ORIGINAL RESEARCH published: 29 June 2022 doi: 10.3389/fimmu.2022.902837 Exposing and Overcoming Limitations Edited by: Giulia Carla Marchetti, of Clinical Laboratory Tests in University of Milan, Italy Reviewed by: COVID-19 by Adding Immunological Davide Firinu, Università di Cagliari, Italy Parameters; A Retrospective Cohort Letizia Santinelli, Sapienza University of Rome, Italy Analysis and Pilot Study *Correspondence: Ricardo Pujol-Borrell Adrián Sánchez-Montalvá 1,2,3,4†, Daniel Álvarez-Sierra 5†, Mónica Martı́nez-Gallo 5,6,7, rpujol@vhio.net Janire Perurena-Prieto 5,7, Iria Arrese-Muñoz 6, Juan Carlos Ruiz-Rodrı́guez 8,9, Manuel Hernández-González Juan Espinosa-Pereiro 1,2,4, Pau Bosch-Nicolau 1,2,4, Xavier Martı́nez-Gómez 10,11,12, manhernandez@vhebron.net Andrés Antón 13,14,15, Ferran Martı́nez-Valle 3,16,17, Mar Riveiro-Barciela 3,18,19, † These authors share first authorship Albert Blanco-Grau 20, Francisco Rodrı́guez-Frias 20, Pol Castellano-Escuder 21, ‡ Elisabet Poyatos-Canton 22, Jordi Bas-Minguet 22, Eva Martı́nez-Cáceres 7,23,24, These authors have contributed Alex Sánchez-Pla 21,25, Coral Zurera-Egea 24, Aina Teniente-Serra 7,23,24, equally to this work and share Manuel Hernández-González 5,6,7*‡, Ricardo Pujol-Borrell 5,6,7*‡ and last authorship the “Hospital Vall d’Hebron Group for the study of COVID-19 immune profile” Specialty section: 1 Infectious Disease Department, Hospital Universitari Vall Hebron, Barcelona, Spain, 2 International Health Program Institut This article was submitted to Català de la Salut, Vall Hebron Research Institute (VHIR), Barcelona, Spain, 3 Department of Medicine, Universitat Autònoma Viral Immunology, Barcelona, Barcelona, Spain, 4 Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), a section of the journal Instituto de Salud Carlos III, Barcelona, Spain, 5 Translational Immunology Research Group, Vall Hebron Research Institute Frontiers in Immunology (VHIR), Barcelona, Spain, 6 Immunology Department, Hospital Universitari Vall Hebron, Barcelona, Spain, 7 Department of Received: 23 March 2022 Cell Biology, Physiology, and Immunology, Universitat Autònoma Barcelona, Barcelona, Spain, 8 Intensive Medicine Accepted: 19 May 2022 Department, Hospital Universitari Vall Hebron, Barcelona, Spain, 9 Organ Dysfunction and Resuscitation Research Group, Vall Published: 29 June 2022 Hebron Research Institute (VHIR), Barcelona, Spain, 10 Epidemiology and Public Health Department, Hospital Universitari Vall Hebron, Barcelona, Spain, 11 Epidemiology and Public Health Research Group, Vall Hebron Research Institute (VHIR), Citation: Barcelona, Spain, 12 Department of Pediatrics, Obstetrics and Gynecology, Epidemiology and Public Health, Universitat Sánchez-Montalvá A, Álvarez-Sierra D, Autònoma Barcelona, Barcelona, Spain, 13 Microbiology Department, Hospital Universitari Vall Hebron, Barcelona, Spain, Martı´nez-Gallo M, Perurena-Prieto J, 14 Microbiology Research Group, Vall Hebron Research Institute (VHIR), Barcelona, Spain, 15 Department of Genetics and Arrese-Muñoz I, Ruiz-Rodrı´guez JC, Microbiology, Autonomous University of Barcelona, Barcelona, Spain, 16 Internal Medicine Department, Hospital Universitari Espinosa-Pereiro J, Bosch-Nicolau P, Vall Hebron, Barcelona, Spain, 17 Systemic Disease Research Group, Valle Hebron Research Institute (VHIR), Barcelona, Martı´nez-Gómez X, Antón A, Spain, 18 Liver Disease Research Group, Valle Hebron Research Institute (VHIR), Barcelona, Spain, 19 CIBERehd - Instituto de Martı´nez-Valle F, Riveiro-Barciela M, Salud Carlos III, Barcelona, Spain, 20 Clinical Biochemistry Department, Hospital Universitari Vall d'Hebron and Clinical Blanco-Grau A, Rodrı´guez-Frias F, Biochemistry Research Group, Valle Hebron Research Institute (VHIR), Barcelona, Spain, 21 Bioinformatics and Statistics Castellano-Escuder P, Group, University of Barcelona, Barcelona, Spain, 22 Immunology Division, Bellvitge University Hospital, Hospitalet de Poyatos-Canton E, Bas-Minguet J, Llobregat, Barcelona, Spain, 23 Immunology Group, Germans Trias i Pujol Health Sciences Research Institute (IGTP), Martı´nez-Cáceres E, Sánchez-Pla A, Badalona (Barcelona), Spain, 24 Immunology Department, Hospital Universitari Germans Trias Pujol, Badalona (Barcelona), Zurera-Egea C, Teniente-Serra A, Spain, 25 Statistics and Bioinformatics Unit, Vall Hebron Research Institute (VHIR), Barcelona, Spain Hernández-González M, Pujol- Borrell R and the “Hospital Vall d’Hebron Group for the study of Background: Two years since the onset of the COVID-19 pandemic no predictive COVID-19 immune profile” (2022) Exposing and Overcoming algorithm has been generally adopted for clinical management and in most algorithms Limitations of Clinical Laboratory the contribution of laboratory variables is limited. Tests in COVID-19 by Adding Immunological Parameters; Objectives: To measure the predictive performance of currently used clinical laboratory A Retrospective Cohort tests alone or combined with clinical variables and explore the predictive power of Analysis and Pilot Study. Front. Immunol. 13:902837. immunological tests adequate for clinical laboratories. Methods: Data from 2,600 doi: 10.3389/fimmu.2022.902837 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory Frontiers in Immunology | www.frontiersin.org 1 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. Findings: 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil- lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage- related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. Conclusions: Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible. Keywords: SARS-CoV-2 infection, predictive risk-profile, clinical laboratory tests, cytokines, chemokines, acute phase reactants, CXCL10, flow cytometry INTRODUCTION Reports originating from the analysis of electronic health records have confirmed the predictive value of clinical laboratory Over two years after the onset of the coronavirus disease (COVID- tests usually associated with poor outcomes in other infections 19) pandemic, the clinical, laboratory, and imaging features of i.e., blood cell counts, acute-phase reactants (APRs), and patients with severe acute respiratory syndrome coronavirus 2 coagulation factors (14–22) but none of the proposed (SARS-CoV-2) infection have been widely described (1–4). The predictive algorithms combining demographic, clinical, and wide clinical spectrum of COVID-19 became obvious during the laboratory data have been widely adopted. In small case series, first wave, and although the effect of inoculum size should be the state of the immune system in COVID-19 patients has been considered (5, 6), variation has been mainly attributed to host analyzed using the latest tools (23–30) leading to the detection of factors, as variants of concern only appeared later (7) (8). The deep perturbations in the immune system. However, inferences analysis of the first wave has therefore obvious advantages for the of the effect of these perturbations in the efficiency of the identification of host factors and their biomarkers. Among host immune response and their clinical consequences are not factors that affect the severity of illness, age, sex, genetic background, simple and, to date, no new predictive tests have been immunological status and prior immunity to coronaviruses (9) have validated and added to the clinical laboratory toolbox for been evaluated. Gene mutations of the type interferon (IFN) COVID-19 management, reflecting not only intrinsic technical pathway (10) and antibodies to type IFNs play a clear role in a difficulties, but also the excessive separation between research small proportion of cases (11); polymorphisms in several genes and clinical laboratories. associated with immune response have been identified in genome- We report a retrospective analysis of data from a cohort of wide association studies (12, 13); however, to date, the genotypes 1,579 consecutive patients treated at the Vall d’Hebron that convey a risk of severe COVID-19 have not been defined in a University Hospital (HUVH) during the first wave of COVID- way that is practically applicable for prediction in clinical practice. 19 in Barcelona. We validated the main conclusions by Frontiers in Immunology | www.frontiersin.org 2 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations comparison with cohorts from two other academic hospitals that Outcomes belong to the same healthcare provider (the Catalan Institute of Final outcomes for comparison included survival vs. death, and Health [ICS]) in Catalonia, Spain. maximum clinical severity. For the validation cohorts the only We initiated the study with the hypothesis that the predictive available outcome was survival for 28 days (survivors) and power of clinical laboratory tests had not been fully exploited and death (deceased). with the main objective of improving their interpretation. A secondary objective was to explore a selection of robust Clinical Laboratory Tests immunological tests that might identify an early dysregulated Detection of SARS-CoV-2 was first performed by an in-house immune response associated with severe COVID-19, with the PCR assay with primers and probes from 2019-nCoV CDC PCR hypothesis that these tests could provide additional non- panel, using the One-Step RT-PCR kit (Qiagen, Germany). redundant prediction power. When commercial assays became available, a real-time multiplex RT-PCR assay (Laplet 2019-nCoV Assay, Seegene, South Korea) was used. PATIENTS AND METHODS The clinical laboratories were equipped with Beckman Coulter (Brea, CA, USA) and Roche Diagnostics (Indianapolis, IN, USA) Patients automatic analyzers that were integrated with two TECAN (Zug, The database of the HUVH cohort was obtained by merging data Switzerland) continuous lines and two automatic cold storage and sets from the Infectious Disease, Epidemiology and Public retrieval units that ensure sample integrity. IL-6 levels were Health, and Clinical Laboratory departments. Consecutive measured in a Elycsis® Cobas analyzer (Roche). Samples for patients aged ≥18 years with a SARS-CoV-2 positive assessing the predictive performance of clinical laboratory tests polymerase chain reaction (PCR) from any respiratory sample, were taken on admission to the hospital; glomerular filtration rate hospitalized in HUVH between 10 March and 29 April 2020 (GFR) was calculated by applying the algorithm of Levey et al. (32); were included in the study (see Tables 1, S1). This COVID-19 