目的:探讨结构化皮肤护理方案预防经口气管插管口唇部压力性损伤的效果.方法:选择2020年7月至2021年6月在南京大学医学院附属鼓楼医院重症监护病房(ICU)经口气管插管治疗的200例患者作为研究对象,100例接受常规护理作为对照组,100例接受结构化皮肤护理作为观察组,比较两组患者舒适度与口唇部压力性损伤发生率的差异.结果:观察组舒适度优于对照组(P<0.05);口唇部压力性损伤发生率低于对照组(P<0.05).结论:结构化皮肤护理可以提高气管插管患者舒适度,降低经口气管插管患者口唇部压力性损伤发生率.

目的 探讨ICU患者接受连续性肾脏替代治疗(CRRT)开始1 h内低血压发生率的相关影响因素.方法 回顾性收集2019年1-12月我院重症医学科收治的CRRT治疗患者,根据CRRT治疗开始1 h内是否发生低血压,将患者分为低血压组和非低血压组,比较两组患者的一般资料和CRRT相关资料,采用Logistic回归分析CRRT治疗开始1 h内低血压发生的相关危险因素.结果 共226例重症患者行837例次CRRT治疗,CRRT治疗开始1 h内发生低血压患者共117例(51.8％),低血压患者比非低血压患者的序贯器官衰竭估计(SOFA)评分和死亡率更高,两者间有显著性差异(P＜0.05).837例次CRRT治疗中,CRRT低血压组共211例次(25.2％),低血压组单针引血方式、超滤量1～100 mL/h和101～200 mL/h、CRRT治疗开始前使用缩血管药物的比例均显著高于CRRT非低血压组(P＜0.05).Logistic回归分析结果显示重症患者CRRT治疗开始1 h内低血压发生的影响因素是超滤速度和CRRT治疗开始前使用缩血管药物.结论 ICU患者CRRT治疗开始1 h内低血压的发生率较高,低血压发生率受CRRT治疗超滤速度和治疗前使用缩血管药物的影响.

目的 了解ICU患者连续性肾脏替代治疗24 h内低体温发生率及体温变化趋势,为优化连续性肾脏替代治疗体外加温方案提供参考.方法 通过医院电子病历系统、重症监护护理系统、连续性肾脏替代治疗护理记录单回顾性收集2019年行连续性肾脏替代治疗的ICU患者一般资料,连续性肾脏替代治疗相关资料,连续性肾脏替代治疗启动后0～h、4～h、8～h、12～24 h最低体温.结果 共纳入213例ICU患者的784例次数据.84例患者(39.4％)发生低体温(核心体温＜36℃);低体温患者的APACHEⅡ评分和序贯器官衰竭估计评分、病死率显著高于非低体温患者(均P＜0.01).122例次(15.6％)连续性肾脏替代治疗运行过程出现低体温,其中运行4 h内体温下降显著(P＜0.05),随后20 h内体温无明显回升;低体温组机械通气率更高,治疗前体温更低(均P＜0.01).结论 ICU患者连续性肾脏替代治疗低体温发生率较高,且24 h内体温复温效果不理想,常规的保温/复温方案有待进一步优化.

目的 总结成人危重低体温患者复温管理的相关证据,为临床实践提供指导.方法 根据循证护理方法确立循证问题,根据证据的"6S"模型,从"证据金字塔"上层开始检索国内外有关成人危重患者低体温复温管理的相关证据,证据资源类型包括临床决策、推荐实践、证据总结、指南、专家共识.由2名研究人员独立进行文献质量评价,并对符合质量标准的文献进行证据内容提取.结果 共纳入8篇文献,包括2篇临床决策,1篇推荐意见,3篇证据总结,2篇专家共识.共提取出涉及体温监测(3条证据)、复温目标(2条证据)、复温措施选择(5条证据)、复温风险管理(7条证据)、复温并发症监测(5条证据)的22条证据,证据等级1～5级.结论 本研究从5个维度汇总了成人危重患者低体温复温管理证据,为临床实践提供了理论指导.但本研究汇总的证据来源多为国外研究,建议研究者在使用本次汇总的证据时,要充分评估每条证据在临床的可行性、适宜性、临床意义和有效性,并评估证据在临床应用的障碍与促进因素,以确保证据在临床的顺利应用.

