COVID-19 spans a wide clinical spectrum from asymptomatic to severe pneumonia with multiple organ failure (Guan et al., 2020), majorly threatening global health, including that of Europe (Sun et al., 2020a). Early identification of critical patients may reduce mortality by timely interventions (Sun et al., 2020a). Many studies have explored the diagnostic or prognostic value of various factors including age, sex, CT scan, biochemical, and haematological parameters (Galloway et al., 2020; Ji et al., 2020; Lu et al., 2020; Shi et al., 2020a; Shi et al., 2020b; Sun et al., 2020b; Wang et al., 2020a; Yan et al., 2020; Caramelo et al., 2020; Yuan et al., 2020; Chen et al., 2020). Most studies were however geographically limited, had high risk for bias, and had no validation cohort (Wynants et al., 2020). C-reactive protein, ferritin, D-dimer, albumin, urea nitrogen, bilirubin, and lactate dehydrogenase (LDH) levels are cited as indirect indicators of the presence and severity of COVID-19 (Henry et al., 2020; Ji et al., 2020; Liu et al., 2020a; Luo et al., 2020; Sun et al., 2020b; Wu et al., 2020; Yang et al., 2020a; Zhou et al., 2020), as are complete blood count (CBC) and differential count (DIFF) changes, specifically lymphopenia, neutrophilia, high neutrophil-to-lymphocyte ratio (NLR) and thrombocytopenia (Henry et al., 2020; Jiang et al., 2020; Kermali et al., 2020; Khartabil et al., 2020; Lippi et al., 2020a; Lippi and Plebani, 2020b). All aforementioned parameters are widely available, but their value is constrained by significant inter-patient variability and limited specificity.

Newer haematology analysers are capable of functional characterisation of blood cells (Hoffmann, 2014; Buttarello, 2016; Chabot-Richards and George, 2015), including measurement of immune cell activation (Kono et al., 2018; Linssen et al., 2007), which has shown promise in screening for infectious diseases (Prodjosoewojo et al., 2019). The aim of this study is to develop and validate a prognostic score using only haemocytometric data for COVID-19 patients presenting at hospitals, to predict within three days of hospital admission, who will deteriorate and require intensive care unit (ICU) transfer within 14 days of admission. Importantly, our intended purpose of this score is to assist with objective risk stratification to support patient management decision making early on, and thus facilitate timely interventions, such as need for ICU or not, before symptoms of severe illness become clinically overt, with the intention to improve patient outcomes, and not to predict mortality. A secondary aim of this study is to document trends of haematology parameters over time, specifically for the newer parameters, as most of the published data in COVID-19 patients focussed on traditional parameters such as lymphocyte counts, platelet counts, and NLR.

We showed that SARS-CoV-2 infection is accompanied by haemocytometric changes over time and that distinct haemocytometric parameters, combined in a COVID-19 prognostic score, can be used early on to identify those patients likely to deteriorate thereafter and thus may benefit from ICU admission. Moreover, our data suggest that parameters reflecting the activation or functional status of blood cells are better disease severity indicators than traditional parameters, such as lymphocyte or platelet counts.

In COVID-19, lymphopenia has been assigned a key role based on a higher incidence and greater suppression observed in ICU patients (Terpos et al., 2020). Our data indicate that lymphocyte count had no significant prognostic value (AUC 0.550; 95% CI 0.523-0.576), although relative presence of lymphocytes did, hence the incorporation of NLR, IGLR, and AS-LYMPH%/L (reflecting lymphoplasmacytoid lymphocytes [Linssen et al., 2007]) in the prognostic score. Increased lymphocyte activation has been documented in COVID-19 (Fan et al., 2020; Chong et al., 2020). Our findings concur with a previous study (Yip et al., 2020) that reported AS-LYMPH as a strong predictor of clinical severity in COVID-19 patients. Furthermore, a specific “hourglass” appearance on the WBC scattergram on Sysmex analysers, representative of lymphoplasmacytoid lymphocytes, was reported to have a high positive predictive value to detect COVID-19 (Osman et al., 2020).

