Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Prognostic value of copeptin in patients with acute coronary syndrome: A systematic review and meta-analysis

  • Jiapeng Lu ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing – original draft

    jiapeng.lu@fwoxford.org

    Affiliation National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    ⨯
  • Siming Wang,

    Roles Data curation, Investigation, Writing – review & editing

    Affiliation National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    ⨯
  • Guangda He,

    Roles Data curation, Investigation, Writing – review & editing

    Affiliation National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    ⨯
  • Yanping Wang

    Roles Investigation, Writing – review & editing

    Affiliation National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    ⨯

Abstract

Background

The aim of this study was to evaluate the value of copeptin in predicting mortality including both short-term and long-term mortality in patients with acute coronary syndrome (ACS).

Methods

Potential studies were searched and selected through PubMed, Embase and Cochrane databases up to December 2019. The predictive performance was evaluated by the pooled sensitivity and specificity, and summary receiver operating characteristic curves. Cochran’s Q test and I2 index were used to assess between-study heterogeneity, and Deek’s test and funnel plots were used to assess publication bias.

Results

Total six studies comprising 2269 patients were included in this meta-analysis. The area under the receiver operating characteristic curve of copeptin in predicting mortality in patients with ACS was 0.73 (95% CI: 0.69–0.77). The pooled sensitivity and specificity of copeptin were 0.77 (95% CI: 0.59–0.89) and 0.60 (95% CI: 0.47–0.71), respectively. Significant between-study heterogeneity was identified in both sensitivity (P = 0.01; I2 = 69.76%) and specificity (P<0.001; I2 = 97.32%) among the six included studies. The meta-regression analysis indicated that the number of study centers was significantly associated with the heterogeneity of sensitivity (P = 0.03), whereas the study design (P = 0.03) and duration of follow-up (P<0.001) were significantly associated with the heterogeneity of specificity.

Conclusions

Copeptin has acceptable prognostic value for mortality in patients with ACS. Further studies based on multimarker strategy are needed to evaluate the prognostic value of copeptin for ACS in conjunction with other well-established biomarkers.

Citation: Lu J, Wang S, He G, Wang Y (2020) Prognostic value of copeptin in patients with acute coronary syndrome: A systematic review and meta-analysis. PLoS ONE 15(8): e0238288. https://doi.org/10.1371/journal.pone.0238288

Editor: Vincenzo Lionetti, Scuola Superiore Sant'Anna, ITALY

Received: May 17, 2020; Accepted: August 11, 2020; Published: August 28, 2020

Copyright: © 2020 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: JL was supported by the National Natural Science Foundation of China (81903399) and the National Key Research and Development Program (2017YFC1310801, 2017YFC1310803) from the Ministry of Science and Technology of China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coronary artery disease (CAD) is the leading cause of death worldwide. Patients with CAD, especially acute coronary syndrome (ACS), are always at the high risk of recurrent cardiovascular events and death [1, 2]. Thus, there is a need to improve risk stratification in ACS using biomarkers beyond traditional cardiovascular risk factors to optimize treatment in clinical practice.

To date, several biomarkers such as natriuretic peptide, cardiac troponins and arginine vasopressin (AVP) have been studied as potential biomarkers for risk stratification in patients with ACS [3–6]. Among them, AVP, a nonapeptide produced in the hypothalamus, has been proved that it contributes to osmoregulation and cardiovascular homeostasis [7, 8]. However, AVP cannot be reliably measured in plasma due to its low stability. Copeptin, the C-terminal portion of provasopressin, is regarded as the ideal surrogate biomarker for AVP due to its favorable stability in blood [9, 10]. Thus, copeptin has been thought to be a potential biomarker for several acute illness, such as lower respiratory infection, acute pancreatitis, stroke and ACS [11].

Some studies have indicated that copeptin is a strong prognostic predictor on death in patients with ACS [12–17]. In contrast, no significant association of copeptin with the prognosis for survival after ACS was reported in other studies [18, 19]. The controversial findings might result from the differences in study design and limited sample size. Additionally, most of previous studies were conducted in single center, which might influence the generalizability of the results [13–16]. Accordingly, we aim to set out a systematic review and meta-analysis to evaluate the association between copeptin and mortality, and to quantify the value of copeptin in predicting mortality in patients with ACS.

Methods

Literature search and selection

We searched for relevant studies published in English up to December 2019 through the Pubmed/MEDLINE, EMBASE and Cochrane database with the following terms and their combinations: “copeptin”, “C-terminal provasopressin”, “coronary artery disease”, “coronary heart disease”, “angina”, “myocardial infarction”, “death” and “mortality”.

We selected eligible studies from all the relevant literatures found in databases by orderly reviewing title, abstract and full text. The inclusion criteria were as follows: (1) studies focused on the value of copeptin in predicting mortality including both in-hospital and long-term mortality in patients with ACS or suspected ACS; (2) prospective cohort studies or randomized control trials (RCT); (3) studies with at least 1 month follow-up. Studies were excluded if: (1) we are not able to calculate sensitivity and specificity based on the data in the literature; (2) literatures are conference articles, editorial, letters, reviews or duplicated publications.

Data extraction and quality assessment

All the following information was separately extracted by two investigators: the first author's name, year of publication, study design, number of study centers, study outcome, duration of follow-up, number of patients enrolled, cut-off value of copeptin, number of true positive (TP), number of false positive (FP), number of false negative (FN) and number of true negative (TN). Discrepancies were resolved through discussion with the third investigator.

Quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) [20]. The QUADAS-2 form is composed of four domains: (1) patient selection, (2) index test, (3) reference standard and (4) flow and timing. For each domain, the risk of bias and applicability concerns was analyzed and rated as “low”, “high” and “unclear”. Two investigators independently assessed the quality of the eligible studies and resolved disagreements by discussion.

Statistical analysis

We calculated pooled sensitivity and specificity using a random-effects model [21]. Forest plots of each study and pooled estimates for sensitivity and specificity with 95% confidence intervals (95% CI) were used to visualize these results. We also presented a summary receiver operating characteristic curve (SROC) and calculated the area under the curve (AUC). The AUC close to 1 indicated a good performance for predicting mortality in ACS. Additionally, between-study heterogeneity of sensitivity and specificity was analyzed using the Cochrane’s Q test and I2 index, and P<0.1 or I2>50% indicated statistically significant heterogeneity. Meta-regression analysis was conducted to identify potential between-study heterogeneity. Deek’s test was used to assess the potential publication bias, and P<0.05 indicated statistically significant publication bias. To assess the reliability of the pooled results, sensitivity analyses were conducted by removing one study each time to observe the influence of each study on the pooled estimates. All analyses were conducted using STATA 15.0 (STATA Corporation, College Station, Texas, USA).

Results

Characteristics of the eligible studies

We identified 80 relevant studies by the literature search (Fig 1). After initial review of the title and abstract, 65 studies were excluded. Among them, 18 studies were reviews, 23 studies were not designed to evaluate the prognostic effect of copeptin in patients with ACS, and 24 studies didn’t focus on patients with ACS. Among the 15 studies selected for full-text review, six studies didn’t have sufficient data for the meta-analysis, one study was not prospective cohort study or RCT, one study didn’t focus on mortality, one study didn’t test baseline level of copeptin. Therefore, six studies were finally included in our meta-analysis [13–18]. Among the six included studies, five were prospective cohort studies, two were multi-center studies, and four studies had follow-up duration ≥6 months. A total of 2269 patients with ACS were studied. The characteristics of the final included studies were shown in Table 1.

thumbnail
Download:
Fig 1. Flowchart of study selection.

