COVID-19: How can routine diagnostic parameters support in clinical decision making?

Identification of COVID-19-positive patients upon hospital admission usually relies on RT-PCR tests, which can be time-consuming. However, clinicians need diagnostic information to make decisions during the first examination of a patient. An initial evaluation can be performed from the results of a complete blood count (CBC). In patients who test positive for COVID-19, white blood cells along with the main subpopulations (neutrophils, lymphocytes and monocytes) are reduced [3, 39], whereas haematological parameters that explore the activation status of cell populations, such as high-fluorescent lymphocytes (HFL) are elevated [1, 14]. At the same time, red blood cells, haemoglobin and haematocrit remain unaffected [3, 39]. This information might be indicative of a possible SARS-CoV-2 infection and can assist clinicians in the medical management at the time of admission.

Host response against different pathogens generates distinct patterns of immune reactions, which can be identified by new haematological parameters that evaluate the activation status of immune cells. These patterns can be used to differentiate between a bacterial and a viral infection, assisting during the first contact with a febrile patient [4].

In very early phases of disease, elevated levels of white blood cells (WBCs) in some cases cannot specifically be attributed to infection but might result from haematological malignancy. Like in different types of infections, symptoms in early SARS-CoV-2 infection are often unspecific. Before starting treatment, thorough clarification therefore comprises exclusion of a haematological malignancy as a cause of symptoms. State-of-the-art haematology analysers are able to exclude the presence of circulating blasts or neoplastic lymphocytes with a simple CBC-Diff.

Once a malignancy is excluded, new haematological parameters, such as extended inflammation parameters (EIP) that evaluate the activation status of lymphocytes and neutrophils, can be used to identify patterns of host immune response in infection [5].

SARS-CoV-2 is a very new pathogen. However, the pandemic is evolving rapidly, and the condition of COVID-19 patients can change quickly during hospitalisation. Risk stratification therefore is among the most urgent questions of health care professionals. In that direction, lab parameters are explored as biomarkers of a severe course of the disease, with new articles published very frequently [6].

The infection course among patients is highly variable, but several laboratory parameters have been associated with severe and/or critical disease. Patients with severe disease showed a significantly lower platelet count, a shorter activated partial thromboplastin time (APTT), but higher D-dimer levels, higher fibrin degradation product (FDP) levels, higher fibrinogen levels and a prolonged prothrombin time [7 - 10]. In addition, elevated D-dimer levels of four times higher than normal, prolonged prothrombin time, and decreased platelet count (in decreasing order of importance) are indicators for a poor outcome of the disease and may help in stratifying patients who may need ICU admission and close monitoring during hospitalisation [11]. However, any underlying condition (e.g. liver disease) or medication (e.g. anticoagulants) which may alter the parameters should be accounted for regarding patient stratifying [11, 13].

Referring to the complete blood count (CBC), lymphopenia is observed in the majority of patients upon hospital admission but is more prominent in severe cases [8, 14, 36]. Similarly, neutrophilia is a characteristic of severe infections, but remains within normal ranges in healthy or non-severe cases [2, 15-16, 36]. Patients who develop severe disease also have a higher neutrophil to lymphocyte ratio (NLR) [15, 17, 36]. Finally, increased white blood cell (WBC) and neutrophil counts with concurrent lymphocytopenia can be used as early predictors for patients with high risk for ICU admission [12, 18-19, 37]. 

A meta-analysis of nine studies researching COVID-19 patients with nearly 400 with severe disease identified that the platelet count was significantly lower in patients with more severe COVID-19. Subgroup analysis comparing patients by survival noted lower platelet count correlated with mortality. Thrombocytopenia was also associated with over five-fold increased risk of severe COVID-19 illness. This suggests thrombocytopenia at the time of admission may be a prognosticator, but is not a consistent one [9, 12-13].

Despite these findings, it is still challenging for a single parameter to assess disease severity with high accuracy due to high individual variation and rapid change over time [6]. Correspondingly, studies have looked beyond single cell counts and have implemented scores to assess disease outcome, in which haematology parameters such as lymphocytes [20] and neutrophil to lymphocyte ratio [21] are used, among others.

Furthermore, the severity of COVID-19 infection is reflected in certain parameters from urinalysis: 

Patients with severe disease progression or adverse outcome show a more frequent presence of urinary granular cylinders and renal tubular epithelial cells [22].  Furthermore, COVID-19-associated nephritis is a known symptom of the SARS-CoV-2 infection, leading to respiratory decompensation that requires ICU admission and ventilation. The risk for respiratory decompensation is elevated, if two out of the three conditions leucocyturia, albuminuria and haematuria are present [23]. Accurate, fast measurement of red blood cells (RBCs)/haemoglobin, white blood cells (WBCs), protein and albuminuria, test strip analysis [24], as well as urine particle detection can provide clinical decision support upon ICU admission.

More than 140 models and scores have been developed by May 2020 with the aim to (a) identify risk groups for an unfavourable course of the disease within the general population, (b) identify COVID-19 positives amongst patients suspected of having an infection and (c) predict the course of infection of COVID-19 positive patients [35]. Depending on the type of model and score, the following factors are considered: patient demographics, comorbidities, lab tests and clinical examination results.

