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Zeng Y, Li Y, Zhang W, Lu H, Lin S, Zhang W, Xia L, Hu H, Song Y, Xu F. Proteome analysis develops novel plasma proteins classifier in predicting the mortality of COVID-19. Cell Prolif 2024; 57:e13617. [PMID: 38403992 PMCID: PMC11216943 DOI: 10.1111/cpr.13617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 02/27/2024] Open
Abstract
COVID-19 has been a global concern for 3 years, however, consecutive plasma protein changes in the disease course are currently unclear. Setting the mortality within 28 days of admission as the main clinical outcome, plasma samples were collected from patients in discovery and independent validation groups at different time points during the disease course. The whole patients were divided into death and survival groups according to their clinical outcomes. Proteomics and pathway/network analyses were used to find the differentially expressed proteins and pathways. Then, we used machine learning to develop a protein classifier which can predict the clinical outcomes of the patients with COVID-19 and help identify the high-risk patients. Finally, a classifier including C-reactive protein, extracellular matrix protein 1, insulin-like growth factor-binding protein complex acid labile subunit, E3 ubiquitin-protein ligase HECW1 and phosphatidylcholine-sterol acyltransferase was determined. The prediction value of the model was verified with an independent patient cohort. This novel model can realize early prediction of 28-day mortality of patients with COVID-19, with the area under curve 0.88 in discovery group and 0.80 in validation group, superior to 4C mortality and E-CURB65 scores. In total, this work revealed a potential protein classifier which can assist in predicting the outcomes of COVID-19 patients and providing new diagnostic directions.
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Affiliation(s)
- Yifei Zeng
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yufan Li
- Shanghai Key Laboratory of Lung Inflammation and Injury, Department of Pulmonary Medicine, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Wanying Zhang
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Huidan Lu
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Siyi Lin
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Wenting Zhang
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Lexin Xia
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Huiqun Hu
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yuanlin Song
- Shanghai Key Laboratory of Lung Inflammation and Injury, Department of Pulmonary Medicine, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Feng Xu
- Department of Infectious DiseasesSecond Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
- Key Laboratory of Multiple Organ Failure (Zhejiang University)Ministry of EducationHangzhouChina
- Research Center for Life Science and Human HealthBinjiang Institute of Zhejiang UniversityHangzhouChina
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Taha AM, Najah Q, Omar MM, Abouelmagd K, Ali M, Hasan MT, Allam SA, Hamam YA, Arian R, Abd-ElGawad M. Diagnostic and prognostic value of heparin-binding protein in sepsis: A systematic review and meta-analysis. Medicine (Baltimore) 2024; 103:e38525. [PMID: 38905400 PMCID: PMC11191987 DOI: 10.1097/md.0000000000038525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 05/17/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Sepsis remains a leading cause of death worldwide. In this context, heparin-binding protein (HBP) has emerged as a possible biomarker, drawing significant attention for its diagnostic and prognostic usefulness in septic patients. Despite this advancement, the literature yields conflicting results. This study is intended to critically evaluate the diagnostic and prognostic value of HBP in critically ill septic patients. METHODS We searched multiple databases, including PubMed, SCOPUS, Web of Science, and EBSCO, to identify relevant studies on April 27, 2023. We included studies investigating sepsis or its severe outcomes that reported HBP levels and the required data to create 2 × 2 tables. We used R version 4.2.2 and R Studio to analyze the pooled diagnostic accuracy outcomes. The diagmeta package was utilized to calculate the optimum cutoff value. RESULTS In our meta-analysis, we incorporated 28 studies including 5508 patients. The analysis revealed that HBP has a sensitivity of 0.71 (95% CI: 0.60; 0.79) and a specificity of 0.68 (95% CI: 0.51; 0.81) in diagnosing sepsis, respectively. HBP demonstrated moderate prognostic accuracy for mortality at a cutoff value of 161.415 ng/mL, with a sensitivity and specificity of 72%, and for severe sepsis outcomes at a cutoff value of 58.907 ng/mL, with a sensitivity and specificity of 71%. CONCLUSION Our findings indicate a relatively moderate diagnostic and prognostic accuracy of HBP for sepsis. Future studies are required to verify the accuracy of HBP as a biomarker for sepsis.
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Affiliation(s)
| | - Qasi Najah
- Faculty of Medicine, University of EL-Mergib, Al Khums, Libya
| | | | - Khaled Abouelmagd
- Cardiology Department, Faculty of Medicine, Al-Azhar University, Egypt
| | - Mohammed Ali
- Faculty of Medicine, Al-Azhar University, Cairo, Egypt
| | | | | | - Yasser A. Hamam
- Faculty of Medicine, Al-Quds University, Jerusalem, Palestine
| | - Roua Arian
- Faculty of Medicine, University of Aleppo, Aleppo, Syria
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Zuo L, Li X, Wang L, Yuan H, Liao Z, Zhou S, Wu J, Guan X, Liu Y. Heparin-binding protein as a biomarker for the diagnosis of sepsis in the intensive care unit: a retrospective cross-sectional study in China. BMJ Open 2024; 14:e078687. [PMID: 38858136 PMCID: PMC11168158 DOI: 10.1136/bmjopen-2023-078687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 05/28/2024] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVES This study aims to investigate the diagnostic value of heparin-binding protein (HBP) in sepsis and develop a sepsis diagnostic model incorporating HBP with key biomarkers and disease-related scores for rapid, and accurate diagnosis of sepsis in the intensive care unit (ICU). DESIGN Clinical retrospective cross-sectional study. SETTING A comprehensive teaching tertiary hospital in China. PARTICIPANTS Adult patients (aged ≥18 years) who underwent HBP testing or whose blood samples were collected when admitted to the ICU. MAIN OUTCOME MEASURES HBP, C reactive protein (CRP), procalcitonin (PCT), white blood cell count (WBC), interleukin-6 (IL-6), lactate (LAC), Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) score were recorded. RESULTS Between March 2019 and December 2021, 326 patients were enrolled in this study. The patients were categorised into a non-infection group (control group), infection group, sepsis group and septic shock group based on the final diagnosis. The HBP levels in the sepsis group and septic shock group were 45.7 and 69.0 ng/mL, respectively, which were significantly higher than those in the control group (18.0 ng/mL) and infection group (24.0 ng/mL) (p<0.001). The area under the curve (AUC) value of HBP for diagnosing sepsis was 0.733, which was lower than those corresponding to PCT, CRP and SOFA but higher than those of IL-6, LAC and APACHE II. Multivariate logistic regression analysis identified HBP, PCT, CRP, IL-6 and SOFA as valuable indicators for diagnosing sepsis. A sepsis diagnostic model was constructed based on these indicators, with an AUC of 0.901, a sensitivity of 79.7% and a specificity of 86.9%. CONCLUSIONS HBP could serve as a biomarker for the diagnosis of sepsis in the ICU. Compared with single indicators, the sepsis diagnostic model constructed using HBP, PCT, CRP, IL-6 and SOFA further enhanced the diagnostic performance of sepsis.
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Affiliation(s)
- Lingyun Zuo
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaoyun Li
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Luhao Wang
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Hao Yuan
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zihuai Liao
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Si Zhou
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jianfeng Wu
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiangdong Guan
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yongjun Liu
- Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
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Kyriazopoulou E, Dalekos GN, Metallidis S, Poulakou G, Papanikolaou IC, Tzavara V, Argyraki K, Alexiou Z, Panagopoulos P, Samarkos M, Chrysos G, Tseliou A, Milionis H, Sympardi S, Vasishta A, Giamarellos-Bourboulis EJ. HEPARIN-BINDING PROTEIN LEVELS PREDICT UNFAVORABLE OUTCOME IN COVID-19 PNEUMONIA: A POST HOC ANALYSIS OF THE SAVE TRIAL. Shock 2024; 61:395-399. [PMID: 38517242 DOI: 10.1097/shk.0000000000002315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
ABSTRACT We aimed to evaluate heparin-binding protein (HBP) as a marker of prognosis of unfavorable outcome in COVID-19 pneumonia. This was a post hoc analysis of the SAVE clinical trial investigating anakinra treatment, guided by suPAR (soluble urokinase plasminogen activator receptor) levels ≥6 ng/mL, for the prevention of severe respiratory failure in hospitalized patients with COVID-19 pneumonia. Baseline HBP plasma levels were measured in 534 patients by fluorescence dry quantitative immunoassay using the Jet-iStar 800 analyzer. Concentrations higher than 35 ng/mL predicted 30-day mortality with a moderate specificity of 53.3% and negative predictive value 78.1%; sensitivity was low (29.0%). After multivariate Cox analysis, HBP higher than 35 ng/mL was an independent predictor of 30-day unfavorable outcome (adjusted hazard ratio, 1.77; 95% CI, 1.06-2.94; P = 0.028) and these patients were also at greater risk of death after 90 days (hazard ratio, 1.85; 95% CI, 1.25-2.74; P = 0.002). The cutoff was not predictive of development of severe respiratory failure, septic shock or acute kidney injury. Among patients with baseline HBP levels higher than 35 ng/mL, anakinra treatment was associated with decreased mortality (7.2%) versus comparators (18.1%; P < 0.001). Results confirm that HBP may be an early biomarker of poor outcome among preselected patients at risk from COVID-19 pneumonia.ClinicalTrials.gov registration NCT04357366.
