Search

OBJECTIVETo develop a highly sensitive and specific blood biomarker panel that identifies febrile children with Kawasaki disease (KD).METHODSWe tested blood samples from a single-center cohort of KD (n = 50) and control febrile children (n = 100) to develop a biomarker panel from 11 candidates selected by their assay clinical availability. We used machine learning with least absolute shrinkage and selection operator regression to identify 11 blood markers with values incorporated into a model, which provided a binary predictive risk score for KD determined with Youden’s index. We further reduced the model using least angle regression.RESULTSUsing 10-fold cross-validation with least absolute shrinkage and selection operator regression on these 11 readouts plus patient age resulted in an area under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.90–0.98; P CONCLUSIONSMachine learning identified a highly accurate diagnostic model for KD. The reduced model employs 3 biomarkers currently approved by regulatory bodies and performed on platforms commonly used by certified diagnostic laboratories.

Introduction: Diabetes Mellitus (DM) is a major risk factor for coronary artery disease (CAD) associated with a two-fold increase in mortality. We aimed to validate the performance of a machine learning based multi-biomarker diagnostic panel to predict obstructive coronary artery disease (oCAD) compared to high-sensitivity cardiac troponin-I (hs-cTnI) alone in a subset of patients with DM. Methods: A previously developed multiple biomarker scoring model was utilized. The pooled cohort included 132 patients at Inova Fairfax Hospital, 69 patients at Massachusetts General Hospital and 46 patients at the University Hospital Hamburg-Eppendorf with a mixture of acute and lesser acute presentations. Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. 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 0.77 (95% CI, 0.70, 0.84, p Conclusions: In this multinational pooled cohort, a previously described novel machine learning, multiple protein biomarker panel provided high accuracy to diagnose patients for oCAD in a subset of patients with DM.

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

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)

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: 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.

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

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC), with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance, and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step towards building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

A series of extended length heterobifunctional coupling agents is described. The successive aminocaproic acid homologation of succinimidyl 4-(N-maleimidomethyl)cyclohexane-1-carboxylate, a known 9-atom long maleimide active ester linker, yielded 16-, 23-, and 30-atom long maleimide active ester homologues. The performance study of these coupling agents in automated microparticle enzyme immunoassays showed that, in the alpha fetoprotein assay, in which the linkers were employed in the construction of the alkaline phosphatase-antibody conjugates, the signal increased 64% when the length of the linker was incremented from 9 atoms to 23 atoms and 82% for the 30-atom long linker as compared with the 9-atom homologue. Similar improvements were observed in the performance of carbohydrate antigen, marker of ovarian cancer (CA-125), immunoassay where the linkers were used for conjugation of the capture antibody anti-CA-125 to the microparticle. Thus, a 300% signal improvement resulted when a 30-atom linker was used instead of the 9-atom homologue. The observed differences in the performance of the conjugates are interpreted as resulting from improved antibody binding and lowering of the steric hindrance of the complementarity-determined region of the antibody when longer coupling agents were used.

5563 Background: Urinary HE4 has been reported as a promising biomarker for ovarian cancer (OC). In the current study, a large validation was performed to evaluate urinary HE4 as a biomarker for the stratification of OC from benign pelvic masses. Methods: Normalized HE4 was obtained from the ratio of HE4 (pmol/L measured with HE4 EIA) and creatinine (mg/dL measured with a Jaffe Reaction) in single-point urine samples from female subjects with a pelvic mass (N = 809). The samples were from one case-control prospective study (Moore RG, 2008) and two prospective clinical trials (NCT00315692 and NCT00987649). R Package was used to randomly distribute the subjects into the Training and Testing Sets. Analyze-it was used to analyze the clinical performance. Results: Medians of Normalized HE4, sensitivities (SN) and likelihood ratios (LR) (+) of Normalized HE4 at two levels of specificity (SP) are presented in the Table. Conclusions: Urinary HE4 normalized with urinary creatinine appears to be a valid biomarker for the stratification of OC from a benign pelvic mass. [Table: see text]

