Potential biomarkers for fatal outcome prognosis in a cohort of hospitalized COVID‐19 patients with pre‐existing comorbidities

Abstract

The difficulty in predicting fatal outcomes in patients with coronavirus disease 2019 (COVID‐19) impacts the general morbidity and mortality due to severe acute respiratory syndrome‐coronavirus 2 infection, as it wears out the hospital services that care for these patients. Unfortunately, in several of the candidates for prognostic biomarkers proposed, the predictive power is compromised when patients have pre‐existing comorbidities. A cohort of 147 patients hospitalized for severe COVID‐19 was included in a descriptive, observational, single‐center, and prospective study. Patients were recruited during the first COVID‐19 pandemic wave (April–November 2020). Data were collected from the clinical history whereas immunophenotyping by multiparameter flow cytometry analysis allowed us to assess the expression of surface markers on peripheral leucocyte. Patients were grouped according to the outcome in survivors or non‐survivors. The prognostic value of leucocyte, cytokines or HLA‐DR, CD39, and CD73 was calculated. Hypertension and chronic renal failure but not obesity and diabetes were conditions more frequent among the deceased patient group. Mixed hypercytokinemia, including inflammatory (IL‐6) and anti‐inflammatory (IL‐10) cytokines, was more evident in deceased patients. In the deceased patient group, lymphopenia with a higher neutrophil‐lymphocyte ratio (NLR) value was present. HLA‐DR expression and the percentage of CD39+ cells were higher than non‐COVID‐19 patients but remained similar despite the outcome. Receiver operating characteristic analysis and cutoff value of NLR (69.6%, 9.4), percentage NLR (pNLR; 71.1%, 13.6), and IL‐6 (79.7%, 135.2 pg/mL). The expression of HLA‐DR, CD39, and CD73, as many serum cytokines (other than IL‐6) and chemokines levels do not show prognostic potential, were compared to NLR and pNLR values.

Study Highlights.

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

The search for clinical characteristics or serum molecules in patients with coronavirus disease 2019 (COVID‐19) was from the beginning of the pandemic an objective to be achieved to predict fatal outcome. One of the most reliable biomarkers to recognize patients with COVID‐19 with high risk of fatal outcome was the neutrophil‐lymphocyte ratio (NLR) and the serum concentration of IL‐6.

• WHAT QUESTION DID THIS STUDY ADDRESS?

Our study analyzed patients’ with COVID‐19, clinical and laboratory characteristics at the time of admission and a follow‐up 3 and 7 days after being hospitalized, the variables collected were analyzed in surviving and non‐surviving patients, we found that a statistically significant difference for the presence of comorbidities, such as AHT or IRC as well as for the lymphocyte count, LDH, PT, CRP, PCT, NLR, IL‐6, or SIRI clinical score, however, only the NLR and the IL‐6 serum concentration reached acceptable sensitivity and specificity to predict fatal outcome. Our study also shows a cutoff level for NLR and IL‐6 that may be useful for the Mexican population.

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

We analyzed the expression of markers, such as HLA‐DR and CD39/CD73, on the leukocyte surface. Only the percentage of HLA‐DR+ monocytes are higher in survivors than in non‐survivors after 7 days of hospitalization, however, they did not reach an acceptable sensitivity or specificity after receiver operating characteristic analysis.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

Pandemics allowed the medical staff and clinical researchers to establish cooperation to analyze conventional or new biomarkers that will help to analyze and solve clinical questions for future pandemics.

INTRODUCTION

The severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) acute infection may evoke a strong inflammatory response. ¹ Even a similitude with the macrophage activation syndrome or secondary hemophagocytic lymphohistocytosis was proposed early during the first coronavirus disease 2019 (COVID‐19) pandemic wave. ² In hospitalized patients with COVID‐19, Adult Respiratory Distress Syndrome (ARDS) has arisen as a common complication. ³ ARDS is associated with hyperinflammatory response. Certain comorbidities with a proposed inflammatory landscape, such as hypertension, cardiovascular disease, diabetes, and obesity, are associated with severity and high‐risk mortality in COVID‐19, ⁴ , ⁵ with hyperinflammation as a typical component of severe or critical COVID‐19. ⁶ Inflammation is a complex response in patients with severe COVID‐19 and may result in a clinical picture resembling sepsis. ¹ , ⁷ Like in sepsis, in COVID‐19, some clinical characteristics and hyperinflammatory responses could be used at admission as predictors of in‐hospital death. ⁴ , ⁸ , ⁹ Cytokines, such as IL‐6 and IL‐10, in serum could reach a high concentration in the serum of patients with COVID‐19 and have been explored as predictors of fatal outcomes. ¹⁰ , ¹¹ , ¹² , ¹³ , ¹⁴ Routine laboratory‐derived inflammatory markers, such as neutrophilia, and general and selective lymphopenia, as well as the neutrophil lymphocyte ratio (NLR) may also predict a fatal outcome in these patients. ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ However, these predictors may be less accurate if patients have pre‐existing comorbidities. The difficulty to predict fatal outcomes in patients with COVID‐19 impacts in the general morbidity and mortality due to SARS‐CoV‐2 infection, as it wears out the hospital services that care for these patients.

