In the middle of July, 1996, a massive outbreak of hemorrhagic colitis (HC) occurred among elementary schoolchildren in Sakai city. This is the most widespread outbreak of O157 infection ever experienced to our knowledge. Lunch foods supplied in the elementary schools in Sakai were contaminated by Escherichia coli (E. coli) O157. One hundred and twenty-one cases developed hemolytic uremic syndrome (HUS) from 12,680 symptomatic patients, including putative secondary infections, and three girls died during this outbreak. Sakai City Hospital is one of the core medical facilities in this community; hence, 425 children with HC were treated at the hospital. Antibiotics were used extensively on all patients. Among them, 12 children developed HUS. All 425 children, including the patients with HUS, recovered without significant sequelae. In the present paper, the clinical experiences during this massive outbreak of E. coli O157 infection in Sakai City Hospital are described.
Objectives: Machine learning-based prediction of hospital admissions may have the potential to optimize patient disposition and improve clinical outcomes by minimizing both undertriage and overtriage in crowded emergency care. We developed and validated the predictive abilities of machine learningbased predictions of hospital admissions in a pediatric emergency care center.Methods: A prognostic study was performed using retrospectively collected data of children younger than 16 years who visited a single pediatric emergency care center in Osaka, Japan, between August 1, 2016, and October 15, 2019. Generally, the center treated walk-in children and did not treat trauma injuries. The main outcome was hospital admission as determined by the physician. The 83 potential predictors available at presentation were selected from the following categories: demographic characteristics, triage level, physiological parameters, and symptoms. To identify predictive abilities for hospital admission, maximize the area under the precision-recall curve, and address imbalanced outcome classes, we developed the following models for the preperiod training cohort (67% of the samples) and also used them in the 1-year postperiod validation cohort (33% of the samples): (1) logistic regression, (2) support vector machine, (3) random forest, and (4) extreme gradient boosting.Results: Among 88,283 children who were enrolled, the median age was 3.9 years, with 47,931 (54.3%) boys and 1985 (2.2%) requiring hospital admission. Among the models, extreme gradient boosting achieved the highest predictive abilities (eg, area under the precision-recall curve, 0.26; 95% confidence interval, 0.25-0.27; area under the receiver operating characteristic curve, 0.86; 95% confidence interval, 0.84-0.88; sensitivity, 0.77; and specificity, 0.82). With an optimal threshold, the positive and negative likelihood ratios were 4.22, and 0.28, respectively.Conclusions: Machine learning-based prediction of hospital admissions may support physicians' decision-making for hospital admissions. However, further improvements are required before implementing these models in real clinical settings.
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