Article Data

  • Views 2733
  • Dowloads 244

Original Research

Open Access

Combined suPAR and qSOFA for the prediction of 28-day mortality in sepsis patients

  • Lifeng Wang1,†
  • Chao Tang1,†
  • Shuangjun He1
  • Yi Chen1
  • Cuiying Xie1,*,

1Department of Emergency, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 201112 Shanghai, China

DOI: 10.22514/sv.2021.143 Vol.18,Issue 3,May 2022 pp.119-127

Submitted: 13 May 2021 Accepted: 29 July 2021

Published: 08 May 2022

*Corresponding Author(s): Cuiying Xie E-mail: xiecuiyingrenji@163.com

† These authors contributed equally.

Abstract

To determine the prognostic performance of soluble urokinase-type plasminogen activator receptor (suPAR) and quick Sequential Organ Failure Assessment (qSOFA) in predicting the 28-day mortality of sepsis patients admitted to the emergency department (ED). A prospective, single-center observational study was conducted between June 2018 and June 2019. In total, 175 patients with sepsis and septic shock admitted to the ED were enrolled based on the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). We assessed the qSOFA score on ED admission and measured serum suPAR levels by quantitative enzyme-linked immunosorbent assay. Univariate and multivariate analysis was performed to identify predictors of prognosis. Kaplan–Meier survival curves and areas under the receiver operating characteristic (ROC) curve for 28-day mortality were calculated. We estimated category-free net reclassification improvement (NRI) when suPAR was added to qSOFA. Increased suPAR levels were significantly associated with 28-day mortality [1.74 (1.24–2.51) ng/mL in survivors vs. 1.34 (0.96–2.00) ng/mL in non-survivors, p = 0.011] and with sepsis severity [1.34 (0.99–1.98) ng/mL in sepsis vs. 1.74 (1.22–2.65) ng/mL in septic shock, p = 0.039]. The area under the curve (AUC) for the prediction of 28-day mortality was 0.646 (95% confidence interval (CI): 0.553–0.740) for suPAR, 0.832 (95% CI: 0.692–0.923) for qSOFA and 0.864 (95% CI: 0.802–0.928) for combined suPAR and qSOFA. Serum suPAR did not significantly increase the AUC of the basic qSOFA, but a model containing suPAR and qSOFA had a continuous NRI of 11% (95% CI: 3.5–18.5%; p = 0.004). Serum suPAR was associated with sepsis severity and 28-day mortality. Adding suPAR to qSOFA increased the ROC curve area and improved its discrimination, suggesting that this might be a useful tool in sepsis mortality prediction models.


Keywords

Soluble urokinase-type plasminogen activator receptor; Quick Sequential Organ Failure Assessment; Combined model; Sepsis; 28-day mortality


Cite and Share

Lifeng Wang,Chao Tang,Shuangjun He,Yi Chen,Cuiying Xie. Combined suPAR and qSOFA for the prediction of 28-day mortality in sepsis patients. Signa Vitae. 2022. 18(3);119-127.

References

[1] Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, et al. Assessment of Clinical Criteria for Sepsis: for the third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Journal of the American Medical Association. 2016; 315: 762–774.

[2] Fleischmann C, Scherag A, Adhikari NKJ, Hartog CS, Tsaganos T, Schlattmann P, et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. American Journal of Respiratory and Critical Care Medicine. 2016; 193: 259–272.

[3] Kaukonen K, Bailey M, Suzuki S, Pilcher D, Bellomo R. Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012. Journal of the American Medical Association. 2014; 311: 1308–1316.

[4] Shankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX, Deutschman CS, et al. Developing a New Definition and Assessing New Clinical Criteria for Septic Shock: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Journal of the American Medical Association. 2016; 315: 775.

[5] Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Journal of the American Medical Association. 2016; 315: 801–810.

[6] Vincent J, Martin GS, Levy MM. QSOFA does not replace SIRS in the definition of sepsis. Critical Care. 2016; 20: 210.

