Biomarkers’ performance in the SEPSIS-3 era

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the biomarkers` performance for diagnosis and severity stratification of 41 sepsis has not been properly evaluated anew using the SEPSIS-3 criteria introduced in 42 2016. We evaluated the accuracy of 21 biomarkers classically tested in sepsis research to 43 identify infection, sepsis, and septic shock in surgical patients classified using SEPSIS-3. 44

four groups of adult surgical patients were compared: post -surgical patients 45 with no infection, patients with infection but no sepsis, patients with sepsis, and patients 46 with septic shock were recruited prospectively from the surgery departments and surgical 47 ICUs from four Spanish hospital . The area under the curve (AUC) to differentiate 48 between groups was calculated for each biomarker. 49

A total of 187 patients were recruited (50 uninfected post-surgery controls, 50 50 patients with infection, 47 with sepsis and 40 with septic shock ). The AUCs indicated 51 that none of the biomarkers tested was accurate enough to differentiate those patients with 52 infection from the uninfected controls. In contrast, procalcitonin, lipocalin 2, pentraxin 3, 53 IL-15, TNF-α, IL-6, angiopoietin 2, TREM-1, D-dimer and C-reactive protein yielded 54 AUCs > 0.80 to discriminate the patients with sepsis or septic shock from those with no 55 infection. C-reactive protein and IL-6 were the most accurate markers to differentiate 56 plain infection from sepsis (AUC = 0.82). Finally, our results revealed that sepsis and 57 septic shock shared similar profiles of biomarkers. 58

Revaluation in the “SEPSIS-3 era” identified the scenarios where 59 biomarkers do and do not provide useful information to improve the management of 60 surgical patients with infection or sepsis. 61 62

63 Biomarkers, Diagnosis, Sepsis, Severity 64 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 4

65 Identification of sepsis remains a major challenge to implement prompt treatment in 66 surgical patients suffering from this condition. Correct and quick discrimination between 67 sepsis and surgical related inflammation allows to early implement measures aimed to 68 control the infection source with surgery or antibiotics (1,2). Biomarkers are a potential 69 useful tool to improve sepsis detection, complementary to clinical information and to 70 image and/or microbiological tests, but the information regarding biomarkers must be 71 provided in minutes in order to be useful (3,4) . In addition, the emergence of the SEPSIS-72 3 criteria in 2016 has re-shaped sepsis diagnosis, by proposing a new definition which 73 consider sepsis just those infections causing life-threatening organ failure (5,6). While 74 the introduction of the new SEPSIS-3 criteria has impacted epidemiological studies on 75 sepsis (7–9), how SEPSIS-3 affects the performance of sepsis biomarkers has not been 76 sufficiently studied yet. 77 In this work, we profiled a large number of biomarkers classically tested in sepsis studies 78 by using a rapid microfluidics-based test, to evaluate their performance regarding 79 identification of infection, sepsis and septic shock in surgical patients. 80 81

82 Study design and patients: Adult patients (≥ 18 years) recruited in the first 24 hours 83 following an abdominal surgery with no infection constituted the uninfected control 84 group. Adult patients with infection, sepsis, or septic shock of abdominal source were 85 recruited prospectively from the surgery departments and surgical intensive care unit s 86 (ICUs) of the four participating hospitals (Hospital Universitario Río Hortega de 87 Valladolid, Complejo Asistencial Universitario de Salamanca , Complejo Asistencial 88 Universitario de León and Hospital Universitario Marqués de Valdecilla de Santander), 89 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 5 between January 2020 and July 2022. Infection was defined according to the US Centers 90 for Disease Control and Prevention National Surveillance Definitions for Specific Types 91 of Infections (10). Sepsis and septic shock were defined using the SEPSIS -3 consensus 92 definitions (5,6). A specific standard surve y was employed in the four participating 93 hospitals to collect clinical data along with results of hematological, biochemical, 94 radiological, and microbiological investigations. Healthy controls with similar age and 95 sex characteristics to the patients were r ecruited from the Centro de Hemoterapia y 96 Hemodonación de Castilla y León (CHEMCYL, Valladolid, Spain). 97 Biomarkers profiling: we quantified 20 biomarkers in plasma involved in different 98 biological functions using the Ella-SimplePlex TM system from Biotechnne (San Jose, 99 California, USA) as per manufacturer instructions. The biomarkers studied were the 100 following: Lipocalin-2 (LCN2), Myeloperoxidase (MPO) (Neutrophil degranulation); 101 Intercellular adhesion molecule 1 (ICAM-1), Vascular cell adhesion molecule 1 102 (VCAM-1), Endothelin-1 (ET-1), Angiopoietin 2 (ANGPT2), Angiopoietin 1 103 (ANGPT1) (Endothelial dysfunction); D-dimer, Urokinase-type plasminogen activator 104 (uPA) (Coagulation); Interleukin 6 (IL-6), Interleukin 15 (IL-15), Tumoral necrosis 105 factor α (TNF-α), Procalcitonin (PCT), Matrix metalloproteinase 7 (MMP7), Pentraxin 106 3 (PTX3), TREM-1 (Inflammation); Interleukin 10 (IL-10), Programmed Death-ligand 107 1 (PD-L1) (immunosuppression / immunomodulation), C-X-C motif chemokine ligand 108 10 (CXCL10), Interleukin 7 (IL-7) (lymphocyte biology). Serum C-reactive protein 109 (CRP) was measured by particle enhanced immunoturbidimetric assay (e501 Module 110 Analyser, Roche Diagnostics, Meylan, France); limit of detection 0.15 mg/dL. 111 Statistical analysis: Statistical analysis was performed using IBM SPSS Statistics 25.0 112 (SPSS INC, Armonk, NY, U.S.A). The level of significance was set at 0.05. For clinical 113 characteristics of the patients, differences between groups were assessed using the χ2 test 114 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 6 for categor ical variables. Differences between groups for continuous variables and 115 protein levels were assessed with the Kruskal–Wallis test. The accuracy of protein levels 116 to differentiate between groups of patients was studied by calculating the area under the 117 receiver operating characteristic curve ( AUC). The optimal operating point (OOP) was 118 calculated on the curve as previously described (11). 119 120

121 Our study involved 187 patients, 50 uninfected post-surgery controls, 50 patients with 122 infection without sepsis, 47 with sepsis and 40 with septic shock. Patients with infection 123 were significantly younger than those in the other groups. Proportion of men to women 124 were similar in all the compared groups. Patients with sepsis and septic shock had more 125 frequently hypertension and chronic cardiac disease. Septic shock patients were the most 126 severe as evidenced by their SOFA scores at admission and stayed longer at the hospital. 127 None of the patients of the surgical control group or in the infection group died during 128 hospitalization, compared with 7 out of 47 (14.9 %) patients with sepsis and 10 out of 40 129 (25 %) patients with septic shock (Table S1, Supplementary material). The Kruskall -130 Wallis test evidenced that patients with sepsis and septic shock showed higher levels of 131 PCT, LCN2, PTX3, IL -15, TNF -α, IL-6, ANGPT2, TREM -1, D -DIMER, CXCL10, 132 VCAM-1, PD-L1 and MMP7 than healthy controls, surgical controls and patients with 133 infection but no sepsis, being the levels of PCT, LCN2, PTX3 and IL -15 the highest in 134 patients with septic shock (Fig 1; Table S2, Supplementary material). 135 We next calculated the AUCs for the different biomarkers to discriminate between 136 uninfected post-surgery controls and the patients with infection, sepsis and septic shock 137 (Fig 2; Table S3, Supplementary material ). This analysis revealed that none of the 138 biomarkers tested was accurate enough to differentiate patients with a plain infection from 139 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 7 post-surgery controls, yielding all AUCs 0.80 to 141 discriminate those patients with sepsis or septic shock from post-surgical patients with no 142 infection (Fig 2; Table S3, Supplementary material). The corresponding OOP are shown 143 Table S4 (Supplementary material). We also evaluated the biomarkers performance to 144 stratify severity. This analysis revealed that CRP and IL -6 were good markers to 145 differentiate plain infection from sepsis, yielding both AUCs of 0.82 for this comparison 146 (Table S3, Supplementary material ). The corresponding OOP are shown Table S 4 147 (Supplementary material). In turn, PCT, LCN2, PTX3, IL -15, TNF-α, IL-6, ANGPT2, 148 CRP and IL -10 showed all AUCs > 0.80 to discriminate between infection and septic 149 shock (Table S3, Supplementary material). The corresponding OOP are shown Table S4 150 (Supplementary material). Finally, our results revealed that sepsis and septic shock shared 151 similar profiles of biomarkers, with none of them yielding AUCs > 0.80 to differentiate 152 between these two conditions (Table S3, Supplementary material). 153 154

155 Since the introduction of the new SEPSIS-3 criteria in 2016, studies evaluating the 156 performance of biomarkers to diagnose and to stratify sepsis severity are lacking or 157 focused on a limited number of molecules (12–16). Here we evaluated 21 biomarkers 158 involved in different biological functions in sepsis (inflammation, neutrophil 159 degranulation, endothelial dysfunction, coagulation, immunosuppression and lymphocyte 160 biology), and compared their performance to discriminate between surgical patients with 161 no infection, infection with no sepsis, sepsis or septic shock, as defined by SEPSIS-3. Our 162

evidenced the limitations of the assessed biomarkers to differentiate those patients 163 with infection from those with no infection, revealing that, in absence of significant organ 164 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 8 failure, the biological response to an infectious or to a surgical challenge is similar. While 165 these results evidence that the b iomarkers tested would not be helpful to better allocate 166 antibiotic treatment in patients with suspected infection when sepsis is absent, we 167 identified in contrast a number of them (PCT, LCN2, PTX3, IL -15, TNF -α, IL -6, 168 ANGPT2, TREM -1, D -DIMER, CRP) which definitively could contribute to quickly 169 identify those patients with sepsis or septic shock and to early implement empiric therapy 170 with wide spectrum antibiotics , along with the other bundles rec ommended by the 171 surviving sepsis campaign (hemodynamic management , ICU admission, antimicrobial 172 therapy, implemention of any required source control intervention, ventilation and other 173 additional therapies) (17). In turn, CRP and IL -6 were also good candidates to 174 differentiate surgical patients with infect ion with or without sepsis. This finding is also 175 very important from a translational point of view since quantification of these biomarkers 176 is widely available in hospital settings. Finally, our study revealed that none of the 177 biomarkers evaluated was good enough to differentiate between patients with sepsis and 178 those with septic shock, revealing that both scenarios induce similar alterations in the host 179 response. 180 A strength of our study is that we employed a next-generation immunoassay based on 181 microfluidics (Ella-SimplePlex) which provides biomarkers levels in less than 90 182 minutes, which is a reasonable frame time to provide actionable information in patients 183 with suspected sepsis or septic shock. While the limited sample size makes this a pilot 184 study, our results warrant further evaluation of biomarker profiling using Ella-SimplePlex 185 in larger cohorts of patients. 186 In conclusion, our study re-approached the performance of sepsis biomarkers in the 187 “SEPSIS-3 era”, identifying the scenarios and molecules really adding valuable 188 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 9 information to improve the management of surgical patients suffering this deadly 189 condition. 190 191 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 10 List of abbreviations 192 ANGPT2: Angiopoietin 2 193 AUC: Area under the receiver operating characteristic curve 194 CXCL10: C-X-C motif chemokine ligand 10 195 ICAM-1: Intercellular adhesion molecule 1 196 ICU: Intensive care unit 197 IL-6: Interleukin 6 198 IL-7: Interleukin 7 199 IL-10: Interleukin 10 200 IL-15: Interleukin 15 201 MMP7: Matrix metalloproteinase 7 202 OOP: Optimal operating point 203 PD-L1: Programmed Death-ligand 1 204 PCT: Procalcitonin 205 SOFA: Sepsis related Organ Failure Assessment 206 TNF-α: Tumor necrosis factor α 207 TREM-1: Triggering receptor expressed on myeloid cells 1 208 uPA: Urokinase-type plasminogen activator 209 VCAM-1: Vascular cell adhesion molecule 1 210 211 Ethics approval and consent to participate: The study was approved by the respective 212 Committees for Ethics in Clinical Research of the three participating hospitals. Methods 213 were carried out in accordance with current Spanish law for Biomedical Research, 214 fulfilling the standards indicated by the Declaration of Helsinki. The study was approved 215 by the Committee for Ethical Research of the coordinating institution, “Comite de Etica 216 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 11 de la Investigacion con Medicamentos del Área de Salud de Valladolid Oeste”, code 217 PI142-19. Written informed consent was obtained from patients' relatives or their legal 218 representative before enrolment. 219 Consent for publication: not applicable 220 Availability of data and materials : The datasets generated and/or analysed during the 221 current study are not publicly available since they are still under elaboration for 222 publication by the authors but are available from the corresponding author on reasonable 223 request. 224 Competing interests: The authors declare that they have no competing interests 225 Funding: This study has be en funded by Instituto de Salud Carlos III (ISCIII) and co -226 funded by the European Union: Project “PI19/00590” (JFBM), Sara Borrell 227 program“CD018/0123” (APT) and PFIS program “FI20/00278” (AdF). The funding 228 sources did not play any role in the design of the study and collection, analysis, 229 interpretation of data or writing the manuscript. 230 Authors' contributions : JFBM designed the study. AdlF, JL, LMVR, MMG, M ESB, 231 MVSH, JMMV, JRF, LMB, RGdC, APT, AO, ASdR, EM, CEV and CA contributed with 232 patient recruitment and data acquisition. AdlF and AO profiled biomarker levels in 233 plasma. JFBM and A dlF analyzed and interpretated of data and drafted the manuscript. 234 All the authors critically reviewed the article and provided final approval of the version 235 submitted for publication. 236

