Background This study was performed to develop and validate machine learning models for early detection of ventilator-associated pneumonia (VAP) 24 h before diagnosis, so that VAP patients can receive early intervention and reduce the occurrence of complications. Patients and methods This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the prediction model were evaluated. Furthermore, We also compare the performance of Clinical Pulmonary Infection Score (CPIS)-based model (threshold value ≥ 3) using the same training and test data sets. Results In total, 38,515 ventilation sessions occurred in 61,532 ICU admissions. VAP occurred in 212 of these sessions. We incorporated 42 VAP risk factors at admission and routinely measured the vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84% in the validation, 74% sensitivity and 71% specificity 24 h after intubation. The AUC of our VAP machine learning model is nearly 25% higher than the CPIS model, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively. Conclusions We developed and internally validated an automated model for VAP prediction using the MIMIC-III cohort. The VAP prediction model achieved high performance based on its AUC, sensitivity and specificity, and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.

目的 分析影响重症监护室(ICU)中危重症患者发生谵妄的危险因素.方法 回顾性收集我科收治的301例危重症患者的一般资料,包括既往史(心血管和脑血管疾病、高血压、吸烟、酗酒、糖尿病)、年龄、性别、诊断、是否手术、患者来源(急诊/平诊),以及其他可能影响谵妄发生的相关因素,包括镇静药物使用时间、Richmond躁动镇静评分量表(RASS)评分、家属探视情况、呼吸机使用时间、血管活性药物使用情况、留置引流管情况、入住ICU时间,同时记录患者入ICU 24 h内C反应蛋白、降钙素原、白细胞计数、体温、急性生理学和慢性健康状况评价Ⅱ(APACHEⅡ)评分和序贯性器官衰竭评估(SOFA)评分.根据ICU意识模糊评估法(CAM-ICU)评估患者有无谵妄,之后对患者的一般资料和可能影响谵妄发生的高危因素进行单因素和多因素logistic回归分析.结果 对24项与ICU患者发生谵妄相关的危险因素进行单因素logistic回归分析,结果显示,家属探视、糖尿病史和患者来源等16项危险因素与谵妄发生密切相关.进一步多因素logistic回归分析结果表明,无家属探视、有糖尿病史、急诊来源、手术术后、入住ICU时间长、有吸烟史和APACHEⅡ评分高是ICU危重症患者发生谵妄的独立危险因素.结论 ICU医护人员应对有糖尿病史、有吸烟史、手术术后、APACHEⅡ评分高和急诊转入ICU的患者给予足够重视,并增加家属探视时间,灵活探视制度,早期干预和预防,以降低ICU患者谵妄的发生率.

Background This study was performed to develop and validate machine learning models for early detection of ventilator-associated pneumonia (VAP) 24 h before diagnosis, so that VAP patients can receive early intervention and reduce the occurrence of complications. Patients and methods This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the prediction model were evaluated. Furthermore, We also compare the performance of Clinical Pulmonary Infection Score (CPIS)-based model (threshold value >= 3) using the same training and test data sets. Results In total, 38,515 ventilation sessions occurred in 61,532 ICU admissions. VAP occurred in 212 of these sessions. We incorporated 42 VAP risk factors at admission and routinely measured the vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84% in the validation, 74% sensitivity and 71% specificity 24 h after intubation. The AUC of our VAP machine learning model is nearly 25% higher than the CPIS model, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively. Conclusions We developed and internally validated an automated model for VAP prediction using the MIMIC-III cohort. The VAP prediction model achieved high performance based on its AUC, sensitivity and specificity, and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.

<正>连续性肾脏替代治疗(CRRT)不仅能够用于肾功能衰竭的替代治疗,近年来也广泛应用于全身炎症反应综合征(SIRS)、急性呼吸窘迫综合征(ARDS)、急性重症胰腺炎、多器官功能障碍综合征(MODS)等危重疾病的治疗,已经成为与机械通气

本文结合自身多年临床护理工作切身体验从培养护理美学意识、树立形象美、扎实的基本功、创造良好氛围四个方面阐述护理美学在临床工作中发挥的作用,使患者在治疗过程中享受护理之美,促进患者早日康复。

目的总结ICU床边CRRT患者深静脉导管的"全程护理",提高深静脉导管护理技术。方法对我院65例实施床边CRRT患者深静脉导管"全程护理"过程进行回顾性分析和总结,观察并发症发生情况。结果 65例深静脉置管患者中发生扭曲1例,堵管1例,其余均通畅,无并发症发生。结论 CRRT患者深静脉导管"全程护理"可减少深静脉导管并发症发生,提高CRRT治疗效果。

目的观察醒脑静注射液对脑卒中急性期的神经保护作用。方法将48例缺血性脑卒中急性期患者随机分为两组。对照组24例,予常规西药治疗;治疗组24例,在对照组治疗基础上加用醒脑静注射液。观察治疗前后血浆内皮素(ET)、肿瘤坏死因子(TNF)、血清白介素-6(IL-6)、血白细胞总数(WBC)、中性粒细胞比值(NE%)、神经功能缺损评分(NIHSS)评分及证候积分的变化。结果治疗后两组ET、TNF、IL-6、WBC、NE%均较治疗前下降,与治疗前比较,差异有显著性意义(P<0.05);治疗组与对照组治疗后比较,差异有显著性意义(P<0.05)。治疗后两组NIHSS均增高,与治疗前比较,差异有显著性意义(P<0.05);治疗组与对照组治疗后比较,差异有显著性意义(P<0.05),提示治疗组NIHSS增高幅度小于对照组。治疗后两组证候积分均明显下降,与治疗前比较,差异有显著性意义(P<0.05)。治疗组与对照组治疗后比较,差异有显著性意义(P<0.05)。结论 醒脑静注射液能抑制缺血性脑卒中急性期炎症和免疫反应,减轻脑水肿,改善脑供血,改善中风患者临床症状。