Use of single parameters to derive prognostic and predictive information can be inaccurate due to variations of biological markers over the time course of the infection and due to individual variations within the population. On the contrary, scores combining several parameters have the potential to balance such variations. Moreover, clinical decisions can be implemented faster when referring to one score value rather than interpreting several probably conflicting single parameter values.

A prognostic score to indicate early the need for ICU care and thus support patient management has been developed based on parameters from Sysmex XN-Series haematology analyser. Parameters reflecting patients’ immune cell activation such as antibody synthesising lymphocytes (AS-LYMP) and reactive monocytes (RE-MONO) are used, thus the score characterises the strength of immune response towards the infection. Based on parameter values measured within the first three days after presentation, the score potentially is able to predict the clinical severity of the infection within a two-week timeframe. Thus, the score may help to differentiate between patients who will recover without ventilation or deteriorate during this timeframe, or those who will need intensive care or have a fatal outcome. All required parameters are generated from a complete blood count with differential, the most common routinely requested baseline blood test globally [36]. The score is for research use only.

Although the frequency for bacterial and fungal coinfections seems to be low in early COVID-19 stages [25], urinary tract infection remains one of the most common infections and is also a result of long-term catheterisation, e.g. upon ICU admission. Furthermore, depending on their clinical condition, urinalysis to exclude pneumococcal and legionella infections is recommended for hospital admitted COVID-19 patients [26]. Urine particle analysis can exclude bacteriuria [27] and funguria [28] with high accuracy. As up to 80 % of suspected UTI cases turn out to be negative, the reliable exclusion of UTI within a minute allows for short-term decisions on and reduction of the prescription of antibiotics.

Since acute kidney injury (AKI) increases the risk of developing chronic kidney disease (CKD) [29], COVID-19 patients that went through an AKI or showed proteinuria and haematuria should be monitored thoroughly following their COVID-19 infection. Changes in the albumin output, as well as the albumin-creatinine ratio can detect renal damages at the early stage of reversible, moderately increased albuminuria. This is supported by the detection of renal tubular epithelial cells (RTEC) and haematuria. Sensitive test strip analysis in combination with CMOS allows an accurate and cost-efficient analysis of a broad population [24].

Hospitalisation should be considered for COVID-19-positive patients who have significantly elevated D-dimers. D-dimers with > 2 μg/mL or four times higher values than normal are highly associated with poor disease progression and higher mortality. Those patients, in contrast to patients with slightly elevated levels, often require intensive care support [11].

Prothrombin time (PT) is prolonged in the non-survivors at admission but only rather modestly: 15.5 seconds [range 14.4–16.3 seconds] in non-survivors versus 13.6 seconds [13.0-14.3 seconds] in survivors (normal range:11.5−14.5 seconds). The PT is also mildly prolonged at admission in those who needed critical care support versus the non-ICU cohort (12.2 seconds [range 11.2–13.4] versus 10.7 seconds [range 9.8–12.1], respectively) [11-12].

Patients that succumbed to COVID-19 were presented more frequently with leucocytosis characterised by lymphopenia and neutrophilia than survivors [18-19, 30, 38], although it is important to note that these observations also correlate with increased disease severity and cannot be used as predictive markers for survival.

Furthermore, the platelet count is of relevance for disease outcome: 

A meta-analysis of nine studies researching COVID-19 patients with nearly 400 with severe disease identified that the platelet count was significantly lower in patients with more severe COVID-19. Subgroup analysis comparing patients by survival noted lower platelet count correlated with mortality. Thrombocytopenia was also associated with over five-fold increased risk of severe COVID-19 illness. This suggests thrombocytopenia at the time of admission may be a prognosticator, but is not a consistent one [9, 12-13].

Several drugs have been investigated in COVID-19 patients with regards to improving survival. On 2 September, the WHO published a treatment guideline for the use of corticosteroids in COVID-19 patients based on the evidence from several randomised clinical trials. Systemic corticosteroids reduce 28-day mortality in patients with severe and critical COVID-19 [31]. Corticosteroids may reduce the inflammation in critical patients and therefore improve survival whereas in non-severe patients, corticosteroids may impair the immune response against the virus [32]. Therefore, the WHO strongly recommends not to administer these drugs to COVID-19 patients as they may increase the risk of death.

Thromboembolic events are a common complication of COVID-19 and mainly affect the venous, but also the arterial vasculature. All hospitalised patients should therefore receive low-molecular-weight heparin (LMWH) in a prophylactic dosage approved for prevention of venous thromboembolism (VTE). Observational studies suggest that a standard-dose LMWH prophylaxis is not sufficiently effective, particularly in intensive care patients, so that intensified anticoagulation, e.g. with an intermediate, semi-therapeutic LMWH dose should be considered. Alternatively, for example in the case of heparin intolerance or heparin-induced thrombocytopenia type II (HIT-II), the administration of danaparoid, fondaparinux, bivalirudin, or argatroban is possible [11, 33-34].

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