Development and Validation of a Mortality Prediction Model in Extremely Low Gestational Age Neonates.

INTRODUCTION: Understanding factors that associate with neonatal death may lead to strategies or interventions that can aid clinicians and inform families. OBJECTIVE: The aim of the study was to develop an early prediction model of neonatal death in extremely low gestational age (ELGA, <28 weeks) neonates. METHODS: A predictive cohort study of ELGA neonates was derived from the Swedish Neonatal Quality Register between the years 2011 to May 2021. The goal was to use readily available clinical variables, collected within the first hour of birth, to predict in-hospital death. Data were split into a train cohort (80%) to build the model and tested in 20% of randomly selected neonates. Model performance was assessed via area under the receiver operating characteristic curve (AUC) and compared to validated mortality prediction models and an external cohort of neonates. RESULTS: Among 3,752 live-born extremely preterm infants (46% girls), in-hospital mortality was 18% (n = 685). The median gestational age and birth weight were 25.0 weeks (interquartile range [IQR] 24.0, 27.0) and 780 g (IQR 620, 940), respectively. The proposed model consisted of three variables: birth weight (grams), Apgar score at 5 min of age, and gestational age (weeks). The BAG model had an AUC of 76.9% with a 95% confidence interval (CI) (72.6%, 81.3%), while birth weight and gestational age had an AUC of 73.1% (95% CI: 68.4%,77.9%) and 71.3% (66.3%, 76.2%). In the validation cohort, the BAG model had an AUC of 68.9%. CONCLUSION: The BAG model is a new mortality prediction model in ELGA neonates that was developed using readily available information.

Recruiting trainees to surgery in the United States and Canadian system - What strategies are effective?

BACKGROUND: There has been increasing concerns regarding the declining number of medical students entering surgical residencies. The aim of this study is to analyze strategies and outcomes to enhance recruitment to the surgical specialties. METHODS: A systematic literature PRISMA-based search was performed. Study quality and bias were assessed. Meta-analysis was performed using DerSimonian Laird method. RESULTS: Of 3288 unique titles identified, 73 studies met inclusion criteria. Median study unique sample size was 84 participants (range 15-910). Subjective interest was reported in 59 studies, while objective match rate was reported by only 21 studies. The cumulative odds of students interested in the studied specialty was 1.98 (95% CI 1.47-2.67, I2 = 0%) and in any surgical specialty was 1.40 (95% 1.01-1.95, I2 = 37%) after an intervention compared to baseline. CONCLUSION: While studies show increased odds of "interested in" a surgical specialty, the results may be subject to high selective and confounding biases.