The coronavirus diseases 2019 (COVID-19), positive-sense single-stranded RNA group of viruses, spread rapidly around the world, this novel coronavirus (2019-nCov) was later identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Initially, the whole world was racing to find a drug or to repurpose already existed drugs to treat and manage the COVID-19 associated complications until the recently appearing effective vaccine against the SARS-CoV-2. Historically, the pandemic COVID-19 was firstly originated from Wuhan, China in December 2019, and was declared as pandemic diseases later on in March 2020 [1-3].

Fever is often the major and initial symptom of COVID-19, which can be accompanied by no symptom or other    symptoms,    including     but     not    excluded    to, generalized weakness and muscle ache, dizziness, dry non-productive cough, dyspnea with or without chest pain, headache, sore throat with or without rhinorrhea, chest pain, nausea with or without vomiting, diarrhea. In some case scenarios, some patients experienced dyspnea associated hypoxemia, especially after 1 week from the COVID-19 disease onset [4-7]. Whatever, some vulnerable COVID-19 infected patients may rapidly progress into more critical and advance stages encompassed mostly with cardiovascular complications (e.g., venothrombolic associated ischemia events, especially the pulmonary thromboembolism) and cerebral embolism, severe acute respiratory distress syndrome (ARDS), thrombotic storm associated disseminated intravascular coagulation (DIC), radical storm associated multi-organ failure, hemodynamic instability with acid-base disturbances [8-11].           

COVID-19 has a great attention due to its unique pathogenesis and its reputation of being a killer virus. Corona viruses have several highly active RNA processes that aren’t found in any other RNA viruses. Also, the variable incubation period that ranges from 2-14 days, hugely pervasive tendencies, their ability to modernize themselves, and the inclusion of not only the respiratory system are an additional specific feature for this SARS-CoV-2 phenotype that making COVID-19 infection as a global crisis that requires the combined efforts of all humans to combat it [12-15].

There are many storm theories that explain the COVID-19 associated mortality, especially in mechanically ventilated critically ill cohort, including but not excluded to, hyperinflammatory associated cytokine storm, hyperoxidative associated radical storm, and hypercoagulability associated thrombotic storm [16-18]. Each of these aforementioned emerging theories has its reasonable and rational explanation but up to date, the hyperinflammatory associated cytokine storm is the most accepted theory that answer most of the debatable questions and correlate the complex interrelated immune participation of the COVID-19 infected patients in combating the virus and eliciting cytokines hypersecretion induced hyperinflammatory cascades [19-20]. Generally, the cytokine storm is a dysfunctional reaction in immunopathogenic cascades resulting in hyper-inflammatory cytokines production, including but not excluded to, interleukins 2, 6, 8, and 12 (IL-2, IL-6, IL-8, and IL-12, respectively), and TNF-α during uncontrolled immuno-reaction progression, primarily ending with ARDS and other aforementioned COVID-19 associated complications [21-23].

Almost all the health care sectors are focusing on treating the symptoms and decrease mortality as possible and many repurposed drugs have relatively succeeded to alleviate symptoms and decrease the infection burden. Inhibition of hyperinflammatory response may be the primary adjunctive therapeutic intervention for COVID-19 infection management to standard antiviral treatment. Literature does not currently provide convulsive evidence for or against the use of corticosteroids in the treatment of COVID-19 patients[24-25]. Typically, corticosteroids are used to manage the severe hyperinflammatory consequences, mostly as severe acute respiratory distress, that accompany the cytokine hypersecretion in infections of viral etiology because of their proved and effective anti-inflammatory effect. The inflammatory response could stimulate the production of neutrophils and speed up the apoptosis of lymphocytes but when corticosteroids are used, the neutrophil count is further intravascularly increased in contrast to both monocyte and lymphocyte counts which are oppositely decreased[26-27].

Basically, elevated some of the biochemical levels including for example; c-reactive protein (CRP), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alkaline phosphatase (ALP), D-Dimer, troponin-I, gamma-glutamyl transferase (GGT), Ferritin, and interleukin-6 (IL-6) are significantly associated with the aforementioned COVID-19 associated complications [28-29]. Notably, elevated positive acute phase reactant levels, ferritin and CRP, are highly correlated to diseases prognosticating and both have a high-performance utility in early prediction for COVID-19 trending even before radiological CT findings [30].

