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Background Undetected obstructive coronary artery disease (oCAD) is a global health problem associated with significant morbidity and mortality. A need exists for an accurate and easily accessible diagnostic test for oCAD. Using machine learning, a multi-biomarker blood diagnostic test for oCAD based on high-sensitivity cardiac troponin-I (hs-cTnI) has been developed. Purpose To validate the performance of a previously developed, algorithmically weighted, multiple protein diagnostic panel to diagnose oCAD in a pooled multi-national cohort and to compare the diagnostic panel's performance to predict oCAD to hs-cTnI alone. Methods Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. hs-cTnI blood samples were assayed on the Siemens Atellica and Abbott Diagnostics ARCHITECT immunoassay platforms. Adiponectin and Kidney Injury Molecule-1 were measured with a multiplex assay on blood samples via the Luminex 100/200 xMAP platform. Individual data from a total of 924 patients with a mixture of acute and lesser acute presentations from three centers were pooled (Table 1). oCAD was defined as >50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or >70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel's performance to predict oCAD was also compared to hs-cTnI alone. Results The multiple protein panel had an area under the receiver-operating characteristic curve of 0.80 (95% CI, 0.77, 0.83, p Conclusions In this multinational pooled cohort, a previously described novel machine learning, multiple biomarker panel provided high accuracy to diagnose patients for oCAD. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Prevencio, Inc. Table 1. Pooled Variable DataFigure 1. ROC for HART CADhs and hs-cTnI

SummarySARS-CoV-2 continues to evolve and the vaccine efficacy against variants is challenging to estimate. It is now common in phase III vaccine trials to provide vaccine to those randomized to placebo once efficacy has been demonstrated, precluding a direct assessment of placebo controlled vaccine efficacy after placebo vaccination. In this work we extend methods developed for estimating vaccine efficacy post placebo vaccination to allow variant specific time varying vaccine efficacy, where time is measured since vaccination. The key idea is to infer counterfactual strain specific placebo case counts by using surveillance data that provide the proportions of the different strains. This blending of clinical trial and observational data allows estimation of strain-specific time varying vaccine efficacy, or sieve effects, including for strains that emergent after placebo vaccination. The key requirements are that surveillance strain distribution accurately reflect the strain distribution for a placebo group, throughout follow-up after placebo group vaccination and that at least one strain is present before and after placebo vaccination. For illustration, we develop a Poisson approach for an idealized design under a rare disease assumption and then use a proportional hazards modeling to better reflect the complexities of field trials with staggered entry, crossover, and smoothly varying strain specific vaccine efficacy We evaluate these by theoretical work and simulations, and demonstrate that useful estimation of the efficacy profile is possible for strains that emerge after vaccination of the placebo group. An important principle is to incorporate sensitivity analyses to guard against mis-specfication of the strain distribution. We also provide an approach for use when genotyping of the infecting strains of the trial participants has not been done.

Introduction: Increased systemic inflammation is one of the key triggers in the initiation and progression of atherosclerosis. We investigated interleukin 18 binding protein (IL-18BP), a new inflammatory marker of the IL-1 cytokine family, on its predictive value for cardiovascular disease in subjects under investigation for stenotic atherosclerotic disease. Methods: We performed a prospective, single-center, investigator-initiated, observational cohort study in 927 subjects who underwent coronary and/or peripheral angiography. The cohort was randomly split into a discovery (n=627) and a validation set (n =267). Predictive value for incident cardiovascular events (composite of cardiovascular death, myocardial infarction and stroke), cardiovascular mortality and all-cause mortality was investigated using Cox proportional hazard models, including concordance index analyses and assessment of net reclassification for 10-year cardiovascular risk categories using Weibull modelling. Results: An one standard deviation increase in log-transformed IL-18BP was strongly associated with cardiovascular events (HR 1.96, 95% CI 1.54-2.51), cardiovascular mortality (HR 1.59, 95% CI 1.04-2.44) and all-cause mortality (HR 1.72, 95 CI 1.43-2.07), independent of the traditional cardiovascular risk factors, including body mass index. C-index of a clinical model for prediction of cardiovascular events improved from 0.69 (95% CI to 0.62-0.75) to 0.73 (95% CI 0.67-0.79) with inclusion of IL-18BP in the model; in doing so, a reclassification of 35.9% (p < 0.001) for events was demonstrated. Conclusions: Among a population of patient undergoing coronary angiography, results of IL18-BP measurement improved cardiovascular risk prediction beyond the traditional risk factors. Kaplan-Meier survival curves for CV events during one-year follow up comparing tertiles IL-18BP (discovery set)

We propose platform trials with outcome-adaptive randomization to efficiently select the most effective coronavirus disease 2019 (COVID-19) treatments. The global spread of severe acute respiratory syndrome coronavirus 2 infection is alarming in its geographic scope and in the number of associated deaths. There are currently no treatments proven to decrease mortality from COVID-19 further than what can be achieved through supportive care. Thus far, the choice of therapeutics has been limited to existing, repurposed medications. Given that some of the medications are perceived to have low toxicity, many have been embraced without evidence. Although remdesivir was recently found to shorten time to symptom resolution, evidence for survival benefit is inconclusive

Background Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs‐cTn (high‐sensitivity cardiac troponin)–based proteomic model to diagnose obstructive coronary artery disease. Methods and Results In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs‐cTnI [high‐sensitivity cardiac troponin I], adiponectin, and kidney injury molecule‐1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% ( P P Conclusions A model including hs‐cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs‐cTnI concentrations.

Background: In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). Methodology & results: A previously described prognostic panel for MACE consisting of four biomarkers was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. Conclusion: Among patients with suspected MI, we validated a multibiomarker panel for predicting 1-year MACE. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov)

Background: We previously developed a clinical/proteomics panel for predicting procedural acute kidney injury (AKI) and now examine its performance in those with diabetes (DM). Methods: We measured 109 biomarkers in blood from patients undergoing coronary angiography. Procedural AKI defined as increase in serum creatinine ≥0.3 mg/dL, increase in serum creatinine of ≥50%, or documented oliguria ≤7 days after procedure. Clinical and biomarker predictors of AKI identified using least-angle regression; a final prognostic model was developed with LASSO; the model was then measured in those with DM. Results: Besides DM, 5 predictors were in the final model: 3 (blood urea nitrogen to creatinine ratio, C-reactive protein and osteopontin) had positive association, while 2 (CD5 antigen-like and Factor VII) had negative association with AKI risk. Among 217 patients with DM, 18 (8.3%) developed AKI. The final model had an AUC of 0.87 for predicting procedural AKI (P Conclusions: We describe a clinical and proteomics-supported biomarker model with high sensitivity/NPV for AKI in patients with DM following coronary angiography. Disclosure N.E. Ibrahim: Other Relationship; Self; Novartis Pharmaceuticals Corporation. C.P. McCarthy: None. S. Shrestha: None. H. Gaggin: Other Relationship; Self; Dr. Gaggin has received research grant support from Roche Diagnostics, Jana Care, Ortho Clinical, Novartis; consulting income from Merck, Roche Diagnostics; research payments for clinical endpoint com. R. Mukai: None. C.A. Magaret: Consultant; Self; Prevencio. R.F. Rhyne: Board Member; Self; Prevencio. Employee; Self; Prevencio. Stock/Shareholder; Self; Prevencio. J. Januzzi: Consultant; Self; Abbott, Roche Diagnostic USA. Research Support; Self; Abbott, Prevencio, Quest Diagnostics, Roche Diagnostic USA. Funding Prevencio, Inc.

BackgroundPatients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM.Methods and resultsThe HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up.ConclusionA clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.Trial registration numberNCT00842868.

Undetected coronary artery disease (CAD) carries significant morbidity and mortality. A novel biomarker-based model to predict the presence of CAD has the potential to non-invasively diagnose CAD. We assessed the performance of a previously developed algorithm to diagnose obstructive CAD versus

Background Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI). Hypothesis Clinical and biomarker data can predict AKI more accurately. Methods Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of 6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO). Results Forty‐three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C‐reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen‐like and Factor VII) had a negative association with AKI risk. The final model had a cross‐validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in‐sample AUC of 0.82 (P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001). Conclusions We describe a clinical and proteomics‐supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.

