Nicolas Deye
email: nicolas.deye@aphp.fr

Ph.D Philippe Nguyen

M.D Nicolas Vodovar

Ph.D Malha Sadoune

Ph.D Corinne Collet

MD, Ph.D Sebastian Voicu

Ph.D Isabelle Malissin

M.D Etienne Gayat

Ph.D Jeanne-Lise Samuel

Ph.D Claude Delcayre

Ph.D Jean-Marie Launay

Ph.D Alain Cohen-Solal

Ph.D Bruno Mégarbane

Ph.D Alexandre Mebazaa

Protein S100B as a reliable tool for early prognostication after cardiac arrest

Keywords: tables, figures, and supplement): 3000 Figures: 2. Tables: 4. References: 34 Early Protein S100B usefulness after cardiac arrest heart arrest, prognostication, outcome, Protein S100B, NSE, biomarker

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Favourable outcome after cardiac arrest (CA) remains poor. [START_REF] Bougouin | Characteristics and prognosis of sudden cardiac death in Greater Paris: population-based approach from the Paris Sudden Death Expertise Center (Paris-SDEC)[END_REF][START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF] Early and reliable prognostication in CA patients seems of major importance. It could avoid futile treatment in patients with low chance of good outcome and could help clinicians to maximize treatment in patients who have a high likelihood of good outcome. However, guidelines emphasize that prognostication needs to be delayed, especially when targeted temperature management (TTM) is applied, and that decisions to limit care should be supported by a multimodal approach including clinical, biological, electrophysiological, and/or imaging parameters. [START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF] The usefulness of biomarkers to help clinicians in optimizing outcome prediction after CA is described mainly for Neuron Specific Enolase (NSE). [START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF][START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF][START_REF] Wijdicks | Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology[END_REF][START_REF] Wiberg | Single versus serial measurements of Neuron-Specific Enolase and prediction of poor neurological outcome in persistently unconscious patients after out-of-hospital cardiac arrest -A TTM-trial substudy[END_REF][START_REF] Mörtberg | S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment[END_REF][START_REF] Stammet | Neuron Specific Enolase as a predictor of death or poor neurological outcome after out-of-hospital cardiac arrest and targeted temperature management[END_REF] The initial NSE threshold validated in 2006 did not remain 100%-predictive after a broader evaluation in TTM-treated patients, [START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF][START_REF] Wijdicks | Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology[END_REF] while NSE accuracy seemed better at a delayed phase after return of spontaneous circulation (ROSC). [START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF][START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF][START_REF] Mörtberg | S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment[END_REF][START_REF] Stammet | Neuron Specific Enolase as a predictor of death or poor neurological outcome after out-of-hospital cardiac arrest and targeted temperature management[END_REF][START_REF] Vondrakova | Association of neuron-specific enolase values with outcomes in cardiac arrest survivors is dependent on the time of sample collection[END_REF] . Other biomarkers, such as lactate, pH, and creatinine levels on admission, have also been proposed to early predict outcome after CA. [START_REF] Hasper | Changes in serum creatinine in the first 24 hours after cardiac arrest indicate prognosis: an observational cohort study[END_REF][START_REF] Shinozaki | Blood ammonia and lactate levels on hospital arrival as a predictive biomarker in patients with out-of-hospital cardiac arrest[END_REF][START_REF] Dell'anna | Prognostic implications of blood lactate concentrations after cardiac arrest: a retrospective study[END_REF][START_REF] Maupain | The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after out-of-hospital cardiac arrest[END_REF] However, specificity of these biomarkers is not 100%, leading to difficulties in assessing their precise cut-off values and predictive accuracy. [START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF][START_REF] Shinozaki | S-100B and neuron-specific enolase as predictors of neurological outcome in patients after cardiac arrest and return of spontaneous circulation: a systematic review[END_REF] S100B protein (PS100B) is also presumed to be an interesting prognostication tool besides NSE. [START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF][START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF][START_REF] Wijdicks | Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology[END_REF][START_REF] Wiberg | Single versus serial measurements of Neuron-Specific Enolase and prediction of poor neurological outcome in persistently unconscious patients after out-of-hospital cardiac arrest -A TTM-trial substudy[END_REF][START_REF] Mörtberg | S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment[END_REF][START_REF] Stammet | Neuron Specific Enolase as a predictor of death or poor neurological outcome after out-of-hospital cardiac arrest and targeted temperature management[END_REF][START_REF] Shinozaki | S-100B and neuron-specific enolase as predictors of neurological outcome in patients after cardiac arrest and return of spontaneous circulation: a systematic review[END_REF][START_REF] Deye | Changes in cardiac arrest patients' temperature management after the 2013 "TTM" trial: results from an international survey[END_REF][START_REF] Derwall | Changes in S-100 protein serum levels in survivors of out-of-hospital cardiac arrest treated with mild therapeutic hypothermia: a prospective, observational study[END_REF][START_REF] Shinozaki | Serum S100B is superior to neuron-specific enolase as an early prognostic biomarker for neurological outcome following cardiopulmonary resuscitation[END_REF][START_REF] Wojtczak-Soska | S-100B protein: An early prognostic marker after cardiac arrest[END_REF][START_REF] Einav | Modeling Serum Biomarkers S100 Beta and Neuron-Specific Enolase as Predictors of Outcome After Out-of-Hospital Cardiac Arrest. An Aid to Clinical Decision Making[END_REF][START_REF] Einav | Brain biomarkers and management of uncertainty in predicting outcome of cardiopulmonary resuscitation: A nomogram paints a thousand words[END_REF][START_REF] Rundgren | Neuron specific enolase and S-100B as predictors of outcome after cardiac arrest and induced hypothermia[END_REF][START_REF] Stammet | Protein S100 as outcome predictor after outof-hospital cardfiac arrest and targeted temperature management at 33°C and 36°C[END_REF] S100 protein is an intracellular dimeric protein with at least 4 sub-types, with S100A1B and S100BB being presently measured by usual tests. [START_REF] Wojtczak-Soska | S-100B protein: An early prognostic marker after cardiac arrest[END_REF] Besides S100A1 protein, S100B protein (PS100B) is normally found in astroglial and Schwann cells, and in neuroctodermal tumoral cells in pathological context, while small amount of PS100 was also found in adipocytes, muscles and chondrocytes.

However, the large sub-TTM report by Stammet et al. showed that PS100B could be better than NSE at H24 to predict poor outcome, but the added information was limited in all prognosticating models with or without NSE. [START_REF] Stammet | Protein S100 as outcome predictor after outof-hospital cardfiac arrest and targeted temperature management at 33°C and 36°C[END_REF][START_REF] Nielsen | Targeted temperature management at 33°C versus 36°C after cardiac arrest[END_REF] However, biomarkers were here only sampled 24 hours after ROSC and thereafter. Therefore, NSE contrary to PS100B is preferred in international guidelines as a validated and useful biomarker since it is superior at H48 and H72. [START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF] Considering the relative short half-life of PS100B compared to NSE, we hypothesized that PS100B could more accurately predict the outcome of CA patients in the early phase after ROSC compared to biomarkers such as lactate, pH, creatinine, and NSE. The aim of the present study was to evaluate the usefulness of early PS100B sampling for prognostication in a large cohort of successfully resuscitated CA patients.

Methods

This prospective single-center study was carried out between March 2010 and May 2016 in the medical ICU of a university hospital (Lariboisiere Hospital, Assistance Publique des Hôpitaux de Paris, France). The Ethics Committee of our institution approved the study (Institutional Review Board of Paris North Hospital: CERB GHU Nord, N°00006477). All surviving patients hospitalized for CA -or their next of kin if necessary-gave their written informed consent. The study was declared at National Clinical Trial (NCT01374880).

Aims

Primary endpoint was to assess PS100B performance in discriminating patients with good outcome versus those with poor outcome at 3-month follow-up as soon as the early phase after CA (i.e. as early as possible and within the first 24 hours after ROSC). PS100B was compared to other biomarkers (lactate, pH, creatinine, and NSE) in its ability to correctly discriminate outcome.

Secondary endpoints were: 1/ to evaluate PS100B performance in the early phase after CA in discriminating patients with good outcome versus those with poor outcome at hospital discharge; 2/ to evaluate PS100B performance according to the timeframe serum sampling after ROSC versus other biomarkers in its ability to discriminate patients with good outcome versus those with poor outcome (i.e. when sampled within the first 72 hours after ROSC).

Patient selection

Were included all consecutive comatose adult patients ≥18 years old, suffering from nontraumatic and successfully resuscitated CA (out-of-hospital or in-hospital CA: OH/IHCA), with sustained ROSC (defined as possibility to maintain ROSC with palpable pulse for >20 min), hospitalized in ICU and TTM-treated. Conscious patients, with unsustained ROSC, refractory CA or cardiogenic shock after CA necessitating an Extra-Corporeal Life Support (ECLS) were excluded. Patients experiencing a do-not-resuscitate order and patients without any PS100B values measured during the ICU course were excluded from this study.

Protocol

The protocol applied for all CA patients has been previously described and was in accordance with international guidelines. [START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF][START_REF] Voicu | Influence of α-stat and pH-stat blood gas management strategies on cerebral blood flow and oxygenation in patients treated with therapeutic hypothermia after out-of-hospital cardiac arrest: a crossover study[END_REF] Particularly, TTM was initiated as soon as possible for all patients regardless of the initial cardiac rhythm and ranged from 32 to 36°C for 24 hours.

Prognostication and withdrawal of life sustaining therapies, if necessary, were performed according to international guidelines. [START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF] Outcome assessment and data collection Data collection followed the Utstein style recommendations. [START_REF] Perkins | Cardiac arrest and cardiopulmonary resuscitation outcome reports: update of the Utstein Resuscitation Registry Templates for Out-of-Hospital Cardiac Arrest: a statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa, Resuscitation Council of Asia); and the American Heart Association Emergency Cardiovascular Care Committee and the Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation[END_REF] Outcome was evaluated using the Cerebral Performance Category (CPC) scoring, CPC 1-2 representing a good outcome whereas CPC 3-4-5 a poor outcome. CPC was systematically collected at hospital discharge and 3 months after CA, during a face-to-face appointment or by phone, by an independent researcher unblinded to patient medical history.

Blood samples were obtained in ICU for all patients as early as possible after ROSC, and hospital admission for OHCA (corresponding to admission: Adm.), at day 1 (H24: corresponding to the day occurring between 6h00 AM the day following ROSC and the following 24 hours), and at day 2 (H48: corresponding to the following 24 hours after H24).

Two serial blood samples were used to evaluate biomarker variations within the first 3 days after CA. Physicians in charge of the patients were all blinded to PS100B measurements, but not to other biomarkers. PS100B and NSE markers from serum were measured by the sandwich electro-chemi-luminescent immuno-assay (ECLIA) methods using a Cobas E601 analyzer (Roche Diagnostics, Meylan, France), which is similar to those published in largest studies. [START_REF] Stammet | Neuron Specific Enolase as a predictor of death or poor neurological outcome after out-of-hospital cardiac arrest and targeted temperature management[END_REF][START_REF] Stammet | Protein S100 as outcome predictor after outof-hospital cardfiac arrest and targeted temperature management at 33°C and 36°C[END_REF]

Statistics

Results are expressed as median (IQR 25-75), unless expressed otherwise. Non-parametric tests were used considering the non-Gaussian distribution of many parameters. Spearman tests for correlations or Wilcoxon tests for serial measurements were used to compare quantitative values. Kruskal-Wallis and Mann-Whitney tests with Bonferroni correction for multiple comparisons were used to compare quantitative and qualitative parameters. Comparisons for qualitative values were performed using chi-2 and the exact Fisher tests. To assess the biomarker ability to correctly evaluate its prognostic value in terms of good outcome predictions, receiver operating characteristic (ROC) curves with deriving sensitivities, specificities, positive and negative predictive values, associated to different cut-off values were performed. ROC curves and Area Under the Curve (AUC) were compared using the Delong's test. These calculations were performed considering each parameter at admission (Adm: primary endpoint), H24 and H48 on its own, as well as changes over admission to H48. PS100B values at admission were compared versus other major prognostic clinical parameters and biomarkers using univariate and multivariate analyses with backward elimination for multiple linear regressions. Main significant clinical parameters and biological values at admission were used in the multivariate analysis using logistic regression to evaluate early predictors independently associated with good outcome at discharge. The Net Reclassification Index (NRI) was used to assess biomarkers added value in the final model. Pvalues were considered as significant when <0.01. All statistics were performed using XL-stat Biomed (version 2018.3, Addinsoft, Μicrosoft, USA), except the NRI performed using R statistical software version 3.1.1 (The "R" Foundation for Statistical Computing, Vienna, Austria).

Results

Among 370 CA without ECLS implementation, 351 patients without exclusion criteria were included, of which 21 were lost to 3-month follow-up (Supplemental Figure S1). Patients' general characteristics are described in Table 1 and Supplemental Table S1 Biomarker values within the first 3 days are described in Table 2. At admission, all biomarkers were significantly associated with good outcome at 3-month (P≤0.001), P-value for NSE being 0.03. At H24, all biomarkers except pH were significantly associated with good outcome at hospital discharge (P<0.0001). At H48, PS100B, NSE, and creatinine values were significantly associated with good outcome at hospital discharge (P≤0.004), but not pH and lactate. Results were similar at hospital discharge (Supplemental Table S2). PS100B and NSE values according to CPC at 3-month are detailed in Figure 1 and Supplemental Table S3. initial non-shockable rhythm, clinical seizures, and absence of therapeutic hypothermia (Supplemental Table S7, AUC of the model: 0.91 [95%-CI: 0.86-0.95]). NRI was 70%, 64%, and 81% when PS100B-Adm was added to the clinical model alone, to the clinical model with NSE-Adm, and to the model including clinical with standard biological parameters, respectively (Table 4 and Supplemental Table S8).

Discussion

Results can be summarized as follows: 1/ Initial PS100B after admission was significantly associated with good outcome at hospital discharge and 3-month after CA; 2/ PS100B-Adm.

was the most accurate biomarker to correctly predict good outcome as evaluated by ROCs analyses, compared with lactate, creatinine, pH, and NSE; 3/ An increasing PS100B value from admission to H24 was significantly associated with poor outcome (1 false negative); 4/ A PS100B-Adm. threshold of 3.78μg/L correctly classified all patients except one as belonging to poor outcome group; 5/ In multivariate analysis, PS100B-Adm. was associated with poor outcome at 3-month, besides no-flow duration, initial non-shockable rhythm, lactate-Adm. and pH-Adm. values, presence of clinical seizures, and absence of therapeutic hypothermia; 6/ NRI was 70%, 64%, and 81% when PS100B-Adm was added to the clinical model alone, to the clinical model with NSE-Adm, and to the model including clinical with standard biological parameters, respectively; 7/ PS100B and NSE were highly discriminative at H24 and H48 to correctly predict good outcome.

Prognostication after CA using PS100B/NSE

In the meta-analysis describing biomarkers and outcome before TTM era, biomarkers values associated with a 0% false positive rate (FPR) varied considerably within studies with a very low level of evidence according to the timing of sampling. [START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 1: Patients not treated with therapeutic hypothermia[END_REF] After TTM implementation, other studies were reported. [START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF] Two studies observed PS100B values of 0.18μg/L and 0.21μg/L at H24 as predicting poor outcome in 100% of cases. [START_REF] Mörtberg | S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment[END_REF][START_REF] Tianen | Serum neuron-specific enolase and S-100B protein in cardiac arrest patients treated with therapeutic hypothermia[END_REF] One of these studies showed a sensitivity of 65%, FPR being 4%, and the other that PS100B measured 24h after ROSC was the best predictor of poor outcome (sensitivity: 87%, specificity: 100%). Another study described a threshold of 0.3μg/L at H48 associated with outcome with a 21%-sensitivity and 0% FPR. [START_REF] Stammet | Modeling Serum level of S100B and Bispectral Index to predict outcome after cardiac arrest[END_REF] None of these 3 studies evaluated PS100B in large enough populations.

The largest study sampling PS100B after 24h post-CA showed that PS100B could be better than NSE at H24 to predict outcome, but the added information was limited in all prognosticating models with or without NSE. [START_REF] Stammet | Protein S100 as outcome predictor after outof-hospital cardfiac arrest and targeted temperature management at 33°C and 36°C[END_REF] Interestingly, AUC for PS100B-H24 observed in our paper and this study were globally similar. However, early prognostication was herein not evaluated, biomarkers not being sampled immediately after admission. Our study affirms in another large cohort that PS100B adds a significant value regarding prognostication, while NSE is robust to accurately predict outcome at a delayed phase after CA, as previously suggested. [START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF][START_REF] Wiberg | Single versus serial measurements of Neuron-Specific Enolase and prediction of poor neurological outcome in persistently unconscious patients after out-of-hospital cardiac arrest -A TTM-trial substudy[END_REF][START_REF] Stammet | Neuron Specific Enolase as a predictor of death or poor neurological outcome after out-of-hospital cardiac arrest and targeted temperature management[END_REF][START_REF] Einav | Modeling Serum Biomarkers S100 Beta and Neuron-Specific Enolase as Predictors of Outcome After Out-of-Hospital Cardiac Arrest. An Aid to Clinical Decision Making[END_REF][START_REF] Stammet | Protein S100 as outcome predictor after outof-hospital cardfiac arrest and targeted temperature management at 33°C and 36°C[END_REF] By contrast to PS100B keeping interesting NRI values from admission to H48, NSE's NRI increases progressively after H24 where its value contributes to neuroprognostication, these biomarkers describing two aspects of brain injury (i.e. glial versus neuronal cells). According to PS100B high specificity/low sensitivity on admission and the validated NSE value at H24-H48, we could use PS100B from admission to H48 and NSE samples from H24 to H72 to better refine the neuroprognostication process. However, this suggestion needs to be adapted in each centre and to strictly follow prognostication guidelines using a multimodal approach performed after the first 2 days post-CA. Some studies also suggested that PS100B could potentially help the early prognostication strategy, [START_REF] Calderon | Combining NSE and S100B with clinical examination findings to predict survival after resuscitation from cardiac arrest[END_REF] confirming our findings that PS100B on admission was significantly associated with outcome. Combining NSE >41.1μg/L with PS100B >0.461μg/L at H24 was the most specific criteria of poor long-term outcome. [START_REF] Zellner | NSE and S-100B are not sufficiently predictive of neurologic outcome after therapeutic hypothermia for cardiac arrest[END_REF] An initial PS100B threshold of 0.92μg/L predicted poor outcome with a 90%-specificity, besides initial shockable rhythm, PS100B and NSE being highly predictive of hospital survival. [START_REF] Derwall | Changes in S-100 protein serum levels in survivors of out-of-hospital cardiac arrest treated with mild therapeutic hypothermia: a prospective, observational study[END_REF] Mörtberg et al. observed that PS100B levels significantly differed between good and poor outcome groups as early as 1h after ROSC, this difference persisting thereafter. [START_REF] Mörtberg | S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment[END_REF] Only one study did not reach significance regarding the discriminative value for PS100B on admission, AUC of ROC curves on admission being lower than at H24 and H48. [START_REF] Duez | Neuron-specific enolase and S-100b in prolonged targeted temperature management after cardiac arrest: A randomised study[END_REF] However, its limited sample size could explain this observation. PS100B half-life is (0.5 to) 2h with a molecular weight of 21kDa, compared with 24h and 78kDa for NSE. [START_REF] Wojtczak-Soska | S-100B protein: An early prognostic marker after cardiac arrest[END_REF][START_REF] Stammet | Modeling Serum level of S100B and Bispectral Index to predict outcome after cardiac arrest[END_REF] This possibly explains discrepancies in case of interrupted integrity of the blood-brain barrier after brain damages with an earlier release of PS100B into cerebral spinal fluid and blood. [START_REF] Gul | Prognostic utility of neuroinjury biomarkers in post out-of-hospital cardiac arrest (OHCA) patient management[END_REF] More understanding of the "early" PS100B kinetic is warranted.

Serial PS100B measurements

In the first study published in 2001 before the TTM era and the modern post-CA syndrome management, [START_REF] Böttiger | Astroglial Protein S-100 Is an Early and Sensitive Marker of Hypoxic Brain Damage and Outcome After Cardiac Arrest in Humans[END_REF] serial PS100B measurements showed significant differences over time according to outcome as soon as 30min after resuscitation.. In the recent study by Duez et al, [START_REF] Duez | Neuron-specific enolase and S-100b in prolonged targeted temperature management after cardiac arrest: A randomised study[END_REF] the daily change remained non-significant for PS100B, but the decrease was significant in the group showing good outcome. Other studies have previously described these PS100B variations in smaller cohorts. [START_REF] Mörtberg | S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment[END_REF][START_REF] Einav | Modeling Serum Biomarkers S100 Beta and Neuron-Specific Enolase as Predictors of Outcome After Out-of-Hospital Cardiac Arrest. An Aid to Clinical Decision Making[END_REF][START_REF] Huang | Predicting the outcome for out-of-hospital patients using multiple biomarkers and suspension microarray assays[END_REF] Mörtberg suggested that levels of PS100B decreased in the good outcome group from the acute phase after ROSC to 24h post-CA, and increased between these time points in the poor outcome group. [START_REF] Mörtberg | S-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment[END_REF] Einav et al. observed comparable results as ours with a "steep decrease" between admission compared to day 3, while the decrease between day 1 to day 3 was less pronounced. [START_REF] Einav | Modeling Serum Biomarkers S100 Beta and Neuron-Specific Enolase as Predictors of Outcome After Out-of-Hospital Cardiac Arrest. An Aid to Clinical Decision Making[END_REF] Our study highlights the time course of PS100B within the first hours after CA and is to date the largest study confirming that serial PS100B measurements, especially in the early phase after CA, can correctly predict outcome.