additional laboratory test data for the 28-day follow-up period were HUVH cohort consisted of 1,579 patients (Figure 1). All patient available from 9,475 samples corresponding to 1,079 of the 1,579 medical records included the main symptoms, days from patients in the HUVH cohort. symptom onset (DFSO), initial assessment of vital signs, comorbidities, length of hospital stay (LOS), intensive care unit Immunological Tests (ICU) admission, oxygen supplementation and supportive The levels of CCL2, CXCL10, GM-CSF, IFN-alpha, IFN-gamma, ventilation requirements, outcome during the hospitalization IL-10, IL-12 p70, IL-13, IL-15, IL-17A, IL-1RA, IL-2, IL-4, IL-6, IL- and results from clinical laboratory tests. Data were censored 7, TNF and granzyme B were measured in sera using the ELLA on the date of discharge, death, or 28 days after admission, microfluidic platform (Biotechne®, Minneapolis, MN, USA); whichever occurred first. The outcome of all patients discharged sCD163 levels were measured by a commercial ELISA (CD163 before the 28th day was ascertained through a review of the human kit, Thermo Fisher Societies, Waltham, MA, USA). primary care electronic health record annotations. The Human Immune Phenotyping Consortium protocol (33, Comorbidities were classified as 1) cardiovascular disease and/or 34) was adapted for the study of COVID-19 patients. The antibodies hypertension, 2) chronic lung disease, 3) diabetes, 4) neurological used are shown in Table S2. Blood was collected in EDTA disease, 5) chronic kidney disease, 6) active non-terminal vacutainer tubes (BD-Plymouth, UK) and processed within 4 malignancy, 7) obesity, and 8) chronic liver disease. Each hours. Lymphocytes were selected by CD45 and SSC including comorbidity was assigned value of 1, and a global comorbidity 105 cells in the gate. In samples with marked lymphopenia a lower index (1 to 8) was generated. The clinical severity category was number were selected. Cells were analyzed in a NAVIOS EX flow assigned as the maximal score attained during hospitalization, using cytometer (Beckman Coulter). Data were analyzed with Kaluza a simplified version of the World Health Organization (WHO) 10- Beckman Software v.2.1. Absolute values were generated by loading point COVID-19 disease clinical progression score (31) as follows: counts from the hematological analyzer (XN-2000; Sysmex, Japan) 1) Mild, no activity limitations or not requiring hospitalization; 2) parallel sample analysis. Moderate, hospitalized, not requiring high-flow oxygen therapy or ventilation support; 3) Severe, hospitalized requiring high-flow Statistical Analysis oxygen therapy or ventilation support; and 4) Deceased, those Categorical variables were summarized as frequencies and who died before day 28 of hospitalization. These categories proportions and continuous variables as means, standard correspond to the WHO scores 1–3, 4–5, 6–9, and 10, deviations, and 95% confidence intervals (CIs) or medians and respectively. For some analyses, the mild and moderate categories interquartile ranges (IQRs), depending on their distribution. were combined into a non-severe category, and the severe and Pairwise comparisons used the Mann–Whitney U-test and deceased categories were combined into a severe category. Kruskal-Wallis test, adjusted for the false-discovery rate (FDR) The validation cohorts from the Bellvitge University Hospital using the Benjamini and Hochberg, or corrected by the (HUB) and the Germans Trias i Pujol University Hospital Bonferroni method where indicated. C-reactive protein (CRP), (HUGTP) included 598 and 423 patients, respectively, and, IL-6, ferritin, and D-dimer values were logarithmically together with the HUVH cohort, at total of 2,600 patients were transformed. A threshold of 30% of laboratory missing data included in the analysis (Figure 1). was used as the exclusion criteria for data analyses. The initial Frontiers in Immunology | www.frontiersin.org 3 June 2022 | Volume 13 | Article 902837 Frontiers in Immunology | www.frontiersin.org Sánchez-Montalvá et al. TABLE 1 | Summary of the clinical and demographic features of HUVH cohort vs outcome. Survivors vs deceased Non-severe vs severe Patients n (%) Survivors n (%) Deceased n (%) p-value Non-severe n (%) Severe n (%) p-value All 1,579 (100) 1324 (83.9) 255 (16.1) 1040 (63.8) 539 (34.1) Female 699 (44.3) 592 (44.7) 107 (42.0) 0.449 491 (47.2) 208 (38.6) 0.001 Male 880 (55.7) 732 (55.3) 148 (58.0) 549 (52.8) 331 (61.4) ICU 236 (14.9) 203 (15.3) 33 (13.9) 0.4 1 (0.1) 235 (43.6) 1.612E-126 Median (IQR) Median (IQR) Median (IQR) Median (IQR) Median (IQR) Age, years 62 (50–75) 57 (48–70) 82 (74–87) 7.26E-81 58 (47–71) 70 (54–82) 2.4872E-29 DFSO 7 (4–9) 7 (5–10) 5 (2–7) 9.40E-19 7 (5–10) 6 (3–8) 2.9561E-14 LOS 7 (2–20) 7 (2–24) 7 (4–11) 0.4226 5 (2–10) 14 (6–36) 1.5748E-44 Disease duration, days 15.0 (10–28) 15 (10–34) 12 (9–18) 4.45E-11 13 (9–21) 22 (11–43) 1.4952E-17 Clinical Presentation n (%) n (%) n (%) n (%) n (%) General Fever 1314 (83.2) 1132 (85.4) 199 (75.3) 0.040 886 (85.2) 438 (81.3) 0.051 Respiratory Upper respiratory symptoms (only) 94 (5.9) 85 (6.5) 9 (3.4) 0.063 69 (6.5) 25 (4.6) 0.118 Lower respiratory symptoms 1351 (85.6) 1148 (86.7) 203 (79.6) 0.020 892 (85.8) 459 (85.2) 0.763 Pneumonia 1524 (96.5) 1271 (96.0) 253 (99.2) 0.008 989 (95.1) 535 (99.3) 3.62E-06 Digestive All 491 (31.1) 440 (33.2) 51 (20.0) 1.182E-05 353 (33.9) 138 (25.6) 7.11E-04 Comorbidities n (%) n (%) n (%) n (%) n (%) Cardiovascular & hypertension 712 (45.1) 508 (38.4) 204 (80.0) 8.534E-37 394 (37.9) 318 (59.0) 1.463E-15 Chronic lung disease 278 (17.6) 198 (15.0) 80 (31.4) 5.134E-09 145 (13.9) 133 (24.7) 2.143E-07 Diabetes 293 (1850.0) 223 (16.8) 70 (28.3) 1.90E-05 170 (16.3) 123 (22.8) 0.002 Neurological disease 227 (14.4) 156 (11.8) 71 (27.8) 1.29E-09 121 (11.6) 106 (19.7) 2.89E-05 Chronic kidney disease 134 (8.5) 79 (6) 55 (21.6) 1.03E-13 55 (5.3) 79 (14.7) 9.101E-10 4 Active non-terminal malignancy 113 (7.2) 62 (4.7) 51 (20.0) 1.10E-15 41 (3.9) 72 (13.4) 4.197E-11 Obesity 261 (16.5) 227 (17.3) 34 (12.8) 0.85 145 (13.9) 116 (21.5) 2.00E-04 Chronic liver disease 61 (3.9) 48 (3.7) 13 (4.9) 0.139 37 (3.6) 24 (4.5) 0.409 Comorbidity index* 1 (0-2) 2 (1-3) 2 (1-3) 2.2961E-38 1 (0-2) 2 (1-3) 1.0118E-26 *The breakdown of patients by comorbidity index is in Table S4. Exact p-values from the Mann–Whitney U test, Significancy should be considered for p ≤ 0.001 after Bonferroni. F, female; M, male; DFSO, days from symptom onset; LOS, length of stay; significant p values in bold. Overcoming COVID-19 Laboratory Tests Limitations June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations FIGURE 1 | Selection of patients for the cohorts from Vall d’Hebron University Hospital (HUVH), Bellvitge University Hospital (HUB), and Germans Trias i Pujol University Hospital (HUGTP). All patients were confirmed by polymerase chain reaction (PCR) to have coronavirus disease (COVID-19). The details of the excluded patients are provided in Table S1. The data from HUVH corresponds to patients who were admitted to the emergency division between 10 March and 29 April 2020; to HUGTP between 17 March and 12 May 2020; and to HUB between 16 March and 23 September 2020. The number of deceased patients corresponds to the 28-day follow-up period. The HUB and HUGTP cohorts include only hospitalised patients but in the HUVH cohort, 46 patients were discharged home or to a medicalized hotel within 24h and monitored by the primary care network. oxygen saturation to fraction of inspired oxygen ratio (SpO2/ curve (AUC) values were calculated, using age as a variable for FiO2) was available for a subset of 827 patients. The data from comparison. In addition, random forest simulation, as machine these patients were either analyzed separately, or when this learning method, and principal component analysis (PCA) were parameter was included in a general analysis, this was performed to further compare the influence of the laboratory and indicated. Data from treatments were available in 981 patients clinical variables on the outcomes in each hospital dataset. with comparable clinical and demographic features as patients in Statistical tests were 2-sided and used a significance threshold of severity categories moderate, severe, and deceased of the HUVH at least p <0.05. R, version 4.1.0 (The R Foundation for Statistical cohort (Table S3). Computing, Vienna, Austria) and Prism 9® (GraphPad, San Diego, Bivariable logistic regression was used to calculate the age- CA, USA) packages were used for all analyses. Statistical analysis adjusted odd ratios (OR) and effect size (Z score) of each variable. was conducted by the Statistics and Bioinformatics Unit (UEB), Vall Multivariable logistic regression was used to calculate the predictive d’Hebron Hospital Research Institute, and by co-authors PC-E and power of different combinations of variables. Correlation among RP-B under the supervision of the UEB. variables was analyzed using the non-parametric Spearman test. For analysis of follow-up data of the HUVH cohort, locally weighted smoothing (LOESS) was applied to clinical laboratory variables to RESULTS visualize the relationship between the mean and CI of each variable, time and 28-day outcome, as described in 4. To assess the Overall Clinical Features of HUVH Cohort performance of each clinical laboratory test, the receiver-operating The HUVH cohort included 1,579 PCR-confirmed COVID-19 characteristic (ROC) curve and the corresponding area under the patients with a median age of 62 years (IQR: 50–75 years), of Frontiers in Immunology | www.frontiersin.org 5 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations whom 255 (16.1%) died during the first 28 days after hospitalization days (IQR: 10–18 days) and was progressively longer with (Figure 2A). Eight hundred eighty (55.7%) patients were male. The increasing disease severity. Age had a strong effect on proportion of males was higher than females (58.0%) among the mortality: for patients in the age groups 50–59, 60–69, 70–79, deceased patients and this proportion of males was significantly 80–89 and >90 years, with 28-day case fatality rates of 1.82%, higher than their proportion in the Barcelona metropolitan area at 10.9%, 26.4%, 49.7% and 60.6% respectively (Table S5). the time (47.5% male, p <0.001, (35). A total of 236 (14.9%) patients The treatment was available in 981cases in the exploratory were admitted to the ICU with a 28-day case fatality rate of 13.9% for cohort and can be consulted in Table S3. Most patients received this subgroup (36). hydroxychloroquine (90.5%) and antivirals (87.7%) following the The presenting symptoms are shown in Table 1. Cardiovascular recommendations of treatments at that stage, but a proportion and/or hypertension, chronic lung disease, diabetes, neurological also received the drugs that were later found to be effective such disease, chronic kidney disease, and active non-terminal malignancy corticosteroids (18.3%) and Tocilizumab (25.1%). were significantly associated to decreased 28-day survival, but not In the dichotomous disease severity grouping, there were 1,040 chronic liver disease nor obesity. Of note, digestive symptoms were and 539 patients in the non-severe and severe categories, more frequent in survivors (31.1% vs. 20.0%, p <0.001). The respectively. Deceased patients accounted for 43.7% of the comorbidity index was significantly higher in deceased patients and severe category. The disease severity was significantly associated patients with severe disease than in survivors and patients with non- with age, DFSO, LOS, disease duration, and comorbidities other severe disease. Each comorbidity added 10% mortality risk up to an than chronic liver disease (p=2.4·10-29, p=2,9·10-14, p=1.5·10-44, index of 4 (Table S4). p=1.4·10-17 respectively). Disease severity was greater in males The distribution of disease severity was as follows, 71 (4.5%), than in females, but after adjusting for multiple comparisons the 969 (61.4%), 284 (17.9%), and 255 (16.1%) in the mild, moderate, statistical significance was moderate compared with the other severe, and deceased categories, respectively. Among the mild significant associations (exact p =0.001, after Bonferroni’s patients, 46 were discharged within 24h. The age of patients correction p=0.03) (Table 1). increased with disease severity category, except between the moderate and severe disease groups (Figure 2B and Table S4). Predictive Power of Current Clinical The LOS increased with disease severity for the three initial Laboratory Tests disease severity categories but was shorter among the deceased The exploratory statistical analysis of the HUVH cohort revealed because 24.9% of obits occurred during the initial 4 days of strong association of 22 of the 30 variables with 28-day outcomes hospitalization (Figure 2C). The median disease duration was 18 (Figure 3 and Tables 2 and S6). However, the analysis of the A C B FIGURE 2 | The structure and outcomes of the Vall d’Hebron University Hospital cohort. (A) Left panel, age distribution of the survivors and that of the deceased is markedly different (median [IQR]: 62 years [50–75 years] vs. 82 years [74–87 years], p<0.001) as are comorbidities (central panel) and SpO2/FiO2 (right panel). (B) Distribution of the patients in the HUVH cohort among the four severity categories, based on the World Health Organization criteria (described in the Material and Methods section). The number of patients in the mild category is small (n=71) as only patients with bilateral pneumonia or severe associated pathologies were hospitalised during this period of the pandemic. (C) Survival after admission: this graph highlights mortality during the initial 10 days, with a high number of patients older than 80 years dying in the initial 3–4 days (see text “Overall Clinical Features of HUVH cohort”). HUVH, Vall d’Hebron University Hospital ****p < 0.0001. Frontiers in Immunology | www.frontiersin.org 6 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations FIGURE 3 | Univariate comparisons of a selection of clinical laboratory-derived variables at admission and 28-days survival for the survival/decease and non-severe, severe outcomes in the Vall d’Hebron University Hospital cohort. n, number of cases plotted; NLR, neutrophil-to-lymphocyte ratio; CRP, C-reactive protein; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GFR, glomerular filtration rate. ****p < 0.0001. When non-significant, the numerical p-values are given. The exact p-values are given in Table 2. The distribution of age and GFR are markedly different in both the severity and survival analysis. classification tables generated by iterative logistic regression analysis and a 20–30% increase in the neutrophil count. At the individual using different sets of variables showed that, despite good ROC curves level, the reduction of lymphocytes was disproportionate to the (see below), their power in predicting poor outcomes, either decease or increase in neutrophils. severe disease, was under the 60% (Table 3 and Supplementary The Acute Phase Reactants had a broad range of variation Tables in excel format “Repeated Multiple Logistic Regression”). Of e.g., >10,000 and 50-fold for IL-6 and CRP, respectively, and in concern, prediction was very dependent on age (Table 3). Laboratory most patients the values were out of the normal range, while the variables by themselves -without SpO2/FiO2- are poor predictors aspartate aminotransferase/alanine aminotransferase (AST/ specially of decease (34.78% of correctly classified patients). Analyses ALT) ratio and kidney function test results were only with a reduced set of eight variables i.e., age, comorbidities, SpO2/ moderately altered and often remained within the FiO2, NLR, CRP, AST/ALT, fibrinogen and GFR, gave similar results normal range. as those using all variables, confirming the redundancy of many Multiple correlation (Figure 4), multivariable logistic variables. Machine learning analysis using random forest confirmed regression analyses (Table 3), age-adjusted logistic regression this low prediction power of laboratory variables on their own. See (Table 4), and examining their respective shifts from the normal below "Selection of a core panel of clinical laboratory tests". range (Table S7), suggested that these variables could be classified into three broad categories, clinic-demographic (CD), including Improving Interpretation of Current age, sex and the comorbidity index; inflammation related Clinical Laboratory Tests biomarkers (IFRB) including blood cell counts, levels of APRs, The white blood cell differential counts showed marked imbalance and coagulation factors; and organ damage-related biomarkers due to an approximately 250% reduction in the lymphocyte count (ODRB), including liver and kidney function tests and SpO2/ Frontiers in Immunology | www.frontiersin.org 7 June 2022 | Volume 13 | Article 902837 Frontiers in Immunology | www.frontiersin.org Sánchez-Montalvá et al. TABLE 2 | Pairwise comparison of biomarkers for decease and severity outcomes, HUVH cohort. Patients, Total Survivors Non-survivors p-value Non-Severe Severe and deceased p-value (n=1,579) (n=1,324) (n=255) (exact) (n = 1040) (n = 539) (exact) Age, years, median and IQR 62 (50–75) 57 (48–70) 82 (74–87) 7.26E-81 58 (47-71) 70 (54-82) 2.49E-29 Comorbidities 1 (0-2) 2 (1-3) 2 (1-3) 2.30E-38 1 (0-2) 2 (1-3) 1.0118E-26 INFLAMMATION RELATED BIOMARKERS (IFRB) Blood (normal range) Hb (12–15 g/dL) 13.5 (12.3–14.5) 13.7 (10.7–11.9) 12.6 (11.4–13.8) 4.95E-15 13.7 (12.5-14.6) 13.1 (12.0-14.3) 5.64E-09 WBC (4–11 109/L) 6.5 (5.0–8.8) 6.5 (5.0–8.2) 7.5 (5.32–10) 1.51E-05 6.36 (4.95-8.10) 7.39 (5.29-10.4) 1.26E-10 Neutrophils, % (40–80) 76.1 (68–83.2) 74.8 (68.3–81.0) 82.9 (75.4–87.9) 1.07E-20 73.3 (65.9-79.8) 82.4 (74.3-87.6) 6.07E-39 Neutrophils (2–7 109/L) 4.8 (3.5–6.7) 4.7 (3.4–7.6) 6.1 (3.9–8.6) 7.18E-10 4.56 (3.33-6.23) 5.92 (4.01-8.68) 1.63E-19 Lymphocytes, % (20–50) 16 (10.5–23) 17 (12–24) 10 (7.2–17.0) 1.07E-23 18.1 (12.8-24.8) 11.6 (7.50-18.2) 8.34E-41 Lymphocytes (1.2–3.5 109/L) 1 (0.7–1.4) 1.1 (0.8–1.4) 0.7 (0.6–1.0) 3.69E-22 1.11 (0.82-1.50) 0.85 (0.60) 1.80E-28 Monocytes, % (2–10) 6.7 (4.8–8.8) 6.8 (5.0–8.8) 5.4 (3.6–7.80) 1.01E-10 7.20 (5.50-9-20) 5.40 (3.70-7.80) 1.92E-24 Monocytes (0.1–1 109/L) 0.43 (0.30–0.59) 0.44 (0.31–0.59) 0.39 (0.28–0.56) 0.013 0.45 (0.33-0.61) 0.39 (0.28-0.58) 1.02E-05 Eosinophils, % (0.0–5.0) 0.0 (0–0.3) 0.00 (0.00–0.10) 0 (0.00–0.00) 4.40E-07 0.1 (0.00-0.40) 0.00 (0.00-0.10) 1.87E-14 Eosinophils (0.0–0.5 109/L) 0.00 (0.00–0.01) 0.0 (0–0.01) 0 (0.00–0.00) 0.006 0.0 (0–0.03) 0 (0.00–0.01) 9.71E-12 Basophils (0.0–0.2 E9/L) 0 (0.01–0.03) 0.0 (0.01–0.03) 0.02 (0.01–0.03) 0.454 0.02 (0.01–0.02) 0.02 (0.01–0.03) 0.1591 Neutrophil–Lymphocyte ratio 4.8 (3.0–7.0) 4.4 (3.2–6.7) 7.7 (4.3–12.2) 1.39E-21 4.07 (2.68-4.21) 7.07 (4.21-11.7) 4.71E-39 Platelets (140–400 109/L) 197 (154–251) 202 (170–286) 174 (133–227) 2.87E-07 292 (159-254) 190 (143-190) 2.63E-04 APR and related parameters CRP (0.03–0.5 mg/dL) 8.9 (3.8–16.6) 8.1 (3.8–15.5) 17.9 (10.2–24.5) 2.05E-19 7.07 (2.57-12.58) 15.8 (8.51-23.44) 5.99E-43 IL-6 (0.0–4.3 pg/mL) 45.1 (23.6–80.0) 41.41 (24.7–85.9) 90.5 (55.3–162.8) 1.88E-24 34.6 (19.0-61.65) 81.2 (50.1-138.0) 1.01E-55 Ferritin (25–400 ng/mL) 539 (282.5–1011.5) 527 (155.5–709.5) 671 (355–1153) 0.04 467 (251.-891.2) 724 (426.-1348.) 1.06E-12 Coagulation 8 Fibrinogen (2.39–6.1 g/L) 5.1 (4.4–6) 5.2 (4.4–5.7) 4.9 (4.2–5.9) 0.04 5.06 (4.42-5.86) 5.31 (4.45-6.15) 0.0129 D-dimer (0–243 ng/mL) 263 (168–463.5) 248.5 (197.8–591.0) 477 (292.5–860.8) 4.65E-16 234 (151-389) 371 (223-692) 3.80E-20 Prothrombin time, INR (0.7–1.3) 1.1 (1.0–1.2) 1.1 (1.0–1.2) 1.1 (1.1–1.3) 9.32E-08 1.09 (1.02-1.17) 1.11 (1.04-1.23) 1.25E-05 ORGAN DAMAGE RELATED BIOMARKERS (ODRB) SpO2/FiO2 448 (354-462) 452 (377-465) 358 (156-443) 5.55E-22 457 (438-467) 369 (230-448) 8.58E-44 Liver function tests AST (12–50 IU/L) 40 (30–60) 39 (26.0–49.5) 44.5 (31–68) 0.01 38 (28-55) 45 (33-68) 6.55E-11 AST/ALT (<1.5) 1.39 (1.06-1.88) 1.3 (1.0-1.8) 1.9 (1.4-2.6) 6.06E-31 1.3 (1-1.71) 1.67 (1.24-2.21) 2.97E-24 Bilirubin, Direct (0.1–0.57 mg/dL) 0.30 (0.24–0.38) 0.30 (0.24–0.37) 0.35 (0.27–0.46) 8.37E-05 0.29 (0.24-0.36) 0.32 (0.26-0.43) 1.34E-05 Bilirubin, Total (0.3–1.2 mg/dL) 0.57 (0.45–0.74) 0.57 (0.44–0.73) 0.63 (0.47–0.85) 0.03 0.56 (0.45-0.73) 0.58 (0.44-0.81) 0.185 Kidney function tests Overcoming COVID-19 Laboratory Tests Limitations Urea (17–42 mg/dL) 35 (25–51) 32 (24.0–55.0) 58 (42–87) 8.77E-47 31 (24-44) 46 (31.8-72.3) 2.23E-31 Creatinine (0,67 - 1.17 mg/dL) 0.80 (0.60–0.97) 0.79 (0.65–0.94) 1.01 (0.78–1.36) 2.20E-20 0.79 (0.64-0.95) 0.92 (0.74-1.29) 2.48E-23 GFR (>75 mL/min/1,73m2) 88.5 (67.8–90.0) 90.0 (76.4–90.0) 56.9 (35.9–79.5) 2.37E-101 90 (78.9-90) 76.3 (50.3-90) 1.42E-37 June 2022 | Volume 13 | Article 902837 AST, Aspartic Amino Transferase; ALT, Alanine Amino Transferase; CRP; C Reactive Protein; GFR, Glomerular Filtration Rate; Hb, Hemoglobin; NLR, Neutrophil Lymphocyte Ratio; SpO2/FiO2, Oxygen saturation to fraction of inspired oxygen ratio; WBC, White Blood cell Count. Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations TABLE 3 | Classification tables from multiple logistic regression including different sets of variables for survival vs decease or severe vs non-severe as outcome. Predicted outcome, 19 clinical and laboratory variables* for survival versus decease Classification table Predicted survival Predicted decease Total % Correctly classified Observed Survival 357 8 365 97.81 Observed Decease 21 25 46 54.35 Total 378 33 411 92.94 Predicted outcome, 19 clinical and laboratory variables* severe versus non-severe Classification table Predicted non-severe Predicted severe Total % Correctly classified Observed non-severe 244 26 270 90.37 Observed severe 64 77 141 54.61 Total 308 103 411 78.10 Predicted outcome, 16 non-clinical variables** for survival versus decease Classification table Predicted survival Predicted decease Total % Correctly classified Observed survival 356 9 365 97.53 Observed decease 30 16 46 34.78 Total 386 25 411 90.51 Predicted outcome, 16 non-clinical variables** for severe versus non-severe Classification table Predicted non-severe Predicted severe Total % Correctly classified Observed non-severe 245 25 270 90.74 Observed severe 59 82 141 58.16 Total 304 107 411 79.56 Predicted outcome, 15 non-clinical variables for survival versus decease (no SpO2/FiO2).*** Classification table Predicted survival Predicted decease Total % Correctly classified Observed survival 897 9 906 99.01 Observed decease 68 20 88 22.73 Total 965 29 994 92.25 Predicted outcome, 15 non-clinical variables for severe versus non-severe (no SpO2/FiO2)*** Classification table Predicted non-severe Predicted severe Total % Correctly classified Observed non-severe 691 49 740 93.38 Observed severe 162 92 254 36.22 Total 853 141 994 78.77 *The 19 variables included were age, gender, comorbidity index, SpO2/FiO2, Hb, Neutrophil %, Lymph %, monocyte %, eosinophils %, NLR, Platelets, CRP, IL-6, D-dım ́ er, ferritin, fibrinogen, prothrombin time INR, AST/ALT ratio and GFR. ** The 16 variables included were the same minus for age, sex, and comorbidities. *** The 15 variables were the same minus for age, sex, comorbidities and, SpO2/FiO2. FiO2. These analyses also revealed that the neutrophil- Predictive Power of Laboratory Variables lymphocyte ratio (NLR) and the AST/ALT ratio captured most During Hospitalization of the predictive value of lymphocyte and neutrophils variations The analysis of the 7,586-follow-up observations showed that and of liver function test variations, respectively, and that SpO2/ association of biomarkers with survival varied during the 28 days FiO2 conveyed much of the predictive power of the ODRBs (see of follow-up. The daily average curves of most IFRB for survivors supplementary text “Sequence of statistical biomarker analysis”). and deceased remained separated during the first few days of Applying this classification to assess clinical parameters and hospitalization with maximum separation around day 5 test performance using ROC curve analysis (Table 5), it (Figure 6). Interpretation of the values in patients with longer emerged that the CD and ODRB performed better at hospital stays was difficult due to the decreasing sample size and predicting survival, while IFRB performed better at predicting complications arising from medical interventions. The survival disease severity (Figure 5A and Table 3). The strong influence curves for ODRBs, GFRs and AST/ALT ratio maintained their of age was more evident in the analysis of survival curves separation for most of the follow-up period. (Figure 5S) using Youden index for the cut-off values (Table 5); the hazard ratio (HR) for patients age under or above 60 years Selection of a Core Panel of Clinical was 32, while the next highest HR was for GFR 9.3 (Figure 5B). Laboratory Tests The predictors of disease severity in descending order were age, At present in HUVH, as in many hospitals, approximately 30 clinical GFR, urea, IL-6, D-dimer, and comorbidities (Figure 5B). The laboratory variables and SpO2/FiO2 are routinely measured in predictive power of both the ODRB and IFRB variables was COVID-19 patients as part of the work-up on admission. maintained in the age-adjusted logistic regression analysis Correlation analysis and multivariable logistic regression showed (Table 4) but reduced when ROC analysis was stratified by that these variables had a high level of multicollinearity which was age intervals (Table S8). The random forest simulation further confirmed by PCA and random forest simulation (Figure 3S and confirmed that age was the single best predictor of outcome, Table S9). Using iterative logistic regression analysis with different and that the combination with all laboratory variables was only variable combinations, a reduced set of eight variables: age, partially additive (Table S9). comorbidity index, SpO2/FiO2, hemoglobin, NLR, CRP, AST/ALT Frontiers in Immunology | www.frontiersin.org 9 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations FIGURE 4 | Overall correlograms of selected data on demographics and clinical laboratory variables that were organised in categories. [1] The blue rectangle highlights the negative correlation between neutrophils and the cluster of lymphocytes, monocytes, and eosinophils. [2] The green rectangle highlights the blood cell variables that correlate positively with the acute-phase reactants (APRs) and coagulation factors. [3] The orange rectangle highlights the negative correlation between lymphocytes, monocytes, and eosinophils with APRs and coagulation factors. [4] The magenta rectangle highlights the correlations between age, disease severity, comorbidities with liver and kidney function and SpO2/FiO2. The cells following the diagonal highlights the seven families of variables: clinical, blood cells, APR-coagulation, liver, kidney and lung tests, which show the expected strong correlations among themselves. The thick lines between rows separate the main categories. APR, acute-phase reactants; SpO2/FiO2, oxygen saturation/fraction of inspired oxygen; NLR, neutrophil-to-lymphocyte ratio; CRP, C-reactive protein; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GFR, estimated glomerular filtration rate. The r- and p-values of the data represented in the heatmap are in xlsx format files in the supplementary material “Correlation of variables, r-values” and “Correlation of variables, p-values”. TABLE 4 | Bivariate age adjusted logistic regression for 28-days survival/decease and non-severe/severe outcomes. 28d decease outcome age adjusted 28 maximal severity age adjusted Variables OR CI Z value Pr(>|z|) Z value Rank OR CI Z value Pr(>|z|) Z value Rank IL-6 (pg/mL) 2.59 2.06-3.31 7.84 <0.001 1 12.24 8.24-18.56 12.10 <0.001 1 CRP (mg/dL) 3.20 2.38-4.41 7.40 <0.001 2 9.55 6.47-14.40 11.06 <0.001 2 SpO2/FiO2 ratio 0.99 0.99-1.00 7.05 <0.001 3 0.99 0.98-0.99 10.17 0.001 4 Neutrophils (%) 1.06 1.04-1.08 6.38 <0.001 4 1.19 1.14-1.23 10.76 <0.001 3 NLR x 100 1.08 1.06-1.11 6.33 <0.001 5 1.13 1.10–1.16 9.87 0.001 5 Monocytes (%) 0.86 0.81-0.90 6.20 <0.001 6 0.86 0.83-0.89 7.89 <0.001 8 Neutrophils (109/L) 1.14 1.09-1.19 5.74 <0.001 7 1.19 1.14-1.23 8.86 <0.001 7 GFR (mL/1.73 m2) 0.98 0.97-0.99 5.44 <0.001 8 0.98 0.97-0.98 6.94 <0.001 11 Lymphocytes (%) 0.95 0.92-0.97 5.04 <0.001 9 1.03 1.03-1.04 9.79 <0.001 6 WBC (109/L) 1.11 1.06-1.16 4.82 <0.001 10 1.14 1.01-1.18 7.29 <0.001 9 AST/ALT ratio 1.49 1.26-176 4.74 <0.001 11 1.53 1.32-1.80 5.47 <0.002 13 D dimer (ng/mL) 1.51 1.26-1.83 4.35 <0.001 12 2.60 1.92-3.56 6.05 <0.001 12 Creatinine (mg/dL) 1.32 1.14-1.53 3.68 <0.001 13 1.39 1.20-1.65 4.02 <0.001 16 Lymphocytes (109/L) 0.51 0.35-0.73 3.61 <0.001 14 0.41 0.32-0.52 6.95 <0.001 10 Ferritin (ng/mL) 1.43 1.12-1.85 2.85 0.004 15 1.00 1.000-1.001 5.42 <0.001 14 Eosinophils (%) 0.63 0.45-0.85 2.79 0.005 16 0.62 0.49-0.76 4.35 <0.001 15 Hb (g/dL) 0.89 0.81-0.98 2.41 0.016 17 1.12 1.04-1.19 3.23 0.001 17 Eosinophils (109/L) 0.02 0.00-0.62 2.07 0.039 18 0.04 0.003-0.360 2.67 0.007 18 Monocytes (109/L) 0.72 0.45-1.07 NA 0.127 NA 0.74 0.51-1.04 1.66 0.097 20 Platelets (109/L) 1.00 1.00-1.00 NA 0.203 NA 1.00 0.997-1.000 1.80 0.070 19 AST, Aspartic Amino Transferase; ALT, Alanine Amino Transferase; CRP; C Reactive Protein; GFR, Glomerular Filtration Rate; Hb, Hemoglobin; NLR, Neutrophil Lymphocyte Ratio; NA, not applicable (Z values not directly comparable), WBC, White Blood cell Count. Frontiers in Immunology | www.frontiersin.org 10 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations TABLE 5 | ROC curve analysis as for clinical laboratory test performance maintained in the three cohorts in the random forest comparison for survival/decease and non-severe/severe outcomes. simulations (Table S10). Decease Outcome Prediction Performance of Variable AUC CI p YOUDEN forHazard Ratios cut off Immunological Tests Despite the limited size of the group analyzed in the cytokines Age 0.87 0.85-0.89 0.001 >60.50 pilot study (n=74, Table S10), CXCL10 had the best ROC curve Comorbidities 0.75 0.72 -0.78 5.95E-25 >1.50 (AUC=0.83, p=2.3·10-6) of all variables including age, IFRB and GFR 0.80 0.77-0.83 2.45E-53 < 87.30 ODRB, and performed better than any of the other variables IL-6 0.77 0.73-0.81 5.24E-26 > 50.50 AST/ALT 0.73 0.69-0.77 6.08E-31 > 1.64 considered. IL1RA and CCL2 with AUCs of 0.77, p=0.002, and SpO2/FiO2 0.73 0.70-0.78 1.79E-43 < 439.50 0.69, p=0.006 respectively also showed promise as biomarkers CRP 0.73 0.70-0.78 1.44E-20 >11.13 (Table 7 and Figures 5C, 7). IL-6, also an immunological test, D-dimer 0.73 0.69-0.77 7.42E-20 > 353.00 measured both as part of the routine clinical tests (n=1269) and in Creatinine 0.71 0.67-0.74 4.35E-25 > 1.12 Lymphocyte % 0.69 0.65-0.73 1.18E-21 < 12.05 this smaller group (n=74), gave the similar AUCs of 0.77, p = NLR 0.69 0.65-0.73 1.33E-21 >6.85 5 . 2 · 1 0 - 2 6 a n d 0. 7 6 , p = 1 . 9 · 1 0 - 4 in the tw o se t o f Neutrophil % 0.69 0.65-0.73 1.61E-43 > 82.15 measurements respectively. Hb 0.65 0.62-0.69 5.67E-09 < 13.45 The immune phenotype was analyzed in 41 patients Monocytes 0.63 0.59-0.67 5.05E-11 < 6.65 (Table S11). There was a steep reduction in the size of all T-cell Prothrombin time (INR) 0.61 0.56-0.65 1.37E-07 > 1.21 Platelets 0.60 0.56-0.64 9.94E-07 < 162.50 subsets, which was more marked for CD8 effector and memory Eosinophils % 0.59 0.55-0.62 1.32E-5 < 0.15 cells, and an increase in activation markers that was like the Ferritin 0.57 0.51-0.62 0.019 > 668.00 pattern observed in other time-series analyses (25, 37), revealing a Severity outcome deep disturbance of the immune response in severely ill patients IL6 0.78 0.75-0.80 8.65E-56 NC (see Expanded phenotype analysis in supplementary). Naïve T SpO2/FiO2 0.77 0.74-0.81 1.79E-43 NC CRP 0.75 0.71-0.77 5.77E-43 NC cells seemed associated to higher mortality (Figures 8 and 11S). NLR 0.71 0.68-0.73 1.74E-41 NC GFR 0.69 0.65-0.71 1.69E-33 NC Age 0.67 0.64-0.70 2.55E-29 NC D-dimer 0.67 0.63-0.69 2.63E-20 NC DISCUSSION Monocytes % 0.66 0.62-0.68 1.92E-24 NC AST/ALT 0.66 0.62-0.68 2.97E-24 NC The analyses of our COVID-19 patients series expose the Comorbidity 0.65 0.62-0.68 5.95E-25 NC limitations of the clinical laboratory tests currently applied to Ferritin 0.64 0.60-0.67 1.08E-12 NC assess the prognosis of patients with COVID-19 but also show Eosinophils % 0.61 0.57-0.63 3.14E-12 NC that they can be better interpreted if grouped into categories that Hb 0.59 0.55-0.62 5.67E-09 NC reflect the two main biological processes that are measured, i.e., Monocytes n 0.57 0.53-0.59 1.02E-05 NC inflammation and organ damage. Since their prognostic AST, Aspartic Amino Transferase; ALT, Alanine Amino Transferase; CRP; C Reactive limitations are due to redundancy, clinical laboratory panels Protein; GFR, Glomerular Filtration Rate; Hb, Hemoglobin; NLR, Neutrophil Lymphocyte Ratio; NC, not calculated as HR applies to mortality. for COVID-19 could be simplified, but additional biomarkers with real independent additional predictive power are urgently needed. This study also exposes the lack of tests for early ratio, and GFR, were found to capture the best prediction power (see prediction of the immune response to SARS-CoV-2. Such tests supplementary material, “Sequence of statistical biomarkers analyses: could provide critical non-redundant information required for complexity reduction” and tables “Repeated multivariable logistic clinical management during the early clinical course. The results regression deceased” and “Repeated multivariable logistic binary of the reported pilot study using a selection of robust severity” among the supplementary excel tables). As age and immunological tests in use in other areas of clinical comorbidities are non-time-varying, only six of the eight variables immunology (primary immunodeficiencies, transplantation, are required for clinical management. These results apply to the etc.), indicates that such tests exist, and their value should be cohort but do not imply that IL-6, ferritin, lactate dehydrogenase, systematically investigated. triglycerides, procalcitonin, D-dimer, and coagulation tests do not Beyond this central message, the findings can be summarized provide valuable information in clinical practice depending on as follows: 1) The three cohorts confirmed the strong association the context. of: SpO2/FiO2, neutrophilia, lymphopenia, acute phase reactants, coagulation factors, kidney function and the AST/ Comparison With the Two ALT ratio with disease outcome. 2) There was a high level of Validation Cohorts collinearity (redundancy) among the different laboratory The comparison among the three cohorts confirmed the variables, which explains their disappointing prediction power prognostic power of the main IFRB and ODRB variables, even when combined. 3) After reducing overall redundancy, the best though the statistical ranking of their positions showed small combination of variables was age, comorbidity index, SpO2/ variations between cohorts (Table 6, and Figures 5C, D). In FiO2, NLR, CRP, AST/ALT ratio, fibrinogen, and GFR. 4) The addition, biomarker performance as predictors of outcome was classification of biomarkers into inflammation and organ- Frontiers in Immunology | www.frontiersin.org 11 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations A B C D FIGURE 5 | Relative weight of different variables in prediction and performance. (A) Heatmap summarizing the values under the curve (AUC) generated by applying Receiver Operating Characteristic (ROC) curve routinely used to assed the performance of clinical laboratory tests, to each the main variables; the performance was assessed by survival/decease and for non-severity/severity outcomes in the HUVH cohort. (B) Hazard ratios corresponding to survival curves for Youden index cut- off. Red, significant values for the HUVH cohort. (C) Heatmap of the area under the curve (AUC) of ROC curves corresponding to the variables available for the three cohorts (HUB, HGTP and HUVH). The values have grouped by unbiased hierarchical clustering. IL-6, CRP, urea, lymphocytes, and neutrophils occupy central positions. At the bottom, the AUC for some variables available only from the HUVH cohort and the AUC values for the three cytokines that perform better in the group of 74 patients who were analyzed in the HUVH cohort. The numbers within the cells are the AUC values. (D). Multivariable logistic regression analysis, age- adjusted, for the main variables of the three cohorts (HUB, HGTP and HUVH). The three forest plots show how, after correcting for age, the APRs rank above the glomerular filtration rate (GFR) in the HUVH cohort and have a similar ranking in the three cohorts. The horizontal whiskers represent the 95% confidence intervals; values in red indicate positive predictive and blue negative predictive value for the 28-day survival/deceased outcome. The dotted lines indicate variables only available for HUVH. The OR rankings -differently from the ROC AUCs- are useful only to compare the different hospital cohorts, but not to compare the weight of the variables within each cohort, as ORs are derived from variables that use different units and ranges of variation. APR, acute-phase reactants. APR, acute-phase reactants; SpO2/FiO2, oxygen saturation/fraction of inspired oxygen; NLR, neutrophil-to-lymphocyte ratio; CRP, C-reactive protein; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GFR, glomerular filtration rate; IL, interleukin; LDH, lactate dehydrogenase; Hb, hemoglobin; ROC, receiver-operating characteristic curve; AUC, area under the curve; HUB, Hospital Universitari Bellvitge, HUGTP, Hospital Universitari Germans Trias Pujol Hospital; HUVH, Universitari Vall Hebron. damage related helps with their interpretation and revealed that although without significant association. Inflammation and organ damage are better predictors of survival than severity while organ damage related variables were significantly higher in the inflammatory are better predictors of severity than of decease. 5) IFN-positive patients (data not shown). Differences in design For the clinician at the bedside, some laboratory organ damage preclude to incorporate these data into the current project changes such as GFR reduction, may be less conspicuous than The study here reported is similar to a number of studies acute phase reactants increase but they may deserve attention carried out during the first year of the pandemic that already when they deteriorate, even when they are still close to the detected age, sex, comorbidities and the laboratory parameters normal range. used to assess severity in sepsis, to be associated with poor It is relevant that as part of another ongoing study lead by our prognosis in COVID-19 patients (14, 16, 17, 20, 22). Differently institution (manuscript in preparation) antibodies to IFN-alpha2 from many of the studies that analysed cohorts over 1000 and IFN-omega were measured in 917 patients of the HUVH patients, in our studies the main database was generated by the cohort; 50 (5.6%) were found positive for one of the IFNs; the physicians attending the patients and curated by medically demographic features of these positive patients were concordant qualified staff this providing a reliable medium granularity data with previous reports (11, 38) in which age, mortality and male/ set relatively unique. The statistical analysis included a variety of female ratio were higher in IFN-antibody positive patients, techniques that eventually revealed the weaknesses of the clinical Frontiers in Immunology | www.frontiersin.org 12 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations FIGURE 6 | Vall d’Hebron University Hospital cohort, variations in the average clinical laboratory variables during the 28-day follow-up period. The blue and red lines represent the mean ± CI values of the parameter for each day of follow-up for the survivors and deceased respectively. The blue bars indicate the number of values available for each day. Notice that samples were not obtained every day and therefore the averages result from plotting together all available values for each day of follow-up, as in 4. Data correspond to 7,586 samples, 6,589 from survivors and 997 from deceased out of 1,079 patients of the HUVH cohort. NLR, neutrophil-to- lymphocyte ratio; CRP, C-reactive protein; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GFR, glomerular filtration rate; IFRB, inflammation-related biomarkers, ODBRs, organ damage-related biomarkers. APRs, acute-phase reactants. laboratory tests. The explanation became only obvious by the be a valuable tool for patient management. However, in our study, careful analysis of the overall correlogram, the random forest and the two patients with the combined higher cytokines values the iterative differential logistic regression. Among organ damage survived, probably because they were relatively young (see associated biomarkers, the best was SpO2/FiO2, but this is a legend to Figure 7). Of note, the best candidate biomarker that bedside test performed by health personnel that reflects we detected, CXCL10 was also reported as such in 39 study, impairment in gases exchange due to important lung or There are several limitations to this study, starting with its circulatory system damage. However, it is a late predictor of retrospective nature, the relative smaller size of the mild group the disease severity and only appear when the damage is and the absence of non-hospitalized patients. In common to other well stablished. retrospective studies, the a priori power calculation of the sample We followed 4 study in the analysis of the variation of size was not carried out and the strategy was just to collect the laboratory parameters along the period of hospitalisation; in maximal number of informative cases. The large cohorts obtained their study sTNFRSF1A, sST2 (IL-33 soluble receptor), IL-10 proved sufficient to detect the strong association of most variables and IL-15 maintained separated trajectories for survivors vs considered but we cannot exclude that minor associations have deceased over the hospitalization period. In our case, been missed. The group of patients with mild COVID-19 was 71, lymphocyte and neutrophil percentage, the corresponding NLR, small compared to the other groups in our study, but comparable to and CRP also maintained a different trajectory over almost 30 other retrospective studies e.g., 4. Regarding the lack of a non- days. Of note, IL-6 showed a marked peak at day 3-5 in deceased hospitalised group, in fact the overall median hospital stay of the patient that is reminiscent. but sharper, than that of sST2 curve in mild patients’ group was 2 days, including 46 discharged within 24h. 4. Probably, close monitoring of these and other immunological The study here reported is being followed by a prospective study, parameters during these critical 1-5 days of hospitalisation would recruitment now closed, in which we put special care in recruiting Frontiers in Immunology | www.frontiersin.org 13 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations TABLE 6 | Pairwise comparison of demographic and clinical laboratory biomarkers in the exploratory (HUVH) and the two validation cohorts (HUGTP and HUB). Patients Cohort HUVH Cohort HUB Cohort HUGTP HUVH vs. HUB HUVH vs. HUGTP HUB vs. HUGTP (n = 1579) (n = 598) (n = 423) (p-value) (p-value) (p-value) Demographics Age Median (IQR) 62 (50–75) 65 (53–74) 62 (52–71) 0.07 0.94 0.03 Females n (%) 699 (44.2 %) 208 (34.8 %) 157 (37.1 %) <0.0001 0.46 0.009 Males n (%) 880 (55.7 %) 389 (65.2 %) 266 (62.90 %) Mortality Global n (%) 255/1579 (16.14 %) 154/598 (25.7%) 52/423 (12.3 %) <0.0001 <0.0001 0.57 Females n (%) 107/699 (15.3 %) 52/208 (25.0 %) 14/157 (8.9%) 0.002 0.0001 0.42 Males n (%) 148/880 (16.8%%) 101/389 (25.9 %) 38/266 (14.3) 0.0002 0.3443 0.0003 DFSO Median (IQR) 7 (4–10) 11 (8–15) 7 (4–10) <0.0001 >0.99 <0.0001 Laboratory variables Median (IQR) Median (IQR) Median (IQR) Blood Hb (g/dL) 13.5 (12.3–14.5) 12.8 (11.5–13.9) 13.6 (12.5–14.7) <0.0001 0.14 <0.0001 WBC (109/L) 6.6 (5.1–8.8) 7.2 (5.3–11.1) 6.9 (5.2–9.3) <0.0001 0.07 0.07 Neutrophils, % 76.1 (68–83.2) 80.4 (68.8–87.9) 79.5 (71.2–85.4) <0.0001 0.0001 0.33 Neutrophils (109/L) 4.9 (3.5– 6.9) 5.7 (3.6–9.1) 5.2 (3.8–7.8) <0.0001 0.005 0.17 Lymphocytes (%) 16 (10.5–23) 12.7 (7.3–20.7) 12.5 (8.1–19.1) <0.0001 <0.0001 0.83 Lymphocytes (109/L) 1 (0.7–1.4) 0.9 (0.6–1.3) 0.8 (0.6–1.2) <0.0001 <0.0001 0.09 Monocytes (%) 6.7 (4.8–8.8) 5.8 (3.6–8.9) 6.8 (4.9–9.1) <0.0001 0.29 <0.0001 Monocytes (109/L) 0.4 (0.31–0.6) 0.4 (0.3–0.6) 0.5 (0.3–0.7) 0.34 0.009 0.003 Eosinophils (%) 0 (0–0.3) 0.1 (0–0.9) 0.1 (0–0.2) <0.0001 0.47 0.006 Eosinophils (109/L) 0 (0–0.02) 0.01 (0–0.06) 0 (0–0) <0.0001 <0.0001 <0.0001 Basophils (109/L) 0 (0.01–0.03) 0.02 (0.01–0.03) 0 (0–0) 0.06 <0.0001 <0.0001 NLR 4.8 (3.0–7.0) 6.3 (3.4–11.8) 6.3 (3.7–10.8) <0.0001 <0.0001 0.93 Platelets (109/L) 197 (154–251) 234.5 (174.3–327.8) 203 (160–254) <0.0001 0.35 <0.0001 Clinical Chemistry CRP (mg/dL) 8.9 (3.8–16.6) 8.1 (3.5–16.7) 9.5 (4.8–16.7) 0.31 0.64 0.69 IL-6 (pg/mL) 45.1 (23.6–80.0) 53.5 (20.4–131.1) 47.1 (24.6–92.6) 0.01 0.21 0.21 Ferritin (ng/mL) 539 (282.5–1011.5) 1210.4 (617.0–1903.3) 614 (316.5–1080) <0.0001 0.16 <0.0001 Triglycerides (mg/dL) 121 (92–161) 173.9 (123.5–251.3) NA (NA) <0.0001 NA NA LDH (UI/L) 336 (271–421) 330.1 (261.3–448.1) 313 (224–441.5) 0.50 0.02 0.04 Urea (mg/dL) 35 (25–51) 39 (26.8–58.9) 34.9 (25–48.5) 0.009 0.17 0.009 AST (IU/L) 40 (30–60) 35.4 (23.9–58) NA (NA) <0.0001 NA NA ALT (IU/L) 28 (19–50) 33.1 (19.9–60.5) 30 (21–53) <0.0001 0.008 0.11 Direct bilirubin (mg/dL) 0.3 (0.2–0.4) NA (NA) NA (NA) <0.0001 NA NA Total bilirubin (mg/dL) 0.6 (0.5–0.7) 0.5 (0.4–0.7) 0.6 (0.5–0.8) <0.001 0.03 <0.0001 D dimer (ng/mL) 263 (168–463.5) 590 (355–2108) 670 (415–1188.5) <0.0001 <0.0001 0.69 Fibrinogen (g/L) 5.1 (4.4–6) 5.8 (5.1–6.6) 7.6 (6.5–8.8) <0.0001 <0.0001 <0.0001 Prothrombin time INR 1.1 (1.0–1.2) 1.1 (1.1–1.2) 1.22 (1.1–1.4) <0.0001 <0.0001 <0.0001 AST, Aspartic Amino Transferase; ALT, Alanine Amino Transferase; CRP; C Reactive Protein; GFR, Glomerular Filtration Rate; Hb, Hemoglobin; NLR, Neutrophil Lymphocyte Ratio; WBC, White Blood cell Count, NA, Not Available. asymptomatic and symptomatic non-hospitalized cases. The initial (Table S3); it shows that, as in many centres in Europe and the US, analysis revealed that the inflammatory, cytokine and serological hydroxychloroquine, lopinavir/ritonavir and azithromycin were profiles show continuously progressive alterations throughout these administered to most patients, while corticosteroids and categories (manuscript in preparation); this supports the notion tocilizumab, only later confirmed to be effective, were administered that the group of mild patients here reported is similar to non- to 18 to 25 percent of patients respectively with a rapid clinical hospitalized patients in other series e.g., Jehi et al (16). deterioration; soon later these were the predominant treatments in Another limitation is the absence of information regarding two patients requiring oxygen supplementation. We did not strictly key factors: the SARS-CoV-2 viral load and markers of the adaptive follow the transparent reporting of a multivariable prediction immune response. The SARS-CoV-2 detection techniques used model for individual prognosis or diagnosis recommendations, as during this period were diverse and not quantitative. This, added to generating a prediction mode was not an objective, but most the variability of swab sampling efficiency, made non-viable to requirements were fulfilled (41). include this important parameter (6) in the present study. The analyses presented here are intended for improved Serological markers need 7–21 days to become detectable and are interpretation of available tests, but no algorithm is proposed. not too helpful as a tool to predict the prognosis of the patients during Most algorithms with good predictive power include parameters, the initial medical assessment (39, 40). Finally, the effect of treatment such as oxygen requirements and imaging data, that reflect organ on the outcomes was not analysed because therapeutic protocols and damage in patients that are already on the path to severe disease; in inclusion criteria were not uniform during the first wave. A summary fact they predict what was already starting to happen (14–22, 41). By of pharmacological treatments is however provided for reference contrast, the ideal test/algorithm should be able to identify patients Frontiers in Immunology | www.frontiersin.org 14 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations TABLE 7 | Performance of expanded immunological parameters in the special immunological studies group as assessed by ROC curve analysis and compared with other variables in the same group. Survival/decease as outcome Variables AUC CI p-value AUC t1 CI p-value AUC t2 CI p-value Age 0.55 0.42-0.69 0.438 Clin Lab Biomarkers Cytokines GFR 0.55 0.46-0.74 0.176 CXCL10 0.83 0.74-0.92 2.34E-06 0.77 0.65-0.88 1.24E-04 IL-6 0.60 0.66-0.88 0.000 IL-1RA 0.77 0.60-0.84 0.002 0.74 0.61-0.85 7.71E-04 SpO2/FiO2 ratio 0.77 0.70-0.91 <0.001 IL-6 0.76 0.65-0.87 1.903E-04 0.83 0.73-0.93 2.34E-06 CRP 0.80 0.61-0.86 0.002 CCL2 0.69 0.56-0.82 0.006 0.81 0.69-0.91 1.60E-05 D-dimer 0.73 0.43-0.71 0.353 IL-10 0.67 0.54-0.79 0.019 0.72 0.60-0.84 0.0014 Creatinine 0.57 0.49-0.78 0.069 IL-15 0.67 0.54-0.80 0.012 0.74 0.63-0.85 5.00E-04 Lymphocytes, % 0.63 0.62-0.86 0.001 IL-7 0.65 0.52-0.79 0.027 0.73 0.62-0.85 8.036E-04 NLR ×100 0.74 0.63-0.87 0.001 TNF-a 0.65 0.51-0.79 0.033 0.73 0.55-0.81 0.008 Neutrophils, % 0.75 0.66-0.89 0.000 Hb 0.52 0.37-0.68 0.775 Monocytes, % 1.00 1.00-1.00 <0.001 Platelets 0.52 0.38-0.66 0.789 Flow cytometry Eosinophils, % 0.58 0.45-0.72 0.263 CD3+CD62L+ 0.73 0.54-0.92 0.032 NA NA NA Naive T cells (%) Ferritin 0.61 0.46-0.76 0.147 TH17 (n) 0.67 0.45-0.88 0.131 NA NA NA Non-severe vs severe as outcome Variables AUC CI p-value AUC t1 CI p-value AUC t2 CI p-value Age 0.55 0.42 to 0.69 0.438 Clin Lab Biomarkers Cytokines GFR 0.60 0.45 to 0.74 0.180 CXCL10 0.83 0.74-0.92 2.340E-06 0.77 0.65-0.89 1.24E-04 IL-6 0.77 0.65 to 0.88 2.72E-04 IL-6 0.77 0.66-0.88 1.903E-04 0.83 0.73-0.94 2.34E-06 SpO2/FiO2 ratio 0.80 0.70 to 0.91 3.08E-05 IL1-RA 0.73 0.60-0.84 7.714E-04 0.74 0.62-0.86 7.71E-04 CRP 0.73 0.61 to 0.86 0.002 IL-15 0.72 0.55-0.81 0.012 0.75 0.63-0.86 4.98E-04 D-dimer 0.57 0.43 to 0.71 0.353 CCL-2 0.69 0.57-0.82 0.065 0.81 0.69-0.92 1.60E-05 Creatinine 0.63 0.49 to 0.78 0.069 IL-10 0.67 0.54-0.79 0.019 0.73 0.61-0.85 0.001 Lymphocytes, % 0.74 0.62 to 0.86 0.001 TNF-alpha 0.65 0.51-0.79 0.033 0.69 0.56-0.82 0.008 NLR ×100 0.75 0.63 to 0.87 6.08E-04 IL-7 0.64 0.50-0.77 0.051 0.74 0.62-0.85 8.04E-04 Neutrophils, % 0.77 0.66 to 0.89 1.77E-04 IL-2 0.6 0.46-0.73 0.176 0.78 0.66-0.90 7.23E-05 Hb 0.52 0.37 to 0.68 0.775 IL-17 0.52 0.38-0.66 0.777 0.70 0.57-0.83 0.005 Monocytes, % 0.72 0.60 to 0.84 0.003 Platelets 0.52 0.38 to 0.66 0.789 Flow cytometry Eosinophils, % 0.58 0.45 to 0.72 0.263 CD3+CD62L+ 0.61 0.40 to 0.83 0.3080 Naive T (%) Ferritin 0.61 0.46 to 0.76 0.147 TH17 (n) 0.57 0.34 to 0.79 0.5490 Cytokine group of patients, n=74, Flowcytometry phenotype group of patients n= 41, for details see Table S10. T1, time 1, initial data, T2 time 2, 2-3 days after onset. CRP; C Reactive Protein; NLR, Neutrophil/Lymphocyte Ratio; GFR, Glomerular Filtration Rate; Hb, Hemoglobin; NA, Not Available. P valued in bold, significant. at risk before organ damage occurs. Our results suggest that this is better biomarkers and the corresponding tests. This would require difficult with current tests because inflammation has limited overcoming some technical difficulties but also the bureaucratic discriminatory power and by the time organ damage biomarkers obstacles in transferring tests from the research to the clinical are elevated, the progress towards severity of the process is already diagnostic immunology laboratories. Reliable early biomarkers set in motion and beyond the ideal window for an would reduce the rate of hospitalisation and guide new treatment immunomodulatory intervention. If, as postulated, the main prescription to benefit patients at high-risk; generation of such determinant of COVID-19 severity is a dysregulated innate biomarkers is urgent and should be feasible. immune response leading to an delayed adaptive immune response, immunological biomarkers of this failure should be investigated in more detail during initial infection period (42, 43). HOSPITAL VALL D’HEBRON COVID-19 The severity of COVID19 in patients with IFN pathway genetic IMMUNE PROFILE GROUP defects (10) or autoantibodies to type I IFNs (11) supports this notion and suggests that anti-IFN antibodies should be included in Artur Llobell Uriel MD, Romina Dieli MD, PhD and Roger future protocols. Alternatively, being the generation of specific Colobran PhD, Immunology Department; Gemma Codina MD, cytotoxic T lymphocytes the main defense mechanism against an PhD and Tomas Pumarola MD, PhD, Microbiology Department; acute infection to a novel virus, monitoring these cells or a surrogate Roser Ferrer PhD and Vicente Cortina BSc, Clinical Laboratories marker of them could help to predict patient outcome (4, 23, 25, 37, Department; Magda Campins MD, PhD, Epidemiology and 44–46). These are two obvious approaches, among many, to identify Public Health Department; Isabel Ruiz MD, Nuria Fernaı́ndez Frontiers in Immunology | www.frontiersin.org 15 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations A B FIGURE 7 | Levels of cytokines and related factors in the Vall d’Hebron University Hospital cytokine studies sub-cohort. The levels of cytokines were measured in the ELLA® platform cytokines on days 0 and +2, and the changes in the levels are shown as before/after graphs. (A) cytokines mediating innate immunity and (B) Granzyme N, IFN-alpha plus cytokines mediating mostly adaptive immunity. Outliers, defined as values above mean + 2SD were identified for seven values. The three single confirmed outlier values for IL6, CXCL10 and CCL2 correspond to an early sample (day 3) of the same patient, a 44-years-old male born, in South America, without comorbidities, with severe COVID; despite the cytokine storm this patient survived and was discharged after over six weeks in hospital, four of them at the ICU with mechanical ventilation. He received two doses of TCZ as part of the treatment. Another three confirmed outlier values for IL-15, IL-12p70 and GM- CSF corresponded to early samples from a single patient, a 61-year-old female with severe pneumonia, chronic lung disease, hypertension, and obesity as risk factors; she survived and was discharged after 30 days in hospital most of them in the ICU with mechanical ventilation. A third patient, with had a single outlier value for IFN-alpha corresponded to a 61-year-old female, with diabetes and hypertension; she suffered moderate COVID, remained at the regular hospital ward and was discharged after two weeks; patients was doing well at censoring time *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. Frontiers in Immunology | www.frontiersin.org 16 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations A B C D E F G H I FIGURE 8 | Representative flow cytometry plots from the Vall d’Hebron University Hospital sub-cohort. (A) CD4 and (B) CD8 T lymphocyte subpopulations distributed by phenotypes based on CD45RA and CCR7. (C) CD4 T lymphocyte Th-polarisation by CXCR3 and CCR6 expression. (D) Monocyte subpopulations (classical, intermediate monocytes [IM] and non-classical monocytes) in a comparison of patients belonging to the deceased, severe, and moderate patient categories. (E) Distribution of CD4 naïve and memory subsets among non-severe and severe patients. (F) Distribution of CD8 naïve and memory subsets among non-severe and severe patients; (G) Distribution of CD4 Th polarized subsets among non-severe and severe patients; (H) Distribution of monocytes subsets among non-severe and severe patients; (I) Mean Fluorescent Intensity (MFI) of CD14 and CD16 in the different monocyte subsets among non-severe and severe patients. Non severe patients n=32 and severe patients n=9, for all plots; ****p < 0.0001 by non-parametric FDR corrected Kruskal-Wallis test. MD, Esteban Ribera MD and Joan Roig MD, Infectious Diseases the datasets should be directed to RP-B, Ricardo.pujol@uab.cat; Department; Ricardo Ferrer MD and Adolfo Ruiz-Sanmartı́n MH-G, manhernandez@vhebron.net. MD, Intensive Care Medicine Department; Albert Selva MD, PhD and Moises Labrador MD, PhD, Internal Medicine Department; Marı́a José Soler Romeo MD PhD, Nephrology ETHICS STATEMENT Department; Jaume Ferrer MD, PhD, Eva Polverino MD, PhD This project was approved by the institutional ethics board of the and Antonio Alvarez MD, PhD, Pneumology Department; Marı́a three institutions (HUVH, HUGTP, and HUB) which waived the Queralt Gorgas PhD and Marta Miarons PhD, Clinical requirement for individual informed consent (protocol R(AG)242/ Pharmacy Department; Pere Soler-Palacin, MD, PhD and 2020). In the HUVH cohort, residual sera samples were transferred Andrea Martin, MD, Pediatrics Department; Anna Suy MD, to the Vall d’Hebron University Hospital Biobank (PT17/0015/ Obstetrics and Gynecology Division; Maria Jose Buzó n PhD and 0047) part of the Carlos III Institute of Health network of biobanks Meritxell Genescà PhD, Infectious Disease Research Group; (Number C.0006012). Written informed consent for participation Santiago Perez-Hoyos and Miriam Mota-Foix, Statistics and was not required for this study in accordance with the national Bioinformatics Unit, Vall Hebron Research Institute (VHIR). legislation and the institutional requirements. AUTHOR CONTRIBUTIONS DATA AVAILABILITY STATEMENT AS-M, MH-G, MM-G, and RP-B: Conceived, designed the work and Deidentified data tables will shared on request after approval of a reviewed the final manuscript. DÁ -S carried out laboratory proposal, with a signed data access agreement. Requests to access experiments, data analysis, figure generation, data base Frontiers in Immunology | www.frontiersin.org 17 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations management and reviewed the Ms. RP-B generated the relational ACKNOWLEDGMENTS data base, performed the exploratory data analysis and wrote the first draft of manuscript. JP-P and IA-M collected the samples and carried The authors thank all the patients and health staff of the out the flowcytometric blood phenotyping; PC-E performed part of Hospitals Vall d’Hebron, Bellvitge, and Germans Trias i Pujol the bioinformatic and statistical analysis; CZ-E, AT-S, and EM-C and of the associated hospitals mentioned in the text who provided the HGTP patients data and reviewed the Ms. EP-C and JB- endured and did their best to overcome the first wave of M provided the HUB patients data and reviewed the Ms. MR-B and COVID-19 in Barcelona, an experience that none of us would FM-V provided data of internal medicine patients HUVH and ever forget. The authors are grateful to Dr Isabel Novoa Garcia reviewed the Ms; AA provided the SARS-CoV-2 data and reviewed and Ms. Sheyla Pascual Martin for their invaluable help in the Ms; AS-P contribute to design the study, data collection and organizing and maintain the COVID-19 collection in the Bio- analysis, and reviewed the final version of the manuscript; AB-G Bank in particular to Sergio Navarro-Velázquez, Blanca Angelica provided the clinical laboratory data; JR provided the clinical the ICU Urban-Vargas, Mario Framil- Seoane, Virgínia Mas-Bosch, data and reviewed the final version of the manuscript. All authors Francisco Morandeira-Reg who collected the clinical data at contributed to the article and approved the submitted version. HUB to Mr. Àlex Pé rez Rodrı́guez, Ms. Jessica Muñoz, Ms. Cinta Rabaza Martı́, and Ms. Aina Aguiló Cucurull, the technical staff HUVH of the HUVH immunology laboratory who collected FUNDING and organized the COVID-19 patient samples; and to Ms. This study was funded by Instituto de Salud Carlos III, Madrid, Spain, Adelaida Parada Ramos, the secretary of the Immunology grants COV20/00416, Cov20/00654 and COV20/00388 to RP–B, Division HUVH, who helped retrieve the electronic medical AT-S, and JB-M respectively and co–financed by the European records and discharge notes in regard to this study for review Regional Development Fund (ERDF). DÁ –S is recipient of a by the medical personnel for this study. doctoral fellowship from the Vall d’Hebron Research Institute, Barcelona, Spain. AS-M was supported by a postdoctoral grant “Juan Rodé s” (JR18/00022) from Instituto de Salud Carlos III through the Ministry of Economy and Competitiveness, Spain. AS- SUPPLEMENTARY MATERIAL P was financially supported by the Spanish Ministerio de Ciencia e Innovacion, grant PID2019-104830RB-I00, and by the Departament The Supplementary Material for this article can be found online at: d’Economia i Coneixement de la Generalitat de Catalunya, grant https://www.frontiersin.org/articles/10.3389/fimmu.2022.902837/ 2017SGR622 (GRBIO). full#supplementary-material REFERENCES Spain: Does the Geographical Origin Matter? J Clin Med (2021) 10:1–9. doi: 10.3390/jcm10225213 1. Chams N, Chams S, Badran R, Shams A, Araji A, Raad M, et al. COVID-19: A 9. Meyerholz DK, Perlman S. Does Common Cold Coronavirus Infection Multidisciplinary Review. Front Public Health (2020) 8:383. doi: 10.3389/ Protect Against Severe No pages available (only on line) SARS-CoV-2 fpubh.2020.00383 Disease? J Clin Invest (2021) 131. doi: 10.1172/JCI144807 2. Ghayda RA, Lee J, Lee JY, Kim DK, Lee KH, Hong SH, et al. Correlations of 10. Zhang Q, Bastard P, Liu Z, le Pen J, Moncada-Velez M, Chen J, et al. Inborn Clinical and Laboratory Characteristics of Covid-19: A Systematic Review and Errors of Type I IFN Immunity in Patients With Life-Threatening COVID-19. Meta-Analysis. Int J Environ Res Public Health (2020) 17:1–15. doi: 10.3390/ Science (2020) 370:eabd4570. doi: 10.1126/science.abd4570 ijerph17145026 11. Bastard P, Rosen LB, Zhang Q, Michailidis E, Hoffmann H-H, Zhang, et al. 3. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical Course and Risk Autoantibodies Against Type I IFNs in Patients With Life-Threatening Factors for Mortality of Adult Inpatients With COVID-19 in Wuhan , China : COVID-19. Science (2020) 370:eabd4585. doi: 10.1126/science.abd4585 A Retrospective Cohort Study. Lancet (2020) 6736:1–9. doi: 10.1016/S0140- 12. Ellinghaus D, Degenhardt F, Bujanda L, Buti M, Albillos A, Invernizzi P, et al. 6736(20)30566-3 Genomewide Association Study of Severe Covid-19 With Respiratory Failure. 