[7] Spoto S, Cella E, de Cesaris M, Locorriere L, Mazzaroppi S, Nobile E, et al. Procalcitonin and MR-Proadrenomedullin Combination with SOFA and qSOFA Scores for Sepsis Diagnosis and Prognosis: a Diagnostic Algorithm. Shock. 2018; 50: 44–52.

[8] Giamarellos-Bourboulis EJ, Tsaganos T, Tsangaris I, Lada M, Routsi C, Sinapidis D, et al. Validation of the new Sepsis-3 definitions: proposal for improvement in early risk identification. Clinical Microbiology and Infection. 2017; 23: 104–109.

[9] Andriolo BN, Andriolo RB, Salomão R, Atallah ÁN. Effectiveness and safety of procalcitonin evaluation for reducing mortality in adults with sepsis, severe sepsis or septic shock. the Cochrane Database of Systematic Reviews. 2017; 1: CD010959.

[10] Rivers EP, Jaehne AK, Nguyen HB, Papamatheakis DG, Singer D, Yang JJ, et al. Early biomarker activity in severe sepsis and septic shock and a contemporary review of immunotherapy trials: not a time to give up, but to give it earlier. Shock. 2013; 39: 127–137.

[11] Velissaris D, Dimopoulos G, Parissis J, Alexiou Z, Antonakos N, Babalis D, et al. Prognostic Role of Soluble Urokinase Plasminogen Activator Receptor at the Emergency Department: a Position Paper by the Hellenic Sepsis Study Group. Infectious Diseases and Therapy. 2020; 9: 407–416.

[12] Desmedt S, Desmedt V, Delanghe JR, Speeckaert R, Speeckaert MM. The intriguing role of soluble urokinase receptor in inflammatory diseases. Critical Reviews in Clinical Laboratory Sciences. 2017; 54: 117–133.

[13] Donadello K, Scolletta S, Covajes C, Vincent J. SuPAR as a prognostic biomarker in sepsis. BMC Medicine. 2012; 10: 2.

[14] Huang Q, Xiong H, Yan P, Shuai T, Liu J, Zhu L, et al. The Diagnostic and Prognostic Value of suPAR in Patients with Sepsis: a Systematic Review and Meta-Analysis. Shock. 2020; 53: 416–425.

[15] Pregernig A, Müller M, Held U, Beck-Schimmer B. Prediction of mortality in adult patients with sepsis using six biomarkers: a systematic review and meta-analysis. Annals of Intensive Care. 2020; 9: 125.

[16] Giamarellos-Bourboulis EJ, Norrby-Teglund A, Mylona V, Savva A, Tsangaris I, Dimopoulou I, et al. Risk assessment in sepsis: a new prognostication rule by APACHE II score and serum soluble urokinase plasminogen activator receptor. Critical Care. 2012; 16: R149.

[17] Koch A, Voigt S, Kruschinski C, Sanson E, Dückers H, Horn A, et al. Circulating soluble urokinase plasminogen activator receptor is stably elevated during the first week of treatment in the intensive care unit and predicts mortality in critically ill patients. Critical Care. 2011; 15: R63. Hall A, Crichton S, Varrier M, Bear DE, Ostermann M. SuPAR as a marker of infection in acute kidney injury - a prospective observational study. BMC Nephrology. 2018; 19: 191.

[19] Cook NR, Paynter NP. Performance of reclassification statistics in comparing risk prediction models. Biometrical Journal. Biometrische Zeitschrift. 2011; 53: 237–258.

[20] Suberviola B, Castellanos-Ortega A, Ruiz Ruiz A, Lopez-Hoyos M, Santibañez M. Hospital mortality prognostication in sepsis using the new biomarkers suPAR and proADM in a single determination on ICU admission. Intensive Care Medicine. 2013; 39: 1945–1952.

[21] knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Critical Care Medicine. 1985; 13: 818–829.

