Big Data for Clinical Trials: Automated Collection of SpO2 for a Trial of Oxygen Targets during Mechanical Ventilation.

Abstract

“Smoothing” is the phenomenon by which manually recorded vital signs are less likely to document values at the extremes compared to automated systems [1]. Intermittent manual recording of the peripheral oxygen saturation (SpO2) has the potential to inaccurately reflect patients’ true physiological states. Taenzer et al. observed that manually charted SpO2 values were, on average, 6.5 percentage points greater than SpO2 values collected by automated sampling [2]. Automated high-frequency SpO2 monitoring may more reliably quantify the incidence and severity of hypoxemia and hyperoxemia. Intensive Care Units (ICUs) have traditionally been more vigilant in preventing hypoxemia than hyperoxemia, and ICU patients frequently receive more supplemental oxygen than required to maintain normal values for SpO2 [3]. Hyperoxemia has been associated with worse patient outcomes in observational studies [4, 5], but randomized trials evaluating oxygen saturation targets for mechanically ventilated adults have reported conflicting results [6–9]. These trials have used intermittent recording of SpO2 by study personnel every 4 to 24 h to assess oxygenation. Automating data collection for SpO2 has the potential to facilitate the design, conduct, and analysis of pragmatic clinical trials examining SpO2 targets in mechanically ventilated ICU patients [10]. We developed a technique for automated extraction of large-volume data on SpO2 values for use in a randomized clinical trial. We aimed to quantify the completeness and density of SpO2 data among mechanically ventilated patients. We evaluated the reliability of automated extraction of SpO2 data from pulse oximetry using physician manual review of photoplethysmographic waveforms.