Human epididymis protein 4 (HE4) has been shown to be a promising serum biomarker for epithelial ovarian cancer (EOC). Recently, Hellstrom et al. reported the detection of HE4 in urine as a biomarker for ovarian neoplasms (Cancer Lett.2010;296:43–8). We investigated the feasibility of urinary HE4 in detection of EOC in a pilot study. METHODS: This retrospective study measured the HE4 levels in urine samples obtained from an institutional review board-approved sample bank. HE4 was measured with HE4 EIA (Fujirebio Diagnostics, Inc.). Creatinine was measured via colorimetric determination by the Jaffe Reaction and used to normalize the HE4 levels. Clinical diagnosis of the subjects was based on the pathological diagnosis. Single-point urine samples were analyzed from 232 subjects; 72 with EOC or low malignant potential tumors and 160 with benign disease. RESULTS: The medians of HE4 levels in subjects with EOC and with benign disease were 29833 pmol/L (95% Confidence Interval (CI): 21185–54728) and 8515 pmol/L (95% CI: 7077–9314) respectively. The medians of HE4/Creatinine ratios in subjects with EOC and with benign disease were 420 (95% CI: 269–491) and 75 (95% CI: 68–83), respectively. Both the medians of HE4 levels and HE4/Creatinine ratios in EOC subjects were significantly higher than that in subjects with benign disease (P0.05). Using the shoulder point of each ROC curve as the cut-off, the cut-off values were 152.5 for HE4/Creatinine ratio and 19161 pmol/L for HE4 level respectively. The sensitivity, specificity, positive predictive value and negative predictive value at the set cut-off values were 70.8% (95% CI = 58.9% – 81.0%), 93.1% (95% CI = 88.0% – 96.5%), 82.3% and 87.6% for HE4/Creatinine ratio, and 65.3% (95% CI = 53.1% – 76.1%), 89.4% (95% CI = 83.5% – 93.7%), 73.4% and 85.1% for HE4 level, respectively. Concordance rates versus clinical diagnosis were 86.2% for HE4/creatinine ratio and 81.9% for HE4 level. CONCLUSIONS: This pilot study demonstrated the utility of urinary HE4 level alone or HE4/Creatinine ratio as potential biomarkers for the detection of EOC. The HE4/Creatinine ratio appeared to be better than the HE4 level in correlating with the clinical pathological diagnosis of EOC. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-451. doi:10.1158/1538-7445.AM2011-LB-451

Human epididymis protein 4 (HE4) is a whey acidic four-disulfide core protein. Serum HE4 has been shown to be a promising biomarker for epithelial ovarian cancer (EOC) with manual HE4 EIA assays. The ARCHITECT system is one of the major automated formats for cancer immunodiagnostics. This abstract describes the clinical performance of an investigational ARCHITECT HE4 assay in the measurement of serum HE4 from longitudinal samples of subjects with EOC and single-point samples from healthy subjects, and subjects with malignancy, benign diseases, EOC or other malignant diseases. PROCEDURES: The ARCHITECT HE4 Assay is a paramagnetic microparticle chemiluminescent assay adopting the sensitive CHEMIFLEX® technology. Single-point and longitudinal serum samples were tested with an investigational ARCHITECT HE4 Assay reagent lot on the ARCHITECT i2000SR. Single-point sera were collected from healthy subjects (N = 400), subjects with pregnancy (N = 50) and benign disease (N = 612), and subjects which were diagnosed with EOC (N = 314) and other cancers (N = 250) including endometrial, breast, gastrointestinal, lung, and bladder cancer. Longitudinal sera (N = 506), including initial time point and follow-up visits (average = 5.7 per subject), were collected from subjects (N = 76) which were diagnosed with EOC. All of the single-point and longitudinal samples were handled in accordance with IRB-approved and GCP-compliant protocols. RESULTS: Using an ARCHITECT HE4 cut-off value of >140 pmol/L, the percent distributions of ARCHITECT HE4 by cohort are: 3% of healthy subjects, 0% with pregnancy, 7.2% of subjects with benign diseases, 53.5% with EOC and 28.4% with other cancers. Using the upper 95% point of the distribution of the HE4 ratio at 14% to categorize the successive HE4 readings into those that were and were not significantly elevated for the longitudinal samples, the sensitivity, specificity and positive predictive value of HE4 elevation versus disease progression were 53.4% (95% CI: 44.1%-62.5%), 78.8% (95% CI: 74.7% - 82.4%) and 43.2% (95% CI: 32.9% - 54.1%) respectively. The total concordance between HE4 elevation and disease progression was 73%. Receiver Operator Characteristic analysis showed an area under the curve of 0.685 (SE = 0.033) for the diagnosis of progression from the ratio of successive HE4 readings. When comparing the change of the HE4 ratio with the diagnostic response to therapy, a decrease in HE4 reflected response to therapy with a sensitivity of 82% and a specificity of 53%. CONCLUSIONS: The study with single-point samples has shown the utility of ARCHITECT HE4 values as a serum biomarker for EOC. The longitudinal increase of ARCHITECT HE4 values in sera seemed to be effective in recognizing the progression of EOC. The longitudinal decrease of ARCHITECT HE4 values in sera seemed to correlate with the response of EOC subjects to therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4653.

Summary The Cu(II) induced lysis of rabbit erythrocytes occurs in two phases. First there is a lag period of 60 min where few cells lyse, followed by a period of rapid lysis where most of the remaining cells undergo hemolysis. Lysis is effectively inhibited if the incubation is conducted in nitrogen or if the erythrocytes are pre-saturated with carbon monoxide. These results suggest that oxygenated hemoglobin is necessary for lysis. It has been reported that copper binds to oxyhemoglobin and releases superoxide ion. This additional oxidative stress can initiate lipid peroxidation which leads to cell hemolysis.

Illustrates column chromatography and an example of the effect of pH on the charge and affinity of a protein.