The SPRINT trial made waves in the field of hypertension, showing evidence for reduced hard clinical endpoints (a combined outcome of myocardial infarction, acute coronary syndrome not resulting in myocardial infarction, stroke, acute decompensated heart failure, or death from cardiovascular causes) in those patients with a BP target of 120 mm Hg systolic.
At the same time, it was clearly shown than more intensive blood pressure control drove increased risk of adverse events, including hypotension, syncope, electrolyte abnormalities, and acute kidney injury or failure.
The challenge with randomized clinical trials is that the results, while considered valid for the total population studied, may not apply to individual patients who simply meet the enrolment criteria for the study.
Some of those patients would derive more benefit and less harm, while others will see more harm and less benefits. As the world moves towards personalized medicine, it would be ideal to be able to individualize how we apply the results of a clinical trial.
Whether the interventions are beneficial for an individual patient appears to be dependent on the individual clinical circumstances and the preferences of the patient. We would strongly recommend the development of methods for improving shared decision making with patients on this topic before recommending this intervention be part of routine practice.
Well, enter the SPRINT data analysis challenge, and researchers like Noa Dagan, MD, MPH, the head of data at Clalit Research Institute, Rahul Aggarwal, Boston University School of Medicine, and Joseph Rigdon, PhD from Stanford University.
Researchers like these re-analyzed the SPRINT data, made available by the NIH and its researchers, in order to derive more specific recommendations for individual patients.
Each researcher had a unique approach to personalizing the trial to individual patients or subgroups of patients. Perhaps most fascinating was the approach taken by content winner Dr. Dagan and her group, where they adapted the concept of the Number Needed to Treat (NNT) and calculated iNNT – the individualized number needed to treat (or harm). Unlike the NNT which suggests how many all-comers would need to be treated to see a benefit of treatment, the iNNT represents the number of people, identical to the person in front of you, who would need to be treated to see one of them benefit.
Attempts to individualize treatment recommendations based on the balance of benefit vs harm obviously requires some subjective evaluation of how much real patients might value a potential benefit versus fear a potential harm.
It is clear that this type of analyses will require more patient input on the values, wishes and beliefs which would determine acceptability of treatment. That being said, it is exceptionally exciting to see raw clinical trial being opened to the public, with researchers finding novel ways to personalize the applicability of a landmark clinical trial.
On a personal note, as medical director of QxMD, I’m eager to collaborate with researchers doing this type of work to adapt personalized decision support tools into the app Calculate by QxMD, so that we can make the results of this work accessible to the 1.5 million clinicians who use our platform every year.