ACS: acute coronary syndrome.

https://doi.org/10.1371/journal.pone.0238288.g001

thumbnail
Download:
Table 1. Characteristics of included studies.

https://doi.org/10.1371/journal.pone.0238288.t001

The result of quality assessment was shown in Fig 2. In general, all included studies in the meta-analysis showed good quality in methodology. In this study, we only included prospective cohort studies or RCTs. Therefore, all included studies had low risk of bias in patient selection. One study had unclear risk of bias in index test domain because the cut-off value of copeptin was not pre-specified [16]. One study was judged to be unclear risk of bias in flow and timing because only part of study patients were included in the final analysis [17]. However, none of the six eligible studies needed to be excluded from the meta-analysis due to methodological defects.

thumbnail
Download:
Fig 2. Quality assessment of included studies using QUADAS-2.

https://doi.org/10.1371/journal.pone.0238288.g002

Performance of copeptin in predicting mortality in patients with ACS

The AUC of copeptin in predicting mortality in patients with ACS was 0.73 (95% CI: 0.69–0.77) (Fig 3). The pooled sensitivity and specificity of copeptin in predicting mortality in patients with ACS was 0.77 (95% CI: 0.59–0.89) and 0.60 (95% CI: 0.47–0.71), respectively (Fig 4). The positive likelihood ratio was 1.90 (95% CI: 1.60–2.30) and the negative likelihood ratio was 0.38 (95% CI: 0.23–0.64). The diagnostic odds ratio was 5.00 (95% CI: 3.00–9.00). The sensitivity analyses of diagnostic odds ratio showed that the combined estimate was 4.76 and the estimates varied between 3.57 and 5.39, which indicated that no single study could significantly influenced the combined estimates (Table 2).

thumbnail
Download:
Fig 3. Summary receiver operating characteristic curve of copeptin in predicting mortality in patients with acute coronary syndrome.

https://doi.org/10.1371/journal.pone.0238288.g003

thumbnail
Download:
Fig 4. Forest plot of the sensitivity and specificity of copeptin in predicting mortality in patients with acute coronary syndrome.

https://doi.org/10.1371/journal.pone.0238288.g004

thumbnail
Download:
Table 2. Sensitivity analysis of included studies.

https://doi.org/10.1371/journal.pone.0238288.t002

Between-study heterogeneity

Significant between-study heterogeneity was identified in both sensitivity (P = 0.01; I2 = 69.76%) and specificity (P<0.001; I2 = 97.32%) among the six included studies. Meta-regression was performed to examine the sources of potential heterogeneity of sensitivity and specificity (Table 3). In the meta-regression, the following covariates were included: study design (prospective cohort study or RCT), number of study centers (multicenter or single-center), sample size (<500 or ≥500), average age (<70 years or ≥70 years), duration of follow-up (<180 days or ≥180 days), study outcome (all-cause mortality or cardiovascular mortality) and cut-off value of copeptin (pre-specified or not pre-specified). The results showed that number of study centers was significantly associated with the heterogeneity of sensitivity (P = 0.03). Studies conducted in multiple centers (0.54, 95%: 0.23–0.85) had lower pooled sensitivity compared with studies with single center (0.85, 95%: 0.75–0.94). The study design (P = 0.03) and duration of follow-up (P<0.001) were significantly associated with the heterogeneity of specificity. The pooled specificity in clinical trial (0.78, 95%: 0.62–0.94) was much higher than that in prospective cohort studies (0.55, 95%: 0.45–0.66), and the pooled specificity obtained from studies with ≥180 days follow-up (0.75, 95%: 0.64–0.85) was much higher than that obtained from studies with <180 days follow-up (0.51, 95%: 0.41–0.61). No significant heterogeneity of sensitivity and specificity was identified across studies categorized by sample size, average age, study outcome and pre-specified cut-off value of copeptin.

thumbnail
Download:
Table 3. Meta-regression analysis to examine the sources of potential heterogeneity of sensitivity and specificity.

https://doi.org/10.1371/journal.pone.0238288.t003

Publication bias

The Deek’s funnel plot asymmetry test was not statistically significant (P = 0.44), which suggested that there is no evident indication of publication bias in the included studies (Fig 5).

thumbnail
Download:
Fig 5. Funnel plot for detecting publication bias.

https://doi.org/10.1371/journal.pone.0238288.g005

Discussion

To our knowledge, this is the first meta-analysis to evaluate the prognostic value of copeptin in patients with ACS. Our meta-analysis including 2269 patients from six studies showed that elevated copeptin was associated with the higher risk of mortality in patients with ACS. The pooled sensitivity and specificity was 0.77 (95% CI: 0.59–0.89) and 0.60 (95% CI: 0.47–0.71), respectively. The AUC of copeptin was 0.73 (95% CI: 0.69–0.77), indicating that copeptin has acceptable performance for predicting mortality in patients with ACS.

Similar to present study, previous studies showed copeptin had prognostic value in patients with heart failure or acute ischemic stroke. Zhong et al. [22] conducted a meta-analysis and reported that elevated copeptin level was associated with an increased risk of all-cause mortality in patients with heart failure (relative risk = 2.64, 95% CI: 2.09–3.32), and the performance for predicting mortality in patients with heart failure was acceptable (AUC: 0.70, 95% CI: 0.66–0.74). In another meta-analysis based on data from 1976 patients in six studies, it indicated that elevation in plasma copeptin level carried a higher risk of all-cause mortality in patients with acute ischemic stroke (odds ratio = 4.16, 95% CI: 2.77–6.25) [23].

At the current state of knowledge, copeptin could be a potential prognostic marker for ACS due to its acceptable predictive performance. However, there are still several weaknesses for copeptin as prognostic marker for ACS. First, the relationship of copeptin with the risk of mortality in patients with ACS may be influenced by concomitant diseases, such as stroke, renal failure or sepsis, which will affect its application in clinical practice [9, 24]. Second, current evidences don’t support that copeptin is a superior predictor of mortality in patients with ACS compared with cardiac troponin T (cTnT) and NT-proBNP, which are well-established biomarkers for diagnosis and prognosis of ACS [25, 26]. Bahrmann et al. found that high-sensitivity cTnT ≥ 0.014 μg/L alone was significantly associated with cardiovascular mortality, while the net reclassification improvement for cardiovascular mortality was not significant (P = 0.809) when copeptin ≥ 14 pmol/L was added [14]. Another study using data from the MERLIN-TIMI 36 Trial reported that copeptin significantly improved the prognostic value when added to traditional clinical factors (net reclassification index = 0.25, P<0.001), but copeptin and BNP showed similar discrimination capability (c-statistic: 0.69 for copeptin and 0.69 for BNP) [12]. Similar results were also reported in another hospital-based prospective cohort study [27]. Given that several biomarkers reflect different pathophysiological pathways of response post-MI, we speculated an additive predictive value for combining biomarkers including those of myonecrosis, myocardial strain or stress, and vascular inflammation in CAD prognosis. Previous studies used the multimarker strategy to investigate the risk prediction tool for prognosis of ACS [19, 28, 29], but the prognostic value of copeptin added to other biomarkers in patients with ACS has not been reported.

There are some potential limitations in this study. First, the number of included studies is relatively small. The Deek’s funnel plot asymmetry test was not statistically significant, but publication bias may still exist. Second, the cut-off value of copeptin varied across different studies, and we could not determine the optimized cut-off value due to the lack of raw data. It’s worthy to explore the optimized cut-off value of copeptin in future studies. Third, most of included studies were conducted in patients with suspected ACS, as a result, we cannot further evaluate the predictive value of copeptin in patients with non-ST-elevation myocardial infarction and ST-elevation myocardial infarction, respectively in meta-regression analysis.

In conclusion, our study shows that copeptin has prognostic value for mortality in patients with ACS. Future studies based on multimarker strategy are needed to evaluate the prognostic value of copeptin in conjunction with other well-established biomarkers for risk stratification in patients with ACS.