Some surface molecules related with leukocyte activation during systemic inflammatory response (infectious and non‐infectious) may be more stable potential prognostic biomarkers. HLA‐DR diminished expression in monocytes or dendritic cells is associated with poor prognosis in patients with sepsis or acute pancreatitis. ²⁰ , ²¹ , ²² In addition, HLA‐DR diminished expression has been reported in myeloid‐derived suppressor cells from patients with COVID‐19, ²³ as well as in monocytes (CD14+ cells) from critically ill patients. ²⁴ Because COVID‐19 evokes both inflammatory and anti‐inflammatory responses, CD39/CD73 on leucocytes could also help monitor patients with COVID‐19. ²⁵ , ²⁶ CD39 and CD73 are surface enzymes on monocytes, and T and B cells that could elicit an anti‐inflammatory ²⁷ , ²⁸ or even an immunosuppressive response. ²⁹ This co‐existence of multiple mediators with antagonistic functions could explain why functional capacities can be compromised, resulting in more tortuous clinical evolutions. Indeed, the CD8+ T cell population undergoes quantitative and qualitative changes in patients with COVID‐19 that are associated with suboptimal responses and higher disease severity. ³⁰

To determine, even in patients with pre‐existing comorbidities, whether the differential expression of HLA‐DR, CD39, and CD73 on the surface of circulating leucocyte might represent early biomarkers of COVID‐19 outcome, we analyzed the immunophenotype of circulating leucocyte in patients hospitalized for severe COVID‐19 during the first COVID‐19 pandemic wave. Other inflammatory markers, such as cytokines, chemokines, and serum D‐dimer levels, as well as white blood cell count (WBC) and NLR or percentage NLR (pNLR), were also explored as indicators of inflammation and potential biomarkers.

MATERIALS AND METHODS

Patients

One hundred forty‐seven patients hospitalized at the UMAE Hospital de Especialidades, Centro Médico Nacional Siglo XXI “Dr. Bernardo Sepúlveda” Instituto Mexicano del Seguro Social for COVID‐19 from April 17 to November 8, 2020 were enrolled for a single center, prospective, observational, longitudinal study approved by the National Research and Ethics Committee (Research project: DI/20112/04/45, and R‐2020‐785‐095). Upon admission, all patients who had signs suggestive of COVID‐19 and agreed to participate after signing an informed consent were recruited, considering a non‐probabilistic sample for convenience. These patients could present as comorbidities: type 2 diabetes mellitus , systemic arterial hypertension, chronic obstructive pulmonary disease, and chronic kidney disease. Other comorbidities, including cancer, immunodeficiencies, autoimmune diseases, hepatitis B and C, and/or HIV infections, as well as pregnancy were non‐inclusion criteria.

SARS‐CoV‐2 infection was confirmed by specific reverse transcription–polymerase chain reaction (RT‐PCR) and was taken as a confirmatory diagnosis of COVID‐19.

After a patient signed the informed consent to participate in the study, blood samples were collected in silicone‐coated heparinized tubes (BD Vacutainer).

A group of healthy donors (non‐COVID‐19, n = 11) were matched by age and comorbidities with patients with COVID‐19. This group was made up of the health personnel of the hospital who cared for the patients and/or participated in the development of our study. The participants included in the control group underwent RT‐PCR tests, as well as interrogation and examination in general to rule out the presence of active respiratory infection. Only those apparently healthy and SARS‐CoV‐2 negative who agreed to sign the informed consent were recruited for the study. In addition, the inclusion of the group of healthy donors was approved by the National Research and Ethics Committee (Research project: DI/20112/04/45, and R‐2020‐785‐095).

Clinical evaluation

Upon admission to the hospital once enrolled in the study, the anthropometric data of each subject, as well as the signs and symptoms and known comorbidities, were recorded on the corresponding collection sheet. This includes age, gender, weight, height, body mass index (BMI), body temperature, cardiac rate, respiratory rate, systolic and diastolic arterial tension; and the presence or absence of cough, rhinorrhea, odynophagia, myalgia, arthralgia, anosmia, dyspnea, and diarrhea. The Sequential Organ Failure Assessment (SOFA) score and days of hospital stay were also recorded. Clinical data and medical treatment were collected at days 3 and 7 after inclusion and followed up until hospital discharge. The outcome was recorded as survivor or non‐survivor.

Laboratory data

The results of the routine laboratories performed on the patients that were collected on days 1, 3, and 7 postadmission were blood count (including: WBC, neutrophil, lymphocytes, and platelets frequencies (%) and absolute number (#), coagulation profile (prothrombin time and partial thromboplastin time), serum fibrinogen, D‐dimer, C‐reactive protein (CRP), procalcitonin (PCT), and satO₂. Systemic Inflammation Response Index (SIRI) score, platelet lymphocyte ratio (PLR), Pan‐Immune‐Inflammation Value (PIIV), NLR and pNLR were calculated as previously reported. ³¹

Blood samples

The first blood sample was taken once the patients was admitted to the hospital and after the patient or legal representative agreed to participate in the study by signing the informed consent. Follow‐up samples were taken at days 3 and 7 of hospitalization. All blood samples (6 mL) were collected in silicone‐coated heparinized tubes (BD Vacutainer). One millilitre of the total blood was used for immunophenotyping staining. The rest of the blood was centrifuged at 1467 g revolutions per min (rpm) for 10 min and the plasma was collected and stored at −70°C until use for cytokines/chemokines quantification.

Plasma cytokine and chemokine assessment

The plasma concentrations of cytokines (IL‐1β, IL‐6, TNF‐α, IFN‐λ‐1, IL‐12p70, IFN‐α2, IFN‐λ2/3, GM‐CSF, and IFN‐β e IFN‐γ) and chemokines (CXCL8/IL‐8, CXCL10/IP‐10, CCL11/Eotaxin, CCL17/TARC, CCL2/MCP‐1, CCL5/RANTES, CCL3/MIP‐1a, CXCL9/MIG, CXCL5/ENA‐78, CCL20/MIP‐3a, CXCL1/GROa, CXCL11/I‐TAC, and CCL4/MIP‐1b) were determined using bead based immunoassays (LEGENDplex, Cat. 740350, Cat. 740003, BioLegend), according to the manufacturer's instructions.

Leucocyte immunophenotyping

Whole fresh blood samples (50 μL) were incubated with titrated volumes of antibodies according to the following panel: anti‐CD45‐PerCP (Clone: HI30), anti‐CD3‐APC/Cy7 (Clone: UCHT1), anti‐CD14‐PECy7 (Clone: M5E2), anti‐CD16‐FITC (Clone: 3G8), anti‐CD73‐PE (Clone: AD2), anti‐CD39‐BV421 (Clone: A1), and anti‐HLA‐DR‐PE/Dazzle594 (Clone: L243). After 15 min of incubation, erythrolysis was performed using FACS Lysing Solution (Cat. 349202; BD Biosciences). Samples were washed with phosphate buffer solution (PBS; 528 g, 5 min) and resuspended in PBS (100 μL). At least 30,000 leucocytes (CD45+ cells) were acquired in a FACSAria IIu flow cytometer (BD Biosciences). The FACS files were analyzed with Infinicyt software 1.8 (Cytognos) and FlowJo v10 software (Becton Dickinson). Single cells were defined with a Forward Scatter (FSC‐A) versus FSC‐H plot, and leucocytes were identified using a Side Scatter versus CD45 plot. Lymphocytes were gated as SSC^lowFSC^lowCD45⁺⁺, monocytes as SSC^midFSC^midCD45⁺CD14^+/‐CD16^+/−, and neutrophils as SSC^midFSC^midCD45⁺CD16⁺. Lymphocyte subtypes were identified according to CD3‐HLA‐DR+ (B‐like cells) and CD3+ (T cells). Monocyte subpopulations were identified as classical CD14⁺⁺ CD16⁻, intermediate as CD14⁺CD16⁺, and no classical as CD14⁻CD16⁺. The percentages and mean fluorescence intensities of HLA‐DR, CD39, and CD73 were calculated.

Statistical analysis

The analysis was performed using GraphPad Prism version 6 software (GraphPad Software), the IBM SPSS Statistics version 25.0 (IBM), and the R i386 3.5.2 terminal (Microsoft Corp.). Normality statistic tests were run for every variable (Shapiro–Wilk test). Nonparametric ANOVA test (Kruskall‐Wallis test) with Dunn post‐test were applied. Categorical variables were expressed as number (%) and compared by Fisher's exact test. To define biomarker prognostic value and the optimal cutoff, we used the C‐statics of receiver operator characteristic (ROC), and the optimal cutoff was estimated in accordance with Youden Index (J). Kaplan–Meier survival analysis was used to compare outcomes and the log‐rank test was applied to validated hazard ratios (HRs) and 95% confidential intervals (95% CIs). A p < 0.05 was considered for statistical significance.

RESULTS

The sample obtained was for convenience. Nine hundred patients were hospitalized in our center during the reporting period in our study, due to limited access to the core facilities. Only those patients admitted in the morning from Monday to Friday who met the inclusion criteria were invited to participate. In addition, only those patients or their legal representatives who accepted to participate and signed the informed consent were enrolled (n = 147). Patients’ outcomes were recorded, as survivor (n = 78) or non‐survivor groups (n = 69). Table 1 shows clinical and laboratory characteristics in patients with COVID‐19 at admission, the mean age in the non‐survivor group was higher (59 ± 14 years, mean ± SD) than in survivor group (49 ± 14 years, mean ± SD, p < 0.0001). No statistically significant difference was observed between the survivor and non‐survivor groups for sex, BMI, respiratory rate, heart rate, and temperature (Table 1). In addition, we did not observe a statistically significant difference between the survivor and non‐survivor groups for some comorbidities like diabetes mellitus or obesity. However, we found a statistically significant difference in the frequency of hypertension (p = 0.011) and IRC (p = 0.016). The SOFA score is higher in the non‐survivor group than in the survivor group and reaches high significance (p = 0.0001). On the other hand, we observed some laboratory parameters with no statistically significant difference, like in glucose, creatinine, ferritin, PTT, D‐Dimer, fibrinogen, PLR, or PIIV. The laboratory parameters that showed statistically significant differences between survivor and deceased groups, such as leucocyte and lymphocyte count, NLR, DHL, PT, CRP, PCT, SAT O₂, and the clinical score SIRI (Table 1).

TABLE 1.

Clinical and laboratory characteristics in patients with COVID‐19.

	Total (n = 147)	Survivor (n = 78)a	Non‐survivor (n = 69)b	p
Age (years)	53 ± 18	49 ± 14	59 ± 14	<0.0001
Male/Female	100/45	56/21	44/24	0.297
BMI	29.8 ± 6.1	29.0 ± 4.9	30.7 ± 7.3	0.114
Respiratory rate (breaths per min)	24.8 ± 8.1	23.9 ± 6.3	25.5 ± 5.8	0.141
Heart rate (beats per min)	101 ± 19.5	103.6 ± 20.5	101.2 ± 17.3	0.494
Temperature (°C)	36.7 ± 0.5	36.5 ± 0.9	36.6 ± 0.6	0.524
Diabetes mellitus (pos/neg)	54/92	24/54	30/38	0.095
Obesity (pos/neg)	59/81	28/49	31/32	0.125
Hypertension (pos/neg)	49/97	19/59	30/38	0.011
CRF (pos/neg)	16/129	4/73	12/56	0.016
Leucocytes	9.9 ± 4.6	9.1 ± 4.0	10.8 ± 4.7	0.022
Lymphocytes	0.9 ± 0.5	0.9 ± 0.4	0.7 ± 0.5	0.038
NLR	15.1 ± 14.1	10.8 ± 11.2	17.0 ± 15.5	0.006
pNLR	13.6 ± 12.9	10.3 ± 8.5	17.5 ± 15.9	0.0013
Glucose	149.9 ± 80	143.5 ± 87	157.1 ± 87	0.345
Ferritin	1543 ± 1248	1455 ± 1285	1644 ± 2495	0.590
Creatinine	2.1 ± 2.1	1.4 ± 3.5	2.8 ± 5.6	0.059
LDH	538 ± 211	436.5 ± 162	647 ± 537	0.001
PT	16 ± 15.6	14.5 ± 1.3	15.6 ± 2.3	0.0008
PTT	31.3 ± 16.2	31.6 ± 17.3	35 ± 33.8	0.458
CRP	17.3 ± 10.3	14.8 ± 9.4	19.8 ± 10.3	0.004
PCT	2.9 ± 3.3	1.1 ± 2.8	4.8 ± 11.5	0.021
D‐Dimer	4.4 ± 3.6	3.5 ± 9.2	6.8 ± 24.9	0.281
Fibrinogen	698 ± 200	716 ± 172	677 ± 179	0.188
Sat O2	88.7 ± 7.4	90.4 ± 7.7	86.7 ± 10.6	0.028
SOFA	3.8 ± 2.8	2.8 ± 1.9	5 ± 3.4	0.0001
SIRI	6.3 ± 9.6	4.4 ± 7.4	8.3 ± 12.2	0.021
PLR	402 ± 234	371.4 ± 216.8	438.5 ± 261.2	0.091
PIIV	1781 ± 3083	1474 ± 2446	2139 ± 2814	0.129