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Affiliation(s)
- Evdoxia Kyriazopoulou
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - George N Dalekos
- Department of Medicine and Research Laboratory of Internal Medicine, National Expertise Center of Greece in Autoimmune Liver Diseases, Full Member of the European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece
| | - Symeon Metallidis
- First Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Garyphalia Poulakou
- Third Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Ilias C Papanikolaou
- Department of Pulmonary Medicine, General Hospital of Corfu "Agia Eirini," Athens, Greece
| | - Vasiliki Tzavara
- First Department of Internal Medicine, Korgialeneion-Benakeion General Hospital, Athens, Greece
| | - Katerina Argyraki
- Department of Internal Medicine, Sotiria Athens Hospital of Chest Diseases, Athens, Greece
| | - Zoi Alexiou
- Second Department of Internal Medicine, Thriasio General Hospital of Eleusis, Athens, Greece
| | - Periklis Panagopoulos
- Second Department of Internal Medicine, Democritus University of Thrace, Alexandropoilis, Greece
| | - Michael Samarkos
- First Department of Internal Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - George Chrysos
- Second Department of Internal Medicine, Tzaneio General Hospital of Piraeus, Athens, Greece
| | - Aikaterini Tseliou
- First Department of Internal Medicine, G. Gennimatas General Hospital of Athens, Athens, Greece
| | - Haralampos Milionis
- First Department of Internal Medicine, University of Ioannina, Medical School, Ioannina, Greece
| | - Styliani Sympardi
- First Department of Internal Medicine, Thriasio General Hospital of Eleusis, Athens, Greece
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Sun Y, Sun B, Ren Z, Xue M, Zhu C, Liu Q. Heparin-binding protein as a predictor of mortality in patients with diabetes mellitus and community-acquired pneumonia in intensive care unit : a propensity score matched study. World J Emerg Med 2024; 15:263-272. [PMID: 39050224 PMCID: PMC11265634 DOI: 10.5847/wjem.j.1920-8642.2024.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/20/2023] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Patients with diabetes mellitus (DM) are vulnerable to community-acquired pneumonia (CAP), which have a high mortality rate. We aimed to investigate the value of heparin-binding protein (HBP) as a prognostic marker of mortality in patients with DM and CAP. METHODS This retrospective study included CAP patients who were tested for HBP at intensive care unit (ICU) admission from January 2019 to April 2020. Patients were allocated to the DM or non-DM group and paired with propensity score matching. Baseline characteristics and clinical outcomes up to 90 days were evaluated. The primary outcome was the 10-day mortality. Receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, and Cox regression were used for statistical analysis. RESULTS Among 152 enrolled patients, 60 pairs were successfully matched. There was no significant difference in 10-day mortality, while more patients in the DM group died within 28 d (P=0.024) and 90 d (P=0.008). In the DM group, HBP levels at ICU admission were higher in 10-day non-survivors than in 10-day survivors (median 182.21 [IQR: 55.43-300] ng/ml vs. median 66.40 [IQR: 34.13-107.85] ng/mL, P=0.019), and HBP levels could predict the 10-day mortality with an area under the ROC curve of 0.747. The cut-off value, sensitivity, and specificity were 160.6 ng/mL, 66.7%, and 90.2%, respectively. Multivariate Cox regression analysis indicated that HBP was an independent prognostic factor for 10-day (HR 7.196, 95%CI: 1.596-32.455, P=0.01), 28-day (HR 4.381, 95%CI: 1.449-13.245, P=0.009), and 90-day mortality (HR 4.581, 95%CI: 1.637-12.819, P=0.004) in patients with DM. CONCLUSION Plasma HBP at ICU admission was associated with the 10-day, 28-day, and 90-day mortality, and might be a prognostic factor in patients with DM and CAP.
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Affiliation(s)
- Yuhan Sun
- Translational Medicine Center, Department of Emergency Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Baoqing Sun
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510230, China
| | - Zhigang Ren
- Department of Infectious Disease, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Mingshan Xue
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510230, China
| | - Changju Zhu
- Henan Medical Key Laboratory of Emergency and Trauma Research, Department of Emergency Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Qi Liu
- Translational Medicine Center, Department of Emergency Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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de Nooijer AH, Pickkers P, Netea MG, Kox M. Inflammatory biomarkers to predict the prognosis of acute bacterial and viral infections. J Crit Care 2023; 78:154360. [PMID: 37343422 DOI: 10.1016/j.jcrc.2023.154360] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/07/2023] [Indexed: 06/23/2023]
Abstract
Mortality in acute infections is mostly associated with sepsis, defined as 'life-threatening organ dysfunction caused by a dysregulated host response to infection'. It remains challenging to identify the patients with increased mortality risk due to the high heterogeneity in the dysregulated host immune response and disease progression. Biomarkers reflecting different pathways involved in the inflammatory response might improve prediction of mortality risk (prognostic enrichment) among patients with acute infections by reducing heterogeneity of the host response, as well as suggest novel strategies for patient stratification and treatment (predictive enrichment) through precision medicine approaches. The predictive value of inflammatory biomarkers has been extensively investigated in bacterial infections and the recent COVID-19 pandemic caused an increased interest in inflammatory biomarkers in this viral infection. However, limited research investigated whether the prognostic potential of these biomarkers differs between bacterial and viral infections. In this narrative review, we provide an overview of the value of various inflammatory biomarkers for the prediction of mortality in bacterial and viral infections.
