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Adusumalli S, Mohebi R, McCarthy CP, Megaret CA, Rhyne RF, Jaffer FA, Januzzi JL. Multiple Biomarkers to Predict Major Adverse Cardiovascular Events in Patients With Coronary Chronic Total Occlusions. medRxiv 2023:2023.07.19.23292911. [PMID: 37503157 PMCID: PMC10371101 DOI: 10.1101/2023.07.19.23292911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background There are limited tools available to predict the long-term prognosis of persons with coronary chronic total occlusions (CTO). Objectives We evaluated performance of a blood biomarker panel to predict cardiovascular (CV) events in patients with CTO. Methods From 1251 patients in the CASABLANCA study, 241 participants with a CTO were followed for an average of 4 years for occurrence of major adverse CV events (MACE, CV death, non-fatal myocardial infarction or stroke) and CV death/heart failure (HF) hospitalization. Results of a biomarker panel (kidney injury molecule-1, N-terminal pro-B-type natriuretic peptide, osteopontin, and tissue inhibitor of metalloproteinase-1) from baseline samples were expressed as low-, moderate-, and high-risk. Results By 4 years, a total of 67 (27.8%) MACE events and 56 (23.2%) CV death/HF hospitalization events occurred. The C-statistic of the panel for MACE through 4 years was 0.79. Considering patients in the low-risk group as a reference, the hazard ratio of MACE by 4 years was 6.65 (95% confidence interval [CI]: 2.98-14.8) and 12.4 (95% CI:5.17-29.6) for the moderate and high-risk groups (both P <0.001). The C-statistic for CVD/HF hospitalization by 4 years was 0.84. Compared to the low-risk score group, the moderate and high-risk groups had hazard ratios of 5.61 (95% CI: 2.33-13.5) and 15.6 (95% CI: 6.18, 39.2; both P value <0.001). Conclusion A multiple biomarker panel assists in evaluating the risk of adverse outcomes in patients with coronary CTO. These results may have implications for patient care and could have a role for clinical trial enrichment. Clinical Trial CASABLANCA, ClinicalTrials.gov Identifier: NCT00842868.
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Mohebi R, van Kimmenade R, McCarthy CP, Magaret CA, Barnes G, Rhyne RF, Gaggin HK, Januzzi JL. Performance of a multi-biomarker panel for prediction of cardiovascular event in patients with chronic kidney disease. Int J Cardiol 2023; 371:402-405. [PMID: 36202172 PMCID: PMC9977515 DOI: 10.1016/j.ijcard.2022.09.074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Patients with chronic kidney disease (CKD) undergoing coronary catheterization are at increased risk of cardiovascular events (CVE). Measuring biomarkers before the procedure may guide clinicians in identifying patients at higher risk of future cardiovascular events. METHODS In this sub-study the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA), 927 patients underwent coronary catheterization and were followed up for two years. Using machine learning algorithm and targeted proteomics from samples of patients with CKD, 4 biomarkers (kidney injury molecule-1, N-terminal pro B-type natriuretic peptide, osteopontin, and tissue inhibitor of metalloproteinase-1) were integrated into a prognostic algorithm to predict CVE. Results from the panel are expressed in a graded fashion (CVE higher risk and lower risk) using a data-driven cutoff optimized for balanced sensitivity and specificity. RESULTS During the 2-year follow-up, 74 CVE were ascertained. 51 (rate: 51/378 = 13.5%) events occurred in stage 1-2 CKD and 23 (rate: 23/68 = 33.8%) events occurred in stage 3-5 CKD. The C-statistic for predicting 2-years cardiovascular events in all 446 patients was 0.77 (0.72, 0.82). The model was well-calibrated (Hosmer-Lemeshow test p-value >0.40). Considering patients at CVE lower-risk within each CKD staging group as a reference, the hazard ratio (95% confidence interval) of cardiovascular events was 2.82 (1.53, 5.22) for CKD stage 1-2/CVE higher-risk, and 8.32 (1.12, 61.76) for CKD stage 3-5/CVE higher-risk. CONCLUSION Measuring biomarker panel prior to coronary catheterization may be useful to individualize CVE risk assessment among patients with CKD.
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Affiliation(s)
- Reza Mohebi
- Massachusetts General Hospital, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America
| | | | - Cian P McCarthy
- Massachusetts General Hospital, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America
| | | | - Grady Barnes
- Prevencio, Inc., Kirkland, WA, United States of America
| | | | - Hanna K Gaggin
- Massachusetts General Hospital, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America
| | - James L Januzzi
- Massachusetts General Hospital, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America; Baim Institute for Clinical Research, Boston, MA, United States of America.
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3
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Mohebi R, McCarthy CP, Magaret CA, Barnes G, Rhyne RF, Peters C, Gaggin HK, Januzzi JL. Performance of a protein biomarker panel for prediction of cardiovascular events in patients with diabetes mellitus. Eur J Prev Cardiol 2022; 29:e270-e271. [PMID: 35258630 PMCID: PMC10039396 DOI: 10.1093/eurjpc/zwac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/12/2022]
Affiliation(s)
- Reza Mohebi
- Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Cian P. McCarthy
- Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | | | | | | | | | - Hanna K. Gaggin
- Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - James L. Januzzi
- Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Baim Institute for Clinical Research, Boston, MA
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Neumann JT, Sorenson NA, McCarthy CP, Magaret CA, Rhyne RF, Peters CC, Barnes G, Defilippi CR, Westermann D, Januzzi JL. A pooled multi-national validation study of a machine learning, high-sensitivity troponin-based multi-proteomic model to predict the presence of obstructive coronary artery disease. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.1374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
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<0.001) for the presence of oCAD (Figure 1). At optimal cutoff, the score had 74% sensitivity, 72% specificity, and a positive predictive value of 81% for oCAD. The multiple biomarker panel had a diagnostic odds ratio of 7.48 (95% CI 5.55, 10.09, p<0.001). In comparison, in patients without an acute MI, hs-cTnI alone had an area under the receiver-operating characteristic curve of 0.63 (95% CI, 0.60, 0.67, p<0.001)) for oCAD (Figure 1).
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
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Affiliation(s)
- J T Neumann
- University Heart & Vascular Center Hamburg, Cardiology, Hamburg, Germany, Germany
| | - N A Sorenson
- University Heart & Vascular Center Hamburg, Cardiology, Hamburg, Germany, Germany
| | - C P McCarthy
- Massachusetts General Hospital, Medicine, Division of Cardiology, Boston, United States of America
| | - C A Magaret
- Prevencio, Inc., Kirkland, United States of America
| | - R F Rhyne
- Prevencio, Inc., Kirkland, United States of America
| | - C C Peters
- Prevencio, Inc., Kirkland, United States of America
| | - G Barnes
- Prevencio, Inc., Kirkland, United States of America
| | - C R Defilippi
- Inova Heart and Vascular Institute, Falls Church, VA, United States of America
| | - D Westermann
- University Heart & Vascular Center Hamburg, Cardiology, Hamburg, Germany, Germany
| | - J L Januzzi
- Massachusetts General Hospital, Medicine, Division of Cardiology, Boston, United States of America
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Januzzi JL, Canty JM, Das S, DeFilippi CR, Gintant GA, Gutstein DE, Jaffe A, Kaushik EP, Leptak C, Mehta C, Pina I, Povsic TJ, Rambaran C, Rhyne RF, Salas M, Shi VC, Udell JA, Unger EF, Zabka TS, Seltzer JH. Gaining Efficiency in Clinical Trials With Cardiac Biomarkers: JACC Review Topic of the Week. J Am Coll Cardiol 2021; 77:1922-1933. [PMID: 33858628 DOI: 10.1016/j.jacc.2021.02.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 02/18/2021] [Indexed: 02/01/2023]
Abstract
The momentum of cardiovascular drug development has slowed dramatically. Use of validated cardiac biomarkers in clinical trials could accelerate development of much-needed therapies, but biomarkers have been used less for cardiovascular drug development than in therapeutic areas such as oncology. Moreover, there are inconsistences in biomarker use in clinical trials, such as sample type, collection times, analytical methods, and storage for future research. With these needs in mind, participants in a Cardiac Safety Research Consortium Think Tank proposed the development of international guidance in this area, together with improved quality assurance and analytical methods, to determine what biomarkers can reliably show. Participants recommended the development of systematic methods for sample collection, and the archiving of samples in all cardiovascular clinical trials (including creation of a biobank or repository). The academic and regulatory communities also agreed to work together to ensure that published information is fully and clearly expressed.
