Background Pulmonary artery acceleration time measured by echocardiography inversely correlates with pulmonary artery pressures in adults and children older than 1 year of age. There is a paucity of data investigating this relationship in young children, particularly among preterm infants. Objective To characterize the relationship between pulmonary artery acceleration time (PAAT) and pulmonary artery pressures in infants. Design/Methods Patients ≤ 1 year of age at Children's Hospital of Philadelphia between 2011 and 2017 were reviewed. Infants with congenital heart disease were excluded, except those with a patent ductus arteriosus (PDA), atrial septal defect (ASD), or ventricular septal defect (VSD). Linear regression analysis was used to assess the correlation between PAAT measured by echocardiography and systolic pulmonary artery pressure, mean pulmonary artery pressure, and indexed pulmonary vascular resistance from cardiac catheterization. Results Fifty‐seven infants were included, of which 61% were preterm and 49% had a diagnosis of bronchopulmonary dysplasia. The median postmenstrual age and weight at catheterization were 51.1 weeks (IQR 35.8–67.9 weeks) and 4400 g (IQR 3100–6500 g), respectively. Forty‐four infants (77%) had a patent ductus arteriosus (PDA). There was a weak inverse correlation between PAAT with mPAP (r = −0.35, P = 0.01), sPAP (r = −0.29, P = 0.03), and PVRi (r = −0.29, P = 0.03). Conclusion There is a weak inverse relationship between PAAT and pulmonary artery pressures. This relationship is less robust in our population of infants with a high incidence of PDAs compared to previous studies in older children. Thus, PAAT may be less clinically meaningful for diagnosing pulmonary arterial hypertension in infants, particularly those with PDAs.

forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (eD) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of facebook statuses and electronic medical records. An ensemble machine learning model forecasted eD visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits.