Elective caesarean section and bronchiolitis hospitalization: A retrospective cohort study.

BACKGROUND: We sought to evaluate whether elective caesarean section is associated with subsequent hospitalization for bronchiolitis. METHODS: This is a retrospective cohort study that used the electronic medical record database of Clalit Health Services, the largest healthcare fund in Israel, serving over 4.5 million members and over half of the total population. The primary outcome was bronchiolitis admission in the first 2 years of life. We performed logistic regression analyses to identify independent associations. We repeated the analysis using boosted decision tree machine learning techniques to confirm our findings. RESULTS: There were 124 553 infants enrolled between 2008 and 2010, and 5168 (4.1%) were hospitalized for bronchiolitis in the first 2 years of life. In logistic regression models stratified by seasons, elective caesarean section birth was associated with 15% increased odds (95% CI: 1.02-1.30) for infants born in the fall season, 28% increased odds (95% CI: 1.11, 1.47) for those born in the winter, 35% increased odds (95% CI: 1.12-1.62) for those born in the spring and 37% increased odds (95% CI: 1.18-1.60) for those born in the summer. In the boosted gradient decision tree analysis, the area under the curve for risk of bronchiolitis admission was 0.663 (95% CI: 0.652, 0.674) with timing of birth as the most important feature. CONCLUSION: Elective caesarean section, a potentially modifiable risk factor, is associated with increased odds of hospitalization for bronchiolitis in the first 2 years of life. These data should be considered when scheduling elective caesarean sections especially for infants born in spring and summer months.

Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study.

BACKGROUND: Currently, postpartum depression (PPD) screening is mainly based on self-report symptom-based assessment, with lack of an objective, integrative tool which identifies women at increased risk, before the emergent of PPD. We developed and validated a machine learning-based PPD prediction model utilizing electronic health record (EHR) data, and identified novel PPD predictors. METHODS: A nationwide longitudinal cohort that included 214,359 births between January 2008 and December 2015, divided into model training and validation sets, was constructed utilizing Israel largest health maintenance organization's EHR-database. PPD was defined as new diagnosis of a depressive episode or antidepressant prescription within the first year postpartum. A gradient-boosted decision tree algorithm was applied to EHR-derived sociodemographic, clinical, and obstetric features. RESULTS: Among the birth cohort, 1.9% (n = 4104) met the case definition of new-onset PPD. In the validation set, the prediction model achieved an area under the curve (AUC) of 0.712 (95% confidence interval, 0.690-0.733), with a sensitivity of 0.349 and a specificity of 0.905 at the 90th percentile risk threshold, identifying PPDs at a rate more than three times higher than the overall set (positive and negative predictive values were 0.074 and 0.985, respectively). The model's strongest predictors included both well-recognized (e.g., past depression) and less-recognized (differing patterns of blood tests) PPD risk factors. CONCLUSIONS: Machine learning-based models incorporating EHR-derived predictors, could augment symptom-based screening practice by identifying the high-risk population at greatest need for preventive intervention, before development of PPD.

Validating reported cause of death using integrated electronic health records from a nation-wide database.

BACKGROUND: To compare the underlying cause of death reported by the Israeli Central Bureau of Statistics (CBS) with diagnoses in the electronic health records (EHR) of a fully integrated payer/provider healthcare system. METHODS: Underlying cause of death was obtained from the CBS for deaths occurring during 2009-2012 of all Clalit Health Service members in Israel. The final cohort consisted of members who had complete medical records. The frequency of a supportive diagnosis in the EHR was reported for 10 leading causes of death (malignancies, heart disease, cerebrovascular disease, diabetes, kidney disease, septicemia, accidents, chronic lower respiratory disease, dementia and pneumonia and influenza). RESULTS: Of the 45 680 members included in the study, the majority of deaths had at least one diagnosis in the EHR that could support the cause of death. The lowest frequency of supportive diagnosis was for septicemia (52.2%) and the highest was for malignancies (94.3%). Sensitivity analysis did not suggest an alternative explanation for the missing documentation. CONCLUSIONS: The underlying cause of death coded by the CBS is often supported by diagnoses in Clalit's EHR. Exceptions are septicemia or accidents that cannot be anticipated from a patient's EHR, and dementia which may be under-reported.