Supporting information

S1 File. PRISMA 2019 checklist.

https://doi.org/10.1371/journal.pone.0238288.s001

(PDF)

References

  1. 1. Ma Q, Wang J, Jin J, Gao M, Liu F, Zhou S, et al. Clinical characteristics and prognosis of acute coronary syndrome in young women and men: A systematic review and meta-analysis of prospective studies. International journal of cardiology. 2017;228:837–43. pmid:27888764
  2. 2. Zhang Q, Zhang RY, Qiu JP, Zhang JF, Wang XL, Jiang L, et al. One-year clinical outcome of interventionalist- versus patient-transfer strategies for primary percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction: results from the REVERSE-STEMI study. Circulation Cardiovascular quality and outcomes. 2011;4(3):355–62. pmid:21521833
  3. 3. Haaf P, Reichlin T, Twerenbold R, Hoeller R, Rubini Gimenez M, Zellweger C, et al. Risk stratification in patients with acute chest pain using three high-sensitivity cardiac troponin assays. European heart journal. 2014;35(6):365–75. pmid:23821402
  4. 4. Thygesen K, Mair J, Mueller C, Huber K, Weber M, Plebani M, et al. Recommendations for the use of natriuretic peptides in acute cardiac care: a position statement from the Study Group on Biomarkers in Cardiology of the ESC Working Group on Acute Cardiac Care. European heart journal. 2012;33(16):2001–6. pmid:21292681
  5. 5. Wang J, Tan GJ, Han LN, Bai YY, He M, Liu HB. Novel biomarkers for cardiovascular risk prediction. Journal of geriatric cardiology: JGC. 2017;14(2):135–50. pmid:28491088
  6. 6. Parizadeh SM, Ghandehari M, Parizadeh MR, Ferns GA, Ghayour-Mobarhan M, Avan A, et al. The diagnostic and prognostic value of copeptin in cardiovascular disease, current status, and prospective. Journal of cellular biochemistry. 2018;119(10):7913–23. pmid:30011137
  7. 7. Katan M, Morgenthaler N, Widmer I, Puder JJ, Konig C, Muller B, et al. Copeptin, a stable peptide derived from the vasopressin precursor, correlates with the individual stress level. Neuro endocrinology letters. 2008;29(3):341–6. pmid:18580851
  8. 8. Szinnai G, Morgenthaler NG, Berneis K, Struck J, Muller B, Keller U, et al. Changes in plasma copeptin, the c-terminal portion of arginine vasopressin during water deprivation and excess in healthy subjects. The Journal of clinical endocrinology and metabolism. 2007;92(10):3973–8. pmid:17635944
  9. 9. Morgenthaler NG, Struck J, Jochberger S, Dunser MW. Copeptin: clinical use of a new biomarker. Trends in endocrinology and metabolism: TEM. 2008;19(2):43–9. pmid:18291667
  10. 10. Morgenthaler NG, Struck J, Alonso C, Bergmann A. Assay for the measurement of copeptin, a stable peptide derived from the precursor of vasopressin. Clinical chemistry. 2006;52(1):112–9. pmid:16269513
  11. 11. Katan M, Christ-Crain M. The stress hormone copeptin: a new prognostic biomarker in acute illness. Swiss medical weekly. 2010;140:w13101. pmid:20872295
  12. 12. O'Malley RG, Bonaca MP, Scirica BM, Murphy SA, Jarolim P, Sabatine MS, et al. Prognostic performance of multiple biomarkers in patients with non-ST-segment elevation acute coronary syndrome: analysis from the MERLIN-TIMI 36 trial (Metabolic Efficiency With Ranolazine for Less Ischemia in Non-ST-Elevation Acute Coronary Syndromes-Thrombolysis In Myocardial Infarction 36). Journal of the American College of Cardiology. 2014;63(16):1644–53. pmid:24530676
  13. 13. Afzali D, Erren M, Pavenstadt HJ, Vollert JO, Hertel S, Waltenberger J, et al. Impact of copeptin on diagnosis, risk stratification, and intermediate-term prognosis of acute coronary syndromes. Clinical research in cardiology: official journal of the German Cardiac Society. 2013;102(10):755–63.
