One score to rule them all: severity assessment in laboratory mice

Animal welfare and the refinement of experimental procedures are fundamental aspects of biomedical research. They provide the basis for robust experimental designs and reproducibility of results. In many countries, the determination of welfare is a mandatory legal requirement and implies the assessment of the degree of the severity that an animal experiences during an experiment. However, for an effective severity assessment, an objective and exact approach/system/strategy is needed. In light of these demands, we have developed the Relative Severity Assessment (RELSA) score. This comprehensive composite score was established on the basis of physiological and behavioral data from a surgical mouse study. Body weight, the Mouse Grimace Scale score, burrowing behavior, and the telemetry-derived parameters heart rate, heart rate variability, temperature, and general activity were used to investigate the quality of indicating severity during postoperative recovery. The RELSA scores not only revealed individual severity levels but also allowed a comparison of severity in distinct mouse models addressing colitis, sepsis, and restraint stress using a k-means clustering approach with the maximum achieved RELSA scores. We discriminated and classified data from sepsis nonsurvivors into the highest relative severity level. Data from mice after intraperitoneal transmitter implantation and sepsis survivor al were located in the next lower cluster, while data from mice subjected to colitis and restraint stress were placed in the lowest severity cluster. Analysis of individual variables and their combinations revealed model- and time-dependent contributions to severity levels. In conclusion, we propose the RELSA score as a validated tool for objective real-time applicability in severity assessment and as a first step towards a unified and accessible risk assessment tool in biomedical research. As an effective severity assessment system, it will fundamentally improve animal welfare, as well as data quality and reproducibility.