Creating a High-Frequency Electronic Database in the PICU: The Perpetual Patient*

Abstract

Objective: Our objective was to construct a prospective high-quality and high-frequency database combining patient therapeutics and clinical variables in real time, automatically fed by the information system and network architecture available through fully electronic charting in our PICU. The purpose of this article is to describe the data acquisition process from bedside to the research electronic database. Design: Descriptive report and analysis of a prospective database. Setting: A 24-bed PICU, medical ICU, surgical ICU, and cardiac ICU in a tertiary care free-standing maternal child health center in Canada. Patients: All patients less than 18 years old were included at admission to the PICU. Interventions: None. Measurements and Main Results: Between May 21, 2015, and December 31, 2016, 1,386 consecutive PICU stays from 1,194 patients were recorded in the database. Data were prospectively collected from admission to discharge, every 5 seconds from monitors and every 30 seconds from mechanical ventilators and infusion pumps. These data were linked to the patient’s electronic medical record. The database total volume was 241 GB. The patients’ median age was 2.0 years (interquartile range, 0.0–9.0). Data were available for all mechanically ventilated patients (n = 511; recorded duration, 77,678 hr), and respiratory failure was the most frequent reason for admission (n = 360). The complete pharmacologic profile was synched to database for all PICU stays. Following this implementation, a validation phase is in process and several research projects are ongoing using this high-fidelity database. Conclusions: Using the existing bedside information system and network architecture of our PICU, we implemented an ongoing high-fidelity prospectively collected electronic database, preventing the continuous loss of scientific information. This offers the opportunity to develop research on clinical decision support systems and computational models of cardiorespiratory physiology for example.

Qualitative subjective assessment of a high‐resolution database in a paediatric intensive care unit—Elaborating the perpetual patient's ID card

“…The DQC data quality report was partially fulfilled (Table ) based on the database description and survey results. Details on data validation rules (item 10, section data processing) and data elements characterization were incomplete as the validation process is ongoing .…”

“…Based on the survey results and on the previous description of the database gathering process, DQC data quality report was edited by the Sainte‐Justine PICU HRD designers.…”

“…Over the last two decades, numerous health care electronic databases were created, especially in intensive care units (ICUs) . Among these databases, high‐frequency electronic databases (data collection rate > 1 per min) were developed using the existing bedside information systems and hospital network architecture to automatically collect and store the data . Our team recently published the descriptive report and analysis of a prospective automatically collected database in the paediatric ICU (PICU), ie, PICU high‐resolution database (HRD) .…”

“…Among these databases, high‐frequency electronic databases (data collection rate > 1 per min) were developed using the existing bedside information systems and hospital network architecture to automatically collect and store the data . Our team recently published the descriptive report and analysis of a prospective automatically collected database in the paediatric ICU (PICU), ie, PICU high‐resolution database (HRD) . Given the tremendous amount of data that are automatically collected, it is crucial that database quality is validated based on standardized and well‐defined criteria before using the data for research purposes .…”

Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: a single center pilot study

“…Patients’ data were excluded if the patient was hemodynamically unstable defined as 2 or more vasoactive drugs delivered at the same time (ie., epinephrine, norepinephrine, dopamine or vasopressin) or with an uncorrected cyanotic heart disease defined by no SpO 2 > 97% during all PICU stay. All the respiratory data from included patients were extracted from the PICU research database [4], after study approval by the ethical review board of Sainte-Justine hospital (number 2017 1480).…”