Department of Gynaecology, Obstetrics and Neonatology, First Faculty of Medicine Charles University and General University Hospital in Prague, Prague, Czech Republic.
Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine Charles University and General University Hospital in Prague, Prague, Czech Republic.
Pediatr Pulmonol. 2024 Dec;59(12):3585-3592. doi: 10.1002/ppul.27265. Epub 2024 Sep 12.
The current generation of neonatal ventilators enables periodic storage of set, measured, and calculated ventilatory parameters.
Retrospective observational study.
To evaluate and identify the ventilatory, demographic, and clinical pre-extubation variables that are significant for estimating extubation readiness.
Eligible subjects included premature infants <33 weeks of gestation weaned from mechanical ventilation (MV) lasting >24 h. A total of 16 relevant ventilator variables, each calculated from 288 data points over 24 h, together with eight demographic and three clinical pre-extubation variables, were used to create the generalized linear model (GLM) for a binary outcome and the Cox proportional hazards model for time-to-event analysis. The achievement of a 120-h period without reintubation was defined as a successful extubation attempt (EA) within the binary outcome.
We evaluated 149 EAs in 81 infants with a median (interquartile range) gestational age of 25 (24-26) weeks. Of this, 90 EAs (60%) were successful while 59 (40%) failed. GLM identified dynamic compliance per kilogram, percentage of spontaneous minute ventilation, and postmenstrual age as significant independent positive variables. Conversely, dynamic compliance variability emerged as a significant independent negative variable for extubation success. This model enabled the creation of a probability estimator for extubation success with a good proportion of sensitivity and specificity (80% and 73% for a cut-off of 60%, respectively).
Ventilator variables reflecting lung mechanical properties and the ability to spontaneously breathe during MV contribute to better prediction of extubation readiness in extremely premature infants with chronic lung disease.
目前这一代新生儿呼吸机能够周期性存储设定的、测量的和计算的通气参数。
回顾性观察研究。
评估和确定与预测拔管准备相关的通气、人口统计学和临床预拔管变量。
合格的研究对象包括从持续>24 小时的机械通气(MV)中撤机的<33 周龄早产儿。总共使用了 16 个相关的呼吸机变量,每个变量均来自 24 小时内 288 个数据点,以及 8 个人口统计学和 3 个临床预拔管变量,用于创建二项结局的广义线性模型(GLM)和用于时间事件分析的 Cox 比例风险模型。以 120 小时无再插管为定义,将二元结局中的成功拔管尝试(EA)定义为成功。
我们评估了 81 名婴儿的 149 次 EA,中位(四分位距)胎龄为 25(24-26)周。其中,90 次 EA(60%)成功,59 次(40%)失败。GLM 确定了每公斤的动态顺应性、自主分钟通气百分比和校正胎龄为独立的正变量。相反,顺应性变异性成为拔管成功的独立负变量。该模型创建了一个用于拔管成功的概率估计器,具有较好的敏感性和特异性(截值为 60%时分别为 80%和 73%)。
反映肺力学特性和在 MV 期间自主呼吸能力的通气变量有助于更好地预测慢性肺部疾病的极早产儿的拔管准备情况。