[22] Lamontagne F, Harrison DA, Rowan KM. QSOFA for Identifying Sepsis among Patients with Infection. Journal of the American Medical Association. 2017; 317: 267–268.

[23] Antonelli M, Azoulay E, Bonten M, Chastre J, Citerio G, Conti G, et al. Year in review in Intensive Care Medicine 2009: i. Pneumonia and infections, sepsis, outcome, acute renal failure and acid base, nutrition and glycaemic control. Intensive Care Medicine. 2010; 36: 196–209.

[24] Giamarellos-Bourboulis EJ, Norrby-Teglund A, Mylona V, Savva A, Tsangaris I, Dimopoulou I, et al. Risk assessment in sepsis: a new prognostication rule by APACHE II score and serum soluble urokinase plasminogen activator receptor. Critical Care. 2012; 16: R149.

[25] Huttunen R, Syrjänen J, Vuento R, Hurme M, Huhtala H, Laine J, et al. Plasma level of soluble urokinase-type plasminogen activator receptor as a predictor of disease severity and case fatality in patients with bacteraemia: a prospective cohort study. Journal of Internal Medicine. 2011; 270: 32–40.

[26] Rasmussen LJH, Ladelund S, Haupt TH, Ellekilde GE, Eugen-Olsen J, Andersen O. Combining National Early Warning Score with Soluble Urokinase Plasminogen Activator Receptor (suPAR) Improves Risk Prediction in Acute Medical Patients: a Registry-Based Cohort Study. Critical Care Medicine. 2018; 46: 1961–1968.

[27] Mölkänen T, Ruotsalainen E, Thorball CW, Järvinen A. Elevated soluble urokinase plasminogen activator receptor (suPAR) predicts mortality in Staphylococcus aureus bacteremia. European Journal of Clinical Microbiology & Infectious Diseases. 2011; 30: 1417–1424.

[28] Luo Q, Ning P, Zheng Y, Shang Y, Zhou B, Gao Z. Serum suPAR and syndecan-4 levels predict severity of community-acquired pneumonia: a prospective, multi-centre study. Critical Care. 2018; 22: 15.

[29] Yilmaz G, Köksal I, Karahan SC, Mentese A. The diagnostic and prog-nostic significance of soluble urokinase plasminogen activator receptor in systemic inflammatory response syndrome. Clinical Biochemistry. 2011; 44: 1227–1230.

[30] Wittenhagen P, Kronborg G, Weis N, Nielsen H, Obel N, Pedersen SS, et al. The plasma level of soluble urokinase receptor is elevated in patients with Streptococcus pneumoniae bacteraemia and predicts mortality. Clinical Microbiology and Infection. 2004; 10: 409–415.

[31] Savva A, Raftogiannis M, Baziaka F, Routsi C, Antonopoulou A, Koutoukas P, et al. Soluble urokinase plasminogen activator receptor (suPAR) for assessment of disease severity in ventilator-associated pneumonia and sepsis. The Journal of Infection. 2011; 63: 344–350.

[32] Jalkanen V, Yang R, Linko R, Huhtala H, Okkonen M, Varpula T, et al. SuPAR and PAI-1 in critically ill, mechanically ventilated patients. Intensive Care Medicine. 2013; 39: 489–496.

[33] Kofoed K, Eugen-Olsen J, Petersen J, Larsen K, Andersen O. Predicting mortality in patients with systemic inflammatory response syndrome: an evaluation of two prognostic models, two soluble receptors, and a macrophage migration inhibitory factor. European Journal of Clinical Microbiology & Infectious Diseases. 2008; 27: 375–383.

[34] Julián-Jiménez A, Yañez MC, González-Del Castillo J, Salido-Mota M, Mora-Ordoñez B, Arranz-Nieto MJ, et al. Prognostic power of biomarkers for short-term mortality in the elderly patients seen in Emergency Departments due to infections. Enfermedades Infecciosas Y Microbiologia Clinica. 2019; 37: 11–18.

