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

“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.


Introduction
"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 (SpO 2 ) has the potential to inaccurately reflect patients' true physiological states. Taenzer et al. observed that manually charted SpO 2 values were, on average, 6.5 percentage points greater than SpO 2 values collected by automated sampling [2]. Automated high-frequency SpO 2 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 SpO 2 [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][7][8][9]. These trials have used intermittent recording of SpO 2 by study personnel every 4 to 24 h to assess oxygenation. Automating data collection for SpO 2 has the potential to facilitate the design, conduct, and analysis of pragmatic clinical trials examining SpO 2 targets in mechanically ventilated ICU patients [10].
We developed a technique for automated extraction of large-volume data on SpO 2 values for use in a randomized clinical trial. We aimed to quantify the completeness and density of SpO 2 data among mechanically ventilated patients. We evaluated the reliability of automated extraction of SpO 2 data from pulse oximetry using physician manual review of photoplethysmographic waveforms.

Study design and oversight
We conducted an observational methodological study using data prospectively collected as a part of the Preliminary Investigation of optimaL Oxygen Targets (PILOT) trial. PILOT is an on-going, cluster-randomized cluster-crossover trial examining higher (96-100%), intermediate (92-96%) and lower (88-92%) SpO 2 targets among mechanically ventilated critically ill patients (Vanderbilt University IRB #171272).

Patient population
Deidentified data from patients receiving invasive mechanical ventilation at Vanderbilt University Medical Center in the medical ICU between July 1st 2018 and December 31st 2019 were included. Data from patients who were pregnant, prisoners, or admitted during 7-day washout periods specified in the design of the PILOT trial were excluded.

Study outcomes
The primary outcome of interest for this methodological study of automated SpO 2 extraction was the number and frequency of SpO 2 values from the time of intubation until extubation among mechanically ventilated ICU patients. The secondary outcomes were adequacy of plethysmographic waveform on physician manual review and correlation between quality of plethysmographic waveform and values of SpO 2 in a subset of patients.

Electronically-extracted data
In the study ICU, SpO 2 is continuously monitored using Nellcor™ SpO 2 Adhesive Sensors, which report a non-normalized, real-time plethysmographic waveform and SpO 2 values. Plethysmography and SpO 2 are displayed on IntelliVue MP90 bedside patient monitors. SpO 2 values from the IntelliVue monitor are archived every 60 s and exported on a daily basis to our institution's enterprise data warehouse and e l e c t r o n i c a l l y m e r g e d w i t h t h e s t u d y d a t a s e t . Plethysmographic waveform from other sources, such as travel monitors, can be used to generate SpO 2 values, which are manually entered into the electronic health record by bedside clinical personnel. Manually entered SpO 2 values were also extracted from the electronic health record and merged with the study dataset, but were unable to be manually reviewed.

Manually-reviewed plethysmography
For a convenience sample of 49 mechanically ventilated patients located in the study ICU during the study period, 24 h of plethysmographic waveform was manually reviewed by a study physician, beginning at 00:00 and ending at 23:59 on that study day. Based on the study physician's clinical impression of the quality of the plethysmographic waveform, the waveform was segmented into intervals and each interval was categorized as: adequate quality; inadequate quality; or absent waveform. The shortest time interval per segment was set at 30 s. The physician was blinded to the values of SpO 2 associated with the plethysmographic waveforms being reviewed.

Statistical analysis
Categorical values were described using numbers and percentages, and continuous variables were described using medians and interquartile ranges. We sought to review more than 1000 h of plethysmographic waveforms, aiming for both a moderate number of patients and a moderate duration of plethysmography for each patient. No formal sample size calculation was performed. All analyses were performed using R version 3.2.0 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline characteristics
Data from 1331 mechanically ventilated ICU patients were included in this analysis. Baseline characteristics are presented in Table 1. Median age was 58 years. Fifty-six percent were male, and 79% were white.  Three patients for whom plethysmography waveforms were reviewed were admitted in a "washout period" between study blocks in PILOT and therefore did not have data on clinical characteristics or SpO 2 values for analysis in the PILOT dataset. For one patient, the 24 h period of plethysmographic waveform that was manually reviewed was a time period before the patient's transfer to the ICU for initiation of mechanical ventilation, and therefore did not overlap with SpO 2 values in the PILOT dataset. As a result, forty-five patient encounters had overlapping reviewed twenty-four hours plethysmographic waveforms and automated SpO 2 data extraction, yielding 33,092 SpO 2 values associated with reviewed waveform.

