Introduction High flow nasal cannula (HFNC) is commonly used as first step respiratory support in infants with moderate-to-severe acute viral bronchiolitis (AVB). This device, however, fails to effectively manage respiratory distress in about a third of patients, and data are limited on determinants of patient response. The respiratory rate-oxygenation (ROX) index is a relevant tool to predict the risk for HFNC failure in adult patients with lower respiratory tract infections. The primary objective of this study was to assess the relationship between ROX indexes collected before and 1 hour after HFNC initiation, and HFNC failure occurring in the following 48 hours in infants with AVB. Method: This is an ancillary study to the multicenter randomized controlled trial TRAMONTANE 2, that included 286 infants of less than 6 months with moderate-to-severe AVB. Collection of physiological variables at baseline (H0), and 1 hour after HFNC (H1), included heart rate (HR), respiratory rate (RR), fraction of inspired oxygen (FiO 2), respiratory distress score (mWCAS), and pain and discomfort scale (EDIN). ROX was calculated as SpO 2/FiO 2 to RR. Predefined HFNC failure criteria included increase in respiratory distress score or respiratory rate, increase in discomfort, and severe apnea episodes. The accuracy of ROX index to predict HFNC failure was assessed using receiver operating curve analysis. Result: HFNC failure occurred in 111/286 (39%) infants, and for 56 (50% of the failure) of them within the first 6 hours. The area under the curve of ROX indexes at H0 and H1 were, respectively, 0.56 (95% CI 0.48-0.63, p =0.14), 0.56 (95% CI 0.49- 0.64, p =0.09). HFNC failure was associated with higher mWCAS score at H1 (p<0.01) and lower decrease in EDIN scale during the first hour of HFNC delivery (p = 0.02), but none of the physiological variables were predictive of HFNC failure. Conclusion: In this study, neither ROX index, nor physiological variables usually collected in infants with AVB had early discriminatory capacity to predict HFNC failure.

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.