[35] Baumann BM, Greenwood JC, Lewis K, Nuckton TJ, Darger B, Shofer FS, et al. Combining qSOFA criteria with initial lactate levels: Improved screening of septic patients for critical illness. The American Journal of Emergency Medicine. 2020; 38: 883–889.

[36] Kofoed K, Schneider UV, Scheel T, Andersen O, Eugen-Olsen J. Development and validation of a multiplex add-on assay for sepsis biomarkers using xMAP technology. Clinical Chemistry. 2006; 52: 1284–1293.

[37] Pencina MJ, D’Agostino RB, Pencina KM, Janssens ACJW, Greenland P. Interpreting Incremental Value of Markers Added to Risk Prediction Models. American Journal of Epidemiology. 2012; 176: 473–481.

[38] Austin PC, Steyerberg EW. Predictive accuracy of risk factors and markers: a simulation study of the effect of novel markers on different performance measures for logistic regression models. Statistics in Medicine. 2013; 32: 661–672.

[39] Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Annals of Internal Medicine. 2006; 145: 21–29.

[40] Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O’Leary D, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. Journal of the American Medical Association. 2012; 308: 788–795.

[41] Freund Y, Lemachatti N, Krastinova E, Van Laer M, Claessens Y, Avondo A, et al. Prognostic Accuracy of Sepsis-3 Criteria for in-Hospital Mortality among Patients with Suspected Infection Presenting to the Emergency Department. Journal of the American Medical Association. 2017; 317: 301–308.

[42] Theilade S, Lyngbaek S, Hansen TW, Eugen-Olsen J, Fenger M, Rossing P, et al. Soluble urokinase plasminogen activator receptor levels are elevated and associated with complications in patients with type 1 diabetes. Journal of Internal Medicine. 2015; 277: 362–371.

[43] Koller L, Stojkovic S, Richter B, Sulzgruber P, Potolidis C, Liebhart F, et al. Soluble Urokinase-Type Plasminogen Activator Receptor Improves Risk Prediction in Patients with Chronic Heart Failure. JACC: Heart Failure. 2017; 5: 268–277.

[44] Pepe MS, Fan J, Feng Z, Gerds T, Hilden J. The Net Reclassification Index (NRI): a Misleading Measure of Prediction Improvement even with Independent Test Data Sets. Statistics in Biosciences. 2015; 7: 282–295.


Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,200 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Chemical Abstracts Service Source Index The CAS Source Index (CASSI) Search Tool is an online resource that can quickly identify or confirm journal titles and abbreviations for publications indexed by CAS since 1907, including serial and non-serial scientific and technical publications.

Index Copernicus The Index Copernicus International (ICI) Journals database’s is an international indexation database of scientific journals. It covered international scientific journals which divided into general information, contents of individual issues, detailed bibliography (references) sections for every publication, as well as full texts of publications in the form of attached files (optional). For now, there are more than 58,000 scientific journals registered at ICI.

Geneva Foundation for Medical Education and Research The Geneva Foundation for Medical Education and Research (GFMER) is a non-profit organization established in 2002 and it works in close collaboration with the World Health Organization (WHO). The overall objectives of the Foundation are to promote and develop health education and research programs.

Scopus: CiteScore 1.0 (2022) Scopus is Elsevier's abstract and citation database launched in 2004. Scopus covers nearly 36,377 titles (22,794 active titles and 13,583 Inactive titles) from approximately 11,678 publishers, of which 34,346 are peer-reviewed journals in top-level subject fields: life sciences, social sciences, physical sciences and health sciences.

Embase Embase (often styled EMBASE for Excerpta Medica dataBASE), produced by Elsevier, is a biomedical and pharmacological database of published literature designed to support information managers and pharmacovigilance in complying with the regulatory requirements of a licensed drug.

Submission Turnaround Time

Conferences

Top