SpO 2 values by automated data extraction
SpO 2 values associated with adequate plethysmographic waveform accounted for 31,754 of 33,092 SpO 2 values (96.0%). A total of 359 of 33,092 SpO 2 values (1.1%) were associated with inadequate plethysmographic waveform and 979 of 33,092 SpO 2 values (3.0%) were associated with periods where plethysmographic waveform was not available from bedside monitors in the study ICU. Six SpO 2 values from inadequate plethysmographic waveform and seven SpO 2 values from absent plethysmographic waveform were 85-88%. Five SpO 2 values from inadequate plethysmographic waveform and two SpO 2 values from absent plethysmographic waveform were less than 85%. In total, a maximum of 20 out of 33,092 SpO 2 values (0.06%) represented episodes of hypoxemia (SpO 2 ≤ 88%) associated with inadequate or absent plethysmographic waveform.
The 979 automatically abstracted SpO 2 values during periods when bedside monitors did not record any plethysmographic waveform corresponded to twenty-three separate intervals from 16 patients. Five intervals in four patients accounted for 922 (94.1%) of the SpO 2 values obtained during absent waveform. Of these, three intervals and 604 SpO 2 values originated, not from the ICU monitor for which the absent plethysmographic waveform had been reviewed, but from patient monitors in the operating room during a period in which the patient had been transported out of the ICU to undergo a procedure. The origin of 318 SpO 2 values during the two additional intervals of absent plethysmographic waveform could not be accounted for on manual chart review.

Discussion
This study found that automated extraction of SpO 2 data from bedside monitors and the electronic health record provided a median of 1336 individual SpO 2 values per patient during invasive mechanical ventilation in the emergency department and ICU, at a median frequency of one value per minute. SpO 2 values were available for extraction on average 8.3 min after intubation until 0.68 min (41 s) before extubation. A total of 96% of SpO 2 values were associated with an adequate plethysmographic waveform on physician review, and inadequate or missing plethysmographic waveform was an infrequent cause of spuriously low SpO 2 values.
The use of continuous pulse oximetry has previously been validated as a more reliable method of detecting hypoxemia compared to routine intermittent nursing checks [2]. In the operating room and post-anesthesia care unit, it decreases activation of rescue teams and patient transfer to the ICU for pulmonary reasons [11]. Automatic data collection also eliminates the smoothing effect and is more likely to demonstrate the wide swings in physiological parameters that occur in clinical practice. Smoothing has previously been demonstrated in intermittent recordings of SpO 2 values by ICU nurses compared to automatically archived data [12].
Methods to monitor oxygenation of mechanically ventilated patients in randomized clinical trials have differed [6][7][8][9]. Time-weighted averages and intermittent sampling every 4 to 24 h of the fraction of inspired oxygen, partial pressure of oxygen, SpO 2 , and ventilator settings have all been employed. The largest study to date employed SpO 2 patient-hours and recorded approximately 170 values per patient [9]. As pragmatic trials recruit a larger number of patients, automated extraction of SpO 2 could allow collection of more granular data on oxygenation for conduct, monitoring, and analysis.
Our study has several strengths. Automated high frequency SpO 2 collection yielded nearly ten times as many SpO 2 values per patient as available in prior trials, potentially facilitating assessment of separation between groups and episodes of hypoxemia or hyperoxemia [9]. Despite concerns about poor signal quality from motion artifact [12,13], in our study manual review of plethysmography waveform found 96% of SpO 2 values were associated with adequate plethysmography waveform and only 20 SpO 2 values out of 33,092 values (0.06%) w e r e 8 8 % o r l e s s d u e t o i n a d e q u a t e o r a b s e n t plethysmography.
Our study has limitations. Plethysmography waveform review was limited to 3.7% of patients due to its labor-intensive nature. Plethysmography waveform was reviewed by one physician only. No established criteria for adequacy exist against which waveform could be graded. During intervals in which the plethysmography is inadequate or absent, information are not available as to the true underlying SpO 2 values. High-frequency values for SpO 2 were archived for research starting at the time of administrative admission to the institutional telemetry system, which resulted in missing values immediately after tracheal intubation for a significant number of patients intubated in the ED. SpO 2 values may not always reflect partial pressure of oxygen [14].

Conclusions
Automated high-frequency extraction of SpO 2 values from bedside monitors in a clinical trial is feasible and allows measurement of SpO 2 as frequently as every minute. The vast majority of SpO 2 values from patient monitors are associated with plethysmographic waveforms of adequate quality.