Schaik, Ron H. N. van

Background We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high‐sensitivity cardiac troponin T, N‐terminal pro‐B‐type natriuretic peptide, high‐sensitivity C‐reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long‐term mortality risk. Methods and Results BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high‐frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker‐based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all‐cause death were evaluated using accelerated failure time models (median follow‐up, 9.1 years; 141 deaths). Three biomarker‐based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long‐term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44–0.93; P =0.018) compared with cluster 1, and 0.71 (95% CI, 0.39–1.32; P =0.281) compared with patients with a repeat ACS. Conclusions Patients with subphenotypes of post‐ACS with different all‐cause mortality risks during long‐term follow‐up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

Abstract Aims Evidence regarding the role of serial measurements of biomarkers for risk assessment in post-acute coronary syndrome (ACS) patients is limited. The aim was to explore the prognostic value of four, serially measured biomarkers in a large, real-world cohort of post-ACS patients. Methods and results BIOMArCS is a prospective, multi-centre, observational study in 844 post-ACS patients in whom 12 218 blood samples (median 17 per patient) were obtained during 1-year follow-up. The longitudinal patterns of high-sensitivity cardiac troponin T (hs-cTnT), N-terminal-pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), and growth differentiation factor 15 (GDF-15) were analysed in relation to the primary endpoint (PE) of cardiovascular mortality and recurrent ACS using multivariable joint models. Median age was 63 years, 78% were men and the PE was reached by 45 patients. The average biomarker levels were systematically higher in PE compared with PE-free patients. After adjustment for 6-month post-discharge Global Registry of Acute Coronary Events score, 1 standard deviation increase in log[hs-cTnT] was associated with a 61% increased risk of the PE [hazard ratio (HR) 1.61, 95% confidence interval (CI) 1.02–2.44, P = 0.045], while for log[GDF-15] this was 81% (HR 1.81, 95% CI 1.28–2.70, P = 0.001). These associations remained significant after multivariable adjustment, while NT-proBNP and hs-CRP were not. Furthermore, GDF-15 level showed an increasing trend prior to the PE (Structured Graphical Abstract). Conclusion Longitudinally measured hs-cTnT and GDF-15 concentrations provide prognostic value in the risk assessment of clinically stabilized patients post-ACS. Clinical Trial Registration The Netherlands Trial Register. Currently available at URL https://trialsearch.who.int/; Unique Identifiers: NTR1698 and NTR1106.

Abstract: When mycophenolic acid (MPA) was originally marketed for immunosuppressive therapy, fixed doses were recommended by the manufacturer. Awareness of the potential for a more personalized dosing has led to development of methods to estimate MPA area under the curve based on the measurement of drug concentrations in only a few samples. This approach is feasible in the clinical routine and has proven successful in terms of correlation with outcome. However, the search for superior correlates has continued, and numerous studies in search of biomarkers that could better predict the perfect dosage for the individual patient have been published. As it was considered timely for an updated and comprehensive presentation of consensus on the status for personalized treatment with MPA, this report was prepared following an initiative from members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT). Topics included are the criteria for analytics, methods to estimate exposure including pharmacometrics, the potential influence of pharmacogenetics, development of biomarkers, and the practical aspects of implementation of target concentration intervention. For selected topics with sufficient evidence, such as the application of limited sampling strategies for MPA area under the curve, graded recommendations on target ranges are presented. To provide a comprehensive review, this report also includes updates on the status of potential biomarkers including those which may be promising but with a low level of evidence. In view of the fact that there are very few new immunosuppressive drugs under development for the transplant field, it is likely that MPA will continue to be prescribed on a large scale in the upcoming years. Discontinuation of therapy due to adverse effects is relatively common, increasing the risk for late rejections, which may contribute to graft loss. Therefore, the continued search for innovative methods to better personalize MPA dosage is warranted.

Objective To examine the use of the Prostate Health Index ( PHI ) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. Materials and Methods The study population included 728 men, with prostate‐specific antigen ( PSA ) levels of 2–10 ng/ mL and a negative digital rectal examination, enrolled in a prospective, multi‐site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [ PCPT ] and European Randomised Study of Screening for Prostate Cancer [ ERSPC ] risk calculators). We also designed and internally validated a new PHI ‐based multivariable predictive model, and created a nomogram. Results Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI , with an area under the curve of 0.746. The bootstrap‐corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision‐curve analysis. Conclusion Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis.