2010—2016年重庆市中毒病例流行病学特征及影响因素分析

对2010年1月—2016年7月重庆市中毒救治指定医疗机构收治的2 497例中毒患者进行回顾性分析。结果显示，中毒患者中男性1 230例（49.3%）、女性1 267例（50.7%）；分布最多的3个年龄段依次是41~50岁（18.5%）、≤10岁（16.4%）和21~30岁（13.0%）；死亡16例，病死率0.6%。中毒类型构成排前三位的是细菌（33.8%）、化学品（32.2%）、植物（14.8%）。中毒预后多因素Logistic回归分析结果显示重度中毒患者结局死亡的可能性是轻度患者的1 102倍。提示重庆市应重点关注农村地区41~50岁人群的细菌、化学品、药物中毒，同时应该加强儿童中毒防控工作。     

Relationship between EMG-detected and ultrasound-detected fasciculations in amyotrophic lateral sclerosis: A prospective cohort study

ObjectivesFasciculation potentials (FP) are an important consideration in the electrophysiological diagnosis of ALS. Muscle ultrasonography (MUS) has a higher sensitivity in detecting fasciculations than electromyography (EMG), while in some cases, it is unable to detect EMG-detected fasciculations. We aimed to investigate the differences of FP between the muscles with and without MUS-detected fasciculations (MUS-fas).MethodsThirty-one consecutive patients with sporadic ALS were prospectively recruited and in those, both needle EMG and MUS were performed. Analyses of the amplitude, duration, and number of phases of EMG-detected FPs were performed for seven muscles per patient, and results were compared between the muscles with and without MUS-fas in the total cohort.ResultsThe mean amplitude and phase number of FP were significantly lower in patients with EMG-detected FP alone (0.39 ± 0.25 mV and 3.21 ± 0.88, respectively) than in those with both FP and MUS-fas (1.22 ± 0.92 mV and 3.74 ± 1.39, respectively; p < 0.0001 and p = 0.017, Welch’s t-test).ConclusionSmall FP may be undetectable with MUS. MUS cannot replace EMG in the diagnostic approach for ALS.SignificanceClinicians should use a combination of EMG and MUS for the detection and quantitative analysis of fasciculation in ALS.     

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

单孔胸腔镜下肺癌术后胸腔引流时间的影响因素分析北大核心

目的:探讨单孔胸腔镜下肺癌手术术后胸腔引流时间的影响因素。方法:本研究采用回顾性分析方法,回顾我院2018年01月至2019年12月原发性肺癌患者经单孔胸腔镜手术治疗的病例199例。按照术后胸腔引流时间分为两组,Ⅰ组(术后胸腔引流时间<5天)和Ⅱ组(术后胸腔引流时间≥5天)。对于影响术后胸腔引流时间的可能因素在两组间先采用单因素分析的方法筛选,再将筛选出来的对术后胸腔引流时间可能有意义的影响因素进行二项Logistic多因素回归分析。结果:经单因素分析及二项Logistic多因素回归分析结果显示:年龄≥60岁、手术部位、肺段切除术、胸膜粘连、手术时间≥180 min、术后早期下床活动是术后胸腔引流时间的独立影响因素(P<0.05)。结论:对于具有多个延长术后胸腔引流时间的独立影响因素的患者,应制定个体化管理方案,尽可能减少术后胸腔引流时间,减少住院天数,加快患者康复。