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Affiliation(s)
- Aline H de Nooijer
- Department of Internal Medicine, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands; Department of Intensive Care Medicine, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands; Radboud University Medical Center for Infectious Diseases, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands
| | - Peter Pickkers
- Department of Intensive Care Medicine, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands; Radboud University Medical Center for Infectious Diseases, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands
| | - Mihai G Netea
- Department of Internal Medicine, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands; Radboud University Medical Center for Infectious Diseases, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands; Department of Immunology and Metabolism, Life & Medical Sciences Institute, University of Bonn, 53115 Bonn, Germany
| | - Matthijs Kox
- Department of Intensive Care Medicine, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands; Radboud University Medical Center for Infectious Diseases, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands.
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Neb H, Talbot SR, Ruskowski K, Brkic D, Sonntagbauer M, Adam EH, von Knethen A, Zacharowski K, Heinicke U. HIGH HEPARANASE LEVEL IN SURVIVORS OF COVID-19 - INDICATOR OF VASCULAR AND PULMONARY RECOVERY? Shock 2022; 58:514-523. [PMID: 36548643 DOI: 10.1097/shk.0000000000002021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
ABSTRACT Background: Severe progression of coronavirus disease 2019 (COVID-19) causes respiratory failure and critical illness. Recently, COVID-19 has been associated with heparanase (HPSE)-induced endothelial barrier dysfunction and inflammation, so called endothelitis, and therapeutic treatment with heparin or low-molecular-weight heparin (LMWH) targeting HPSE has been postulated. Because, up to this date, clinicians are unable to measure the severity of endothelitis, which can lead to multiorgan failure and concomitant death, we investigated plasma levels of HPSE and heparin-binding protein (HBP) in COVID-19 intensive care patients to render a possible link between endothelitis and these plasma parameters. Therefore, a prospective prolonged cohort study was conducted, including 47 COVID-19 patients from the intensive care unit. Plasma levels of HPSE, and HBP were measured daily by enzyme-linked immunosorbent assay in survivors (n = 35) and nonsurvivors (n = 12) of COVID-19 from admission until discharge or death. All patients were either treated with heparin or LMWH, aiming for an activated partial thromboplastin time of ≥60 seconds or an anti-Xa level of >0.8 IU/mL using enoxaparin, depending on the clinical status of the patient (patients with extracorporeal membrane oxygenation or >0.1 μg/kg/min noradrenaline received heparin, all others enoxaparin). Results: We found significantly higher plasma levels of HPSE and HBP in survivors and nonsurvivors of COVID-19, compared with healthy controls. Still, interestingly, plasma HPSE levels were significantly higher ( P < 0.001) in survivors compared with nonsurvivors of COVID-19. In contrast, plasma HBP levels were significantly reduced ( P < 0.001) in survivors compared with nonsurvivors of COVID-19. Furthermore, when patients received heparin, they had significantly lower HPSE ( P = 2.22 e - 16) and significantly higher HBP ( P = 0.00013) plasma levels as when they received LMWH. Conclusion: Our results demonstrated that patients, who recover from COVID-19-induced vascular and pulmonary damage and were discharged from the intensive care unit, have significantly higher plasma HPSE level than patients who succumb to COVID-19. Therefore, HPSE is not suitable as marker for disease severity in COVID-19 but maybe as marker for patient's recovery. In addition, patients receiving therapeutic heparin treatment displayed significantly lower heparanse plasma level than upon therapeutic treatment with LMWH.
