
Antibiotics get used a lot in dogs and cats (and other species) that undergo surgery. A lot of the time, the antibiotics are unnecessary. I suspect in many cases veterinarians know it’s unnecessary, but they still use them out of habit, fear of complications, fear of complaints from owners, lack of consideration of the potential downsides, and because we are programmed to want to “do something” to avoid adverse outcomes, even when not doing anything may be the best approach.
A big challenge with developing reliable veterinary antimicrobial prophylaxis guidelines is the lack of good studies. There are lots of small observational studies in animals for various types of surgery, which are useful but only provide quite low-certainty evidence. We’d love to have randomized controlled trials for each type of surgery in each species, but such trials are expensive and complex. Another major challenge for any surgical site infection study is enrolling enough cases (i.e. sample size). Since surgical site infections are uncommon after most procedures (which is a good thing!), studies usually need very large sample sizes (e.g. hundreds to thousands of animals) to detect differences between treatment groups, or to confidently say there’s no difference (i.e. non-inferiority trial). I frequently have discussions with people who want to do studies looking at use of antimicrobials and surgical infections. Once we go over the numbers, they tend to quickly get turned off because sample size calculations show the required size would be way too big for what they can manage (or afford) to do. Unfortunately, there simply is not enough funding in this area, so we’re not likely to get these kinds of large trials anytime soon. That doesn’t mean we’re stuck with no evidence at all on which to base our guidelines, it just means we have to rely on different types of evidence that have less certainty.
That can be disheartening to feel like the kinds of studies we want to do are constantly out of reach, but it doesn’t mean that smaller studies are futile even if they can’t answer all the questions. “Don’t let perfection be the enemy of the good” as they say. We just have to have realistic expectations (and avoid over interpreting the results).
We recently published a commentary highlighting this, entitled Small sample sizes in clinical trials: a pragmatic approach to clinical research in veterinary medicine (Weese et al. J Small Anim Pract 2026). A single small study may not be able to answer our big questions, but we can put data together from multiple small studies (if they’re well designed) using meta-analyses to draw stronger / broader conclusions. The concept is that there are no underpowered studies, there are only underpowered analyses.
- Small studies may not be amenable to much or any statistical analysis on their own, yet weak or futile analyses are often attempted, likely because the authors feel it’s expected. However, improper conclusions from underpowered analyses can range from useless to even harmful in some cases.
- We still want small studies to be published, but they may just be data with no analysis, and that can be hard for researchers and readers alike to wrap their heads around.
I raise this concept in the context of a nice recently published little study about bacterial endocarditis in dogs that underwent balloon dilatation because of congenital pulmonary stenosis (Zeedijk & Szatmári et al. 2026). The authors evaluated dogs that underwent this procedure and that had adequate post-operative follow up, with focus on the 83 dogs that didn’t get peri-operative antimicrobials. None developed an infection. There was a smaller group of 11 dogs that did antimicrobials. None of them developed an infection either. We could run a rather futile statistical analysis and conclude that there is no statistically significant difference, but we’d have no confidence in that analysis. The study was not adequately powered for that comparison, so it’s great they actually did not try to do it, but we still have those data for a future meta-analysis.
The 83 dogs that didn’t get antimicrobials can also provide some additional insight. With zero infections in 83 dogs, the 95% confidence interval for the true infection rate would be 0-3.6%, i.e. the true incidence of infection could be between 0 and 36 infections per 1000 dogs that underwent the procedure without antimicrobial treatment.
- Based on this low rate of infection, the potential severity of disease if infection occurs, the ability to treat such an infection, and the potential for complications from prophylaxis, we can consider the balance between risks and benefits of antimicrobial use in these patients. That’s still challenging, but the point is it’s important to consider all these different factors and not just the infection rate.
We can take it a step further, too. Consider that not all infections are preventable, even when antimicrobials are used, so we shouldn’t base our calculations on the assumption antimicrobial use would eliminate all these infections, it would only lower the infection rate.
- Using an infection rate of 3.6% (the upper limit of the confidence interval) and an estimate that antimicrobials would reduce the infection risk by 25% (remembering that we don’t know if they will in fact reduce it at all), the calculated absolute risk reduction would be 0.9% (i.e. 9 infections per 1000 treated dogs).
In this scenario, the number needed to treat (NNT) to prevent a single infection is 111 dogs. (NNT is an underused but very helpful concept to put rates like this into context.) At first glance, an NNT of 111 might seem quite reasonable, especially since infective endocarditis can be a severe disease. However, that’s based on a very conservative assumption that the true infection rate is at the upper end of the confidence interval, and we have yet considered other factors:
- Some of those 111 dogs will experience adverse effects from the antimicrobial. Most will be minor side effects, but occasionally they can be significant. So we need to weight the benefit to some dogs versus the potential harm to others.
- Also remember that we’re basing this number on a very (and likely unrealistically) high infection rate. If the infection rate is 1% and antimicrobials still reduce that risk by 25%, the absolute risk reduction would be only 0.25%, or 2.5 infections prevented per 1000 treated dogs. The NNT would be 400. Drop that infection rate to 0.5%, and the NNT is 800.
The endocarditis rate for this procedure in humans has been reported at 0.12%, which would make the NNT 3333. I think it’s pretty safe to say we’d do a lot of damage treating a few thousand dogs to prevent one case of endocarditis (and that’s not even taking into consideration the risks of antimicrobial resistance selection). Yet, antimicrobials are still very commonly being used in dogs for catheter-based cardiac interventions that are very low risk for infection (Blok et al. 2025).
Too often we don’t do the math. We don’t think about absolute risk reduction, NNT, number needed to harm, broader risks like selection for resistance, or even cost to owners. We get tunnel vision about incidence rates and p-values, and act as though they provide the answers, when in reality they rarely tell the whole story, and are sometimes even misleading.










