infarcts (OR, 4.61; 95% CI, 2.45-8.55; P<.001); even after age and sex adjustment (OR, 2.09; 95% CI, 1.06-4.07; P=.03).ConclusionThe AI-ECG-AF score correlated with worse baseline cognition and gradual global cognition and attention decline. High AF probability by AI-ECG-AF score correlated with MRI cerebral infarcts. However, most infarcts observed in our cohort were subcortical, suggesting that AI-ECG not only predicts AF but also detects other non-AF cardiac disease markers and correlates with small vessel cerebrovascular disease and cognitive decline.