Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention.

Acknowledgements: The study was funded by the Wellcome Trust (223016/Z/21/Z). TJ is supported by a grant from UK Medical Research Council (MC_UU_00002/14). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Tetris® has been licenced for use within i-spero® from The Tetris Company. The authors would like to thank the Intensive Care Society, in particular Dr Sandy Mather and Alex Day; Our Data Monitoring Committee members comprising Prof Andreas Reif (chair, Frankfurt am Main—Goethe University), Prof Steve Hollon (Vanderbilt University), Prof Ian Penton-Voak (Bristol University), and adjunct member Prof Michael B. Bonsall (University of Oxford); Trial Steering Committee including Prof Guy Goodwin, Pooyan Behbahani, and Rebecca Dias; Expert Advisors including Dr Nick Grey, Prof Sir Simon Wessely, and Prof Jonathon Bisson. Members of study team including Veronika Kubičková, Alfred Markham, Zunaid Islam and Marie Kanstrup for support; Our Data Management Team including Mark Dziedzic, Sameer Iqbal, and Nikita Shukan.Intensive care unit (ICU) staff continue to face recurrent work-related traumatic events throughout the COVID-19 pandemic. Intrusive memories (IMs) of such traumatic events comprise sensory image-based memories. Harnessing research on preventing IMs with a novel behavioural intervention on the day of trauma, here we take critical next steps in developing this approach as a treatment for ICU staff who are already experiencing IMs days, weeks, or months post-trauma. To address the urgent need to develop novel mental health interventions, we used Bayesian statistical approaches to optimise a brief imagery-competing task intervention to reduce the number of IMs. We evaluated a digitised version of the intervention for remote, scalable delivery. We conducted a two-arm, parallel-group, randomised, adaptive Bayesian optimisation trial. Eligible participants worked clinically in a UK NHS ICU during the pandemic, experienced at least one work-related traumatic event, and at least three IMs in the week prior to recruitment. Participants were randomised to receive immediate or delayed (after 4 weeks) access to the intervention. Primary outcome was the number of IMs of trauma during week 4, controlling for baseline week. Analyses were conducted on an intention-to-treat basis as a between-group comparison. Prior to final analysis, sequential Bayesian analyses were conducted (n = 20, 23, 29, 37, 41, 45) to inform early stopping of the trial prior to the planned maximum recruitment (n = 150). Final analysis (n = 75) showed strong evidence for a positive treatment effect (Bayes factor, BF = 1.25 × 106): the immediate arm reported fewer IMs (median = 1, IQR = 0-3) than the delayed arm (median = 10, IQR = 6-16.5). With further digital enhancements, the intervention (n = 28) also showed a positive treatment effect (BF = 7.31). Sequential Bayesian analyses provided evidence for reducing IMs of work-related trauma for healthcare workers. This methodology also allowed us to rule out negative effects early, reduced the planned maximum sample size, and allowed evaluation of enhancements. Trial Registration NCT04992390 ( www.clinicaltrials.gov )