Biomarkers and scores for early prognostication

Our study is the first to evaluate simultaneously all "usual" biomarkers using ROCs, biomarkers that were correlated with outcome but with lower AUCs than PS100B-Adm.

Interestingly, PS100B, pH and lactate were correlated in multivariate analyses, suggesting that these biomarkers could finally find a place in scores used in the early phase post-CA. [START_REF] Maupain | The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after out-of-hospital cardiac arrest[END_REF] Creatinine remained significantly but moderately different between good and poor outcome groups, whereas pH was no longer significant at H24 and H48, neither lactate at H48. Our results are concordant with literature for lactate variations over time [START_REF] Dell'anna | Prognostic implications of blood lactate concentrations after cardiac arrest: a retrospective study[END_REF] and suggest that lactate or pH could be useful predictors early after CA but less thereafter. Except PS100B-Adm, overlaps for these biomarkers between good and poor outcome were important as early as admission. In our study, only PS100B was blinded to physicians preventing the risk of "selffulfilling prophecy". Therefore, according to literature and our data, early PS100B could interestingly be incorporated in such prognosticating scores.

Limitations

Our study is a single-center study. However, regarding our main endpoint, this fact guarantees a correct homogeneity of samplings and biochemical tests, considering that the ECLIA method with immunoluminescent sandwich used in our unit is the most used method in literature. [START_REF] Stammet | Neuron Specific Enolase as a predictor of death or poor neurological outcome after out-of-hospital cardiac arrest and targeted temperature management[END_REF][START_REF] Einav | Modeling Serum Biomarkers S100 Beta and Neuron-Specific Enolase as Predictors of Outcome After Out-of-Hospital Cardiac Arrest. An Aid to Clinical Decision Making[END_REF][START_REF] Stammet | Protein S100 as outcome predictor after outof-hospital cardfiac arrest and targeted temperature management at 33°C and 36°C[END_REF][START_REF] Stammet | Modeling Serum level of S100B and Bispectral Index to predict outcome after cardiac arrest[END_REF][START_REF] Duez | Neuron-specific enolase and S-100b in prolonged targeted temperature management after cardiac arrest: A randomised study[END_REF] First sampling was performed after a 4-hour delay post-CA, consequence of the important deployment of resources performed to find the aetiology of CA. [START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF] Choice of the days of sampling could be criticized as we did not pursue the biomarker evaluation after H48. However, our main goal was to specifically evaluate biomarkers in the early phase after CA.

Several exclusion criteria were prospectively applied in our study. However, this selection of patients added precision, as biomarkers could be modified by ECLS and haemolysis. [START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: A systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF] Both OHCA and IHCA were enrolled in our pragmatic trial. However, similar results were observed in the OHCA subgroup (data not shown). It could be argued that extra-cerebral sources of PS100B are potentially confounders, decreasing its specificity to predict outcome. [START_REF] Gul | Prognostic utility of neuroinjury biomarkers in post out-of-hospital cardiac arrest (OHCA) patient management[END_REF] However, in our study, neither body mass index nor CPK were associated with PS100B values at admission (data not shown).

Finally, the cost of PS100B dosages, the duration of its measurement, or its unavailability in some laboratories could limit its spreading. [START_REF] Deye | Changes in cardiac arrest patients' temperature management after the 2013 "TTM" trial: results from an international survey[END_REF] Meanwhile, more studies are warranted before any generalization of PS100B measurement: multicentre prospective trials to definitively confirm our results, refining early PS100B added value and its cut-off levels, and metaanalyses to pool all PS100B data in its ability to early predict outcome.

Conclusions

In our cohort of comatose patients resuscitated from CA without ECLS implementation, PS100B after hospital admission was the biomarker with the best accuracy for outcome's prognostication, and the sole biomarker with a high accuracy persisting during the first 3 days after CA. Box plots represent PS100B sampling over the first 3 days after CA. Data are presented as median, quartiles 1 and 3, lower and upper fence.

Legend (x axis) = Adm: day of sampling after admission in ICU, H24 and H48 correspond to the day of sampling during the following 24h and 48h. « C » denotes CPC assessment, ranging from CPC-1 (no sequellae) to CPC-5 (death or brain death). No CPC-4 was observed in our study at hospital discharge.

At admission, significant differences were observed between CPC5 vs. CPC1, 2, and 3, and between CPC1 vs. CPC2 (P<0.01). At H24 and H48, significant differences were observed between CPC5 vs. CPC1, 2, and 3. Footnote. ΔPS100B Adm-H24 is defined as the difference between the first blood sample on admission (n=278) and the sample at H24 (n=257), the ΔPS100B Adm-H48 is the difference between the first blood sample on admission (n=278) and the sample at H48 (n=239), and ΔPS100B H24-H48 is the difference between the blood sample at H24 and the sample at H48.

Pos. means a positive difference between the first and second samples (i.e. decreasing PS100B value measured on the second sampling). Neg. means a negative difference between the first and second samples (i.e. increasing PS100B value measured on the second sampling).

  . The delay between CA and the first blood sample measuring NSE and PS100B (Admission) was 245 min [180-338], the delay between ROSC and the first sample being 220 min [155-316]. The delay between ROSC and the second sample (H24) was 23.5 hours [15.3-36.0], while it was 48.1 hours [39.2-61.2] between ROSC and the third sample (H48).

Figure 1 .

 1 Figure 1. Protein S100 time course according to CPC assessment at 3 months after

Figure 2 .PS100B

 2 Figure 2. Forest plot for biomarker Receiver Operating Characteristic (ROC) curves

3 .

 3 Abbreviation.

a:

  PS100B values for this patient were 0.102 at D0 and 0.123 at D1. b : Fisher exact tests are expressed.

