Systematic Reviews (SRs) of experimental animal studies are not yet common practice, but awareness of the merits of conducting such SRs is steadily increasing. As animal intervention studies differ from randomized clinical trials (RCT) in many aspects, the methodology for SRs of clinical trials needs to be adapted and optimized for animal intervention studies. The Cochrane Collaboration developed a Risk of Bias (RoB) tool to establish consistency and avoid discrepancies in assessing the methodological quality of RCTs. A similar initiative is warranted in the field of animal experimentation.

Cochrane Reviews are intended to help providers, practitioners and patients make informed decisions about health care. The goal of the Cochrane Applicability and Recommendation Methods Group (ARMG) is to develop approaches, strategies and guidance that facilitate the uptake of information from Cochrane Reviews and their use by a wide audience with specific focus on developers of recommendations and on healthcare decision makers. This paper is part of a series highlighting developments in systematic review methodology in the 20 years since the establishment of The Cochrane Collaboration, and its aim is to present current work and highlight future developments in assessing and presenting summaries of evidence, with special focus on Summary of Findings (SoF) tables and Plain Language Summaries.

Laboratory animal studies are used in a wide range of human health related research areas, such as basic biomedical research, drug research, experimental surgery and environmental health. The results of these studies can be used to inform decisions regarding clinical research in humans, for example the decision to proceed to clinical trials. If the research question relates to potential harms with no expectation of benefit (e.g., toxicology), studies in experimental animals may provide the only relevant or controlled data and directly inform clinical management decisions. Systematic reviews and meta-analyses are important tools to provide robust and informative evidence summaries of these animal studies. Rating how certain we are about the evidence could provide important information about the translational probability of findings in experimental animal studies to clinical practice and probably improve it. Evidence summaries and certainty in the evidence ratings could also be used (1) to support selection of interventions with best therapeutic potential to be tested in clinical trials, (2) to justify a regulatory decision limiting human exposure (to drug or toxin), or to (3) support decisions on the utility of further animal experiments. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach is the most widely used framework to rate the certainty in the evidence and strength of health care recommendations. Here we present how the GRADE approach could be used to rate the certainty in the evidence of preclinical animal studies in the context of therapeutic interventions. We also discuss the methodological challenges that we identified, and for which further work is needed. Examples are defining the importance of consistency within and across animal species and using GRADE’s indirectness domain as a tool to predict translation from animal models to humans.

Aims : Lipoprotein(a) [Lp(a)] is a causal and independent risk factor for cardiovascular disease (CVD). People with elevated Lp(a) are often prescribed statins as they also often show elevated low-density lipoprotein cholesterol (LDL-C) levels. While statins are well-established in lowering LDL-C, their effect on Lp(a) remains unclear. We evaluated the effect of statins compared to placebo on Lp(a) and the effects of different types and intensities of statin therapy on Lp(a). Methods and results : We conducted a systematic review and meta-analysis of randomized trials with a statin and placebo arm. Medline and EMBASE were searched until August 2019. Quality assessment of studies was done using Cochrane risk-of-bias tool (RoB 2). Mean difference of absolute and percentage changes of Lp(a) in the statin vs. the placebo arms were pooled using a random-effects meta-analysis. We compared effects of different types and intensities of statin therapy using subgroup- and network meta-analyses. Certainty of the evidence was determined using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Overall, 39 studies (24 448 participants) were included. Mean differences (95% confidence interval) of absolute and percentage changes in the statin vs. the placebo arms were 1.1 mg/dL (0.5–1.6, P < 0.0001) and 0.1% (−3.6% to 4.0%, P = 0.95), respectively (moderate-certainty evidence). None of the types of statins changed Lp(a) significantly compared to placebo (very low- to high-certainty evidence), as well as intensities of statin therapy (low- to moderate-certainty evidence). Conclusion : Statin therapy does not lead to clinically important differences in Lp(a) compared to placebo in patients at risk for CVD. Our findings suggest that in these patients, statin therapy will not change Lp(a)-associated CVD risk.