Note: Mean ± SD. Kruskal‐Wallis test. Significant p < 0.05.

Abbreviations: BMI, Body Mass Index; CRF, chronic renal failure; CRP, C‐reactive protein; LDH, lactate dehydrogenase; NLR, neutrophil lymphocyte ratio; PCT, procalcitonin; PIIV, pan‐immune‐inflammation value; PLR, platelet lymphocyte ratio; pNLR, percentage NLR; PT, prothrombin time; PTT, thromboplastin partial thromboplastin time; SIRI, Systemic Inflammation Response Index; SOFA, sequential organ failure assessment.

Plasma cytokines and chemokines were assessed and shown in Figure 1. IL‐6, CXCL8, IL‐10, CCL2, CCL4, CCL20, CXCL10, and CXCL11 levels were significantly higher in non‐survivor patients than in healthy donors. In addition, IL‐6, IL‐10, CXCL10, and CXCL11 is higher in survivor patients than in healthy donors (Figure 1). However, cytokines/chemokines plasma concentrations did not show a statistically significant difference between survivor and non‐survivor patients (Figure S1).

FIGURE 1.

Plasma cytokine/chemokine concentration in patients with COVID‐19. Cytokine and chemokines were assessed as in Methods. Black circles: Healthy controls. Green squares: patients with COVID‐19 who survived. Red triangles: patients with COVID‐19 who had fatal outcome. Dash line show the low limit of detection for each analyte. Results are expressed as mean ± SD. Kruskal‐Wallis and Dunn's multiple comparisons test was calculated. Significant p < 0.05. COVID‐19, coronavirus disease 2019.

Based on the differences we showed in Table 1 for NLR and pNLR, an ROC curve analysis was performed and showed a prognostic value for clinical outcome. In the case of the NLR with an area under the curve (AUC) of 0.700 (95% CI: 0.624–0.777, p < 0.0001). The cutoff for NLR was 9.4 with 66% and 64.9% of sensitivity and specificity, respectively (Figure 2c). Kaplan–Meier curves show it as a predictor for value greater than 9.4 (p = 0.0008, HR: 43, 95% CI: 0.261–0.685; Figure 2d). The best discriminant of clinical outcome was pNLR with an AUC of 0.711 (95% CI: 0.635–0.786, p < 0.0001). The optimal cutoff value was 13.6% with a sensitivity of 50.7% and specificity of 80.0% (Figure 2e; Kaplan–Meier curves results were significant as a predictor for values >13.6% [p = 0.011, HR: 55, 95% CI: 0.312–0.843]; Figure 2f). The ROC curve analysis for some cytokine/chemokine is shown in Figure S2, where IL‐6 has an AUC 0.797 (95% CI: 0.580–0.870, p = 0.005). The optimal cutoff value for IL‐6 to predict mortality was 135.2 pg/mL (Figure 2a) with a sensitivity of 61.5% and a specificity of 95.2%. Kaplan–Meier survival curves were performed, showing that IL‐6 levels greater than 135.2 pg/mL is a significant predictor for a fatal outcome (Figure 2b; p = 0.005, HR: 43, 95% CI: 0.283–0.786). ROC curve analysis of the IL‐6 level in combination of pNLR value show an increase in AUC compared to the individual curves 85.4% specificity; CI: 76.9–93.8 (data not shown).

FIGURE 2.

ROC and survival curves for IL‐6, NLR, and pNLR in patients with COVID‐19. Two different ROC curves were performed for IL‐6 (a, b), NLR (c, d), and pNLR (e, f), optimal cutoff was calculated in accordance with Youden index. Kaplan–Meier of survival curves were made according to the optimal cutoff and HR expressed in percentage. Significant p < 0.05. AUC, area under the curve; HR, hazard ratio; NLR, neutrophil‐lymphocyte ratio; pNLR, positive neutrophil‐lymphocyte ratio; ROC, receiver operating characteristic.

