项目管理在降低呼吸重症监护室环境物品表面多重耐药菌检出率中的应用

目的:探讨项目管理在降低呼吸重症监护室(RICU)环境物品表面多重耐药菌检出率中的应用效果。方法:采用目的抽样方法,选取医院呼吸与危重症医学科RICU 17张编制床位、32处高频接触环境物品表面及42名工作人员为观察对象。2019年1月—2019年3月实施RICU常规管理,2019年4月—2019年6月实施项目管理。比较项目管理前后RICU各类别工作人员手卫生依从率、RICU环境物品表面清洁合格率及多重耐药菌检出率。结果:在项目管理后RICU各类别工作人员手卫生依从率均高于项目管理前(P<0.05);项目管理后RICU环境物品表面清洁合格率较项目管理前升高(P<0.05);项目管理后多重耐药菌(MDRO)病人床单位及病区办公区域环境物体表面的耐甲氧西林金黄色葡萄球菌(MRSA)和耐碳青霉烯鲍曼不动杆菌(CRAB)检出率均较项目管理前降低(P<0.05)。结论:项目管理可有效提高RICU各类别工作人员手卫生依从性及环境物品表面清洁合格率,降低RICU环境物品表面多重耐药菌检出率。     

Lipase elevation on admission predicts worse clinical outcomes in patients with COVID-19

Background and objectivesHyperlipasemia is highly prevalent among coronavirus disease 2019 (COVID-19) patients. The aim of this study was to assess the effect of lipase activity, measured at the time of admission, on the clinical course and mortality in COVID-19 patients.MethodsThe population of this study comprised 12,139 patients who were hospitalized due to COVID-19 between June 2020 and June 2021 in a pandemic hospital. Of these, 8819 patients were excluded from the study due to missing data, four patients were excluded due to a diagnosis of acute pancreatitis (according to the revised Atlanta criteria), and 72 patients were excluded due to alcohol use or having a history of chronic pancreatitis. The final study sample consisted of the remaining 3244 COVID-19 patients. Laboratory results, intensive care unit (ICU) follow-up periods, the need for mechanical ventilation, and mortality rates were compared between the normal lipase activity and high lipase activity groups.ResultsThere were 968 (29.8%) patients with high lipase activity at the time of admission. The rate of ICU admission was 36.1% vs. 9.9% (p < 0.001), mechanical ventilation requirement rates were 33.7% vs. 8.3% (p < 0.001), and mortality rates were as 24.6% vs. 6.4% (p < 0.001) in the high lipase activity group compared to the normal lipase activity group. Multivariate regression analysis revealed that high lipase activity was an independent factor in predicting mortality in hospitalized COVID-19 patients (odds ratio [OR]: 3.191, p < 0.001).ConclusionElevated lipase activity without acute pancreatitis at the time of admission in COVID-19 patients was determined as an independent predictor of poor prognosis.     