1 Table 1 . Patient general characteristics according to outcome at 3 months

 11

	(Table 1, continued)		Overall	Good outcome	Poor outcome	P
	Characteristic SAPS II		Overall (N=330) (N=330) 69 [56-83]	Good outcome (N=109) (N=109) 63 [53-73]	(N=221) Poor outcome (N=221) 73 [59-86] <0.0001 P
	Age, yr Cardiac cause responsible for or participating to	61 [49-72] 190 (57.4)	55 [45-67] 91 (83.5)	63 [52-76] 99 (44.8)	0.0002 <0.0001
	Male sex, no. (%) CA, including APE and PE, no (%) b	230 (70)	84 (77.1)	146 (66.1)	0.043
	Body Mass Index, kg/m 2 CA related to acute coronary occlusion, no (%) b	26 [23-29] 115/220 (52.3)	25 [24-29] 65/90 (72.2)	26 [23-30] 50/130 (38.5) <0.0001 0.54
	Previous hypertension, no. (%) Previous coronary disease PCI with stenting, no (%) b	126 (38.1) 60 (18.1) 82/220 (37.3)	36 (33.0) 13 (11.9) 49/90 (54.4)	90 (40.7) 47 (21.3) 33/130 (25.4) <0.0001 0.23 0.034
	Previous chronic heart failure Post-CA shock, no. (%)	29 (8.8) 234 (70.7)	6 (5.5) 70 (64.2)	23 (10.4) 164 (74.2)	0.15 0.092
	Diabetes, no. (%) Chronic renal failure, no. (%) Clinical seizures, no. (%)	72 (21.8) 25 (7.6) 62 (18.7)	15 (13.8) 1 (0.9) 10 (9.2)	57 (25.8) 24 (10.9) 52 (23.5)	0.01 0.001 0.002
	Location of arrest: in-hospital, no. (%) Acute hepatic injury and failure, no. (%)	79 (23.9) 103 (31.1)	24 (22.0) 27 (24.8)	55 (24.9) 76 (34.4)	0.59 0.078
	Location of arrest: out-of-hospital, no. (%): DIC, no. (%)	251 (76.1) 64 (19.3)	85 (78.0) 16 (14.7)	166 (75.1) 48 (21.7)	0.14
	Renal replacement, no. (%)	Home, no. (%)	127 (50.6) 50 (15.1)	36 (48.0) 9 (8.3)	91 (54.8) 41 (18.6)	0.014
	32-34°C TTM, no (%) c	Public place, no. (%)	124 (49.4) 266 (80.4)	49 (52.0) 102 (93.6)	75 (45.2) 164 (74.2) <0.0001
	Bystander, no (%) Mechanical ventilation duration d , days	312 (94.3) 6 [3-10]	103 (94.5) 5 [3-10]	209 (94.6) 6 [3-10]	0.99 0.75
	Bystander CPR, no. (%) Decision to limit treatment(s), no. (%)	226 (68.3) 149 (45.0)	87 (79.8) 0	139 (62.9) 149 (67.4) <0.0001 0.002
	Delay to first treatment limitation decision, days	4 [2-7]	0	4 [2-7]	0.10
	Initial documented rhythm, no. (%) Duration of hospitalization in ICU, days	7 [4-13]	9 [6-16]	7 [4-12]	<0.0001 0.001
	Shockable rhythm: VF / pulseless VT, no. (%) In-hospital length of stay, days	125 (37.8) 10 [5-22]	80 (73.4) 21 [14-31]	45 (20.4) 7 [4-13]	<0.0001
	Non-shockable rhythm: asystole / PEA, no. (%)	206 (62.2)	29 (26.6)	176 (79.6)
	No-flow, min a		2 [0-8]	1 [0-4]	4 [0-10]	0.0001
	Low-flow, min a		17 [10-27]	13 [7-23]	19 [12-28]	0.0005
	Adrenaline bolus, mg (total IV, N=243)	2 [0-4]	0 [0-2]	2 [1-4]	<0.0001
	Initial catecholamine perfusion, mg/h (N=162)	0.5 [0-1.5]	0 [0-1]	1 [0-2]	<0.0001

Table 4 . Biomarkers to predict outcome at 3 months after CA: sensitivity, specificity, AUC, NRI versus clinical model, and NRI versus clinical model + NSE-Adm.

 4 Abbreviation. Se: sensitivity. Sp: specificity. AUC: area under the curve. NRI: Net Reclassification Index.Footnote. a : Best sensitivity / specificity (with their criterion) according to the maximum accuracy. b : Biomarkers Receiver Operating Characteristic (ROC) curves are represented with their area under the curve (AUC) and CI-95% for their ability to discriminate good versus poor outcome at 3-month follow-up. c : Net Reclassification Index (NRI) of each biomarker at all time-points against clinical model (including clinical parameters with statistical significance in the multivariate analysis: initial shockable rhythm, no-flow duration, clinical seizures, and therapeutic hypothermia). d : NRI against clinical model (with parameters with statistical significance in the multivariate analysis) + NSE-Adm as the reference biomarker. e : no added value according to NRI. Biomarkers with NRI >50% with and without NSE are PS100B at all time-points, NSE after H24, and pH and lactate on admission.

	Biomarker	Se a	Sp a	Criterion	AUC b	NRI (%) vs. clinical model c NRI (%) vs. clinic + NSE d
	PS100B Admission	76.7	79.0	0.539	0.831 [0.779 -0.883]	69.8 [48.1 -91.6]	63.8 [41.6 -86.1]
	PS100B H24	88.8	63.6	0.321	0.825 [0.772 -0.879]	101.2 [79.5 -122.9]	66.1 [36.2 -96.0]
	PS100B H48	79.5	75.9	0.139	0.818 [0.761 -0.874]	53.4 [31.8 -75.0]	97.8 [71.6 -124.0]
	NSE Admission	71.4	45.9	31.92	0.585 [0.512 -0.658]	-21.7 [-46.3 -2.9] e	Reference
	NSE H24	98.7	44.3	49.28	0.761 [0.702 -0.820]	77.1 [52.9 -101.3]	67.4 [37.5 -97.2]
	NSE H48	92.3	59.6	38.30	0.776 [0.716 -0.837]	99.8 [77.7 -121.8]	89.7 [64.4 -115.1]
	Lactate Admission	77.8	57.7	7.00	0.709 [0.650 -0.767]	64.4 [42.5 -86.2]	52.2 [27.0 -77.5]
	Lactate H24	71.3	55.0	2.77	0.647 [0.582 -0.712]	42.2 [19.9 -64.4]	19.1 [-5.8 -44.1] e
	Lactate H48	31.6	82.8	1.24	0.551 [0.480 -0.622]	34.2 [10.5 -57.8]	-2.9 [-28.1 -22.2] e
	pH Admission	57.0	63.9	7.23	0.618 [0.554 -0.682]	57.6 [35.5 -79.8]	57.6 [35.5 -79.8]
	pH H24	82.7	29.7	7.23	0.549 [0.483 -0.616]	22.8 [-0.8 -46.4] e	-7.5 [-31.1 -16.1] e
	pH H48	77.2	36.4	7.32	0.510 [0.442 -0.578]	32.8 [9.4 -56.2]	-6.0 [-30.9 -18.9] e
	Creat. Admission	85.2	44.7	126	0.658 [0.598 -0.718]	67.6 [48.0 -87.2]	32.9 [8.4 -57.4]
	Creat. H24	70.2	62.8	81	0.672 [0.610 -0.735]	51.7 [29.5 -73.8]	32.3 [7.5 -57.1]
	Creat. H48	84.5	42.9	116	0.605 [0.539 -0.672]	42.3 [21.6 -63.0]	30.2 [8.0 -52.4]

No specific grant was received for this study. However, this study was performed under the coordination of both "Inserm UMR S-942 et le

Footnote. The total number of samples for PS100B and NSE were on admission: 262 and 256, at H24: 243 and 237, at H48: 224 and 214, respectively. The number of PS100B and NSE samples per patient during the first 72h after cardiac arrest were 2 [2-3] and 3 [START_REF] Bougouin | Characteristics and prognosis of sudden cardiac death in Greater Paris: population-based approach from the Paris Sudden Death Expertise Center (Paris-SDEC)[END_REF][START_REF] Monsieurs | on behalf of the ERC Guidelines 2015 Writing Group. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary[END_REF][START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015. Section 5 of the European Resuscitation Council Guidelines for Resuscitation[END_REF], respectively. Good outcome depicts CPC 1 and 2 at M3 after cardiac arrest, and poor outcome CPC 3 to 5 at M3-follow-up.

Disclosures

The authors declared no competing interest in relation with the paper. Outside the present work, Nicolas Deye declared lecture and travel fees from Bard and Zoll companies.

Biomarker ROC curves according to the day of sampling are depicted in Figure 2 and Supplemental Table S4. At admission, best AUC for prediction of outcome was observed for PS100B-Adm compared with all other biomarkers (P<0.0001), AUC for lactate-Adm being also higher than for NSE-Adm (Supplemental Figure S2a). At H24, AUC for PS100B-H24 was significantly higher than for all other biomarkers except NSE-H24 (P≤0.0001, Supplemental Figure S2b), while AUC for NSE-H24 was higher than AUCs for lactate-H24 and pH-H24. At H48, best AUCs were observed for both PS100B-H48 and NSE-H48 versus all other biomarkers for outcome prediction (P<0.001, Supplemental Figure S2c). No differences were observed between all PS100B AUCs from admission to H48, whereas AUC for NSE-Adm was significantly lower than AUCs for NSE-H24 and NSE-H48 (P<0.0001, Supplemental Figure S3). PS100B serial measurements are described in Table 3. PS100B values significantly decreased over time (P<0.0001). An increasing value between admission and H24 was significantly associated with poor outcome compared to patients with decreased PS100B values over time (P=0.001, only 1 patient with good outcome experiencing an increasing PS100B value).

Comparable results were observed for PS100B differences between admission and H48 measurements but not between H24 and H48.

Biomarkers sensitivity and specificity according to the maximum accuracy at all time-points are detailed in Table 4. A PS100B-Adm threshold of 3.78μg/L correctly classified all patients except one as experiencing poor outcome with a 99%-specificity (95%-CI [94-99]) and a positive predictive value of 98% (1 false positive; Supplemental Figure S4, Tables S5 andS6).

In multivariate analysis, early predictors independently associated with poor outcome at hospital discharge were: no-flow duration, PS100B-Adm, pH-Adm, and lactate-Adm values, coagulation; PCI: percutaneous coronary intervention; TTM: targeted temperature management.

Footnote.

a : Time between collapse to ROSC was 22 min [14-33]; b : coronary angiogram was performed in 220/330 patients (67%), brain and chest CT-scans in 194/330 (59%) and 132/330 (40%), respectively; c : all patients were TTM-treated between 32°C and 36°C; d : all patients were initially intubated and mechanically ventilated after cardiac arrest. Good outcome depicts CPC 1 and 2 at hospital discharge, and poor outcome CPC 3 to 5. Survival and good outcome were observed in 45% and 32% at ICU discharge, in 44% and 36% at hospital discharge, and in 39% and 33% at 3 months after CA, respectively.


J.-B De Fréminville

M Azizi

Inhibiteurs du système-rénine-angiotensine au cours du COVID-19 : protecteurs ou dangereux ? Are renin-angiotensin system inhibitors protective or deleterious in patients with COVID-19?

. Dans d'autres cas, le virus est responsable d'affections graves, principalement pulmonaires, avec un Syndrome de Détresse Respiratoire Aigu (SDRA) pouvant entrainer une hospitalisation prolongée en soins intensifs accompagnée d'un taux de décès élevé. Un des principaux enjeux de la recherche en cours sur la maladie COVID-19 est l'identification des patients à risque de développer une forme critique de la maladie. Les facteurs de risque prédisposant à la gravité du COVID-19 les plus fréquemment rapportés sont l'âge, le sexe masculin, les comorbidités cardiovasculaires (maladie coronaire, insuffisance cardiaque, hypertension artérielle), métaboliques (diabète de type 2, obésité) et pulmonaires (bronchopneumopathie chronique obstructive et tabagisme) et l'insuffisance rénale chronique. La maladie COVID-19 est liée de près au système rénine-angiotensine (SRA), puisque la pénétration du SARS-CoV-2 dans les cellules se fait via l'enzyme de conversion de l'angiotensine 2 (ACE2, angiotensin-converting enzyme 2). L'ACE2 tire son nom d'une homologie de séquence avec l'angiotensin-converting enzyme1 (ACE1). Les traitements bloqueurs du SRAA sont utilisés dans multiples indications, principalement chez les patients atteints d'hypertension artérielle, qui représentent entre 20 % et 30 % de la population mondiale [2], mais aussi chez les patients insuffisants cardiaques et insuffisants rénaux. L'impact de ces médicaments sur le risque d'infection par le SARS-CoV-2, ainsi que sur la gravité et la mortalité liées à ces infections, a donc été particulièrement surveillé et étudié.

La pénétration cellulaire du SARS-CoV-2 se fait via l'enzyme de conversion de l'angiotensine 2.

Physiopathologie

Rappels sur le SRA et l'ACE2 Le système rénine-angiotensine-aldostérone (SRAA) est un système de régulation endocrinien et enzymatique régulant l'homéostasie hydrosodée, ainsi que l'autorégulation du débit sanguin rénal et la pression artérielle [START_REF] Sparks | Classical Renin-Angiotensin System in Kidney Physiology[END_REF]. La sécrétion et la libération de rénine sont sous la dépendance de nombreux facteurs, en particulier la pression de perfusion de l'artériole afférente du glomérule, le système nerveux sympathique et les catécholamines circulantes via le système béta-1 adrénergique, la charge sodée au niveau de la macula densa, le rétrocontrôle négatif de l'angiotensine II via l'activation du récepteur AT1 (AT1R), les prostaglandines et le NO. La rénine clive l'angiotensinogène pour libérer un décapeptide inactif, l'angiotensine I. L'angiotensine I est ensuite transformée en angiotensine II grâce à l'angiotensin-converting enzyme1 (ACE1). L'angiotensine II stimule la vasoconstriction des artérioles (via le récepteur membranaire AT1) et entraine la sécrétion d'aldostérone par la surrénale, responsable de la réabsorption de sodium et de la sécrétion de potassium au niveau du tubule collecteur rénal. Outre son rôle dans l'homéostasie, l'angiotensine II a un effet pro-inflammatoire et pro-thrombotique. L'ACE2 clive l'angiotensine II en une « angiotensine (1-7) ». L'ACE1 est inhibée par les inhibiteurs de l'enzyme de conversion (IEC) de l'angiotensine, alors que l'ACE2 ne l'est pas. L'ACE2, à une moindre échelle, clive aussi l'angiotensine I en angiotensine (1-9), qui est ensuite convertie en angiotensine (1-7) par l'ACE1. L'angiotensine (1-7) s'oppose à l'action de l'angiotensine IIvia la liaison au récepteur Mas et au récepteur de l'angiotensine II de type 2 (AT2) -en abaissant la pression artérielle via une légère vasodilatation médiée par le NO et les prostacyclines, en favorisant l'excrétion rénale de sodium et d'eau, et en exerçant un effet anti-inflammatoire [START_REF] Srivastava | Imbalance between Angiotensin II -Angiotensin (1-7) system is associated with vascular endothelial dysfunction and inflammation in type 2 diabetes with newly diagnosed hypertension[END_REF]. La régulation de l'ACE2 n'est pas parfaitement élucidée. Certains modèles expérimentaux précliniques ont montré que l'utilisation d'inhibiteurs de l'enzyme de conversion (IEC) et les antagonistes du récepteur de l'angiotensine 2 (ARA2), exerçaient une régulation positive sur l'ACE2 [START_REF] Igase | Increased Expression of Angiotensin Converting Enzyme 2 in Conjunction with Reduction of Neointima by Angiotensin II Type 1 Receptor Blockade[END_REF] et réciproquement [START_REF] Ferrario | Effect of angiotensin-converting enzyme inhibition and angiotensin II receptor blockers on cardiac angiotensin-converting enzyme 2[END_REF], alors que d'autres n'ont pas retrouvé cette association [START_REF] Burrell | Myocardial infarction increases ACE2 expression in rat and humans[END_REF]. Quelques études ont par ailleurs étudié cette association chez l'Homme, avec des résultats partagés, certaines montrant une régulation positive de l'ACE2 par les IEC [START_REF] Vuille-Dit-Bille | Human intestine luminal ACE2 and amino acid transporter expression increased by ACE-inhibitors[END_REF] ou les ARA2 [START_REF] Furuhashi | Urinary angiotensin-converting enzyme 2 in hypertensive patients may be increased by olmesartan, an angiotensin II receptor blocker[END_REF] et d'autres ne retrouvant pas cette association [START_REF] Ramchand | Elevated plasma angiotensin converting enzyme 2 activity is an independent predictor of major adverse cardiac events in patients with obstructive coronary artery disease[END_REF].

