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OBJECTIVES:. To validate a method for continuously estimating the Pao2/Fio2 (PF) ratio in all critically ill pediatric patients using only standard continuous data monitoring. DESIGN:. Retrospective study on a high temporal resolution database. SETTING:. PICU in Montreal, QC, Canada. PATIENTS/SUBJECTS:. We included any patients admitted from May 2015 to May 2023 who had an arterial blood gas (ABG) with concurrent continuous pulsed oximetry saturation (Spo2) values. We used our previously validated mathematical model to determine the magnitude of hypoxemia by computing the estimated ePao2/Fio2 (ePF) ratio and comparing it to the Spo2/Fio2 (SF), using PF ratio as the reference standard. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. We analyzed a total of 20,828 ABGs. When Spo2 was below or equal to 97%, the ePF ratio showed a significantly better hypoxemia classification (none, light/moderate, or severe) than the SF ratio (0.80 vs. 0.72; p < 0.001), a lower fixed bias (16.26 vs. –35.24; p < 0.001), a lower mean absolute error (37.92 vs. 63.93; p < 0.001) and a lower proportional bias (slope of 1.01 vs. 0.81; p < 0.001). ePF ratio has also a better limits of agreement difference from Bland-Altman plot (248.10 vs. 292.45; p < 0.001) and coefficient of determination (0.68 vs. 0.59; p < 0.001). When Spo2 was above 97%, the ePF ratio had better classification with Kappa (0.53 vs. 0.43; p < 0.001) and lower fixed bias (–0.63 vs. 65.68; p < 0.001). CONCLUSIONS:. The PF ratio based on ePF allows for a continuous estimation of hypoxemia severity with a better performance than the SF ratio.

Abstract Organ donation is not meeting demand, and yet 30–60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949–0.981) for the neural network and 0.940 (0.908–0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.

BACKGROUND:. Pao2 is the gold standard to assess acute hypoxic respiratory failure, but it is only routinely available by intermittent spot checks, precluding any automatic continuous analysis for bedside tools. OBJECTIVE:. To validate a continuous and noninvasive method to estimate hypoxemia severity for all Spo2 values. DERIVATION COHORT:. All patients who had an arterial blood gas and simultaneous continuous noninvasive monitoring from 2011 to 2019 at Boston Children’s Hospital (Boston, MA) PICU. VALIDATION COHORT:. External cohort at Sainte-Justine Hospital PICU (Montreal, QC, Canada) from 2017 to 2020. PREDICTION MODEL:. We estimated the Pao2 using three kinds of neural networks and an empirically optimized mathematical model derived from known physiologic equations. RESULTS:. We included 52,879 Pao2 (3,252 patients) in the derivation dataset and 12,047 Pao2 (926 patients) in the validation dataset. The mean function on the last minute before the arterial blood gas had the lowest bias (bias –0.1% validation cohort). A difference greater than or equal to 3% between pulse rate and electrical heart rate decreased the intraclass correlation coefficients (0.75 vs 0.44; p < 0.001) implying measurement noise. Our estimated Pao2 equation had the highest intraclass correlation coefficient (0.38; 95% CI, 0.36–0.39; validation cohort) and outperformed neural networks and existing equations. Using the estimated Pao2 to estimate the oxygenation index showed a significantly better hypoxemia classification (kappa) than oxygenation saturation index for both Spo2 less than or equal to 97% (0.79 vs 0.60; p < 0.001) and Spo2 greater than 97% (0.58 vs 0.52; p < 0.001). CONCLUSION:. The estimated Pao2 using pulse rate and electrical heart rate Spo2 validation allows a continuous and noninvasive estimation of the oxygenation index that is valid for Spo2 less than or equal to 97% and for Spo2 greater than 97%. Display of continuous analysis of estimated Pao2 and estimated oxygenation index may provide decision support to assist with hypoxemia diagnosis and oxygen titration in critically ill patients.

Objectives: Significant resources are devoted to neonatal prolonged mechanical ventilation (NPMV), but little is known about the outcomes in those children. Our primary objective was to describe the NPMV respiratory, digestive, and neurological outcomes at 18 months corrected age. Our second objective was on the early identification of which patients, among the NPMV cohort, will need to be ventilated for ≥125 days, which corresponded to the 75th percentile in the preliminary data, and to describe that subgroup.Methods: In this retrospective cohort study, we included all children born between 2004 and 2013 who had a NPMV (≥21 days of invasive or noninvasive respiratory support reached between 40 and 44 weeks of postconceptional age). We used random forests, logistic regression with penalization, naive Bayes, and XGBoost to predict which patients will need ≥125 days of ventilation. We used a Monte Carlo cross validation.Results: We included 164 patients. Of which, 40% (n = 66) were female, and the median gestational age was 29 weeks [interquartile range (IQR): 26–36 weeks] with a bimodal distribution. Median ventilation days were 104 (IQR: 66–139 days). The most frequently associated diagnoses were pulmonary hypertension (43%), early pulmonary dysplasia (41%), and lobar emphysema (37%). At 18 months corrected age, 29% (n = 47) had died, 59% (n = 97) were free of any respiratory support, and 45% (n = 74) were exclusively orally fed. A moderate area under the ROC curve of 0.65 (95% CI: 0.54–0.72) for identifying patients in need of ≥125 days of ventilation at inclusion was achieved by random forests classifiers. Among the 26 measured at inclusion, the most contributive ones were PCO2, inspired O2 concentration, and gestational age. At 18 months corrected age, patients ventilated for ≥125 days had a lower respiratory weaning success (76 vs. 87%, P = 0.05), lower exclusive oral feeding proportion (51 vs. 84%, P < 0.001), and a higher neurological impairment (median Pediatric Cerebral Performance Category score 3 vs. 2, P = 0.008) than patients ventilated for < 125 days.Conclusion: NPMV is a severe condition with a high risk of mortality, neurological impairment, and oral feed delay at 18 months. Most survivors are weaned of any respiratory support. We identified the risk factors that allow for the early identification of the most at-risk children of long-term ventilation with a moderate discrimination.

Objectives: Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU). Methods: We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®). Measurements and main results: In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)). Conclusions: Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia.

BackgroundIn an intensive care units, experts in mechanical ventilation are not continuously at patient's bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients' data in real time offers an opportunity to fill this gap.ObjectiveThe objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change.Data sourcesData of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (< 84%, 85% to 91% and c92% to 100%).Performance metrics of predictive modelsDue to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves < 0.75.ConclusionThis single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels.