Automated screening of potential organ donors using a temporal machine learning model.

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.

Long-Term Mechanical Ventilation in Neonates: A 10-Year Overview and Predictive Model.

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.

Assessment of exposure to soils contaminated with lead, cadmium, and arsenic near a zinc smelter, Cassiopée Study, France, 2008.

After 150 years of industrial activity, significant pollution of surface soils in private gardens and locally produced vegetables with lead, cadmium, and arsenic has recently been observed in Viviez (Southern France). A public health intervention was conducted in 2008 to identify individual health risks of Viviez inhabitants and to analyze their environmental exposure to these pollutants. Children and pregnant women in Viviez were screened for lead poisoning. Urinary cadmium testing was proposed to all inhabitants. Those with urinary cadmium levels over 1 µg/g creatinine were then tested for kidney damage. Urinary cadmium and arsenic levels were compared between participants with non-occupational exposure from Viviez and Montbazens, a nearby town not exposed to these two pollutants, in order to identify environmental factors contributing to impregnation. No case of lead poisoning was detected in Viviez, but 23 % of adults had urinary cadmium over 1 µg/g creatinine, 14 % of whom having markers of kidney damage. Viviez adults had higher levels of urinary cadmium, and to a lesser extent, higher levels of urinary arsenic than those from Montbazens. Consumption of local produce (vegetables and animals) and length of residence in Viviez were associated with higher urinary cadmium levels, independently of known confounding factors, suggesting persisting environmental exposure to contaminated soil. To conclude, health risks related to cadmium exposure were identified in the Viviez population living on contaminated soils. Lead and arsenic exposure did not pose health concerns. Interventions were proposed to reduce exposure and limit health consequences.