What is the number needed to treat (NNT)? How does it relate to orthopaedic practice and, ultimately, orthopaedic patient outcomes?
Quite simply, the NNT tells how many patients would need to be treated to prevent one adverse outcome for a particular intervention. A perfect NNT would be 1; this would mean that for every person treated, there would be a reduction of one adverse outcome.
Inversely, the number needed to harm (NNH) is a statistic that would be encountered if the intervention under study is less effective than the control at preventing adverse events. In this scenario, the NNH would specify how many patients would need to be treated to observe one adverse advent that may occur with the use of the intervention under study.
For example, in the AAOS Guideline on the Management of Anterior Cruciate Ligament (ACL) Injuries, the literature review found that for every 109 people participating in a neuromuscular program, the number of ACL injuries would be reduced by one. Relating this NNT statistic to a real-life situation would mean that if an average college soccer team has 20 players and six teams participate in a neuromuscular program, a reduction of one ACL injury over the course of a season could be expected.
With proper understanding and interpretation, the NNT and NNH statistics provide a good overview of the effectiveness of interventions. They can be helpful tools for clinician decision-making and may help spur clinician-patient discussion.
As with all statistics, though, the value of an intervention based on the interpretation of NNT or NNH is heavily dependent on the user. Users may have varying opinions regarding the value of an intervention based on the number of patients who would need to be treated; that is, some may more be comfortable than others recommending a treatment that has a moderate-to-high NNT. Additionally, the NNT and NNH are based on a normal distribution of patients and do not take into account any risk factors that an individual patient may have prior to treatment.
Orthopaedic surgeons who know the inherent limitations and how to properly interpret both NNT and NNH may find them valuable additions to their toolkit when deciding treatment options for a patient.
Kevin Shea, MD, is the guidelines oversight section leader on the AAOS Committee on Evidence-based Quality and Value; Jayson Murray, MA, is the manager, evidence-based medicine; and Peter Shores, MPH, is the statistician in the AAOS department of research and scientific affairs.