BACKGROUND: Mathematical models based on the physiology when programmed as a software can be used to teach cardiorespiratory physiology and to forecast the effect of various ventilatory support strategies. We developed a cardiorespiratory simulator for children called “SimulResp”. The purpose of this study was to evaluate the quality of SimulResp. METHODS: SimulResp quality was evaluated on accuracy, robustness, repeatability and reproducibility. Blood gas values (pH, PaCO 2, PaO 2 and SaO 2) were simulated for several subjects with different characteristics and in different situations and compared to expected values available as reference. The correlation between reference and simulated data was evaluated by the coefficient of determination and Intraclass correlation coefficient. The agreement was evaluated with the Bland & Altman analysis. RESULTS: SimulResp produced healthy child physiological values within normal range (pH 7.40 +/- 0.5; PaCO2 40 +/- 5 mmHg, PaO2 90 +/- 10 mmHg; SaO2 97% +/- 3%) starting from a weight of 25 to 35 kg, regardless of ventilator support. SimulResp failed to simulate accurate values for subjects under 25 kg and/or affected with pulmonary disease and mechanically ventilated. Based on the repeatability was considered as excellent and the reproducibility as mild to good. SimulResp’s prediction remains stable within time. CONCLUSIONS: The cardiorespiratory simulator SimulResp requires further development before future integration into a clinical decision support system.

Objective: High data quality is essential to ensure the validity of clinical and research inferences based on it. However, these data quality assessments are often missing even though these data are used in daily practice and research. Our objective was to evaluate the data quality of our high-resolution electronic database (HRDB) implemented in our pediatric intensive care unit (PICU). Design: A prospective validation study of a HRDB. Setting: A 32-bed pediatric medical, surgical and cardiac PICU in a tertiary care freestanding maternal-child health center in Canada. Population: All patients admitted to the PICU with at least one vital sign monitored using a cardiorespiratory monitor connected to the central monitoring station. Interventions: None Measurements and Main Results: Between June 2017 and August 2018, data from 295 patient days were recorded from medical devices and 4,645 data points were video recorded and compared to the corresponding data collected in the HRDB. Statistical analysis showed an excellent overall correlation (R2=1), accuracy (100%), agreement (bias=0, limits of agreement=0), completeness (2% missing data) and reliability (ICC=1) between recorded and collected data within clinically significant pre-defined limits of agreement. Divergent points could all be explained. Conclusions: This prospective validation of a representative sample showed an excellent overall data quality.