BackgroundWe evaluated the ability of 23 novel biomarkers representing several pathophysiological pathways to improve the prediction of cardiovascular event (CVE) risk in patients with type 2 diabetes mellitus beyond traditional risk factors.Methods and ResultsWe used data from 1002 patients with type 2 diabetes mellitus from the Second Manifestations of ARTertial disease (SMART) study and 288 patients from the European Prospective Investigation into Cancer and Nutrition‐NL (EPIC‐NL). The associations of 23 biomarkers (adiponectin, C‐reactive protein, epidermal‐type fatty acid binding protein, heart‐type fatty acid binding protein, basic fibroblast growth factor, soluble FMS‐like tyrosine kinase‐1, soluble intercellular adhesion molecule‐1 and ‐3, matrix metalloproteinase [MMP]‐1, MMP‐3, MMP‐9, N‐terminal prohormone of B‐type natriuretic peptide, osteopontin, osteonectin, osteocalcin, placental growth factor, serum amyloid A, E‐selectin, P‐selectin, tissue inhibitor of MMP‐1, thrombomodulin, soluble vascular cell adhesion molecule‐1, and vascular endothelial growth factor) with CVE risk were evaluated by using Cox proportional hazards analysis adjusting for traditional risk factors. The incremental predictive performance was assessed with use of the c‐statistic and net reclassification index (NRI; continuous and based on 10‐year risk strata 0–10%, 10–20%, 20–30%, >30%). A multimarker model was constructed comprising those biomarkers that improved predictive performance in both cohorts. N‐terminal prohormone of B‐type natriuretic peptide, osteopontin, and MMP‐3 were the only biomarkers significantly associated with an increased risk of CVE and improved predictive performance in both cohorts. In SMART, the combination of these biomarkers increased the c‐statistic with 0.03 (95% CI 0.01–0.05), and the continuous NRI was 0.37 (95% CI 0.21–0.52). In EPIC‐NL, the multimarker model increased the c‐statistic with 0.03 (95% CI 0.00–0.03), and the continuous NRI was 0.44 (95% CI 0.23–0.66). Based on risk strata, the NRI was 0.12 (95% CI 0.03–0.21) in SMART and 0.07 (95% CI −0.04–0.17) in EPIC‐NL.ConclusionsOf the 23 evaluated biomarkers from different pathophysiological pathways, N‐terminal prohormone of B‐type natriuretic peptide, osteopontin, MMP‐3, and their combination improved CVE risk prediction in 2 separate cohorts of patients with type 2 diabetes mellitus beyond traditional risk factors. However, the number of patients reclassified to a different risk stratum was limited.

BackgroundClinical practice guidelines have traditionally recommended blood pressure treatment based primarily on blood pressure thresholds. In contrast, using predicted cardiovascular risk has been advocated as a more effective strategy to guide treatment decisions for cardiovascular disease (CVD) prevention. We aimed to compare outcomes from a blood pressure-lowering treatment strategy based on predicted cardiovascular risk with one based on systolic blood pressure (SBP) level.Methods and findingsWe used individual participant data from the Blood Pressure Lowering Treatment Trialists’ Collaboration (BPLTTC) from 1995 to 2013. Trials randomly assigned participants to either blood pressure-lowering drugs versus placebo or more intensive versus less intensive blood pressure-lowering regimens. We estimated 5-y risk of CVD events using a multivariable Weibull model previously developed in this dataset. We compared the two strategies at specific SBP thresholds and across the spectrum of risk and blood pressure levels studied in BPLTTC trials. The primary outcome was number of CVD events avoided per persons treated. We included data from 11 trials (47,872 participants). During a median of 4.0 y of follow-up, 3,566 participants (7.5%) experienced a major cardiovascular event. Areas under the curve comparing the two treatment strategies throughout the range of possible thresholds for CVD risk and SBP demonstrated that, on average, a greater number of CVD events would be avoided for a given number of persons treated with the CVD risk strategy compared with the SBP strategy (area under the curve 0.71 [95% confidence interval (CI) 0.70–0.72] for the CVD risk strategy versus 0.54 [95% CI 0.53–0.55] for the SBP strategy). Compared with treating everyone with SBP ≥ 150 mmHg, a CVD risk strategy would require treatment of 29% (95% CI 26%–31%) fewer persons to prevent the same number of events or would prevent 16% (95% CI 14%–18%) more events for the same number of persons treated. Compared with treating everyone with SBP ≥ 140 mmHg, a CVD risk strategy would require treatment of 3.8% (95% CI 12.5% fewer to 7.2% more) fewer persons to prevent the same number of events or would prevent 3.1% (95% CI 1.5%–5.0%) more events for the same number of persons treated, although the former estimate was not statistically significant. In subgroup analyses, the CVD risk strategy did not appear to be more beneficial than the SBP strategy in patients with diabetes mellitus or established CVD.ConclusionsA blood pressure-lowering treatment strategy based on predicted cardiovascular risk is more effective than one based on blood pressure levels alone across a range of thresholds. These results support using cardiovascular risk assessment to guide blood pressure treatment decision-making in moderate- to high-risk individuals, particularly for primary prevention.

Background Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. “CKD Patch” is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures. Methods Utilizing data from 4,143,535 adults from 35 datasets, we developed several “CKD Patches” incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch. Findings We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018–0.036] and 0.010 [0.007–0.013] and categorical net reclassification improvement 0.080 [0.032–0.127] and 0.056 [0.044–0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89–3.40) in very high-risk CKD (e.g., eGFR 30–44 ml/min/1.73m 2 with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m 2 with albuminuria 30–299 mg/g), and 1.37 (1.14–1.69) in moderate risk CKD (e.g., eGFR 60–89 ml/min/1.73m 2 with albuminuria 30–299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37–1.81), 1.24 (1.10–1.54), and 1.21 (0.98–1.46). Interpretation The “CKD Patch” can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available. Funding US National Kidney Foundation and the NIDDK.

Large-scale randomized clinical trials have established the efficacy of cholesterol-lowering, blood pressure-lowering, and anti-platelet therapy to prevent cardiovascular diseases. A challenge for clinicians is to apply group-level evidence from these trials to individual patients. Trials typically report a single treatment effect estimate which is the average effect of all participants, comprising patients who respond poorly, intermediately, and well. Clinicians would preferably make patient-tailored treatment decisions. Therefore, one would require an estimate of an individual patient's response to therapy. Although not yet widely recognized, trials contain this type of information. In this paper, we show how available information from landmark trials can be translated to an individual 'treatment score' through the use of multivariable therapeutic prediction models. These models provide an individual estimate of the absolute risk reduction in cardiovascular events given the specific combination of multiple clinical characteristics of a patient under care. Based on this individualized treatment estimate and metrics such as the individual number-needed-to-treat, clinicians together with their patients can decide whether drug treatment or what treatment intensity is worthwhile. Selective treatment of those who can anticipate the greatest benefit and the least harm on an individualized basis could reduce the number of unnecessary treatments and healthcare costs beyond that currently achievable by subgroup analyses based on single patient characteristics.

All 10 evaluated models had a comparable and moderate discriminative ability. The recalibrated, but not the original, prediction models provided accurate risk estimates. These models can assist clinicians in identifying type 2 diabetes patients who are at low or high risk of developing CVD.