SARS-CoV-2 et ACE2

L'ACE2 joue un rôle central dans l'infection par le SARS-CoV-2, puisqu'elle est une porte d'entrée pour le virus. Le rôle de la protéine Spike (S) et de l'ACE2 dans l'entrée des coronavirus dans les cellules a été bien décrit lors de l'épidémie de SARS-CoV en 2003 [START_REF] Kuba | A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus-induced lung injury[END_REF][START_REF] Li | Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus[END_REF]. Dans le cas du SARS-CoV-2, l'ACE2 agit de même comme récepteur de la protéine S et permet ainsi l'entrée du virus via la sérine protéase TMPRSS2 (Transmembrane protease serine 2) [START_REF] Hoffmann | SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor[END_REF]. L'importance de ce récepteur est encore plus prégnante que pour les autres coronavirus connus. En effet, la séquence génomique du SARS-CoV-2 est connue [START_REF] Zhou | A pneumonia outbreak associated with a new coronavirus of probable bat origin[END_REF], ainsi que sa structure par cryo-microscopie électronique : bien que partageant presque 80 % de sa séquence génomique avec le SARS-CoV de 2003, l'affinité du SARS-CoV-2 pour l'ACE2 est 10 à 20 fois plus importante [START_REF] Wrapp | Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation[END_REF]. L'ACE2 est une ectoenzyme exprimée dans l'intestin grêle, le tissu adipeux, les reins, le coeur, la thyroïde, mais aussi sur les cellules épithéliales des alvéoles bronchiques, le colon, le foie, ou la vessie. En revanche, elle est peu exprimée dans les vaisseaux sanguins, la moelle osseuse et le muscles. Par ailleurs, elle est très peu circulante. Dans le poumon, l'ACE2 est principalement exprimée dans les pneumocytes de type 2, produisant le surfactant nécessaire au maintien de la structure des alvéoles [START_REF] Hamming | Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis[END_REF][START_REF] Li | Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues[END_REF][START_REF] Zou | Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection[END_REF].

Inhibiteurs du SRA : protecteurs ou dangereux ? Les hypothèses physiopathologiques

En exerçant une régulation positive sur l'expression de l'ACE2, les bloqueurs du SRA pourraient favoriser l'entrée du SARS-CoV-2, sa multiplication et sa dissémination dans l'organisme en théorie [START_REF] Fang | Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection?[END_REF]. C'est d'ailleurs l'une des explications proposées pour la moindre fréquence de l'atteinte des enfants par le SARS-CoV-2, qui serait la conséquence d'une expression moins importante de l'ACE2 dans le nez [START_REF] Bunyavanich | Nasal Gene Expression of Angiotensin-Converting Enzyme 2 in Children and Adults[END_REF]. Néanmoins, il n'est pas évident que les bloqueurs du SRA soient effectivement responsables d'une augmentation de l'expression de l'ACE2. Par ailleurs, l'endocytose de l'ACE2, suite à l'entrée du SARS-CoV-2 dans les cellules, est responsable d'une dérégulation de la balance SRA / ACE2 aboutissant à une augmentation des réponses biologiques médiées par l'angiotensine II et d'une diminution de celle médiées par l'angiotensine (1-7). Or, l'angiotensine II semble avoir un rôle néfaste dans la gravité de l'infection pulmonaire à SARS-CoV-2. En effet, chez un petit nombre de patients, le taux d'angiotensine II semblait être corrélé à la gravité de l'atteinte pulmonaire [START_REF] Liu | Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury[END_REF]. Cette hypothèse semble cohérente avec des études précliniques réalisées d'autres infections virales [START_REF] Gu | Angiotensin-converting enzyme 2 inhibits lung injury induced by respiratory syncytial virus[END_REF][START_REF] Zou | Angiotensin-converting enzyme 2 protects from lethal avian influenza A H5N1 infections[END_REF]. Ces effets seraient la conséquence d'une réponse inflammatoire accrue médiée par l'angiotensine II, en l'absence de contre-régulation par l'angiotensine (1-7). Ce déséquilibre entre la voie de signalisation de l'angiotensine II et celle de l'angiotensine (1-7) serait responsable d'une apoptose des pneumocytes, d'une augmentation de la fibrose pulmonaire, d'une augmentation de la sécrétion de cytokines pro-inflammatoires et d'une endothélite associée à une dysfonction endothéliale [START_REF] Ackermann | Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19[END_REF][START_REF] Sriram | A hypothesis for pathobiology and treatment of COVID-19: The centrality of ACE1/ACE2 imbalance[END_REF][START_REF] Varga | Endothelial cell infection and endotheliitis in COVID-19[END_REF]. Cette dysfonction endothéliale pourrait par ailleurs être responsable d'une inflammation accrue et du phénotype pro-thrombotique observée chez les patients atteints de la COVID-19 [START_REF] Matacic | Blood vessel injury may spur disease's fatal second phase[END_REF][START_REF] Verdecchia | COVID-19: ACE2centric infective disease?[END_REF]. Les conséquences de cette rupture d'équilibre entre angiotensine II et angiotensine (1-7) s'appliquent aussi aux autres organes cibles du SARS-CoV-2, en particulier le myocarde [START_REF] Sriram | A hypothesis for pathobiology and treatment of COVID-19: The centrality of ACE1/ACE2 imbalance[END_REF]. La réflexion concernant les effets protecteurs ou nocifs des bloqueurs du SRA doit enfin prendre en compte l'indication initiale de ces médicaments, les effets protecteurs connus chez les patients atteints d'insuffisance cardiaque, d'hypertension artérielle, ou d'insuffisance rénale notamment, et les risques liés à leur arrêt dans ce contexte. Ces effets protecteurs suivent d'ailleurs souvent la même logique de lutte contre le déséquilibre entre les voies de signalisation de l'angiotensine II, pro-inflammatoire et pro-fibrosante, et l'angiotensine (1-7), s'opposant à son action.