Monocytes to lymphocytes ratio (MLR) has taken both the levels of monocytes and lymphocytes into account, and been proposed as a new diagnostic and prognostic biomarker for pathogens and non-pathogens associated systemic inflammation response syndromes [31]. Numerically, elevated MLR results from either increasing monocytes count or decreasing lymphocytes count or both. Clinically, higher value of MLR indicates higher probabilities for bacterial infection and lower probabilities for viral infection. Similarly, higher value of neutrophils to lymphocytes ratio (NLR) also indicates higher probabilities for bacterial infection and lower probabilities for viral infection [32-33]. As previously mentioned, corticosteroids elevate plasma neutrophils level and at same time decrease both monocytes and lymphocytes levels. So, extensive corticosteroids using in ARDS affected COVID-19 infected patients, especially the mechanically ventilated critically ill cohort, may overestimated the predictive, diagnostic, prognostic, and discriminative values of NLR in contrary to MLR [34-35].

The proposed theory “Hyper-Oxidative-Radical Storm” indicate that the COVID-19 pathogenesis maybe partially due to RBCs invasion by the virus in which the SARS-CoV-2 could attack the ß chain of hemoglobin (Hgb) and subsequently releasing free iron to the blood causing iron overload. This iron overload pose harmful effects on the body as iron is a strong oxidizing and free radicals eliciting substrate [36-37]. Since there is an increased rate of RBCs rupture and release of iron, monocytes play a vital role in iron homeostasis and decrease iron toxicity by a process called erythrophagocytosis [38]. A special feature about COVID-19 infection that it is opposite to other viral infection in the elevation of lymphocytes. In this virus most of the patients have shown lymphopenia and monocytosis which is misleading to the over prescription of unneeded antibiotics due to the unique COVID-19 infection pathogenesis related MLR elevation[39].

A need to monitor the prognosis of the disease using a specific affordable, practical, applicable, cost-available, biochemical, and seems to be discriminative, valid, and reliable COVID-19 related marker is a feasible economic and health wise, as seeing the prognosis of COVID-19 give us the ability to take an action in managing the upcoming issues and precede the virus one step ahead. So, in this retrospective research article, we investigate the prognosticating performance of MLR versus monocytes and lymphocytes counts in high COVID-19 infection associated mortality rate cohort of ARDS affected mechanically ventilated critically ill patients who are on corticosteroids.

A single-center, observational, retrospective study was conducted on all mechanically ventilated critically I'll COVID-19 infected patients that were admitted into the intensive care units (ICUs) of our Royal Medical Services (RMS) institutions, Jordan between April 2020 and Dec 2020. Owing to our study’s retrospective design, a signed consent form was waived. This study was approved by our institutional ethical review board (IRB). All eligible mechanically ventilated critically ill COVID-19 infected patients for this study, were admitted from other COVID-19 isolation wards and all had moderate-severe ARDS as explained numerically by persistent average Pa02 / FiO2 <200 and radiologically confirmed. Analytical variables were firstly evaluated for normality of distribution by using Kolmogorov-Smirnov Test. Normally distributed continuous variables were expressed as Mean±SD by using independent T-Test while non-parametric categorical/ordinal variables were expressed as either Number (Percentages) by Chi Square Test or as Median (IQR) by using the Mann-Whitney U Test, respectively. All demographical, co-morbidities, biochemical, clinical, radiological, and pharmacological data were extracted from the institutional electronic system (Hakeem). Comparative variables were compared across Cohort I (Survivors) and Cohort II (Non-Survivors). A receiver operating char¬acteristic (ROC) curve followed by sensitivity analysis was used to determine the area under the ROC curves (AUROCs), predictive performances, and the optimal cut-off values for the three tested prognosticators (Monocytes, Lymphocytes, and MLR). Youden’s indices, sensitivities, specificities, positive and negative predictive val¬ues, and accuracy indices were also calculated. Statistical analyses were performed using IBM SPSS ver. 25 (IBM Corp., Armonk, NY, USA) and P-values ≤0.05 were considered sta¬tistically significant. Other secondary endpoints, including but not excluded to, intensive Care Unit (ICU) and overall hospital length of stay (LOS).