About activation markers as HLA‐DR was analyzed on the surface of leucocytes in survivor and non‐survivor groups of patients with COVID‐19. In addition, Figure 3 shows the percentage of HLA‐DR+ on monocytes and T lymphocytes in healthy donors. Healthy donors express a high percentage of monocytes HLA‐DR+, usually around 95% of monocytes, whereas the percentage of monocytes HLA‐DR+ in patients with COVID‐19 is lower than 90%. Figure 3a shows that on admission day (d0), the percentage of HLA‐DR+ monocytes were significantly lower than in healthy donors. In addition, a lower percentage were detected in patients with COVID‐19 after 7 days of medical treatment in survivor (d7S) or non‐survivors (d7D) patients.

FIGURE 3.

Percentage of HLA‐DR+ leucocytes in healthy donors and patients with COVID‐19. Whole blood was phenotype as in methods. d0: admission day. d7: seventh day in hospital. The blue diamond shows healthy donors. The red full circles show patients whose death occurred during hospitalization not necessarily at d0. Green empty circles show patients who were discharge from the hospital. Red empty triangles show patients whose fatal outcome occurs during hospitalization after 7 days of medical treatment. Monocytes Int: SSC^midFSC^midCD16⁺CD14⁺. Monocytes C: SSC^midFSC^midCD16⁻CD14⁺. Monocytes NC: SSC^midFSC^midCD16⁺CD14⁻. Results are expressed as mean ± SD. Kruskal‐Wallis and Dunn's multiple comparisons test was calculated. Significant p < 0.05. COVID‐19, coronavirus disease 2019.

Nevertheless, Figure 3a shows that the percentage of HLA‐DR+ monocytes are higher in d7S than in d7D (Mann–Whitney test, 95% IC, p < 0.0001). In addition, Figure 3b, c, and d show the percentage of HLA‐DR+ cells in classical (monocytes C), intermediate (monocytes int), and non‐classical monocytes (monocytes NC). We observed that the percentage of HLA‐DR+ in each of them is lower than in healthy donors; however, only in monocytes NC the percentage of HLA‐DR+ are similar between healthy patients and d7S patients (Figure 3d). The HLA‐DR was also analyzed in T cells, and the percentage of these lymphocytes was similar between healthy patients and patients with COVID‐19 (Figure 3e), however, the percentage of T cells HLA‐DR+ is lower in patients with COVID‐19 at the admission day d(0) than in healthy patients (Figure 3f).

The percentage of leucocytes CD39+, CD73+, and CD39+ CD73+ were analyzed on leucocytes from healthy donors and patients with COVID‐19. The patients with COVID‐19 characteristically show a similar percentage of CD39 on monocytes and B‐like cells than in the healthy donors. Our data show that the percentage CD39+ cells remain similar after 7 days of treatment despite the outcome (Figure 4a,g). In addition, and despite the final outcome (survivors or non‐survivors), the percentage of CD39 on T cells is higher in patients with COVID‐19 after the seventh day of hospitalization (Figure 4d), d7D patients show the highest percentage, however, there was no statistically significant difference between d7S and d7D (Mann–Whitney test, p = 0.167). Regarding the percentage of CD73 cells, we observed a higher percentage of CD73 on monocytes from patients with COVID‐19 than in healthy donors (Figure 4b), whereas B‐like cells from patients with COVID‐19 only show a higher percentage of CD73 cells than in healthy donors (Figure 4h). In contrast, the percentage of leucocytes (monocytes, T and B cells) show a similar percentage of CD39+ CD73+ cells from patients with COVID‐19 than in healthy donors (Figure 4c,f,j).

FIGURE 4.

Percentage of CD39+, CD73+, and CD39+ CD73+ leucocytes in patients with COVID‐19. Whole blood was phenotype as in methods. d0: Admission Day. d7: seventh day in hospital. The blue diamond shows healthy donors. The red full circles show patients whose death occurred during hospitalization (not necessary at d0). Green circles show patients who were discharged alive from the hospital. Red triangles show patients who had a fatal outcome during hospitalization after 7 days of medical treatment. Results are expressed as mean ± SD. Kruskal‐Wallis and Dunn's multiple comparisons test was calculated. Significant p < 0.05. COVID‐19, coronavirus disease 2019.