Background: Patients with diabetes mellitus (DM) are at increased risk for prevalent coronary artery disease (CAD) and incident major adverse cardiac events (MACE) such as cardiovascular death, myocardial infarction, and stroke. Prediction of risk in those with DM may be challenging. Using unbiased proteomics we recently described biomarker panels to predict prevalent CAD (HART-CAD) and incident MACE (HART-CVE; both Prevencio, Kirkland, WA) in unselected patients undergoing diagnostic coronary angiography. In this study, we sought to compare accuracy of these panels in patients with and without DM. Methods: The HART-CAD panel includes 2 clinical variables (male sex and prior PCI) and 4 biomarkers: adiponectin, apolipoprotein CI, kidney injury molecule 1 (KIM 1), and midkine. The HART-CVE panel includes 4 biomarkers (amino-terminal pro-B type natriuretic peptide, KIM 1, osteopontin, and tissue inhibitor of matrix metalloproteinase-1). These panels were derived in 167 DM and 482 non-DM patients; performance of each was validated in a second cohort of patients (N=278) with and without DM. Results: In patients with DM, the HART-CAD panel had excellent performance for prediction of coronary stenosis ≥ 70%, with area under the curve (AUC) of 0.81 (p Conclusion: Previously described multivariate proteomic biomarker panels effectively predict prevalent CAD and risk for incident MACE in at-risk patients with DM. Disclosure S. Shrestha: None. C.P. McCarthy: None. N.E. Ibrahim: None. R. van Kimmenade: None. H. Gaggin: Research Support; Self; Roche Diagnostics Corporation, Portola. Consultant; Self; Roche Diagnostics Corporation, Amgen Inc., Ortho clinical. Other Relationship; Self; EchoSense, Radiometer. R. Mukai: None. C.A. Magaret: Consultant; Self; Prevencio. G. Barnes: Employee; Self; Prevencio. R.F. Rhyne: None. J. Januzzi: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc.. Board Member; Self; Jana Care Inc.. Research Support; Self; Prevencio.

Contains fulltext : 196098.pdf (Publisher’s version ) (Open Access) BACKGROUND: Peripheral arterial disease (PAD) is a global health problem that is frequently underdiagnosed and undertreated. Noninvasive tools to predict the presence and severity of PAD have limitations including inaccuracy, cost, or need for intravenous contrast and ionizing radiation. HYPOTHESIS: A clinical/biomarker score may offer an attractive alternative diagnostic method for PAD. METHODS: In a prospective cohort of 354 patients referred for diagnostic peripheral and/or coronary angiography, predictors of >/=50% stenosis in >/=1 peripheral vessel (carotid/subclavian, renal, or lower extremity arteries) were identified from >50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed. RESULTS: The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1, and eotaxin-1). The model had an in-sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross-validated area under the curve of 0.84; higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 65% sensitivity, 88% specificity, 76% positive predictive value (PPV), and 81% negative predictive value (NPV) for obstructive PAD and performed consistently across vascular territories. Partitioning the score into 5 levels resulted in a PPV of 86% and NPV of 98% in the highest and lowest levels, respectively. Elevated score was associated with shorter time to revascularization during 4.3 years of follow-up. CONCLUSIONS: A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.

ObjectiveSevere aortic valve stenosis (AS) develops via insidious processes and can be challenging to correctly diagnose. We sought to develop a circulating biomarker panel to identify patients with severe AS.MethodsWe enrolled study participants undergoing coronary or peripheral angiography for a variety of cardiovascular diseases at a single academic medical centre. A panel of 109 proteins were measured in blood obtained at the time of the procedure. Statistical learning methods were used to identify biomarkers and clinical parameters that associate with severe AS. A diagnostic model incorporating clinical and biomarker results was developed and evaluated using Monte Carlo cross-validation.ResultsOf 1244 subjects (age 66.4±11.5 years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) 2). A final model included age, N-terminal pro-B-type natriuretic peptide, von Willebrand factor and fetuin-A. The model had good discrimination for severe AS (OR=5.9, 95% CI 3.5 to 10.1, pConclusionsWe describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS.Trial registration numberNCT00842868.

BackgroundAnti-HIV-1 broadly neutralizing antibodies (bnAbs) have been developed as potential agents for prevention of HIV-1 infection. The HIV Vaccine Trials Network and the HIV Prevention Trials Network are conducting the Antibody Mediated Prevention (AMP) trials to assess whether, and how, intravenous infusion of the anti-CD4 binding site bnAb, VRC01, prevents HIV-1 infection. These are the first test-of-concept studies to assess HIV-1 bnAb prevention efficacy in humans.MethodsThe AMP trials are two parallel phase 2b HIV-1 prevention efficacy trials conducted in two cohorts: 2700 HIV-uninfected men and transgender persons who have sex with men in the United States, Peru, Brazil, and Switzerland; and 1500 HIV-uninfected sexually active women in seven countries in sub-Saharan Africa. Participants are randomized 1:1:1 to receive an intravenous infusion of 10 mg/kg VRC01, 30 mg/kg VRC01, or a control preparation every 8 weeks for a total of 10 infusions. Each trial is designed (1) to assess overall prevention efficacy (PE) pooled over the two VRC01 dose groups vs. control and (2) to assess VRC01 dose and laboratory markers as correlates of protection (CoPs) against overall and genotype- and phenotype-specific infection.ResultsEach AMP trial is designed to have 90 % power to detect PE > 0 % if PE is ≥ 60 %. The AMP trials are also designed to identify VRC01 properties (i. e., concentration and effector functions) that correlate with protection and to provide insight into mechanistic CoPs. CoPs are assessed using data from breakthrough HIV-1 infections, including genetic sequences and sensitivities to VRC01-mediated neutralization and Fc effector functions.ConclusionsThe AMP trials test whether VRC01 can prevent HIV-1 infection in two study populations. If affirmative, they will provide information for estimating the optimal dosage of VRC01 (or subsequent derivatives) and identify threshold levels of neutralization and Fc effector functions associated with high-level protection, setting a benchmark for future vaccine evaluation and constituting a bridge to other bnAb approaches for HIV-1 prevention.

