Introduction: In SPRINT (Systolic blood PRessure INtervention Trial), non-diabetic patients with hypertension at high cardiovascular risk treated with intensive blood pressure (BP) control (<120mmHg) had fewer major adverse cardiovascular events (MACE) and all-cause deaths but higher rates of serious adverse events (SAE) compared with patients treated with standard BP control (<140mmHg). However, the degree of benefit or harm for an individual patient could vary due to heterogeneity in treatment effect.
Methods: Using patient-level data from SPRINT, we developed predictive models for benefit (freedom from death or MACE) and harm (increased SAE) to allow for individualized BP treatment goals based on projected risk-benefit for each patient. Interactions between candidate variable and treatment were evaluated in the models to identify differential treatment effects. We performed 10 fold cross-validation for both the models.
Results: Among 9361 patients, 8606 (92%) patients had no MACE or death event (benefit) and 3529 (38%) patients had a SAE (harm) over a median follow-up of 3.3 years. The benefit model showed good discrimination (c-index= 0.72; cross-validated c-index= 0.72) with treatment interactions of age, sex, and baseline systolic BP (Figure A), with more benefit of intensive BP treatment in patients who are older, male, and have lower baseline SBP. The SAE risk model showed moderate discrimination (c-index=0.66; cross-validated c-index= 0.65) with a treatment interaction of baseline renal function (Figure B), indicating less harm of intensive treatment in patients with a higher baseline creatinine. The mean predicted absolute benefit of intensive BP treatment was of 2.2% ± 2.5% compared with standard treatment, but ranged from 10.7% lower benefit to 17% greater benefit in individual patients. Similarly, mean predicted absolute harm with intensive treatment was 1.0% ± 1.9%, but ranged from 15.9% lesser harm to 4.9% more harm.
Conclusion: Among non-diabetic patients with hypertension at high cardiovascular risk, we developed prediction models using basic clinical data that can identify patients with higher likelihood of benefit vs. harm with BP treatment strategies. These models could be used to tailor the treatment approach based on the projected risk and benefit for each unique patient.