How would automatic health alerting change your study?

In conjunction with BMS

What would you do if you had animal model that died overnight without warning? No standard methods of monitoring animal health were able to predict sick animals. But, as a researcher, data collection throughout the whole experiment, including the final experiment day, was critical for maintaining experiment power and success. Loss of animals on study, or attrition, can significantly impact total animal numbers required and potential study results [1,2]. This is the challenge of one researcher at Bristol Myer Squibb (BMS).

“Given small sample sizes, loss of animals in preclinical experiments can dramatically alter results”

Tim Sproul at BMS tested out the Vium platform’s ability to detect behavioral or physiological changes that could predict death in this challenging model. The study continuously monitored ~35 animals that belonged to one of 4 different experimental groups. Only one of the four groups contained animals challenging to identify prior to death. Within a month, the first animal was found dead. A review of the animal’s record revealed obvious changes in motion and breathing rates that preceded death by more than 24 hours. Armed with this new knowledge, we diligently monitored all animals moving forward and were able to identify and collect 6 of 7 animals with similar behavioral patterns.

Since this study, Vium has created and is putting to the test an animal alerting system that is designed to detect abnormal patterns in motion (reductions), breathing rate (both reduced and increased) and circadian rhythms. Individual subject data from our experiment was run back through the alerting algorithms.

Further reading

1. Where Have All the Rodents Gone? The Effects of Attrition in Experimental Research on Cancer and Stroke
2. How to calculate sample size in animal studies?
3. https://eda.nc3rs.org.uk/experimental-design-group