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Affiliation(s)
- Holger Neb
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Steven R Talbot
- Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany
| | - Katharina Ruskowski
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Djurdjina Brkic
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Michael Sonntagbauer
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Elisabeth H Adam
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | | | - Kai Zacharowski
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Ulrike Heinicke
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
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Clinical value of serum sTREM-1 and HBP levels in combination with traditional inflammatory markers in diagnosing hospital-acquired pneumonia in elderly. BMC Infect Dis 2022; 22:773. [PMID: 36195852 PMCID: PMC9531631 DOI: 10.1186/s12879-022-07758-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/02/2022] [Accepted: 09/26/2022] [Indexed: 11/17/2022] Open
Abstract
Background The clinical presentation of hospital-acquired pneumonia (HAP) in older patients is often complex and non-specific, posing a diagnostic challenge. This study evaluates the value of serum soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) and heparin-binding protein (HBP) in combination with traditional inflammatory markers procalcitonin (PCT) and C-reactive protein (CRP) in diagnosing HAP in older patients. Methods Thirty-eight elderly male patients with HAP (≥ 80 years old) and 46 age-matched controls, who were hospitalized for other reasons than HAP, were enrolled. The serum sTREM-1, HBP, PCT and CRP levels were measured by ELISA on the first day after enrollment. In addition, routine blood test, blood gas, sputum analysis, clinical pulmonary infection score (CPIS) assessment, and chest X-ray were performed, and the correlations with HAP were analyzed. Results The serum sTREM-1 (n = 38, 170.75 ± 158.33 pg/ml), HBP (2.08 ± 0.50), PCT (9.44 ± 17.73) and CRP (79.63 ± 71.37) were all significantly higher in the HAP group, when compared to the control group (P < 0.05). Furthermore, the values were positively correlated with the CPIS. The ROC curve analysis revealed that the AUC for sTREM-1 (0.667) and HBP (0.711) were lower, when compared to that for PCT (AUC = 0.839) and CRP (AUC = 0.840). The combination of PCT and CRP with sTREM-1 (AUC = 0.927) or HBP (AUC = 0.930) had the highest AUC values. Conclusion Serum sTREM-1, HBP, PCT and CRP can all be used as diagnostic markers for HAP in the elderly. The combination of traditional inflammatory markers PCT and CRP with novel inflammatory marker sTREM-1 or HBP further improves the diagnostic performance.
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Xiang Z, Zhang Z, Chen X, Zhang L, Zhou X, Qin Y, Zhao X, Wang Y, Huang B, Tang H. Development and application of amplified luminescent proximity homogeneous assay for quantitation of heparin-binding protein. Anal Biochem 2022; 657:114906. [PMID: 36152874 DOI: 10.1016/j.ab.2022.114906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 11/29/2022]
Abstract
A fast and highly sensitive amplified luminescent proximity homogeneous assay (AlphaLISA) method was developed for quantitation of plasma heparin-binding protein levels. In this study, a method directly coupling donor and acceptor beads modified with aldehyde groups to anti-HBP antibodies was proposed, which can effectively simplify the steps and shorten the reaction time to achieve faster detection. Therefore, the developed method required only 15 min of reaction time to generate results. Compared with the approved commercial kit, the developed method had a wider linear range (2.78-500 ng/mL). The excellent linear range means that the method can better exploit the value of HBP in clinical applications. Meanwhile, results of amplified luminescent proximity homogeneous assay and fluorescence dry quantitative immunoassay had good correlation and consistency (ρ = 0.9181). Moreover, the plasma HBP concentrations of patients with bacterial infection were significantly higher than those of healthy individuals (P < 0.0001), indicating the potential applicability of the proposed method for predicting the incidence of bacterial infections. Importantly, the newly developed method is expected to serve as an alternative to the traditional assay method and provides a completely new platform for other biomarkers that require rapid detection.
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Affiliation(s)
- Zhongyi Xiang
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Zhibin Zhang
- Clinical Laboratory, Wuxi Hospital of Traditional Chinese Medicine, Wuxi, China
| | - Xindong Chen
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Lele Zhang
- Clinical Laboratory Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiumei Zhou
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Yuan Qin
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Xueqin Zhao
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Yigang Wang
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Biao Huang
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China.
| | - Huqiang Tang
- Clinical Laboratory Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, China.
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Acar T, Ertekin B, Yortanlı M, Koçak S. Prognostic value of heparin-binding protein for mortality in severe COVID-19 pneumonia. Biomark Med 2022; 16:981-991. [PMID: 36052658 PMCID: PMC9443788 DOI: 10.2217/bmm-2022-0265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Aim: The study investigated heparin-binding protein (HBP) levels in patients with severe COVID-19 pneumonia and their relation to prognosis. Methods: A total of 134 patients with serious COVID-19 pneumonia were prospectively analyzed. HBP levels were statistically compared between both the patient and healthy control groups and within the patient group itself. Results: HBP was defined to be significantly higher in the patient group compared with the control group. There was a statistically significant distinction between the patients who survived and those who died with regard to HBP levels. When the cutoff value of HBP was >13.47, sensitivity (89.8%), specificity (74.1%) had area under the curve values of 0.806 (p < 0.001). Conclusion: HBP level may be used for prognosis prediction of patients with COVID-19.
