Department of Pediatrics, Division of Neonatology, Erasmus MC - Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, The Netherlands,
Department of Pediatrics, Division of Neonatology, Erasmus MC - Sophia Children's Hospital, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Neonatology. 2020;117(4):438-445. doi: 10.1159/000509269. Epub 2020 Aug 25.
Evaluation of pharmacotherapy during intensive care treatment is commonly based on subjective, intermittent interpretations of physiological parameters. Real-time visualization and analysis may improve drug effect evaluation. We aimed to evaluate the effects of the respiratory stimulant doxapram objectively in preterm infants using continuous physiological parameters.
In this longitudinal observational study, preterm infants who received doxapram therapy were eligible for inclusion. Physiological data (1 Hz) were used to assess respiration and to evaluate therapy effects. The oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio and the area under the 89% SpO2 curve (duration × saturation depth below target) were calculated as measures of hypoxemia. Regression analyses were performed in 1-h timeframes to discriminate therapy failure (intubation or death) from success (no intubation).
Monitor data of 61 patients with a median postmenstrual age (PMA) at doxapram initiation of 28.7 (IQR 27.6-30.0) weeks were available. The success rate of doxapram therapy was 56%. Doxapram pharmacodynamics were reflected in an increased SpO2 and SpO2/FiO2 ratio as well as a decrease in episodes with saturations below target (SpO2 <89%). The SpO2/FiO2 ratio, corrected for PMA and mechanical ventilation before therapy start, discriminated best between therapy failure and success (highest AUC ROC of 0.83).
The use of continuous physiological monitor data enables objective and detailed interpretation of doxapram in preterm infants. The SpO2/FiO2 ratio is the best predictive parameter for therapy failure or success. Further implementation of real-time data analysis and treatment algorithms would provide new opportunities to treat newborns.
在重症监护治疗期间，药物治疗的评估通常基于对生理参数的主观、间歇性解释。实时可视化和分析可能会改善药物效果评估。我们旨在使用连续的生理参数客观地评估在早产儿中使用呼吸兴奋剂多沙普仑的效果。
在这项纵向观察性研究中，接受多沙普仑治疗的早产儿有资格入组。生理数据（1 Hz）用于评估呼吸并评估治疗效果。氧饱和度（SpO2）/吸入氧分数（FiO2）比值和 89% SpO2 曲线下面积（持续时间×目标饱和度以下的饱和度深度）被计算为低氧血症的指标。在 1 小时的时间框架内进行回归分析，以区分治疗失败（插管或死亡）与成功（未插管）。
61 名患者的监测数据可用于分析，他们的多沙普仑起始时的中位胎龄（PMA）为 28.7（IQR 27.6-30.0）周。多沙普仑治疗的成功率为 56%。多沙普仑的药效学反映在 SpO2 和 SpO2/FiO2 比值增加，以及目标饱和度以下的饱和度发作减少（SpO2 <89%）。在治疗开始前，校正 PMA 和机械通气后，SpO2/FiO2 比值能最好地区分治疗失败和成功（ROC 曲线下面积 AUC 的最高值为 0.83）。
使用连续的生理监测数据可以对早产儿中的多沙普仑进行客观和详细的解释。SpO2/FiO2 比值是预测治疗失败或成功的最佳参数。进一步实施实时数据分析和治疗算法将为治疗新生儿提供新的机会。