4. Abers MS, Delmonte OM, Ricotta EE, Fintzi J, Fink DL, Almeida de Jesus AA, N Engl J Med (2020) 383:1522–34. doi: 10.1101/2020.10.06.20205864 et al. An Immune-Based Biomarker Signature Is Associated With Mortality in 13. COVID-19 Host Genetics Initiative. Mapping the Human Genetic COVID-19 Patients. JCI Insight (2021) 6:m1985. doi: 10.1172/ Architecture of COVID-19. Nature (2021) 600:472–7. doi: 10.1038/s41586- jci.insight.144455 021-03767-x 5. Guallar MP, Meiriño R, Donat-Vargas C, Corral O, Jouvé N, Soriano V. 14. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The Inoculum at the Time of SARS-CoV-2 Exposure and Risk of Disease Severity. Ability of the National Early Warning Score (NEWS) to Discriminate Patients Int J Infect diseases (2020) 97:290–2. doi: 10.1016/j.ijid.2020.06.035 at Risk of Early Cardiac Arrest, Unanticipated Intensive Care Unit Admission, 6. Marks M, Millat-Martinez P, Ouchi D, Roberts C, Alemany A, Corbacho- and Death. Resuscitation (2013) 84:465–70. doi: 10.1016/ Monné M, et al. Transmission of COVID-19 in 282 Clusters in Catalonia, j.resuscitation.2012.12.016 Spain: A Cohort Study. Lancet Infect Dis (2021) 21:629–36. doi: 10.1016/ 15. Gupta RK, Marks M, Samuels THA, Luintel A, Rampling T, Chowdhury H, S1473-3099(20)30985-3 et al. Systematic Evaluation and External Validation of 22 Prognostic Models 7. Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J, Abfalterer W, et al. Among Hospitalised Adults With COVID-19: An Observational Cohort Tracking Changes in SARS-CoV-2 Spike: Evidence That D614G Increases Study. Eur Respir J (2020) 56:2003498. doi: 10.1183/13993003.03498-2020 Infectivity of the COVID-19 Virus. Cell (2020) 182:812–827.e19. doi: 10.1016/ 16. Jehi L, Ji X, Milinovich A, Erzurum S, Merlino A, Gordon S, et al. j.cell.2020.06.043 Development and Validation of a Model for Individualized Prediction of 8. Sempere A, Salvador F, Monforte A, Sampol J, Espinosa-Pereiro J, Miarons Hospitalization Risk in 4,536 Patients With COVID-19. PLoS One (2020) 15: M, et al. Covid-19 Clinical Profile in Latin American Migrants Living in e0237419. doi: 10.1371/journal.pone.0237419 Frontiers in Immunology | www.frontiersin.org 18 June 2022 | Volume 13 | Article 902837 Sánchez-Montalvá et al. Overcoming COVID-19 Laboratory Tests Limitations 17. Knight SR, Ho A, Pius R, Buchan I, Carson G, Drake TM, et al. Risk Reference Values in a Healthy Pediatric Population. Cytometry B Clin Stratification of Patients Admitted to Hospital With Covid-19 Using the Cytometry (2019) 96:223–33. doi: 10.1002/cyto.b.21728 ISARIC WHO Clinical Characterisation Protocol: Development and 35. Barcelona Demography (2020). Available at: https://www.ine.es/jaxiT3/Datos. Validation of the 4C Mortality Score. BMJ (2020) 2:m3339. doi: 10.1136/ htm?t=2861. bmj.m3339 36. Ferrer R, Bá guena M, Balcells J, Bañeras J, Biarnes A, de Nadal M, et al. 18. Riveiro-Barciela M, Labrador-Horrillo M, Camps-Relats L, Gonzalez-Sans D, Planning for the Assistance of Critically Ill Patients in a Pandemic Situation: Ventura-Cots M, Terrones-Peinador M, et al. Simple Predictive Models The Experience of Vall D’hebron University Hospital. Enfermedades Identify Patients With COVID-19 Pneumonia and Poor Prognosis. PLoS Infecciosas y Microbiologia Clinica (2020) 110:697–700. doi: 10.1016/ One (2020) 15(12):e0244627 doi: 10.1371/journal.pone.0244627 j.eimc.2020.08.007 19. Wynants L, van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. 37. Chen Z, John Wherry E. T Cell Responses in Patients With COVID-19. Nat Prediction Models for Diagnosis and Prognosis of Covid-19: Systematic Rev Immunol (2020) 20:529–36. doi: 10.1038/s41577-020-0402-6 Review and Critical Appraisal. BMJ (2020) 369:m1328. doi: 10.1136/ 38. Bastard P, Gervais A, le Voyer T, Rosain J, Philippot Q, Manry, et al. bmj.m1328 Autoantibodies Neutralizing Type I IFNs Are Present in ~ 4% of 20. Ambale-Venkatesh B, Quinaglia T, Shabani M, Sesso J, Kapoor K, Matheson Uninfected Individuals Over 70 Years Old and Account for ~ 20% of MB, et al. Prediction of Mortality in Hospitalized COVID-19 Patients in a COVID-19 Deaths. Sci Immunol (2021) 6(62):eabl4340. doi: 10.1126/ Statewide Health Network. medRxiv (2021). doi: 10.1101/ sciimmunol.abl4340 2021.02.17.21251758 39. Rydyznski Moderbacher C, Ramirez SI, Dan JM, Grifoni A, Hastie KM, 21. Galvá n-Romá n JM, Rodrı́guez-Garcı́a SC, Roy-Vallejo E, Marcos-Jimé nez A, Weiskopf D, et al. Antigen-Specific Adaptive Immunity to SARS-CoV-2 in Sá nchez-Alonso S, Ferná ndez-Dı́az C, et al. IL-6 Serum Levels Predict Severity Acute COVID-19 and Associations With Age and Disease Severity. Cell and Response to Tocilizumab in COVID-19: An Observational Study. (2020) 183:996–1012. doi: 10.1016/j.cell.2020.09.038 J Allergy Clin Immunol (2021) 147:72–80.e8. doi: 10.1016/j.jaci.2020.09.018 40. Röltgen K, Powell AE, Wirz OF, Stevens BA, Hogan CA, Najeeb J, et al. 22. Marcolino MS, Pires MC, Ramos LEF, Silva RT, Oliveira LM, Carvalho RLR, Defining the Features and Duration of Antibody Responses to SARS-CoV-2 et al. ABC2-SPH Risk Score for in-Hospital Mortality in COVID-19 Patients: Infection Associated With Disease Severity and Outcome. Sci Immunol (2020) Development, External Validation and Comparison With Other Available 5:eabe0240. doi: 10.1126/sciimmunol.abe0240 Scores. Int J Infect Dis (2021) 110:281–308. doi: 10.1016/j.ijid.2021.07.049 41. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a 23. Arunachalam PS, Wimmers F, Mok CKP, Perera RAPM, Scott M, Hagan T, Multivariable Prediction Model for Individual Prognosis or Diagnosis et al. Systems Biological Assessment of Immunity to Mild Versus Severe (TRIPOD): The TRIPOD Statement. Ann Internal Med (2015) 162:55–63. COVID-19 Infection in Humans. Science (2020) 369:1210–20. doi: 10.1126/ doi: 10.7326/M14-0697 SCIENCE.ABC6261 42. Osuchowski MF, Winkler MS, Skirecki T, Cajander S, Shankar-Hari M, 24. Kuri-Cervantes L, Pampena MB, Meng W, Rosenfeld AM, Ittner CAG, Lachmann G, et al. The COVID-19 Puzzle: Deciphering Pathophysiology Weisman AR, et al. Comprehensive Mapping of Immune Perturbations and Phenotypes of a New Disease Entity. Lancet Respir Med (2021) 9:622–642. Associated With Severe COVID-19. Sci Immunol (2020) 5. doi: 10.1126/ doi: 10.1016/S2213-2600(21)00218-6 sciimmunol.abd7114 43. Paludan SR, Mogensen TH. Innate Immunological Pathways in COVID-19 25. Lucas C, Wong P, Klein J, Castro TBR, Silva J, Sundaram M, et al. Pathogenesis. Sci Immunol (2022) 7:eabm5505. doi: 10.1126/ Longitudinal Analyses Reveal Immunological Misfiring in Severe COVID- sciimmunol.abm5505 19. Nature (2020) 584:463–9. doi: 10.1038/s41586-020-2588-y 44. Hadjadj J, Yatim N, Barnabei L, Corneau A, Boussier J, Smith N, et al. 26. Mann ER, Menon M, Knight SB, Konkel JE, Jagger C, Shaw TN, et al. Impaired Type I Interferon Activity and Inflammatory Responses in Severe Longitudinal Immune Profiling Reveals Key Myeloid Signatures Associated COVID-19 Patients. Science (2020) 369:718–24. doi: 10.1126/science.abc6027 With COVID-19. Sci Immunol (2020) 5:eabd6197. doi: 10.1126/ 45. Lucas C, Klein J, Sundaram ME, Liu F, Wong P, Silva J, et al. Delayed sciimmunol.abd6197 Production of Neutralizing Antibodies Correlates With Fatal COVID-19. Nat 27. Mathew D, Giles JR, Baxter AE, Oldridge DA, Greenplate AR, Wu JE, et al. Med (2021) 27:1178–86. doi: 10.1038/s41591-021-01355-0 Deep Immune Profiling of COVID-19 Patients Reveals Distinct Immunotypes 46. Sette A, Crotty S. Adaptive Immunity to SARS-CoV-2 and COVID-19. Cell With Therapeutic Implications. Science (2020) 369. doi: 10.1126/ (2021) 184:861–80. doi: 10.1016/j.cell.2021.01.007 science.abc8511 28. Wilk AJ, Rustagi A, Zhao NQ, Roque J, Martı́nez-Coló n GJ, McKechnie JL, Conflict of Interest: The authors declare that the research was conducted in the et al. A Single-Cell Atlas of the Peripheral Immune Response in Patients With absence of any commercial or financial relationships that could be construed as a Severe COVID-19. Nat Med (2020) 26:1070–6. doi: 10.1038/s41591-020- potential conflict of interest. 0944-y 29. Mueller YM, Schrama TJ, Ruijten R, Schreurs MWJ, Dwin GB, Grashof DGB, Publisher’s Note: All claims expressed in this article are solely those of the authors et al. Stratification of Hospitalized COVID-19 Patients Into Clinical Severity and do not necessarily represent those of their affiliated organizations, or those of Progression Groups by Immuno-Phenotyping and Machine Learning. Nat the publisher, the editors and the reviewers. Any product that may be evaluated in Commun (2022) 13:915. doi: 10.1038/s41467-022-28621-0 this article, or claim that may be made by its manufacturer, is not guaranteed or 30. Grau-Expó sito J, Sánchez-Gaona N, Massana N, Suppi M, Astorga-Gamaza A, endorsed by the publisher. Perea D, et al. Peripheral and Lung Resident Memory T Cell Responses Against SARS-CoV-2. Nat Commun (2021) 12:3010 doi: 10.1038/s41467-021-23333-3 ́ Copyright © 2022 Sań chez-Montalva,́ Alvarez-Sierra, Martínez-Gallo, Perurena- 31. Marshall JC, Murthy S, Diaz J, Adhikari N, Angus DC, Arabi YM, et al. A Prieto, Arrese-Muñoz, Ruiz-Rodrıguez,́ Espinosa-Pereiro, Bosch-Nicolau, Martínez- Minimal Common Outcome Measure Set for COVID-19 Clinical Research. Goḿ ez, Antoń , Martínez-Valle, Riveiro-Barciela, Blanco-Grau, Rodríguez-Frias, Lancet Infect Dis (2020) 20:e192–7. doi: 10.1016/S1473-3099(20)30483-7 Castellano-Escuder, Poyatos-Canton, Bas-Minguet, Martínez-Cać eres, Sań chez-Pla, 32. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF3rd, Feldman HI, Zurera-Egea, Teniente-Serra, Hernań dez-Gonzaĺ ez, Pujol-Borrell and the “Hospital et al. A New Equation to Estimate Glomerular Filtration Rate. Ann Internal Vall d’Hebron Group for the study of COVID-19 immune profile”. This is an open- Med (2009) 150:604–12. doi: 10.7326/0003-4819-150-9-200905050-00006 access article distributed under the terms of the Creative Commons Attribution 33. Maecker HT, McCoy JP, Nussenblatt R. Standardizing Immunophenotyping License (CC BY). The use, distribution or reproduction in other forums is permitted, for the Human Immunology Project. Nat Rev Immunol (2012) 12:191–200. provided the original author(s) and the copyright owner(s) are credited and that the doi: 10.1038/nri3158 original publication in this journal is cited, in accordance with accepted academic 34. Garcia-Prat M, Á lvarez-Sierra D, Aguiló -Cucurull A, Salgado-Perandré s S, practice. No use, distribution or reproduction is permitted which does not comply with Briongos-Sebastian S, Franco-Jarava C, et al. Extended Immunophenotyping these terms. Frontiers in Immunology | www.frontiersin.org 19 June 2022 | Volume 13 | Article 902837