From three hundred and seventy-four (N = 374) adult and elderly admitted mechanically ventilated critically ill COVID-19 infected patients in our COVID-19 isolation ICU department at Queen Alia Military Hospital, Royal Medical Services, Amman, Jordan between May 2020 and Dec 2020, one hundred and ninety-three (N = 193) were finally included in this study with forty-six (N = 46), and one-hundred and thirty-five (N=135) cases were excluded from the study due to either not developing moderate-severe ARDS (persistent Pa02: FiO2<200 and radiologically confirmed), incompleted recruited baseline data or follow-up data, respectively.

The mean age of the whole study cohort was 58.37±9.96 years, and the Survivors Cohort were insignificantly older than the Non-Survivors Cohort (58.55±9.95 years versus 58.09±10.05 years, respectively, p>0.05). Significantly, males were distributed in the study in approximately 2: 1 ratio compared to female (127 (65.8%) versus 66 (34.2%), respectively, p<0.05) in which 67.68% (67 COVID-19 critically ill men) and 32.32% (32 COVID-19 critically ill women) were belonged to the Survivors Cohort compared to 63.83% (60 COVID-19 critically ill men) and 36.17% (34 critically ill women) were belonged to the Non-Survivors Cohort.

Haematologically, the white blood cells (WBCs) and their corresponding differential percentages and counts of neutrophils and monocytes were significantly lower in the Survivors Cohort compared with Non-Survivors Cohort (15201±2710 cells/µl, 63.36%±10.19%, 9644±1995 cells/µl, 7.45%±1.73%, and 1108±112 cells/µl versus 19880±2031 cells/µl, 74.20%±28.33%, 14522±2487 cells/µl, 12.85%±2.98%, and 2550±421 cells/µl, respectively, p<0.05). When the ratio of neutrophils and monocytes to lymphocytes were taken, the NLR and MLR were also significantly lower in the Survivors Cohort compared with Non-Survivors Cohort (2.22±0.99 and 0.25±0.10 versus   5.16±2.08 and 0.89±0.19, respectively, p<0.05). Conversely, the percentage and count of lymphocytes were significantly higher in the Survivors Cohort compared with Non-Survivors Cohort (29.66%±9.89% and 4410±422 cells/µl versus 14.24%±3.09% and 2866±210 cells/µl, respectively, p<0.05).

Hemodynamically, the shock index (SI) is significantly lower in the Survivors Cohort compared with Non-Survivors Cohort [1.12±0.03 bpm/mmHg vs 1.29±0.17 bpm/mmHg, respectively, p<0.05] even though there was a statistically insignificant difference regarding Norepinephrine (NE) rate in both aforementioned cohorts [9.27±1.68 mcg/min vs 9.94±1.89 mcg/min, respectively, p>0.05]. Biochemically, the c-reactive protein (CRP) and ferritin (FER), the two widely used diagnostic and prognostic indicators which both belong to the positive acute phase reactants, were significantly higher in the Non-Survivors Cohort than in the Survivors Cohort [143.09±59.28 mg/dl and 891.51±377.82 ng/ml vs 88.38±34.38 mg/dl and 465.76±154.07 ng/ml, respectively, p<0.05]. Other mortality prognosticators, including but not excluded to, lactate dehydrogenase (LDH) and D-Dimer levels were also significantly higher in the Non-Survivors Cohort than in the Survivors Cohort (383.56±54.11 IU/l and 0.79±0.41 mg/l vs 234.55±34.22 IU/l and 0.58±0.21 mg/l, respectively, p<0.05). 

