Zelalem Habtamu, Sibhat Migbar Mekonnen, Yeshidinber Abate, Kehali Habtamu
Saint Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia.
College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia.
BMC Nurs. 2024 Jun 11;23(1):398. doi: 10.1186/s12912-024-02068-8.
The interaction between the patient and the ventilator is often disturbed, resulting in patient-ventilator asynchrony (PVA). Asynchrony can lead to respiratory failure, increased artificial ventilation time, prolonged hospitalization, and escalated healthcare costs. Professionals' knowledge regarding waveform analysis has significant implications for improving patient outcomes and minimizing ventilation-related adverse events. Studies investigating the knowledge of healthcare professionals on patient-ventilator asynchrony and its associated factors in the Ethiopian context are limited. Therefore, this study aimed to assess the knowledge of healthcare professionals about using waveform analysis to detect asynchrony.
A multicenter cross-sectional study was conducted on 237 healthcare professionals (HCPs) working in the intensive care units (ICUs) of federal public hospitals in Addis Ababa, Ethiopia, from December 2022 to May 2023. The data were collected using a structured and pretested interviewer-administered questionnaire. Then, the collected data were cleaned, coded, and entered into Epi data V-4.2.2 and exported to SPSS V-27 for analysis. After description, associations were analyzed using binary logistic regression. Variables with a P-value of < 0.25 in the bivariable analysis were transferred to the multivariable analysis. Statistical significance was declared using 95% confidence intervals, and the strengths of associations were reported using adjusted odds ratios (AORs).
A total of 237 HCPs participated in the study with a response rate of 100%. Half (49.8%) of the participants were females. The mean age of the participants was 29 years (SD = 3.57). Overall, 10.5% (95% CI: 6.9-15.2) of the participants had good knowledge of detecting PVA using waveform analysis. In the logistic regression, the number of MV-specific trainings and the training site had a statistically significant association with knowledge of HCPs. HCPs who attended more frequent MV training were more likely to have good knowledge than their counterparts [AOR = 6.88 (95% CI: 2.61-15.45)]. Additionally, the odds of good knowledge among professionals who attended offsite training were 2.6 times higher than those among professionals trained onsite [AOR = 2.63 (95% CI: 1.36-7.98)].
The knowledge of ICU healthcare professionals about the identification of PVA using waveform analysis is low. In addition, the study also showed that attending offsite MV training and repeated MV training sessions were independently associated with good knowledge. Consequently, the study findings magnify the relevance of providing frequent and specific training sessions focused on waveform analysis to boost the knowledge of HCPs.
患者与呼吸机之间的相互作用常常受到干扰，导致患者 - 呼吸机不同步（PVA）。不同步会导致呼吸衰竭、人工通气时间增加、住院时间延长以及医疗费用上升。专业人员对波形分析的了解对于改善患者预后和尽量减少通气相关不良事件具有重要意义。在埃塞俄比亚背景下，调查医疗保健专业人员对患者 - 呼吸机不同步及其相关因素的了解的研究有限。因此，本研究旨在评估医疗保健专业人员对使用波形分析检测不同步的了解。
2022年12月至2023年5月，对埃塞俄比亚亚的斯亚贝巴联邦公立医院重症监护病房（ICU）工作的237名医疗保健专业人员（HCPs）进行了一项多中心横断面研究。使用结构化且经过预测试的访谈员管理问卷收集数据。然后，对收集到的数据进行清理、编码，并输入Epi数据V - 4.2.2，导出到SPSS V - 27进行分析。在进行描述后，使用二元逻辑回归分析关联。在双变量分析中P值<0.25的变量被转移到多变量分析中。使用95%置信区间声明统计学显著性，并使用调整后的优势比（AORs）报告关联强度。
共有237名HCPs参与了研究，回复率为100%。一半（49.8%）的参与者为女性。参与者的平均年龄为29岁（标准差 = 3.57）。总体而言，10.5%（95%置信区间：6.9 - 15.2）的参与者对使用波形分析检测PVA有良好的了解。在逻辑回归中，特定机械通气（MV）培训的次数和培训地点与HCPs的知识有统计学显著关联。参加更频繁MV培训的HCPs比同行更有可能有良好的知识[AOR = 6.88（95%置信区间：2.61 - 15.45）]。此外，参加非现场培训的专业人员具有良好知识的几率比现场培训的专业人员高2.6倍[AOR = 2.63（95%置信区间：1.36 - 7.98）]。
ICU医疗保健专业人员对使用波形分析识别PVA的知识水平较低。此外，研究还表明，参加非现场MV培训和重复的MV培训课程与良好的知识独立相关。因此，研究结果凸显了提供专注于波形分析的频繁且特定培训课程以提高HCPs知识的相关性。
