Date Published: October 17, 2017
Publisher: Public Library of Science
Author(s): Sanjay Basu, Jeremy B. Sussman, Joseph Rigdon, Lauren Steimle, Brian T. Denton, Rodney A. Hayward, Joshua Z Willey
Abstract: BackgroundIntensive blood pressure (BP) treatment can avert cardiovascular disease (CVD) events but can cause some serious adverse events. We sought to develop and validate risk models for predicting absolute risk difference (increased risk or decreased risk) for CVD events and serious adverse events from intensive BP therapy. A secondary aim was to test if the statistical method of elastic net regularization would improve the estimation of risk models for predicting absolute risk difference, as compared to a traditional backwards variable selection approach.Methods and findingsCox models were derived from SPRINT trial data and validated on ACCORD-BP trial data to estimate risk of CVD events and serious adverse events; the models included terms for intensive BP treatment and heterogeneous response to intensive treatment. The Cox models were then used to estimate the absolute reduction in probability of CVD events (benefit) and absolute increase in probability of serious adverse events (harm) for each individual from intensive treatment. We compared the method of elastic net regularization, which uses repeated internal cross-validation to select variables and estimate coefficients in the presence of collinearity, to a traditional backwards variable selection approach. Data from 9,069 SPRINT participants with complete data on covariates were utilized for model development, and data from 4,498 ACCORD-BP participants with complete data were utilized for model validation. Participants were exposed to intensive (goal systolic pressure < 120 mm Hg) versus standard (<140 mm Hg) treatment. Two composite primary outcome measures were evaluated: (i) CVD events/deaths (myocardial infarction, acute coronary syndrome, stroke, congestive heart failure, or CVD death), and (ii) serious adverse events (hypotension, syncope, electrolyte abnormalities, bradycardia, or acute kidney injury/failure). The model for CVD chosen through elastic net regularization included interaction terms suggesting that older age, black race, higher diastolic BP, and higher lipids were associated with greater CVD risk reduction benefits from intensive treatment, while current smoking was associated with fewer benefits. The model for serious adverse events chosen through elastic net regularization suggested that male sex, current smoking, statin use, elevated creatinine, and higher lipids were associated with greater risk of serious adverse events from intensive treatment. SPRINT participants in the highest predicted benefit subgroup had a number needed to treat (NNT) of 24 to prevent 1 CVD event/death over 5 years (absolute risk reduction [ARR] = 0.042, 95% CI: 0.018, 0.066; P = 0.001), those in the middle predicted benefit subgroup had a NNT of 76 (ARR = 0.013, 95% CI: −0.0001, 0.026; P = 0.053), and those in the lowest subgroup had no significant risk reduction (ARR = 0.006, 95% CI: −0.007, 0.018; P = 0.71). Those in the highest predicted harm subgroup had a number needed to harm (NNH) of 27 to induce 1 serious adverse event (absolute risk increase [ARI] = 0.038, 95% CI: 0.014, 0.061; P = 0.002), those in the middle predicted harm subgroup had a NNH of 41 (ARI = 0.025, 95% CI: 0.012, 0.038; P < 0.001), and those in the lowest subgroup had no significant risk increase (ARI = −0.007, 95% CI: −0.043, 0.030; P = 0.72). In ACCORD-BP, participants in the highest subgroup of predicted benefit had significant absolute CVD risk reduction, but the overall ACCORD-BP participant sample was skewed towards participants with less predicted benefit and more predicted risk than in SPRINT. The models chosen through traditional backwards selection had similar ability to identify absolute risk difference for CVD as the elastic net models, but poorer ability to correctly identify absolute risk difference for serious adverse events. A key limitation of the analysis is the limited sample size of the ACCORD-BP trial, which expanded confidence intervals for ARI among persons with type 2 diabetes. Additionally, it is not possible to mechanistically explain the physiological relationships explaining the heterogeneous treatment effects captured by the models, since the study was an observational secondary data analysis.ConclusionsWe found that predictive models could help identify subgroups of participants in both SPRINT and ACCORD-BP who had lower versus higher ARRs in CVD events/deaths with intensive BP treatment, and participants who had lower versus higher ARIs in serious adverse events.
Partial Text: Elevated blood pressure (BP) is the leading risk factor for death worldwide [1,2], primarily because it increases the risk of cardiovascular disease (CVD) events such as myocardial infarction (MI) and stroke. In the SPRINT trial, patients at high risk for CVD events experienced lower rates of fatal and nonfatal major CVD events when treated with intensive rather than standard BP treatment (goal systolic BP < 120 mm Hg versus <140 mm Hg, respectively) . Yet patients treated with intensive treatment experienced significantly higher rates of some serious adverse events including hypotension, syncope, electrolyte abnormalities, and acute kidney injury or failure. A similar trial conducted on patients with type 2 diabetes mellitus (the ACCORD-BP trial) found lower average benefit of intensive BP treatment than SPRINT . Meta-analyses of randomized trials comparing more intensive to less intensive BP treatment have noted that while CVD events and deaths are typically reduced more among intensively treated participants overall, the increased risk of serious adverse events is not necessarily among the same participants who experience CVD risk reduction—raising the question of whether lower BP targets may best apply to some patient populations than others . In this study, we achieved our principal aim of deriving models that could help identify subgroups of participants in both SPRINT and ACCORD-BP who had lower versus higher ARRs in CVD events/deaths and ARIs in serious adverse events. While numerous models exist for estimating overall CVD risk, the recent availability of individual participant data from randomized intensive BP treatment trials has enabled us to apply a strategy that not only estimates overall risk of CVD events/deaths, but also addresses a different clinically important question: who is most likely to benefit and most likely to experience harm from intensive BP treatment? The models we developed (i) calculate degree of benefit or harm from therapy, rather than only absolute pre-treatment risk; (ii) use data readily available to clinicians, with an online calculator available to provide patient-specific probabilities of benefit and harm to enable individualized patient counseling (and to provide clinicians with individualized NNT values for benefit/harm) ; and (iii) may assist clinician–patient discussions of potential benefits and harms from intensive BP treatment, particularly among patients with concerns about polypharmacy or the occurrence of serious adverse events . An individual practitioner can use the risk calculators for personalized decision-making that may inform treatment choices. Specifically, because many individuals in both SPRINT and ACCORD who were eligible for intensive BP treatment had a higher probability of harm than benefit, or vice versa, the risk calculation may have significant impact on clinical decision-making. Previous studies did not have rigorous calibration testing, or they relied on data from trials that did not have very low systolic BP targets and therefore had very few participants in which very tight BP control was considered [5,10–12]. Our study analyzes ARR rather than only relative risk reduction, and also examines major treatment-related adverse events, which were an uncommon outcome in trials and meta-analyses that had less intensive BP targets than SPRINT or ACCORD-BP . Source: http://doi.org/10.1371/journal.pmed.1002410