DISCUSSION

The D‐Dimer concentration has been proposed as a useful predictor of fatal outcomes, ³² however, we did not observe a statistically significant difference in the plasma concentration of D‐Dimer between survivors and non‐survivors. This could be due to differential endothelial dysfunction states, and may be related to both inflammatory and chronic diseases, because D‐Dimer can be modified due to coagulopathy and endothelial activation. ³³

As reported for sepsis and pancreatitis, our data showed SOFA score is a useful predictor of fatal outcomes in COVID‐19. In addition, lymphopenia is a variable consistently reported to help predict the progression to more severe states in COVID‐19. ³⁴ Our study indicates that the NLR is useful as a predictor of fatal outcomes; moreover, the calculation of the pNLR is also helpful as a predictor of fatal outcome, reinforcing the observation made with the NLR. Both NLR and pNLR are values that can be easily calculated after performing a leukocyte count in peripheral blood. The number or percentage of neutrophils was taken and divided by the number or percentage of lymphocytes, respectively. Such a count is within reach of most countries, and universal standardization exists to achieve it. Notably other demographic, clinical, or laboratory parameters are not always practical as sufficient predictors of the outcome, but the NLR can be consistently helpful even in patients with pre‐existing comorbidities. The higher the NLR value, the greater probability of a fatal outcome in patients with COVID‐19; the higher value may result from the increase in neutrophils and/or the decrease in lymphocytes in the patients with COVID‐19. We think that the chemokine‐dependent neutrophilia could be a factor that explains the massive mobilization of these cells in peripheral blood. However, despite their high protective function, phagocytic or microbicidal capacity could be limited in neutrophils, and neutrophilia could be a mechanism to compensate for the lack of function. Likewise, lymphopenia can result from the redistribution of lymphocytes to inflamed tissue and the subsequent cytotoxicity to limit viral replication in COVID‐19. This condition is favored as toxic for the lymphocyte population, especially for cytotoxic lymphocytes. More studies must be carried out to determine the cause of the increase in the value of the NLR and having a medical treatment that improves the patients' conditions with the use of the NLR as a monitor.

Regarding cytokines and chemokines, we show that IL‐6, IL‐10, CXCL10 (IP‐10), and CXCL11 are higher in patients with COVID‐19 than in healthy patients, indicating that SARS‐CoV‐2 infection evokes a state of hyperinflammation with both pro‐ and anti‐inflammatory response. We speculate that the inflammatory response mainly affects molecules with chemoattractant function because the plasma elevation was noted for CXCL8, CCL4, CCL20, and CCL2 in non‐survivors. However, there were no statistically significant differences between survivors and non‐survivors. This was observed to other soluble candidates as calcium in sera, which in initial studies seem to have prognostic potential, but when transferred to other populations than Chinese it failed as a predictor of severity. This could be due to the different cutoff points instead of the plasma levels. ³⁵

The hypercytokinemia reported in COVID‐19 includes IL‐6, TNF‐α, and IP‐10. Our study shows that patients with a fatal outcome consistently express higher plasma concentrations of inflammatory mediators; however, when performing the ROC curve analysis, only IL‐6 was a good predictor for a fatal outcome. As we observed for the NLR value, the high concentration of IL‐6 could suggest that in COVID‐19 there is a specific dysregulation of the response of various tissues through IL‐6, and is not a condition with general hypercytokinemia. IL‐6 is a cytokine with a broad pleiotropic response and could synergizes several responses to other cytokines. This is in accordance with the improved clinical conditions and reduced mortality reported with anti‐IL‐6R treatments in patients with severe COVID‐19. ³⁶

