Common Variables That Influence Sepsis Mortality in Mice

影响小鼠脓毒症死亡率的常见变量

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Abstract

INTRODUCTION: Sepsis is characterized by a dysregulated host immune response to infection, leading to organ dysfunction and a high risk of death. The cecal ligation and puncture (CLP) mouse model is commonly used to study sepsis, but animal mortality rates vary between different studies. Technical factors and animal characteristics may affect this model in unanticipated ways, and if unaccounted for, may lead to serious biases in study findings. We sought to evaluate whether mouse sex, age, weight, surgeon, season of experiments, and timing of antibiotic administration influenced mortality in the CLP model. METHODS: We created a comprehensive dataset of C57BL/6J mice that had undergone CLP surgery within our lab during years 2015-2020 from published and unpublished studies. The primary outcome was defined as the time from sepsis induction to death or termination of study (14 days). The Log rank test and Cox regression models were used to analyze the dataset. The study included 119 mice, of which 43% were female, with an average age of 12.6 weeks, an average weight of 25.3 g. 38 (32%) of the animals died. RESULTS: In the unadjusted analyses, experiments performed in the summer and higher weight predicted a higher risk of mortality. In the stratified Cox model by sex, summer season (adjusted hazard ratio [aHR]=5.61, p=0.004) and delayed antibiotic administration (aHR=1.46, p=0.029) were associated with mortality in males, whereas higher weight (aHR=1.52, p=0.005) significantly affected mortality in females. In addition, delayed antibiotic administration (HR=1.42, p=0.025) was associated with mortality in the non-summer seasons, but not in the summer season. DISCUSSION: In conclusion, some factors specific to sex and season have a significant influence on sepsis mortality in the CLP model. Consideration of these factors along with appropriate group matching or adjusted analysis is critical to minimize variability beyond the experimental conditions within a study.

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