Informing efficient pilot development of animal trauma models through quality improvement strategies

Poor quality data in preclinical trials can result from inconsistent and unstandardized experimental processes. Unpredictable pre-intervention variability generates unreliable data, biases outcomes and results in needless waste of animals and resources. We applied Define-Measure-Analyse-Improve-Control (DMAIC) quality improvement processes to pilot development of a swine model of trauma, haemorrhagic shock and coagulopathy. The goal was to reduce variability through protocol standardization and error reduction. Six male Sinclair swine were sequentially anesthetized, intubated, mechanically ventilated and instrumented, then subjected to multiple-hit injury, followed by fluid resuscitation monitoring and coagulation testing. Experimental tasks were defined and mapped. Performance measures were task performance times, subject stabilization time and number of task execution errors. Process improvement was assessed by reduced times and errors, and subject stability at target physiological values. Previously-overlooked performance errors and deficiencies were identified. 'Mistake-proofing' actions included personnel retraining, revisions of standard operating procedures and use of checklists. The quality improvement pilot trial produced a stable model with reduced protocol deviations. Data quality can be improved and animal waste minimized, if experimental planning incorporates strategies to ensure protocol adherence and reduced operator performance variation and errors. Properly designed pilot trials can be essential components of refinement and reduction strategies in animal-based research.