Background: An oxygen saturation (SpO2) histogram classification system has been shown to enable quantification of SpO2 instability into five types, based on histogram distribution and time spent at SpO2 ≤ 80%. We aimed to investigate this classification system as a tool to describe response to doxapram treatment in infants with severe apnea of prematurity. Methods: This retrospective study included 61 very-low-birth-weight infants who received doxapram. SpO2 histograms were generated over the 24-h before and after doxapram start. Therapy response was defined as a decrease of ≥1 histogram types after therapy start. Results: The median (IQR) histogram type decreased from 4 (3–4) before to 3 (2–3) after therapy start (p < 0.001). The median (IQR) FiO2 remained constant before (27% [24–35%]) and after (26% [22–35%]) therapy. Thirty-six infants (59%) responded to therapy within 24 h. In 34/36 (94%) of the responders, invasive mechanical ventilation (IMV) was not required during the first 72 h of therapy, compared to 15/25 (60%) of non-responders (p = 0.002). Positive and negative predictive values of the 24-h response for no IMV requirement within 72 h were 0.46 and 0.94, respectively. Conclusions: Classification of SpO2 histograms provides an objective bedside measure to assess response to doxapram therapy and can serve as a tool to detect changes in oxygenation status around respiratory interventions. Impact: The SpO2 histogram classification system provides a tool for quantifying response to doxapram therapy.The classification system allowed estimation of the probability of invasive mechanical ventilation requirement, already within a few hours of treatment.The SpO2 histogram classification system allows an objective bedside assessment of the oxygenation status of the preterm infant, making it possible to assess the changes in oxygenation status in response to respiratory interventions.
|Publication status||E-pub ahead of print - 23 Jun 2022|
Bibliographical noteFunding Information:
Hoang The Tuan was funded by Vingroup JSC and supported by the Postdoctoral Scholarship Programme of the Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VinBigdata), under the code VINIF.2021.STS.17.
© 2022, The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.