Cardiovascular (CV) events are the major cause of morbidity and mortality associated with blood pressure (BP) in hemodialysis (HD) patients. BP varies significantly during HD treatment, and the dramatic variation in BP is a well-recognized risk factor for increased mortality. It is important to develop an intellectual system capable of predicting BP profiles for real-time monitory. Our aim was to build a web-based system to predict the systolic blood pressure (SBP) change during hemodialysis process. This study was based on a large stream of HD parameters collected from a dialysis equipment connected to the Vital Info Portal gateway and linked with the demographic data stored in the hospital information system. The data set was divided into three groups - training, test and new patients. The training group was useful to build a multiple linear regression model, in which the SBP change was the dependent variable and the dialysis parameters and demographic data were the independent variables. We used the test and new patient groups to evaluate the model performance using coverage rates in different thresholds. A web-based interactive system based on the model was built for visualizing the prediction performance. A total of 542,424 BP records were used in the model building. The accuracy was greater than 80% in the prediction error range of 15%, and 20mmHg of true SBP in the test and new patient groups for the SBP change model suggested a good performance of our prediction model. In the case of absolute SBP values (5, 10, 15, 20 and 25 mmHg), the accuracy of SBP prediction increased as the threshold value augmented. This database supported the application of our prediction model in reducing the frequency of intradialytic SBP variability, and therefore, it could aid in the clinical decision when a new patient undertakes HD treatment. Whether the introduction of SBP prediction intelligent system can lower CV events in HD patients, it needs further investigations.

Background: Intradialytic hypotension (IDH) is a serious complication and a major risk factor of increased mortality during hemodialysis (HD). However, predicting the occurrence of intradialytic blood pressure (BP) fluctuations clinically is difficult. This study aimed to develop an intelligent system with capability of predicting IDH. Methods: In developing and training the prediction models in the intelligent system, we used a database of 653 HD outpatients who underwent 55,516 HD treatment sessions, resulting in 285,705 valid BP records. We built models to predict IDH at the next BP check by applying time-dependent logistic regression analyses. Results: Our results showed the sensitivity of 86% and specificity of 81% for both nadir systolic BP (SBP) of <90 mmHg and <100 mmHg, suggesting good performance of our prediction models. We obtained similar results in validating via test data and data of newly enrolled patients (new-patient data), which is important for simulating prospective situations wherein dialysis staff are unfamiliar with new patients. This compensates for the retrospective nature of the BP records used in our study. Conclusion: The use of this validated intelligent system can identify patients who are at risk of IDH in advance, which may facilitate well-timed personalized management and intervention. Copyright (C) 2018, Formosan Medical Association. Published by Elsevier Taiwan LLC.

Background Peripheral artery disease (PAD) is a condition characterized by restricted blood flow to the extremities, and is especially common in the elderly. PAD increases the risk for mortality and morbidity in patients with end-stage renal disease (ESRD), especially those on hemodialysis (HD). Methods The records of 484 patients with end-stage renal disease who were on HD or peritoneal dialysis (PD) were reviewed. PAD was diagnosed based on the ankle-brachial pressure index (ABI). Demographic and clinical characteristics were analyzed. Results PAD had an overall prevalence of 18.2% and was significantly more common in HD patients (21.8%) than in PD patients (4.8%). Advanced age, diabetes mellitus, smoking, low parathyroid hormone level, elevated serum ferritin, elevated serum glucose, and low serum creatinine levels increased the risk for PAD. PAD was independently associated with advanced age, diabetes mellitus, duration of dialysis, low serum creatinine, and hyperlipidemia. PD patients had a significantly lower prevalence of PAD than HD patients, maybe due to their younger age and lower prevalence of diabetes mellitus in this present study. Conclusions The prevalence of PAD was greater in the HD group than the PD group. Most of the risk factors for PAD were specific to HD, and no analyzed factor was significantly associated with PAD in PD patients.