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Affiliation(s)
- James L Januzzi
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Baim Institute for Clinical Research, Boston, Massachusetts, USA.
| | - John M Canty
- Division of Cardiovascular Medicine, University at Buffalo and Department of Veterans Affairs, Western New York Health Care System, Buffalo, New York, USA
| | - Saumya Das
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Gary A Gintant
- Department of Integrative Pharmacology, Integrated Sciences and Technology, AbbVie Pharmaceuticals, Cambridge, Massachusetts, USA
| | - David E Gutstein
- Cardiovascular Metabolism Discovery, Janssen Pharmaceuticals, Titusville, New Jersey, USA
| | - Allan Jaffe
- Department of Cardiovascular Diseases and Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Emily P Kaushik
- Global Drug Safety Research and Evaluation, Takeda Pharmaceuticals, Boston, Massachusetts, USA
| | - Christopher Leptak
- Biomarker Qualification Program, Office New Drugs, Center for Drug Development and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA
| | - Cyrus Mehta
- Harvard TH Chan School of Public Health and Cytel Inc., Boston, Massachusetts, USA
| | - Ileana Pina
- Wayne State University, Detroit, Michigan, USA
| | - Thomas J Povsic
- Duke Clinical Research Institute and Department of Medicine, Duke University, Durham, North Carolina, USA
| | | | | | - Maribel Salas
- Daiichi-Sankyo, Inc., Basking Ridge, New Jersey, USA; University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Victor C Shi
- Novartis Pharmaceuticals Corporation, Basel, Switzerland
| | - Jacob A Udell
- Cardiovascular Division, Women's College Hospital and Peter Munk Cardiac Centre, Toronto General Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Ellis F Unger
- Office of Cardiology, Hematology, Endocrinology, and Nephrology, Office of New Drugs, Center for Drug Development and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tanja S Zabka
- Development Sciences-Safety Assessment, Genentech Inc., San Francisco, California, USA
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McCarthy CP, Neumann JT, Michelhaugh SA, Ibrahim NE, Gaggin HK, Sörensen NA, Schäefer S, Zeller T, Magaret CA, Barnes G, Rhyne RF, Westermann D, Januzzi JL. Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease. J Am Heart Assoc 2020; 9:e017221. [PMID: 32757795 PMCID: PMC7660799 DOI: 10.1161/jaha.120.017221] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
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<0.001). At the optimal cutoff, we observed 80% sensitivity, 71% specificity, a positive predictive value of 83%, and negative predictive value of 66% for ≥70% stenosis. Partitioning the score result into 5 levels resulted in a positive predictive value of 97% and a negative predictive value of 89% at the highest and lowest levels, respectively. In the external validation cohort, the score performed similarly well. Notably, in patients who had myocardial infarction neither ruled in nor ruled out via hs‐cTnI testing (“indeterminate zone,” n=65), the score had an area under the receiver operating characteristic curve of 0.88 (P<0.001). 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.
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Affiliation(s)
- Cian P McCarthy
- Division of Cardiology Massachusetts General Hospital Boston MA
| | - Johannes T Neumann
- Department of Cardiology University Heart & Vascular Center Hamburg Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck Hamburg Germany
| | | | | | - Hanna K Gaggin
- Division of Cardiology Massachusetts General Hospital Boston MA.,Cardiometabolic Trials Baim Institute for Clinical Research Boston MA
| | - Nils A Sörensen
- Department of Cardiology University Heart & Vascular Center Hamburg Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck Hamburg Germany
| | - Sarina Schäefer
- Department of Cardiology University Heart & Vascular Center Hamburg Germany
| | - Tanja Zeller
- Department of Cardiology University Heart & Vascular Center Hamburg Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck Hamburg Germany
| | | | | | | | - Dirk Westermann
- Department of Cardiology University Heart & Vascular Center Hamburg Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck Hamburg Germany
| | - James L Januzzi
- Division of Cardiology Massachusetts General Hospital Boston MA.,Cardiometabolic Trials Baim Institute for Clinical Research Boston MA
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7
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Neumann JT, Sörensen NA, Zeller T, Magaret CA, Barnes G, Rhyne RF, Peters C, Goßling A, Hartikainen TS, Haller PM, Lehmacher J, Schäfer S, Januzzi JL, Westermann D. Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction. Biomark Med 2020; 14:775-784. [PMID: 32462911 DOI: 10.2217/bmm-2019-0584] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
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).