The primary outcome of this study (An overall 28-day ICU mortality) was detected in ninety-four (N = 94) with an overall incidence of  48.70%   during    an    average    of 

Table 1: Mechanically Ventilated Critically Ill COVID-19 Infected Patients’ Comparative Variables and Analyze Outcome Data between Cohort I (Survivors) and Cohort II (Non-Survivors)

Data results of the comparative variables between the Survivors and Non-Survivors groups are statistically analyzed by independent T, Whitney-U, and Chi Square Tests Data (at p-value< 0.05). Data results of the comparative variables between the Survivors and Non-Survivors groups are expressed as Mean±SD, Mean (Range), Number (Percentage). Cohort I: Survivors. Cohort II: Non-Survivors. Yrs: Years. Kg: Kilogram.m: Meter. BW: Actual body weight at admission. BMI: Body mass index at admission. ICU: Intensive care unit. S: Significant (P-Value <0.05). NS: Non-significant (p-Value >0.05).N: Number of study’s critically ill patients

Table 2: Mechanically Ventilated Critically Ill COVID-19 Infected Patients’ Comparative Variables and Analyze Outcome Data between Cohort I (Survivors) and Cohort II (Non-Survivors)

Data results of the comparative variables between the Survivors and Non-Survivors groups are statistically analyzed by independent T, Whitney-U, and Chi Square Tests Data (at p-value< 0.05). Data results of the comparative variables between the Survivors and Non-Survivors groups are expressed as Mean±SD, Mean (Range), Number (Percentage). Cohort I: Survivors. Cohort II: Non-Survivors. N: Number of study’s critically ill patients. bpm: beat per minute. mcg: microgram. min: minute. S: Significant (p-Value <0.05). NS: Non-significant (p-Value >0.05). NE: Norepinephrine. NLR: Neutrophils to lymphocytes ratio. MLR: Monocytes to lymphocytes ratio. SI: Shock index. H.ALB: Human Albumin 20%. GCS: Glasgow coma scale. ALB: Albumin level. CRP: C-reactive protein. FER: Ferritin. LDH: Lactate dehydrogenase. CRP: ALB: CRP to ALB ratio.FER: ALB: FER to ALB ratio 12.40±4.79 days and   16.67±6.81 days of the ICU and overall hospital admission days, respectively, in which both were also significantly higher in the Non-Survivors Cohort compared to the Survivors Cohort (17.30±4.14 days and 24.72±1.98 days vs 9.23±1.06 days and 11.46±2.12 days, respectively, p<0.005). Also, the Non-Survivors Cohort was further sub-divided into two 2 phases, The Early Phase (≤14 days) and the Late Phase (>14 days), in which the incidence of mortality for our eligible studied mechanically ventilated critically ill COVID-19 infected patients, was lower in the Early Phase compared to the Late Phase (31 (16.06%) vs 63 (32.64%), respectively, p<0.05).

There were insignificant differences between the two mortality cohorts (Survivors vs Non-Survivors) regarding Glasgow Coma Scale (GSC), Child-Pugh Score, and the anthropometrics comparative variables of body weight (BW) and body mass inde   (BMI)   (12    (12-13), 6    (6-8), 74.63±10.06 kg, and 26.19±3.85 kg/m2 vs 12 (12-13), 6 (6-7), 73.45±10.56 kg, 25.50±4.22 kg/m2, retrospectively, p>0.05). The mechanically ventilated critically ill COVID-19 infected patients’ comparative variables and analytical outcomes data between Cohort I and Cohort II. Are fully summarized in Tables 1-2.

Table 3 shows the optimal cut-off point, sensitivity (TPR), specificity (TNR), Youden index (YI), positive and nega-tive predictive values (PPV and NPV), and the accuracy index (AI), for the three tested prognosticators. According to our study, the best cut-off values for monocytes, lymphocytes, and MLR in predicting the overall 28-day ICU mortality in mechanically ventilated critically ill COVID-19 infected patients were 1455 cells/µl, 3116 cells/µl, and 0.67, respectively. While the area under the curve (AUC) of the receiver operating characteristic (ROC) for our tested mortality predictor indicators were significantly greater for MLR prognosticator followed by monocytes and lymphocytes [0.923; 95% CI, 0.877-0.969, 0.874; 95% CI, 0.802-0.947, and 0.865; 95% CI, 0.791-0.939 respectively, p<0.05] as fully illustrated in Fig 1.

