Another Nudge to Overcome the Treatment-Risk Paradox in Blood Pressure Management

Incorporating Individual-Level Treatment Effects and Outcome Preferences Into Personalized Blood Pressure Target Recommendations

BACKGROUND

There are no shared decision-making frameworks for selecting blood pressure (BP) targets for individuals with hypertension. This study addressed whether results from the SPRINT (Systolic Blood Pressure Intervention Trial) could be tailored to individuals using predicted risks and simulated preferences.

METHODS AND RESULTS

Among 8202 SPRINT participants, Cox models were developed and internally validated to predict each individual's absolute difference in risk from intensive versus standard BP lowering for cardiovascular events, cognitive impairment, death, and serious adverse events (AEs). Individual treatment effects were combined using simulated preference weights into a net benefit, which represents a weighted sum of risk differences across outcomes. Net benefits were compared among those above versus below the median AE risk. In simulations for which cardiovascular, cognitive, and death events had much greater weight than the AEs of BP lowering, the median net benefit was 3.3 percentage points (interquartile range [IQR], 2.0-5.7), and 100% of participants had a net benefit favoring intensive BP lowering. When simulating benefits and harms to have similar weights, the median net benefit was 0.8 percentage points (IQR, 0.2-2.2), and 87% had a positive net benefit. Compared with participants at lower risk of AEs from BP lowering, those at higher risk had a greater net benefit from intensive BP lowering despite experiencing more AEs (P<0.001 in both simulations).

CONCLUSIONS

Most SPRINT participants had a predicted net benefit that favored intensive BP lowering, but the degree of net benefit varied considerably. Tailoring BP targets using each patient's risks and preferences may provide more refined BP target recommendations.

OUP accepted manuscript

The increasing volume and richness of healthcare data collected during routine clinical practice have not yet translated into significant numbers of actionable insights that have systematically improved patient outcomes. An evidence-practice gap continues to exist in healthcare. We contest that this gap can be reduced by assessing the use of nudge theory as part of clinical decision support systems (CDSS). Deploying nudges to modify clinician behaviour and improve adherence to guideline-directed therapy represents an underused tool in bridging the evidence-practice gap. In conjunction with electronic health records (EHRs) and newer devices including artificial intelligence algorithms that are increasingly integrated within learning health systems, nudges such as CDSS alerts should be iteratively tested for all stakeholders involved in health decision-making: clinicians, researchers, and patients alike. Not only could they improve the implementation of known evidence, but the true value of nudging could lie in areas where traditional randomized controlled trials are lacking, and where clinical equipoise and variation dominate. The opportunity to test CDSS nudge alerts and their ability to standardize behaviour in the face of uncertainty may generate novel insights and improve patient outcomes in areas of clinical practice currently without a robust evidence base.