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Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

ABSTRACT: We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitalized for bronchiolitis, by using routinely-available pre-hospitalization data as predictors, we developed four machine learning models: Lasso regression, elastic net regression, random forest, and gradient boosted decision tree. We compared their predictive performance-e.g., area-under-the-curve (AUC), sensitivity, specificity, and net benefit (decision curves)-using a cross-validation method, with that of the reference model. The outcomes were positive pressure ventilation use and intensive treatment (admission to intensive care unit and/or positive pressure ventilation use). Of 1,016 infants, 5.4% underwent positive pressure ventilation and 16.0% had intensive treatment. For the positive pressure ventilation outcome, machine learning models outperformed reference model (e.g., AUC 0.88 [95% CI 0.84-0.93] in gradient boosted decision tree vs 0.62 [95% CI 0.53-0.70] in reference model), with higher sensitivity (0.89 [95% CI 0.80-0.96] vs. 0.62 [95% CI 0.49-0.75]) and specificity (0.77 [95% CI 0.75-0.80] vs. 0.57 [95% CI 0.54-0.60]). The machine learning models also achieved a greater net benefit over ranges of clinical thresholds. Machine learning models consistently demonstrated a superior ability to predict acute severity and achieved greater net benefit.

SUBMITTER: Raita Y

PROVIDER: S-EPMC7335203 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

Raita Yoshihiko Y Camargo Carlos A CA Macias Charles G CG Mansbach Jonathan M JM Piedra Pedro A PA Porter Stephen C SC Teach Stephen J SJ Hasegawa Kohei K

Scientific reports 20200703 1

We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitalized for bronchiolitis, by using routinely-available pre-hospitalization data as predictors, we developed four machine learning models: Lasso regression, elastic net regression, random forest, and gradient boosted decision tree. We compared their pred ...[more]

PMID: 32620819

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Project description:Acute bronchiolitis treatment in children and infants is largely supportive, but chest physiotherapy is routinely performed in some countries. In France, national guidelines recommend a specific type of physiotherapy combining the increased exhalation technique (IET) and assisted cough (AC). Our objective was to evaluate the efficacy of chest physiotherapy (IET + AC) in previously healthy infants hospitalized for a first episode of acute bronchiolitis.We conducted a multicenter, randomized, outcome assessor-blind and parent-blind trial in seven French pediatric departments. We recruited 496 infants hospitalized for first-episode acute bronchiolitis between October 2004 and January 2008. Patients were randomly allocated to receive from physiotherapists three times a day, either IET + AC (intervention group, n=246) or nasal suction (NS, control group, n=250). Only physiotherapists were aware of the allocation group of the infant. The primary outcome was time to recovery, defined as 8 hours without oxygen supplementation associated with minimal or no chest recession, and ingesting more than two-thirds of daily food requirements. Secondary outcomes were intensive care unit admissions, artificial ventilation, antibiotic treatment, description of side effects during procedures, and parental perception of comfort. Statistical analysis was performed on an intent-to-treat basis. Median time to recovery was 2.31 days, (95% confidence interval [CI] 1.97-2.73) for the control group and 2.02 days (95% CI 1.96-2.34) for the intervention group, indicating no significant effect of physiotherapy (hazard ratio [HR]=1.09, 95% CI 0.91-1.31, p=0.33). No treatment by age interaction was found (p=0.97). Frequency of vomiting and transient respiratory destabilization was higher in the IET + AC group during the procedure (relative risk [RR]=10.2, 95% CI 1.3-78.8, p=0.005 and RR=5.4, 95% CI 1.6-18.4, p=0.002, respectively). No difference between groups in bradycardia with or without desaturation (RR=1.0, 95% CI 0.2-5.0, p=1.00 and RR=3.6, 95% CI 0.7-16.9, p=0.10, respectively) was found during the procedure. Parents reported that the procedure was more arduous in the group treated with IET (mean difference=0.88, 95% CI 0.33-1.44, p=0.002), whereas there was no difference regarding the assessment of the child's comfort between both groups (mean difference=-0.07, 95% CI -0.53 to 0.38, p=0.40). No evidence of differences between groups in intensive care admission (RR=0.7, 95% CI 0.3-1.8, p=0.62), ventilatory support (RR=2.5, 95% CI 0.5-13.0, p=0.29), and antibiotic treatment (RR=1.0, 95% CI 0.7-1.3, p=1.00) was observed.IET + AC had no significant effect on time to recovery in this group of hospitalized infants with bronchiolitis. Additional studies are required to explore the effect of chest physiotherapy on ambulatory populations and for infants without a history of atopy.ClinicalTrials.gov NCT00125450.

Dataset Information

Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

Publications

Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

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