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Affiliation(s)
- Tarık Acar
- Department of Emergency, University of Health Sciences, Beyhekim Training & Research Hospital, Konya, 42060, Turkey
| | - Birsen Ertekin
- Department of Emergency, University of Health Sciences, Beyhekim Training & Research Hospital, Konya, 42060, Turkey
| | - Mehmet Yortanlı
- Department of Emergency, Numune State Hospital, Konya, Turkey
| | - Sedat Koçak
- Department of Emergency, Necmettin Erbakan University Meram Faculty of Medicine, Konya, Turkey
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11
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Akın Şen İ, Karaşahin Ö, Şebin E, Şen C. Use of azurocidin (heparin binding protein) and interleukin-1ß as prognostic indicators in COVID-19 patients. EUR J INFLAMM 2022. [PMCID: PMC9152632 DOI: 10.1177/1721727x221105129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Objective: Considering the published manuscripts on this subject, it is thought
that IL-1B and azurocidin may be diagnostic and prognostic factors in severe
COVID-19 disease. This study aimed to determine whether azurocidin and IL-1β are
useful biomarkers and are associated with disease exacerbation in patients
needing advanced treatment options. Material and Method: Our study was performed
retrospectively. The data of a total of 291 patients followed in the hospital
due to COVID-19 were collected. Among these patients, 66 randomly selected
patients were included in this study. The control group consisted of 24 healthy
individuals referred to the infectious disease outpatient clinic who weren’t no
diagnosis of COVID-19 infection or any other infection. After the samples were
taken into tubes without anticoagulant, they were kept at room temperature for
30 min. Afterwards, it was centrifuged at 1000xg for 15 min at +4° according to
the instructions of the ELISA kit we used. The remaining serum was transferred
to Eppendorf tubes and stored at −80° throughout this study. Results: A
statistically significant strong positive correlation was determined between
IL1β and azurocidin. A statistically significant weak positive correlation was
found between IL1β and CRP, ferritin and neutrophil count, weak negative
correlation with albumin, and moderately strong positive correlation with
leukocyte count. Moreover, a statistically significant weak positive correlation
was found between azurucidin and CRP, ferritin, and neutrophil counts.
Conclusion: Azurocidin and IL-1β may serve as a potential therapeutic target for
patients at risk of developing systemic multi-organ failure, with improved
patient prognosis and prevention of death in severely ill patients. Although
these results may contribute to the improvement of the follow-up and treatment
of the patients and reduce mortality rates, there is a need for randomized
controlled studies with a larger number of patients to be performed in the
future.
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Affiliation(s)
- İrem Akın Şen
- Intensive Care Unit, Department of Internal Medicine, Mersin University, Turkey
| | - Ömer Karaşahin
- Infectious Diseases And Clinical Microbiology, Erzurum Regional Education and Research Hospital, Ministry of Health, Turkey
| | - Engin Şebin
- Biochemistry, Erzurum Regional Education and Research Hospital, Ministry of Health, Turkey
| | - Cem Şen
- Department of Emergency, Mersin State Hospital, Ministry of Health, Turkey
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12
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Katsaros K, Renieris G, Safarika A, Adami EM, Gkavogianni T, Giannikopoulos G, Solomonidi N, Halvatzis S, Koutelidakis IM, Tsokos N, Tritzali M, Koutoukas P, Avgoustou C, Vasishta A, Giamarellos-Bourboulis EJ. Heparin Binding Protein for the Early Diagnosis and Prognosis of Sepsis in the Emergency Department: The Prompt Multicenter Study. Shock 2022; 57:518-525. [PMID: 34907118 DOI: 10.1097/shk.0000000000001900] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND The validation of new biomarkers for the diagnosis and risk stratification of patients with sepsis at an early point is essential for successful treatment. Recent publications prompted us to investigate of heparin binding protein (HBP) for the emergency department (ED) admissions. MATERIALS AND METHODS In this multicenter, cross-sectional study, HBP and procalcitonin (PCT) were measured within the first hour upon admission to the ED in plasma samples of 371 patients with signs of infection. Patients were classified into non-sepsis and sepsis by the Sepsis-3 definitions and were followed up for outcome. RESULTS HBP was significantly higher in patients with sepsis and was positively correlated to PCT and C-reactive protein, absolute neutrophil and monocyte counts, creatinine, bilirubin and lactate. Sensitivity, specificity, positive predictive value, and negative predictive value of HBP more than 19.8 ng/mL for the diagnosis of sepsis was 66.3%, 44.9%, 49.3%, and 62.2%, respectively; and for prediction of early death was 100%, 41.0%, 4.5%, and 100%, respectively. Single HBP and PCT could not predict 28-day mortality; this was performed with sensitivity, specificity, positive predictive value, and negative predictive value 44.8%, 81.8%, 17.3%, and 94.6% when used in combination. CONCLUSION Admission HBP can be used as a tool for the early diagnosis of sepsis and for the risk of early death in the ED.