Item does not contain fulltext We sought to develop a multiple biomarker approach for prediction of incident major adverse cardiac events (MACE; composite of cardiovascular death, myocardial infarction, and stroke) in patients referred for coronary angiography. In a 649-participant training cohort, predictors of MACE within 1 year were identified using least-angle regression; over 50 clinical variables and 109 biomarkers were analyzed. Predictive models were generated using least absolute shrinkage and selection operator with logistic regression. A score derived from the final model was developed and evaluated with a 278-patient validation set during a median of 3.6 years follow-up. The scoring system consisted of N-terminal pro B-type natriuretic peptide (NT-proBNP), kidney injury molecule-1, osteopontin, and tissue inhibitor of metalloproteinase-1; no clinical variables were retained in the predictive model. In the validation cohort, each biomarker improved model discrimination or calibration for MACE; the final model had an area under the curve (AUC) of 0.79 (p

Undetected coronary artery disease (CAD) carries significant morbidity and mortality. A novel biomarker-based model to predict presence of CAD has potential to non-invasively diagnose CAD. To validate a previously developed algorithm to diagnose obstructive CAD in a prospective cohort undergoing

Genetic analysis of breakthrough infections in people vaccinated against HIV-1 show that vaccine efficacy increased by up to 80% against viruses carrying two mutations in Env V2, but also raises the possibility of population-level adaptation to the vaccine. A major clinical trial involving more than 16,000 volunteers, known as the RV144 trial, tested a combination of two vaccines (ALVAC-HIV and AIDSVAX B/E gp120) for its ability to prevent HIV infection, as well as for safety. The vaccine was 31% effective against HIV-1 infection, and antibodies against the HIV-1 envelope variable loop 1 and 2 (V1/V2) domain correlated inversely with infection risk. Rolland et al. present a genetic analysis of breakthrough infections in RV144 trial participants and identify signatures associated with vaccine-induced immune pressure, thereby gaining support for a causal relationship between vaccination and protection. Viral amino-acid changes at positions 169 and 181 in the second variable loop of the viral envelope are shown to be most associated with efficacy — and represent possible targets for future vaccines. The RV144 trial demonstrated 31% vaccine efficacy at preventing human immunodeficiency virus (HIV)-1 infection1. Antibodies against the HIV-1 envelope variable loops 1 and 2 (Env V1 and V2) correlated inversely with infection risk2. We proposed that vaccine-induced immune responses against V1/V2 would have a selective effect against, or sieve, HIV-1 breakthrough viruses. A total of 936 HIV-1 genome sequences from 44 vaccine and 66 placebo recipients were examined. We show that vaccine-induced immune responses were associated with two signatures in V2 at amino acid positions 169 and 181. Vaccine efficacy against viruses matching the vaccine at position 169 was 48% (confidence interval 18% to 66%; P = 0.0036), whereas vaccine efficacy against viruses mismatching the vaccine at position 181 was 78% (confidence interval 35% to 93%; P = 0.0028). Residue 169 is in a cationic glycosylated region recognized by broadly neutralizing and RV144-derived antibodies. The predicted distance between the two signature sites (21 ± 7 A) and their match/mismatch dichotomy indicate that multiple factors may be involved in the protection observed in RV144. Genetic signatures of RV144 vaccination in V2 complement the finding of an association between high V1/V2-binding antibodies and reduced risk of HIV-1 acquisition, and provide evidence that vaccine-induced V2 responses plausibly had a role in the partial protection conferred by the RV144 regimen.