Table 3: The Optimal Cut-Off Point, Sensitivity, Specificity, Positive and Negative Predictive Values, Youden, and Accuracy Indices for the Two Tested Mortality Prognosticators of Monocytes, Lymphocytes, and MLR

CRP: C-reactive protein. FER: Plasma ferritin level. TPR: True positive rate (sensitivity) FPR: False positive rate. YI: Youden index. PPV: Positive predictive value. NPV: Negative predictive value. AI: Accuracy index. TNR: True negative ratio (specificity).MLR: Monocytes to lymphocytes ratio

Figure 1: ROC Curve Analysis of Monocytes, Lymphocytes, and Monocyte-to-Lymphocyte Ratio (MLR) for Predicting 28-Day ICU Mortality

The present study includes two studied outcome cohorts, the Survivors Cohort and the Non-Survivors Cohort, of admitted mechanically ventilated critically ill COVID-19 infected patients with moderate-severe ARDS. To the best of our knowledge, the uniqueness of this study is primarily involved in its direct prognostic performance comparison between three commonly used hematological prognosticators in a specified COVID-19 infected cohort. Mechanically ventilated critically ill patients have the highest probability of mortality in case of being on the storms of hyperinflammatory associated cytokine storm, Hyperoxidative associated radical storm, and hypercoagulability associated thrombotic storm. 

Many cellular components and acellular components are involved in the pathogenesis of COVID-19. Three main immune system elements that play versatile roles in SARS-COV-2. First; the monocytes become macrophages when they migrate to tissues to perform their job. Macrophage serve in different ways, they represent the antigen and release different cytokines that contribute in pro-inflammatory status and activate T-cells, they perform their job by engulfing pathogens like bacteria, debris and apoptotic cells, that explains the seen monocytosis in bacterial infections. However, the monocytosis status that is commonly observed in COVID-19 infection is partially due to RBCs rupture and release of free iron, so macrophages will pursue to phagocyte the free liberated iron in a process called erythrophagocytosis [40-41]. The second hematological involved immune-component in SARS-COV-2 pathogenesis is neutrophils, their role in respiratory viral infections isn’t well known although they are present and contribute in ARDS and lung injury. Many respiratory viral infections as influenza A virus have shown increased amount of neutrophil and its correlation with disease severity, neutrophils function via increasing the release of chemo-attractants and complement C5. In addition, hantavirus infections (causing Hantavirus pulmonary syndrome), Severe acute respiratory syndrome-related coronavirus (SARS-CoV) have all shown increased infiltration of neutrophils in the lung leading to a fatal ARDs[42-43]. Third important component to be discussed is lymphocytes. It is well known that the number of lymphocytes increase during viral infection and decrease during bacterial ones, as T-Lymphocytes exhibit their functions via cell-mediated response against virally infected cells with the help of B-lymphocytes that produce antibodies, and they mediate the production of interferons that aid in viral eradication. While the scenario is inverted in SARS-COV-2 as we see an increase in leukocytes and a decrease in lymphocytes despite the presence of a viral infection, which will lead to over-prescription of antibiotics that will lead to major issues specially in critically ill patients, as they will suffer from antibiotic resistance and they might be infected with multi-drug resistant bacteria [44-45].

As critically ill hospitalized patients need ultimate care and continuous follow up to assess their condition that might change in any second, the follow up procedure costs a lot of money and requires long time to come up, although time factor isn’t in the favor of critically ill patients specifically SARS-COV-2 patients. Therefore, in the context of our ever-shrinking resources we are looking for an urgent early risk stratification with rapid, affordable, valid, reliable, and discriminative predictive and prognostic tool that is easy, less painful, cost-effective and time saving in this high-risk and high uncertainty critically COVID-19 infected cohort to avoid any potential or even possible delay on targeted life-saving interventions. In order to assess severity and prognosis in the view of the study’s tested hematological variables for hospital resident COVID-19 patients, especially our tested mechanically ventilated critically ill cohort, we need to discriminate between different WBCs differentials. In this study we pursued to investigate the mortality prognosticating utility and performances for 3 tested hematological prognosticators including; monocytes, lymphocytes, and their corresponding ratio (MLR) in addition to addressed other major clinical outcomes of ICU and overall hospital LOS. 