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Affiliation(s)
| | - Georgios Renieris
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Asimina Safarika
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Evangelia-Maria Adami
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Theologia Gkavogianni
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | | | - Nicky Solomonidi
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Stamatios Halvatzis
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Ioannis M Koutelidakis
- Second Department of Surgery, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolaos Tsokos
- Department of Internal Medicine, Chalkida General Hospital, Chalkida, Greece
| | - Maroula Tritzali
- Department of Internal Medicine, Syros General Hospital, Ermoupolis, Syros, Greece
| | - Pantelis Koutoukas
- Department of Internal Medicine, Sparti General Hospital, Sparti, Greece
| | - Cristina Avgoustou
- Fourth Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
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13
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Theoharides TC. Could SARS-CoV-2 Spike Protein Be Responsible for Long-COVID Syndrome? Mol Neurobiol 2022; 59:1850-1861. [PMID: 35028901 PMCID: PMC8757925 DOI: 10.1007/s12035-021-02696-0] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 12/10/2021] [Indexed: 02/06/2023]
Abstract
SARS-CoV-2 infects cells via its spike protein binding to its surface receptor on target cells and results in acute symptoms involving especially the lungs known as COVID-19. However, increasing evidence indicates that many patients develop a chronic condition characterized by fatigue and neuropsychiatric symptoms, termed long-COVID. Most of the vaccines produced so far for COVID-19 direct mammalian cells via either mRNA or an adenovirus vector to express the spike protein, or administer recombinant spike protein, which is recognized by the immune system leading to the production of neutralizing antibodies. Recent publications provide new findings that may help decipher the pathogenesis of long-COVID. One paper reported perivascular inflammation in brains of deceased patients with COVID-19, while others showed that the spike protein could damage the endothelium in an animal model, that it could disrupt an in vitro model of the blood-brain barrier (BBB), and that it can cross the BBB resulting in perivascular inflammation. Moreover, the spike protein appears to share antigenic epitopes with human molecular chaperons resulting in autoimmunity and can activate toll-like receptors (TLRs), leading to release of inflammatory cytokines. Moreover, some antibodies produced against the spike protein may not be neutralizing, but may change its conformation rendering it more likely to bind to its receptor. As a result, one wonders whether the spike protein entering the brain or being expressed by brain cells could activate microglia, alone or together with inflammatory cytokines, since protective antibodies could not cross the BBB, leading to neuro-inflammation and contributing to long-COVID. Hence, there is urgent need to better understand the neurotoxic effects of the spike protein and to consider possible interventions to mitigate spike protein-related detrimental effects to the brain, possibly via use of small natural molecules, especially the flavonoids luteolin and quercetin.
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Affiliation(s)
- Theoharis C Theoharides
- Laboratory of Molecular Immunopharmacology and Drug Discovery, Department of Immunology, Tufts University School of Medicine, 136 Harrison Avenue, Suite 304, Boston, MA, 02111, USA.
- School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA, 02111, USA.
- Departments of Internal Medicine and Psychiatry, Tufts University School of Medicine and Tufts Medical Center, Boston, MA, 02111, USA.
- Institute of Neuro-Immune Medicine, Nova Southeastern University, Clearwater, FL, 33759, USA.
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14
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Yu X. Potential value of heparin in COVID-19 related cardiovascular disease. Am J Emerg Med 2021; 50:789. [PMID: 34627645 PMCID: PMC8486575 DOI: 10.1016/j.ajem.2021.09.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 09/27/2021] [Indexed: 11/29/2022] Open
Abstract
There is increasing evidence that COVID-19 is a multi-organ infectious disease with respiratory system as its main clinical manifestation. In particular, its risk of cardiovascular infection calls for effective clinical intervention strategies. The multiple values of heparin in its cardiovascular system deserve to be considered.
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Affiliation(s)
- Xianqiang Yu
- Southeast University, Medical School, Nanjing, China.
