The FDA’s 2023 guidance on baseline covariate adjustment highlights the importance of incorporating prognostic covariates into randomized clinical trials (RCTs) efficacy analyses. Adjusting for such covariates can reduce variability in treatment effect estimates, leading to narrower confidence intervals and more powerful hypothesis testing. In line with this guidance, Placebell baseline prognostic covariates were developed for chronic pain indications. The Placebell covariates, derived from chronic pain RCTs, integrate baseline factors such as disease severity, psychological traits, and demographics. 

 This study aimed to evaluate the applicability and benefits of these covariates in an acute pain indication: severe acute lower back pain (LBP). Their impact on the analysis precision was assessed in a phase II trial of severe acute LBP (SP-103-02 sponsored by Scilex Pharmaceutical). The primary endpoint was the time-weighted Summed Pain Intensity Difference (SPID) score, calculated as the change from baseline in daily average pain scores (Days 1–7). Precision improvement was quantified by comparing the primary analysis model to the same model adding the Placebell covariates. 

 Including these prognostic covariates increased the precision of the treatment effect estimate by 34.75% (p<0.001). Having the same precision without them would have required adding 25 patients to the 72 per protocol from the study. As such, the Placebell covariates, designed to account for contextual effects, demonstrated robust transferability from chronic to acute pain settings. By significantly enhancing assay sensitivity, they offer a practical approach to improving precision equivalent to a larger sample size in acute pain RCTs.