We analyzed the differential expression of HLA‐DR, CD39, and CD73 with good prognostic potential in other hyperinflammatory states, such as sepsis or systemic inflammatory syndrome. We analyzed the percentage of monocytes and T lymphocytes that express HLA‐DR. Our group previously reported that the percentage of monocytes HLA‐DR+ is diminished in septic patients and is associated with poor prognosis. ²⁰ Here, we show that the percentage of HLA‐DR+ in monocytes from patients with COVID‐19 is lower than in healthy subjects (Figure 3a), also d7D patients expressed a lower percentage of HLA‐DR+ monocytes than in d7S patients (Mann–Whitney test, p < 0.0001), this was closer to reach statistically significant difference in non‐classical monocytes (Mann–Whitney test, p = 0.057). However, after ROC curve analysis, it does not reach a high sensitivity or specificity to be a good predictor of fatal outcome in patients with COVID‐19. More studies are necessary to know if HLA‐DR expression on monocytes could be useful to predict fatal outcome in patients with COVID‐19. In addition, we explored the expression of other surface markers (CD39 and CD73) that could limit the inflammatory response in COVID‐19. The percentage of CD39 on T cells was higher in patients with COVID‐19 after 7 days of treatment; this was not the case for monocytes and B‐like cells, indicating that some anti‐inflammatory molecules are differentially expressed in peripheral leucocytes, however, we do not know if leucocytes in tissue expressed this differential condition, the expression of CD39/CD73 could regulate the activation of leucocytes in situ and limit more effectively the inflammatory response to COVID‐19. Our result shows a higher percentage of monocytes and B‐like CD73+ cells than healthy donors, indicating that the expression of CD73 could be more important to regulate anti‐inflammatory response in monocyte and B cells than in T lymphocytes. Our result about CD73 agrees with the report of Díaz García et al., ³⁷ but was still in contrast to Ahmadi et al. ²⁶ In addition, Ahmadi et al. found a lower expression of CD73 in CD4, and CD8 T lymphocytes, whereas we did not observe a difference (Figure 4e); Ahmadi reported a lower expression of CD73 in monocytes and B lymphocytes, whereas we observed a high percentage of CD73 in monocytes that not reach statistically significant difference, this opposite result could be explained because we included the outcome in our analysis. ²⁶ Finally, the expression of both CD39 and CD73 on the same cell was similar in patients with COVID‐19 and healthy donors (Figure 4c,f,j), suggesting that monocytes and B and T cells have the same condition to regulate inflammation by CD39/CD73 molecules. Our result shows that single CD39 or CD73 expression on leucocytes have some difference in peripheral blood, however, double expression of CD39 and CD73 on cells is similar in patients with COVID‐19 and healthy donors, suggesting that this anti‐inflammatory mechanism is working in COVID‐19, taking together these results suggest that SARS‐CoV‐2 infection could dissociate the expression of CD39 and CD73, increasing the single cells and limiting the anti‐inflammatory functionality of the pair CD39/CD73. More studies are necessary to clarify this possibility.

The pandemic came as a new medical challenge, and we had limited medical resources that restrain our capacity to offer the attention of patients with COVID‐19 in the hospital. Our study included a high number of patients with poor outcomes, which focused and limited our study to a specific group of patients with highly severe conditions, which prevented having a common moderate or mild COVID‐19 group to compare and establish if our observations are related to the disease and/or severity and this patients corresponding to the first COVID‐19 wave, therefore, the generalization of the results must be cautious and consider the different variants of the virus and the subsequent vaccination. A disbalance number of observations for some variables is another limitation.

A relative limitation is that this was a single‐center study, and the time of symptom onset is not always certain or has not always been collected for each patient. In addition, we assume that there has been no re‐infection or asymptomatic cases in patients with COVID‐19 or healthy donors, respectively. The interpretation of the results should consider these limitations.

AUTHOR CONTRIBUTIONS

R.L.M.S., and A.C.V. wrote the manuscript. A.C.V., L.A.A.P., and E.F.O. designed the research. R.C.D.B., A.H.V.C., A.E.P., R.R.M., O.U.M., G.F.P., J.C.A.G., L.A.S.H., S.C.A., L.R.G., M.E.S.R., E.S.R., E.D.S.M., R.L.M.S., A.C.V., L.A.A.P., G.L.C.R., E.B.G., P.E.M.C., M.T.G.R., J.L.P.C., R.T.R., S.V.R.A., A.C.C., and D.R.H. performed the research. R.L.M.S., A.C.V., L.A.A.P., C.L.M., L.C.B., R.P., E.M.M., and E.F.O. analyzed the data.

FUNDING INFORMATION

This project was supported by the Mexican National Research Council (CONACyT), Project No. 313494 and by IMSS in the grant call: “Protocolos multidisciplinarios de cohorte o largo aliento sobre temas prioritarios en el IMSS 2023”. Project R‐2020‐785‐095 (both awarded to C.L.M.).

CONFLICT OF INTEREST STATEMENT

The authors declared no competing interests for this work.

Supporting information

Figure S1

Figure S2

Madera‐Sandoval RL, Cérbulo‐Vázquez A, Arriaga‐Pizano LA, et al. Potential biomarkers for fatal outcome prognosis in a cohort of hospitalized COVID‐19 patients with pre‐existing comorbidities. Clin Transl Sci. 2023;16:2687‐2699. doi: 10.1111/cts.13663