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简介：AbstractBackground:Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology.Methods:A retrospective study of patients’ data was conducted using the Emergency Rescue Database of Chinese People’s Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost, and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity, and the areas under receiver operator characteristic curves (ROC-AUC).Results:The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the logistic regression were cardiac troponin T (CTnT) (coefficient = 0.346, OR = 1.413), temperature (T) (coefficient = 0.235, OR = 1.265), respiratory rate (RR) (coefficient= –0.206, OR = 0.814), serum kalium (K) (coefficient = 0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient = –0.101, OR = 0.904), and albumin (ALB) (coefficient = –0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heart rate, and systolic blood pressure.Conclusions:The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB, and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

简介：摘要1例8岁11月龄男性患儿，智力低下，语言落后，1岁9月龄癫痫首次发作，4岁诊断为儿童孤独症，体格检查有特殊面容，视频脑电图示广泛性棘慢波发放。染色体微阵列检测到8q22.2q22.3区域约2.34 Mb片段微缺失，8q22.2q22.3微缺失的病例非常罕见。

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简介：摘要目的对1例先天性心脏病合并腭裂等畸形的新生儿进行遗传学分析。方法应用常规G带对患儿及其父母外周血染色体进行核型分析，用低深度全基因组高通量测序技术(low-coverage massively parallel copy number variation sequencing, CNV-seq)对患儿及其父母进行拷贝数变异(copy number variants, CNVs)分析。结果患儿核型为46, X, add (Y) (q11.23)，Y染色体长臂存在不明来源的染色体片段，CNV-seq结果为seq[hg19]22q12.1q13.3(29 520 001～51 180 000)×3。父母核型及CNV-seq结果均正常。结论患儿Y染色体上的不明来源染色体片段为22q12.1-q13.3重复区域，属新发变异，患儿异常表型为其所导致。CNV-Seq与核型分析技术相互补充，明确诊断，为遗传咨询提供精准指导。

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