  14. 14. Bahrmann P, Bahrmann A, Breithardt OA, Daniel WG, Christ M, Sieber CC, et al. Additional diagnostic and prognostic value of copeptin ultra-sensitive for diagnosis of non-ST-elevation myocardial infarction in older patients presenting to the emergency department. Clinical chemistry and laboratory medicine. 2013;51(6):1307–19. pmid:23314553
  15. 15. Morawiec B, Kawecki D, Przywara-Chowaniec B, Opara M, Muzyk P, Ho L, et al. Copeptin as a Prognostic Marker in Acute Chest Pain and Suspected Acute Coronary Syndrome. Disease markers. 2018;2018:6597387. pmid:29619130
  16. 16. Narayan H, Dhillon OS, Quinn PA, Struck J, Squire IB, Davies JE, et al. C-terminal provasopressin (copeptin) as a prognostic marker after acute non-ST elevation myocardial infarction: Leicester Acute Myocardial Infarction Peptide II (LAMP II) study. Clinical science. 2011;121(2):79–89. pmid:21309746
  17. 17. Vafaie M, Slagman A, Möckel M, Hamm C, Huber K, Müller C, et al. Prognostic Value of Undetectable hs Troponin T in Suspected Acute Coronary Syndrome. American Journal of Medicine. 2016;129(3):274–82. pmid:26524709
  18. 18. Sanchez M, Llorens P, Herrero P, Martin-Sanchez FJ, Pinera P, Miro O, et al. The utility of copeptin in the emergency department as a predictor of adverse outcomes in non-ST-elevation acute coronary syndrome: the COPED-PAO study. Emergency medicine journal: EMJ. 2014;31(4):286–91. pmid:23371977
  19. 19. O'Donoghue ML, Morrow DA, Cannon CP, Jarolim P, Desai NR, Sherwood MW, et al. Multimarker Risk Stratification in Patients With Acute Myocardial Infarction. Journal of the American Heart Association. 2016;5(5).
  20. 20. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine. 2011;155(8):529–36. pmid:22007046
  21. 21. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of clinical epidemiology. 2005;58(10):982–90. pmid:16168343
  22. 22. Zhong Y, Wang R, Yan L, Lin M, Liu X, You T. Copeptin in heart failure: Review and meta-analysis. Clinica chimica acta; international journal of clinical chemistry. 2017;475:36–43.
  23. 23. Xu Q, Tian Y, Peng H, Li H. Copeptin as a biomarker for prediction of prognosis of acute ischemic stroke and transient ischemic attack: a meta-analysis. Hypertension research: official journal of the Japanese Society of Hypertension. 2017;40(5):465–71.
  24. 24. Katan M, Fluri F, Morgenthaler NG, Schuetz P, Zweifel C, Bingisser R, et al. Copeptin: a novel, independent prognostic marker in patients with ischemic stroke. Annals of neurology. 2009;66(6):799–808. pmid:20035506
  25. 25. Katus HA, Remppis A, Neumann FJ, Scheffold T, Diederich KW, Vinar G, et al. Diagnostic efficiency of troponin T measurements in acute myocardial infarction. Circulation. 1991;83(3):902–12. pmid:1999039
  26. 26. Omland T, Persson A, Ng L, O'Brien R, Karlsson T, Herlitz J, et al. N-terminal pro-B-type natriuretic peptide and long-term mortality in acute coronary syndromes. Circulation. 2002;106(23):2913–8. pmid:12460871
  27. 27. Khan SQ, Dhillon OS, O'Brien RJ, Struck J, Quinn PA, Morgenthaler NG, et al. C-terminal provasopressin (copeptin) as a novel and prognostic marker in acute myocardial infarction: Leicester Acute Myocardial Infarction Peptide (LAMP) study. Circulation. 2007;115(16):2103–10. pmid:17420344
  28. 28. Sabatine MS, Morrow DA, de Lemos JA, Gibson CM, Murphy SA, Rifai N, et al. Multimarker approach to risk stratification in non-ST elevation acute coronary syndromes: simultaneous assessment of troponin I, C-reactive protein, and B-type natriuretic peptide. Circulation. 2002;105(15):1760–3. pmid:11956114
  29. 29. Schnabel RB, Schulz A, Messow CM, Lubos E, Wild PS, Zeller T, et al. Multiple marker approach to risk stratification in patients with stable coronary artery disease. European heart journal. 2010;31(24):3024–31. pmid:20852293