RELSA—A multidimensional procedure for the comparative assessment of well-being and the quantitative determination of severity in experimental procedures - Talbot, Steven R.; Struve, Birgitta; Wassermann, Laura; Heider, Miriam; Weegh, Nora; Knape, Tilo; Hofmann, Martine C. J.; von Knethen, Andreas; Jirkof, Paulin; Häger, Christine; Bleich, André (Frontiers in Veterinary Science, 2022)
Good science in translational research requires good animal welfare according to the 3R principle. In many countries, the determination of animal welfare is a mandatory legal requirement, implying a categorization of animal suffering, traditionally dominated by subjective scorings. However, how such methods can be objectified and refined to compare impairments between animals, subgroups, and animal models remained unclear. Therefore, we developed the RELative Severity Assessment (RELSA) procedure to establish an evidence-based method based on quantitative outcome measures such as body weight, burrowing behavior, heart rate, heart rate variability, temperature, and activity to obtain a relative metric for severity comparisons. The RELSA procedure provided the necessary framework to get gradings of severity in TM-implanted mice, yielding four distinct RELSA thresholds L1<0.27, L2<0.59, L3<0.79, and L4<3.45. We show further that severity patterns in the contributing variables are time and model-specific and use this information to obtain contextualized between animal-model and subgroup comparisons with the severity of sepsis > surgery > restraint stress > colitis. The bootstrapped 95% confidence intervals reliably show that RELSA estimates are conditionally invariant against missing information but precise in ranking the quantitative severity information to the moderate context of the transmitter-implantation model. In conclusion, we propose the RELSA as a validated tool for an objective, computational approach to comparative and quantitative severity assessment and grading. The RELSA procedure will fundamentally improve animal welfare, data quality, and reproducibility. It is also the first step towards translational risk assessment in biomedical research.