The authors thank the nursing teams of the participating clinical 237 services for their continuous support to the research programme. They also thank the 238 Biobanco Hospital Cl ínico Universitario de Salamanca, for assistance with sample 239 storing, and CHEMCYL (Valladolid, Spain) for providing the samples from healthy 240 controls. 241 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 12

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Xie Y, Zhuang D, Chen H, Zou S, Chen W, Chen Y. 28-day sepsis mortality prediction model 291 from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective 292 cohort study. Eur J Clin Microbiol Infect Dis. 2023 Jan 1;42(1):77–85. 293 17. Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving 294 Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 295 2021. Crit Care Med. 2021 Nov;49(11):e1063. 296 297 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 15 Tables and figures 298 Figure 1. Levels of biomarkers in healthy control, surgical control, infection, sepsis 299 and septic shock groups. Levels are in pg/mL. ET, endothelin; IL, Interleukin; CRP, C 300 reactive protein; PD -L1, programmed death -ligand 1; TNF, tumor necrosis factor; 301 ANGPT, angiopoietin; CXCL, chemokine ligand; MMP, matrix metalloproteinase; PCT, 302 procalcitonin, TREM, triggering receptor expressed on myeloid cells-1; uPA, urokinase-303 type plasminogen activator; ICAM, intercellular adhesion molecule; VCAM , vascular 304 cell adhesion molecule; PTX, pentraxin; LCN, lipocalin; MPO, myeloperoxidase. * P ≤ 305 0⋅050 versus healthy control; † P ≤ 0⋅050 (Kruskal – Wallis test). 306 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 16 307 308 309 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint 17 Figure 2. “Big Bang” Plot. AUC to differentiate patients with infection, sepsis, and 310 septic shock from surgical controls. ET, endothelin; IL, Interleukin; CRP, C reactive 311 protein; PD-L1, programmed death-ligand 1; TNF, tumor necrosis factor; ANGPT, 312 angiopoietin; CXCL, chemokine ligand; MMP, matrix metalloproteinase; PCT, 313 procalcitonin, TREM, triggering receptor expressed on myeloid cells-1; uPA, 314 urokinase-type plasminogen activator; ICAM, intercellular adhesion molecule; VCAM, 315 vascular cell adhesion molecule; PTX, pentraxin; LCN, lipocalin; MPO, 316 myeloperoxidase. 317 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted January 18, 2023. ; https://doi.org/10.1101/2023.01.18.23284703doi: medRxiv preprint