Pulmonary vein stenosis of ex-premature infants is a complex problem with poor survival, delayed diagnosis, and unsatisfactory treatment. The lack of concordance in twins suggests epigenetic or environmental factors may play a role in the development of pulmonary vein stenosis. In ex-premature infants with pulmonary hypertension and bronchopulmonary dysplasia a focused echocardiographic assessment of the pulmonary veins is required with further imaging if the echocardiogram is inconclusive.

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BackgroundB-type natriuretic peptide (BNP) has not been evaluated in newborns with congenital diaphragmatic hernia (CDH). We hypothesized that BNP and severity of pulmonary hypertension (PH) would predict clinical outcome in these infants.MethodsWe measured BNP levels and assessed severity of PH by echocardiography at one day and one week of life. Outcome was classified by status at 56 days (or prior discharge): Good (n=13) if alive on room air and Poor (n=14) if expired or receiving respiratory support. We estimated area under the curve (AUC) and 95% confidence interval (CI).ResultsBNP levels were higher at one day in newborns with Poor outcome (median 220 pg/mL versus 55 pg/mL, P<0.01). At one week, there was no significant difference in BNP level (median 547 pg/mL versus 364 pg/mL, P=0.70, for Poor and Good outcomes). At one day, BNP level predicted outcome (AUC 0.91, 95% CI 0.77–1.0), but this relationship dissipated by one week (AUC 0.55, 95% CI 0.31–0.79). Severity of PH did not predict outcome at one day (AUC 0.51, 95% CI 0.27–0.74), but prediction improved at one week (AUC 0.80, 95% CI 0.61–0.99).ConclusionBNP is a strong predictor of clinical outcome in newborns with CDH at one day of life.

Objectives To compare registry and EHR data mining approaches for cohort ascertainment in patients with pediatric pulmonary hypertension (PH) in an effort to overcome some of the limitations of registry enrollment alone in identifying patients with disease phenotypes. Study design This study was a single-center retrospective analysis of EHR and registry data at Boston Children’s Hospital. The local Informatics for Integrating Biology and the Bedside (i2b2) data warehouse was queried for billing codes, prescriptions, and narrative data related to pediatric PH. Computable phenotype algorithms were developed by fitting penalized logistic regression models to a physician-annotated training set. Algorithms were applied to a candidate patient cohort and performance was evaluated using a separate set of 136 records and 179 registry patients. We compared clinical and demographic characteristics of patients identified by computable phenotype and the registry. Results The computable phenotype had an area under the ROC curve of 90% (95% CI 85% – 95%), positive predictive value of 85% (95% CI 77% – 93%), and identified 413 patients (an additional 231%) with pediatric PH not enrolled in the registry. Patients identified by the computable phenotype were clinically distinct from registry patients, with greater prevalence of diagnoses related to perinatal distress and left heart disease. Conclusions Mining of EHRs using computable phenotypes identified a large cohort of patients not recruited using a classic registry. Fusion of EHR and registry data can improve cohort ascertainment for the study of rare diseases. Trial Registration ClinicalTrials.gov: NCT02249923