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Affiliation(s)
- Johannes T Neumann
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Nils A Sörensen
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Tanja Zeller
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | | | | | | | | | - Alina Goßling
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany
| | - Tau S Hartikainen
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Paul M Haller
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Jonas Lehmacher
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany
| | - Sarina Schäfer
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - James L Januzzi
- Massachusetts General Hospital, Division of Cardiology, Boston, MA 02114, USA.,Baim Institute for Clinical Research, Cardiometabolic Trials, Boston, MA 02115, USA
| | - Dirk Westermann
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
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Ramirez JL, Magaret CA, Khetani SA, Rhyne RF, Peters C, Barnes G, Grenon SM. PC102. A Novel Machine Learning-Driven Clinical and Proteomic Tool for the Diagnosis of Peripheral Artery Disease. J Vasc Surg 2019. [DOI: 10.1016/j.jvs.2019.04.344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Ekanem E, Shah P, Latta F, Barnes G, Adams E, Magaret CA, Peters C, Rhyne RF, DeFilippi C. VALIDATION OF A NOVEL BIOMARKER-CLINICAL SCORE TO PREDICT THE PRESENCE OF OBSTRUCTIVE CORONARY DISEASE. J Am Coll Cardiol 2019. [DOI: 10.1016/s0735-1097(19)30768-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Ibrahim NE, McCarthy CP, Shrestha S, Gaggin HK, Mukai R, Magaret CA, Rhyne RF, Januzzi JL. A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography. Clin Cardiol 2019; 42:292-298. [PMID: 30582197 PMCID: PMC6712314 DOI: 10.1002/clc.23143] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 12/17/2018] [Accepted: 12/20/2018] [Indexed: 12/30/2022] Open
Abstract
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 <0.5 mL/kg per hour for >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.
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Affiliation(s)
- Nasrien E Ibrahim
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Cian P McCarthy
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
| | - Shreya Shrestha
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
| | - Hanna K Gaggin
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Renata Mukai
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
| | | | | | - James L Januzzi
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts.,Baim Institute for Clinical Research Boston, Boston, Massachusetts
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11
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McCarthy CP, Ibrahim NE, van Kimmenade RRJ, Gaggin HK, Simon ML, Gandhi P, Kelly N, Motiwala SR, Mukai R, Magaret CA, Barnes G, Rhyne RF, Garasic JM, Januzzi JL. A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study. Clin Cardiol 2018; 41:903-909. [PMID: 29876944 DOI: 10.1002/clc.22939] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 02/20/2018] [Accepted: 03/04/2018] [Indexed: 11/08/2022] Open
Abstract
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.
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Affiliation(s)
- Cian P McCarthy
- Department of Medicine, Massachusetts General Hospital, Boston
| | | | | | - Hanna K Gaggin
- Division of Cardiology, Massachusetts General Hospital, Boston.,Baim Institute for Clinical Research, Cardiometabolic Trials, Boston, Massachusetts
| | - Mandy L Simon
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Parul Gandhi
- Division of Cardiology, VA Connecticut Healthcare System and Yale University, New Haven, Connecticut
| | - Noreen Kelly
- Division of Cardiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Shweta R Motiwala
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Renata Mukai
- Division of Cardiology, Massachusetts General Hospital, Boston
| | | | | | | | | | - James L Januzzi
- Division of Cardiology, Massachusetts General Hospital, Boston.,Baim Institute for Clinical Research, Cardiometabolic Trials, Boston, Massachusetts
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McCarthy CP, van Kimmenade RR, Gaggin HK, Simon ML, Ibrahim NE, Gandhi P, Kelly N, Motiwala SR, Belcher AM, Harisiades J, Magaret CA, Rhyne RF, Januzzi JL. Usefulness of Multiple Biomarkers for Predicting Incident Major Adverse Cardiac Events in Patients Who Underwent Diagnostic Coronary Angiography (from the Catheter Sampled Blood Archive in Cardiovascular Diseases [CASABLANCA] Study). Am J Cardiol 2017; 120:25-32. [PMID: 28487034 DOI: 10.1016/j.amjcard.2017.03.265] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 03/31/2017] [Accepted: 03/31/2017] [Indexed: 01/12/2023]
Abstract
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 <0.001), higher than AUC for clinical variables alone (0.75). In net reclassification improvement analyses, addition of other markers to NT-proBNP resulted in significant improvement (net reclassification improvement 0.45; p = 0.008). At the optimal score cutoff, we found 64% sensitivity, 76% specificity, 28% positive predictive value, and 93% negative predictive value for 1-year MACE. Time-to-first MACE was shorter in those with an elevated score (p <0.001); such risk extended to at least to 4 years. In conclusion, in a cohort of patients who underwent coronary angiography, we describe a novel multiple biomarker score for incident MACE within 1 year (NCT00842868).
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Ibrahim NE, Januzzi JL, Magaret CA, Gaggin HK, Rhyne RF, Gandhi PU, Kelly N, Simon ML, Motiwala SR, Belcher AM, van Kimmenade RR. A Clinical and Biomarker Scoring System to Predict the Presence of Obstructive Coronary Artery Disease. J Am Coll Cardiol 2017; 69:1147-1156. [DOI: 10.1016/j.jacc.2016.12.021] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Revised: 11/30/2016] [Accepted: 12/01/2016] [Indexed: 01/09/2023]
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