Optimal Cutoff Values and Utility of High-Sensitivity Troponin T and NT-proBNP for the Risk Stratification of Patients with Acute Pulmonary Embolism

IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Clinical chemistry Pub Date : 2024-12-20 DOI:10.1093/clinchem/hvae212
Timothy M Matthews, Gregory A Peters, Grace Wang, Nora Horick, Kyle E Chang, Savanah Harshbarger, Christiana Prucnal, Drew A Birrenkott, Karsten Stannek, Eun Sang Lee, Isabel Dhar, Jesse O Wrenn, William B Stubblefield, Christopher Kabrhel
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Abstract

Background Guidelines recommend using high-sensitivity troponin T (hsTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) to risk stratify hemodynamically stable patients with acute pulmonary embolism (PE). However, there are no evidence-based cutoff values defined for this clinical application. Methods We performed a single-center, retrospective cohort study of patients with imaging-confirmed PE and hsTnT and/or NT-proBNP (ElecsysTM, Roche) measured 12 h before or 24 h after PE Response Team (PERT) activation. We excluded hypotensive patients. Our primary outcome was a composite of adverse outcomes or critical interventions within 7 days. We calculated the area under the receiver operating curve (AUC, ROC) for hsTnT and NT-proBNP and determined the optimal cutoffs using the distance from (0,1). We performed a subgroup analysis on patients with PE and right ventricular dysfunction on imaging. Results Two hundred thirty-four patients were included in the hsTnT analysis, and 727 in the NT-proBNP analysis. Mean age was 62 years (SD = 17) and 47% were female. The AUC for hsTnT was 0.64 (95% CI, 0.56–0.71) with an optimal cutoff of 46 ng/L, corresponding to a sensitivity of 59% (95% CI, 49–69) and a specificity of 61% (95% CI, 53–69). The AUC for NT-proBNP was 0.56 (95% CI, 0.51–0.61) with an optimal cutoff of 1092 pg/mL, corresponding to a sensitivity of 53% (95% CI, 45–61) and a specificity of 59% (95% CI, 55–63). Conclusion We identified an optimal cutoff of 46 ng/L for hsTnT and 1092 pg/mL for NT-proBNP, though the AUC for both markers suggests low to moderate performance for the risk stratification of initially hemodynamically stable PERT patients. Use of these biomarkers to risk stratify PE may require reconsideration.
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来源期刊
Clinical chemistry
Clinical chemistry 医学-医学实验技术
CiteScore
11.30
自引率
4.30%
发文量
212
审稿时长
1.7 months
期刊介绍: Clinical Chemistry is a peer-reviewed scientific journal that is the premier publication for the science and practice of clinical laboratory medicine. It was established in 1955 and is associated with the Association for Diagnostics & Laboratory Medicine (ADLM). The journal focuses on laboratory diagnosis and management of patients, and has expanded to include other clinical laboratory disciplines such as genomics, hematology, microbiology, and toxicology. It also publishes articles relevant to clinical specialties including cardiology, endocrinology, gastroenterology, genetics, immunology, infectious diseases, maternal-fetal medicine, neurology, nutrition, oncology, and pediatrics. In addition to original research, editorials, and reviews, Clinical Chemistry features recurring sections such as clinical case studies, perspectives, podcasts, and Q&A articles. It has the highest impact factor among journals of clinical chemistry, laboratory medicine, pathology, analytical chemistry, transfusion medicine, and clinical microbiology. The journal is indexed in databases such as MEDLINE and Web of Science.
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Optimal Cutoff Values and Utility of High-Sensitivity Troponin T and NT-proBNP for the Risk Stratification of Patients with Acute Pulmonary Embolism Advances in Cardiac Troponin Composition Assays: A Step Closer to the Clinic? Design and Analytical Evaluation of Novel Cardiac Troponin Assays Targeting Multiple Forms of the Cardiac Troponin I-Cardiac Troponin T-Troponin C Complex and Fragmentation Forms. Correction to: Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels. An AI Model (LORIS) to Predict Immune Checkpoint Blockade Response in Cancer: A Clinical Data Science Perspective.
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