Dr. Bikash Bhojnagarwala

From training to clinical practice: insights from a survey looking at clinician experiences during the ‘Serial Lung Ultrasound in predicting the need for surfactant and Respiratory course in Preterm infants’ (SLURP) study - European Journal of Pediatrics

In this brief report, we present the findings of a survey which explores the learning curve that was achieved during the ‘Serial Lung Ultrasound in predicting the need for surfactant and Respiratory course in Preterm infants’ (SLURP) study. The aim of this survey was to evaluate the training lung ultrasound operators received in order to scan patients who were recruited into the SLURP study. The aim of the SLURP study was to validate the optimal lung ultrasound (LU) ‘cut off’ score using standard and extended LU zones that predict surfactant need in babies born ≤ 34 weeks on non-invasive respiratory support when clinicians had various levels of experience in LU. The study was a prospective observational study conducted between May 2023 to June 2024 in 3 neonatal intensive care units (NICUs) across the UK. A standardised lung ultrasound training programme was delivered across the 3 sites before starting the SLURP study which included an online module and 1 day of didactic lectures with face-to-face hands-on training. We performed a survey towards the end of the SLURP study to evaluate the learning curve of a significant number of inexperienced lung ultrasound operators participating in this study. The results of the survey showed that a structured training package alongside supervision developed clinicians’ ability to recognise common lung pathologies, perform scans independently and improve their scanning speed.Conclusion: The SLURP study was a major driving force in upskilling clinicians in LU across 3 UK NICUs. The study was unique in that it was the first prospective LU study in the UK, and it utilised a large proportion of staff with no prior LU experience. This survey demonstrates that the implementation of a structured training package and supervision led to a rapid learning curve for basic LU skills which can be replicated on a larger scale across the UK. What is Known: • Training and implementation of neonatal lung ultrasound (LU) remains inconsistent across neonatal units. • The effectiveness of neonatal LU training is infrequently evaluated and reported in the literature. What is New: • Novice neonatal LU operators can be trained within a short time frame through a structured training programme. • This training enabled the operators to perform high-quality scans, with reduced scanning time and differentiate between various neonatal lung pathologies.

A Simplified and Data-Driven Lung Ultrasound Approach for Predicting Surfactant Need in Preterm Infants: A Pilot Study Using Machine Learning and Rule-Based Models

This study evaluated whether specific lung ultrasound regions could better predict the need for surfactant in preterm infants ≤34 weeks. Using data from the SLURP cohort, researchers found that a simplified model using a few key regions (especially left lateral, left upper posterior, and right upper anterior) predicted surfactant need more accurately than the traditional 6-region LUS score, making the approach faster and more practical for clinical use.

BAPM Special Interest Group: NeoFOCUS-UK | British Association of Perinatal Medicine

Neonatal Functional Haemodynamics and Point-of-Care Ultrasound.