Données cliniques

Nous n'avons retenu que les études dont l'objectif principal était l'étude des inhibiteurs du SRA dans le COVID-19. Il n'existe pas à l'heure actuelle d'essais prospectifs interventionnels évaluant l'impact des bloqueurs du SRA chez les patients atteints du COVID-19. Cependant, un certain nombre d'études de cohortes rétrospectives ont été réalisées. Les détails des principales études sont résumés dans le tableau 1. Concernant l'hypothèse selon laquelle les bloqueurs du SRA augmenteraient le risque d'infection à SARS-CoV-2, trois études, portant respectivement sur 6272, 12594 et 18472 patients, ne montrent pas de surrisque d'infection associé à ces traitements [START_REF] Mancia | Renin-Angiotensin-Aldosterone System Blockers and the Risk of Covid-19[END_REF][START_REF] Mehta | Association of Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Testing Positive for Coronavirus Disease 2019 (COVID-19)[END_REF][START_REF] Reynolds | Renin-Angiotensin-Aldosterone System Inhibitors and Risk of Covid-19[END_REF]. Ceci est confirmé par quelques autres études de plus petite taille. Concernant la sévérité et la mortalité, il n'existe à ce jour pas d'étude associant les bloqueurs du SRA à une sévérité ou à une mortalité accrue par le COVID-19 [START_REF] De Abajo | Use of renin-angiotensinaldosterone system inhibitors and risk of COVID-19 requiring admission to hospital: a case-population study[END_REF][START_REF] Tedeschi | Clinical impact of renin-angiotensin system inhibitors on in-hospital mortality of patients with hypertension hospitalized for COVID-19[END_REF][START_REF] Yang | Effects Of ARBs And ACEIs On Virus Infection, Inflammatory Status And Clinical Outcomes In COVID-19 Patients With Hypertension: A Single Center Retrospective Study[END_REF]. En revanche, certaines ont mis en évidence une réduction de la mortalité chez les patients traités hypertendus [START_REF] Peng | Association of Inpatient Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Mortality Among Patients With Hypertension Hospitalized With COVID-19[END_REF] ou non [START_REF] Zhou | Comparative impacts of angiotensin converting enzyme inhibitors versus angiotensin II receptor blockers on the risk of COVID-19 mortality[END_REF].

Enfin, une revue de littérature exhaustive [START_REF] Mackey | Risks and Impact of Angiotensin-Converting Enzyme Inhibitors or Angiotensin-Receptor Blockers on SARS-CoV-2 Infection in Adults: A Living Systematic Review[END_REF], ainsi que deux méta-analyses [38,[START_REF] Zhang | ACEI/ARB use and risk of infection or severity or mortality of COVID-19: A systematic review and meta-analysis[END_REF] penchent en faveur d'un bénéfice de ces traitements. Cependant, ces études sont de faible niveau de preuve, portant parfois sur de petits effectifs et si elles plaident en faveur de l'absence de risque lié à la poursuite des bloqueurs du SRA chez les patients ayant une indication de traitement avant le COVID-19, leurs méthodologies ne permettent pas de répondre de manière robuste à la question du bénéfice de ces traitements. Ces conclusions sont reprises par les Fiches de Réponses rapides de la HAS dans le cadre du COVID-19 [START_REF] Laëtitia | Réponses rapides dans le cadre du COVID-19[END_REF] et de la mise au point exhaustive de la Société Européenne de Cardiologie [START_REF]ESC Guidance for the Diagnosis and Management of CV Disease during the COVID-19 Pandemic[END_REF] : « Malgré de nombreuses spéculations, il n'y a actuellement aucune preuve démontrant qu'un traitement antérieur par des IEC ou des ARA2 augmente le risque d'infection au SARS-CoV-2 ou le risque de développer de graves complications de l'infection au SARS-CoV-2 ».

La position des sociétés savantes Conclusion

En résumé, les bloqueurs du SRA ne semblent ni favoriser l'infection par le SARS-CoV-2, ni aggraver le pronostic de cette infection. Il est donc recommandé par toutes les sociétés savantes de ne pas arrêter les traitements par IEC ou ARA2 chez les patients atteints de COVID-19 traités comme tel, en dehors des indications d'arrêt classiques (insuffisance rénale aigüe, déshydratation sévère, état de choc). Plusieurs hypothèses peuvent expliquer cela. En premier lieu, l'infection par le SARS-CoV-2 entraine une baisse de l'ACE-2, internalisé pour permettre au virus d'entrer dans les cellules. Ceci a pour conséquence une dérégulation de l'équilibre entre l'angiotensine II et l'angiotensine (1-7), entrainant une augmentation de l'inflammation, une endothélite avec une dysfonction endothéliale, responsable d'un phénotype vasculaire pro-thrombotique et une fibrose tissulaire accélérée. La fréquence de l'hypokaliémie dans les formes graves de COVID-19 pourrait d'ailleurs s'expliquer par ce mécanisme [START_REF] Chen D Jr | Hypokalemia and Clinical Implications in Patients with Coronavirus Disease 2019 (COVID-19)[END_REF]. Ceci pourrait aussi être plus marqué chez les patients ayant une hyperactivation du SRA lié à une pathologie sousjacente (hypertension artérielle, insuffisance cardiaque, insuffisance rénale) et non traités par bloqueurs du SRA. Il reste à élucider le potentiel effet bénéfique chez les patients initialement sans indication pour un traitement par IEC ou ARA2 atteints de COVID-19. A ce jour, nous ne disposons que d'études rétrospectives, avec une tendance certaine, mais un niveau de preuve insuffisant pour se prononcer. Des d'essais cliniques tournés vers cet objectif sont en cours et permettront de répondre prochainement à cette question (voir www.clinicaltrials.gov).

En pratique

Les bloqueurs du SRA ne semblent ni favoriser l'infection par le SARS-CoV-2, ni en aggraver le pronostic.

Déclaration de liens d'intérêts

Tableau 1 .

 1 Récapitulatif des études.

Depuis mars 2019, l'ensemble des sociétés savantes d'hypertension artérielle, dont l'ESH, l'ESC et la SFHTA, ont statué et recommandé de ne pas arrêter les traitements bloquant le système rénine angiotensine[START_REF] Sparks | The Coronavirus Conundrum: ACE2 and Hypertension Edition[END_REF]. Néanmoins, en cas de fièvre élevée ou de diarrhée importante, il faut adapter ou interrompre transitoirement les traitements par IEC, ARA2 et diurétiques compte tenu du risque de déshydratation et d'insuffisance rénale.

Les auteurs déclarent ne pas avoir de liens d'intérêts.