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15
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Hessami A, Rezaei N. Cardiovascular diseases burden in COVID-19. Am J Emerg Med 2021; 50:790. [PMID: 34627644 PMCID: PMC8486597 DOI: 10.1016/j.ajem.2021.09.066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 09/27/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- Amirhossein Hessami
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran; Systematic Review and Meta-Analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Sari, Iran; Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nima Rezaei
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran; Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran; Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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16
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Yu X. Another new application of heparin in COVID-19: more than anticoagulation and antiviral. J Investig Med 2021; 69:1258. [PMID: 34112704 DOI: 10.1136/jim-2021-001951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2021] [Indexed: 11/04/2022]
Affiliation(s)
- Xianqiang Yu
- Medical School, Southeast University-Dingjiaqiao Campus, Nanjing, Jiangsu, China
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17
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Heparin binding protein in severe COVID-19-A prospective observational cohort study. PLoS One 2021; 16:e0249570. [PMID: 33822821 PMCID: PMC8023466 DOI: 10.1371/journal.pone.0249570] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/20/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND AND AIMS Neutrophil-derived heparin binding protein (HBP; also known as azurocidin or CAP-37) is a key player in bacterial sepsis and a promising biomarker in severe infections. The aims of this study were to assess whether HBP is involved in the pathophysiology of COVID-19 and, if so, whether it can be used to predict severe disease preferably using a point-of-care test. METHODS This was a prospective convenience sample study of biomarkers in patients admitted to Skåne University hospital in Sweden with a confirmed COVID-19 diagnosis. Plasma samples and clinical data were collected within 72h after admission, during hospital stay and at discharge. Plasma HBP concentrations samples were measured both with enzyme-linked immunosorbent assay (ELISA) and with a novel dry immunofluorescence analyzer (Joinstar) point-of-care test. RESULTS Thirty-five COVID-19 patients were enrolled in the study. Twenty-nine patients had blood samples taken within 72h after admission. We compared the highest HBP value taken within 72h after admission in patients who eventually developed organ dysfunction (n = 23) compared to those who did not (n = 6), and found that HBP was significantly elevated in those who developed organ dysfunction (25.0 ng/mL (interquartile range (IQR) 16.6-48.5) vs 10.6 ng/mL (IQR 4.8-21.7 ng/mL), p = 0.03). Point-of-care test measurements correlated well with ELISA measurements (R = 0.83). HBP measured by the POC device predicted development of COVID-induced organ dysfunction with an AUC of 0.88 (95% confidence interval (CI) 0.70-1.0). CONCLUSIONS HBP is elevated prior to onset of organ dysfunction in patients with severe COVID-19 using a newly developed point-of-care test and hence HBP could be used in a clinical setting as a prognostic marker in COVID-19.
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18
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Huang C, Zhang C, Zhang J, Zhang L, Mo Y, Mo L. Heparin-Binding Protein in Critically Ill Children With Severe Community-Acquired Pneumonia. Front Pediatr 2021; 9:759535. [PMID: 34778149 PMCID: PMC8581232 DOI: 10.3389/fped.2021.759535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 09/30/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: The aim of this study was to investigate possible associations between Heparin-binding protein (HBP) and the development of respiratory failure (RF) and sepsis in critically ill children with severe community-acquired pneumonia (CAP). Methods: This study enrolled 157 children with severe CAP admitted to Intensive Care Unit (ICU). At ICU admission, the levels of HBP and other biomarkers, including C-reactive protein, interleukin-6 (IL-6), procalcitonin, white blood cells, neutrophil percentage, and D-dimer, were determined. Results: Of the enrolled patients, 106 developed RF (35 with RF at enrollment and 71 with RF after enrollment), while 51 did not developed RF. The number of patients progressing to sepsis in those with or without RF were 34 (21 with severe sepsis) and 14, respectively. The plasma level of HBP at admission was more than eightfold higher than the upper normal value. HBP, IL-6, and D-dimer could significantly predict the development of RF, and a high level of HBP (odds ratio = 1.008, 95% confidence interval: 1.003-1.013) was independently associated with the development of RF in this population. Compared with other biomarkers, HBP was the best indicator of progression to severe sepsis, with an area under the receiver operating characteristic curve of 0.85, the best specificity at 96.30%, and a positive predictive value of 92.86% at the optimal cut-off value of 340.29 ng/mL. The HBP level was also positively correlated with other conventional biomarkers. Conclusion: HBP might represent a better predictor of disease progression in children with severe CAP than currently used biomarkers.
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Affiliation(s)
- Caizhi Huang
- Department of Laboratory Medicine, Hunan Children's Hospital, Changsha, China
| | - Cong Zhang
- Department of Laboratory Medicine, Hunan Children's Hospital, Changsha, China
| | - Jie Zhang
- Department of Laboratory Medicine, Hunan Children's Hospital, Changsha, China
| | - Lin Zhang
- Department of Laboratory Medicine, Hunan Children's Hospital, Changsha, China
| | - Yi Mo
- Department of Laboratory Medicine, Hunan Children's Hospital, Changsha, China
| | - Liya Mo
- Department of Laboratory Medicine, Hunan Children's Hospital, Changsha, China
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19
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1649] [Impact Index Per Article: 412.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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