Preparing for future pandemics: Automated intensive care electronic health record data extraction to accelerate clinical insights - Lada Lijović; Harm Jan de Grooth; Patrick Thoral; Lieuwe Bos; Zheng Feng; Tomislav Radočaj; Paul Elbers (Journal of Intensive Medicine)
Background: Manual data abstraction from electronic health records (EHRs) for research on intensive care patients is time-intensive and challenging, especially during high-pressure periods such as pandemics. Automated data extraction is a potential alternative but may raise quality concerns. This study assessed the feasibility and credibility of automated data extraction during the coronavirus disease 2019 (COVID-19) pandemic. Methods: We retrieved routinely collected data from the COVID-Predict Dutch Data Warehouse, a multicenter database containing the following data on intensive care patients with COVID-19: demographic, medication, laboratory results, and data from monitoring and life support devices. These data were sourced from EHRs using automated data extraction. We used these data to determine indices of wasted ventilation and their prognostic value and compared our findings to a previously published original study that relied on manual data abstraction largely from the same hospitals. Results: Using automatically extracted data, we replicated the original study. Among 1515 patients intubated for over 2 days, Harris–Benedict (HB) estimates of dead space fraction increased over time and were higher in non-survivors at each time point: at the start of ventilation (0.70±0.13 vs. 0.67±0.15, P <0.001), day 1 (0.74±0.10 vs. 0.71±0.11, P<0.001), day 2 (0.77±0.09 vs. 0.73±0.11, P<0.001), and day 3 (0.78±0.09 vs. 0.74±0.10, P<0.001). Patients with HB dead space fraction above the median had an increased mortality rate of 13.5%, compared to 10.1% in those with values below the median (P<0.005). Ventilatory ratio showed similar trends, with mortality increasing from 10.8% to 12.9% (P=0.040). Conversely, the end-tidal-to-arterial partial pressure of carbon dioxide (PaCO₂) ratio was inversely related to mortality, with a lower 28-day mortality in the higher than median group (8.5% vs. 15.1%, P<0.001). After adjusting for base risk, impaired ventilation markers showed no significant association with 28-day mortality. Conclusion: Manual data abstraction from EHRs may be unnecessary for reliable research on intensive care patients, highlighting the feasibility and credibility of automated data extraction as a trustworthy and scalable solution to accelerate clinical insights, especially during future pandemics.