Blood pressure ranges via non-invasive and invasive monitoring techniques in premature neonates using high resolution physiologic data.

BACKGROUND: There are limited evidence-based published blood pressure ranges for premature neonates. The aim of the study was to determine blood pressure ranges in a large cohort of premature neonates based on gestational and post-menstrual age. METHODS: Retrospective observational study of premature neonates admitted to the neonatal intensive care unit at our institution between January 2009 and October 2015. We stratified data by gestational and post-menstrual age groups as well as by method of blood pressure measurement (non-invasive vs. invasive). RESULTS: Over two billion blood pressure values in 1708 neonates were analyzed to generate heat maps and establish percentile-based reference ranges. The median gestational age of the cohort was 31 weeks (IQR 28-33 weeks). We found moderate correlation (r = 0.57) between simultaneously obtained non-invasive and invasive blood pressure measurements. CONCLUSIONS: Our results can serve as a reference during the bedside assessment of the critically-ill neonate.

Time-series model to predict impact of H1N1 influenza on a children's hospital.

The spring of 2009 witnessed the emergence of a novel influenza A(H1N1) virus resulting in the first influenza pandemic since 1968. In autumn of 2010, the 2009 novel H1N1 influenza strain re-emerged. We performed a retrospective time-series analysis of all patients with laboratory-confirmed H1N1 influenza who presented to our institution during 2009. Cases of influenza were assembled into 3-day aggregates and forecasting models of H1N1 influenza incidence were created. Forecasting estimates of H1N1 incidence for the 2010-2011 season were compared to actual values for our institution to assess model performance. Ninety-five percent confidence intervals calculated around our model's forecasts were accurate to ±3·6 cases per 3-day period for our institution. Our results suggest that time-series models may be useful tools in forecasting the incidence of H1N1 influenza, helping institutions to optimize distribution of resources based on the changing burden of illness.

A multi-tiered time-series modelling approach to forecasting respiratory syncytial virus incidence at the local level.

Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged <18 years with laboratory-confirmed RSV within a network of multiple affiliated academic medical institutions. Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to ±9·3, ±7·5 and ±1·5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.