重组人表皮生长因子凝胶联合CO2点阵激光治疗增生性瘢痕的效果观察

目的观察重组人表皮生长因子凝胶联合CO₂点阵激光治疗烧伤及创伤后增生性瘢痕的临床效果。 方法选择2020年12月至2021年10月安徽医科大学第一附属医院烧伤与创面修复外科收治的20例增生性瘢痕患者，按照随机数字表法将患者分为联合治疗组及单纯激光治疗组，每组10例。术前均对2组患者术区进行拍照存像、消毒、擦干，局部均匀涂抹5%复方利多卡因乳膏后以无菌薄膜封闭保护60 min，擦去乳膏后再次消毒术区。术中使用CO₂点阵激光治疗仪对患者瘢痕部位进行扫描，根据患者增生性瘢痕厚度选定合适的机器参数，维持机器频率300 Hz及密度5%，在20～30 J调整能量参数的大小，时间间隔1 s，保证点阵矩形完全覆盖增生瘢痕，允许2次激光形成的热损伤矩阵之间存在25%面积的重叠。术后即刻冷敷创面60 min，冷敷结束至术后1周，单纯激光治疗组不外用任何药物，联合治疗组术区加用重组人表皮生长因子凝胶外用，3次/d。术后3 d治疗部位不碰水，若出现感染情况，可以加用抗生素软膏，点状损伤创面外痂皮未掉落完全时禁止擦洗创面，禁止直接剥除痂皮，禁止术区涂抹化妆品等，避免接受阳光直射。于第1次激光治疗后2个月进行第2次激光治疗，以2次CO₂点阵激光治疗为1个疗程，1个疗程治疗后间隔2个月待术区稳定后行疗效判断，计算2组患者治疗的总有效率；根据温哥华瘢痕量表(VSS)与患者与观察者瘢痕评估量表(POSAS)对患者治疗前、第1次激光治疗后2个月、第2次激光治疗后2个月瘢痕的各项指标进行评分。数据比较采用重复测量方差分析和χ²检验。 结果(1)治疗1个疗程后2个月，单纯激光治疗组与联合治疗组患者治疗的总有效率分别为80%(8/10)、90%(9/10)，2组间比较差异无统计学意义(χ²＝2.40，P＝0.49)。(2)对2组VSS评分的各项指标进行比较，其中疼痛、瘙痒、柔软度及厚度评分的组内及组间各时相点比较，差异均无统计学意义(P>0.05)。治疗前、第1次激光治疗后2个月及第2次激光治疗后2个月，单纯激光治疗组VSS评分中色泽评分分别为(1.9±0.7)、(1.9±0.1)、(1.8±1.0)分，联合治疗组分别为(2.9±0.7)、(2.8±0.6)、(1.9±0.7)分，2组在不同时相点组内比较，差异有统计学意义(F＝ 26.143，P<0.05)；2组组间不同时相点比较，差异有统计学意义(F＝6.753，P＝0.018)。治疗前、第1次激光治疗后2个月及第2次激光治疗后2个月，单纯激光治疗组VSS评分中血管分布评分分别为(1.8±1.0)、(1.7±0.7)、(1.5±1.0)分，联合治疗组分别为(2.6±0.5)、(2.5±0.5)、(1.7±0.8)分，2组在不同时相点组内比较，差异有统计学意义(F＝17.603，P<0.05)，2组组间不同时相点比较，差异无统计学意义(F＝ 2.538，P＝0.129)。(3)对2组POSAS评分的各项指标进行比较，其中厚度、粗糙度、柔软度和表面积评分的组内及组间各时相点比较，差异均无统计学意义(P>0.05)。治疗前、第1次激光治疗后2个月及第2次激光治疗后2个月，单纯激光治疗组POSAS评分中色泽评分分别为(4.9±2.6)、(4.1±0.8)、(3.5±2.0)分，联合治疗组分别为(7.6±1.1)、(6.8±1.4)、(5.4±1.8)分，2组在不同时相点组内比较，差异有统计学意义(F＝ 26.509，P<0.05)，2组组间不同时相点比较，差异有统计学意义(F＝ 8.973，P＝0.008)。治疗前、第1次激光治疗后2个月及第2次激光治疗后2个月，单纯激光治疗组POSAS评分中血管分布评分分别为(4.4±2.1)、(3.9±0.9)、(3.5±1.6)分，联合治疗组分别为(6.3±1.1)、(5.7±2.0)、(4.5±1.6)分，2组在不同时相点组内比较，差异有统计学意义(F＝ 20.118，P<0.05)，2组组间不同时相点比较，差异有统计学意义(F＝ 5.744，P＝0.028)。 结论重组人表皮生长因子凝胶联合CO₂点阵激光及单独使用CO₂点阵激光治疗增生性瘢痕均效果明确，但联合使用重组人表皮生长因子凝胶能有效促进创面愈合、改善瘢痕组织血管分布和减少局部色素沉着。     

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.