Empirically, both MLR and NLR are economically feasible, both are correlated in a proportional manner with the duration of illness and both are elevated in bacterial infections according to many clinical studies that shown statistically significant increase of both ratios in many infection statuses, significant increase of the ratios with age, insignificant difference between both genders [46]. In some study, for example a study included 1468 patients, had concluded that the use of MLR or NLR provide better predictions and high specificity than CRP or WBCs alone in estimating septicemia in critically ill patients [47]. To justify the reason of selecting MLR over NLR is due to the use of corticosteroids (Cs) by COVID-19 critically infected patients and the reason behind the use of corticosteroids is the high risk of developing ARDS. Corticosteroids exert anti-inflammatory, immune-modulatory and anti-fibrotic activates. Many reports and cohort studies have demonstrated the evidence of Cs in decreasing ARDS in the early stages, reduce lung injury score, organ dysfunctions and increase ventilator free days. However, the use of Cs has affected the ability of using the overestimated NLR, thus the selection of MLR is more practical in those case scenarios due to their same directional movement, which will decrease the misleading results and minimize prescribing unneeded antibiotics[48].

Our study revealed a significant role of the aforementioned tested prognosticators in indicating more severe disease outcomes, including 28-day mortality risk, especially in COVID-19 infected critically ill patients with higher baseline pre-ICU admission days and co-morbidities. The analytical comparative variables revealed a significantly higher baseline pre-ICU admission days and co-morbidity levels in the Non-Survivors Cohort compared with the Survivors Cohort (7.42±4.57 days vs 2.23±1.06 days) and (57 (60.64%) vs 47 (47.47%) for co-morbidities count >2), respectively, p<0.05. The prognostic value of our tested haematological prognosticators have been demonstrated significant performances in many studies and on different diseases and syndromes, including COVID-19 infectious, with variable sensitivities, specificities, accuracies, positive and negative predictive values, and youden’s indices which allow us for early identification, stratification, diagnosis in some circumstances, and morbidities and mortalities prognosis, therefore contributing to overall positive impacts [49-50].

Other ratios thar we analyzed in our study including; C-reactive protein to serum albumin level ratio (CRP: ALB) and ferritin level to serum albumin level ratio (FER: ALB). Both ratios were also significantly higher in the Non-Survivors Cohort compared with the Survivors Cohort (58.71±24.91:1 vs 31.92±19.06: 1 and 346.11±112.85: 1 vs 176.33±76.62: 1, respectively, p<0.05). ALB is considered the primary negative acute-phase protein that is synthesized in the liver and can be significantly and negatively affected in COVID-19 infectious by many complex and interrelated mechanisms rather than nutritional factors, including but not excluded to, CRP associated albumin synthesis downregulation, cytokines associated exaggerated albumin transcapillary escaping rate, stress associated albumin hypercatabolic status. [51] Our study revealed that the Survivors Cohort had a higher insignificant ALB level compared with the Non-Survivors Cohort (2.64±0.12 g/dl vs 2.57±0.13 g/dl, respectively, p>0.05) although the average amount of human albumin (H.ALB) administered in the Survivors Cohort was significantly higher compared with the Non-Survivors Cohort [18.89±3.16 g/day vs 14.06±6.09 g/day, respectively, p<0.05) which may indicate a possible mortality benefit of H.ALB administration for mechanically ventilated critically ill COVID-19 infected patients not related to ALB level.

In summary, there is an urgent necessity to find an affordable and available biomarker with a reasonable performance to prognosticate SARS-COV-2 status in critically ill patients who are in a high risk of developing lethal complications as ARDS and other multi-organ failure. In this study, we concluded that the increasing of monocytes count and monocytes to lymphocytes ratio or decreasing lymphocytes count in COVID-19 infected patients, especially the mechanically ventilated critically ill cohort, can be potentially used for their reasonable performances and prediction utility in early identification and stratification of COVID-19 infected patients severities despite global ever-shrinking in medical teams and facilities, especially in critical care units to make sure optimum resource provision and implement swift management protocols. This study is limited by its retrospective design. A larger, multisite, and prospective study is needed to control for multiple confounders and to clarify the causation between the tested prognosticators and mortality. Despite these limitations, our conclusions may have an added value to the current excessively evolving controversial pieces of evidence, especially in the mechanically ventilated critically ill cohorts.

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