In contrast to lymphocytes, neutrophils, including precursors, have a tendency to increase in COVID-19 (Mitra et al., 2020). IGs, representing metamyelocytes, myelocytes,, and promyelocytes (Briggs et al., 2011), were commonly present in our patient population, especially in those more severely ill. The importance of IGs in management of sepsis has been reported (Ayres et al., 2019; Nierhaus et al., 2013). Of note, increases in neutrophil counts and neutrophil activity (NEUT-RI) were dissimilar, unlike observations in bacterial infections (Park et al., 2015). RE-MONO%/M is abnormal in critical illness cases only, in line with a previous report attributing a key role for monocytes and macrophages in severe COVID-19 (Merad and Martin, 2020).

Erythropoietic changes have been reported, mostly showing low HGB levels (Fan et al., 2020; Lippi and Plebani, 2020b; Sun et al., 2020c). We found that whilst HGB levels decrease in COVID-19 patients, the erythropoietic response to anaemia, indicated by RET and reticulocyte production index (data not shown), were mostly normal. Haemoglobinisation of reticulocytes, as indicated by negative DELTA-He levels, is however significantly compromised, specifically in more severe cases, possibly due to ongoing inflammation (Weimann et al., 2016). NRBCs are absent in peripheral blood of healthy adults. Their presence, without reticulocytosis, in severe COVID-19 cases indicates haematopoietic stress, probably due to prolonged hypoxia or inflammation (Danise et al., 2011). Furthermore, NRBCs, were reported as a marker of disease severity in ARDS patients, indicating a higher risk of death (Menk et al., 2018).

Contrary to other studies (Henry et al., 2020; Jiang et al., 2020; Wang et al., 2020b; Yang et al., 2020b), PLT at presentation were similar between CI and NC cases, mostly within normal limits with no sign of increased platelet consumption as IPF also remained normal. PLT, and IPF#, tended to increase with disease severity. Higher PLT have been previously reported in severe COVID-19 (Qu et al., 2020). So thrombocytosis, more than thrombocytopenia may be linked to severe COVID-19 which is in contrast to guidelines to identify severe pneumonia (Metlay et al., 2019).

A recent meta-analysis (Zeng et al., 2020) concluded that severe COVID-19 patients had higher neutrophil counts and NLR, and lower lymphocyte counts than those with non-severe COVID-19, and that these basic parameters might help clinicians to predict the severity and prognosis of COVID-19. Although our findings concur with their observed clinical severity-based WBC differences, the discriminating power, early on during hospitalisation and thus value to determine prognosis, was insufficient. A previous report about the prognostic value of NLR (Liu et al., 2020b) is also not supported by our data. Altogether, our findings indicate that new parameters, reflecting functional status of blood cells, are more frequently outside reference ranges in COVID-19 than classical parameters such as lymphocytes, neutrophils or platelets. However, none of the measured parameters, traditional or novel, alone could discriminate patients based on disease severity. The prognostic score we developed used multiple parameters, representing the three haemopoietic cell lines, with the aim to distinguish CI from NC COVID-19 patients. Different cell lines may not all be equally affected by COVID-19 at the same time. Thus, it is not unexpected that multiple parameters together are better at predicting disease severity, as duration of symptoms at presentation differs widely in individual patients.

The patient group is inherently heterogeneous as health-seeking behaviour may vary widely between patients: some patients may present early whilst others present late at health facilities. Furthermore, recall when symptoms started may be unclear at time of presentation to the hospital. Therefore, a patient on day 3 of hospitalisation could be in an earlier phase of infection than a patient on day 0. In this regard, as the intended use of the prognostic score was to identify at an early stage (once medical care had been sought) who is likely to deteriorate, to keep it as simple and practical as possible, we analysed the data from day 0–3 as a collective dataset for the purpose of identifying which parameters, and at what cut-off values, should be incorporated in the score. Likewise, we did not compare score values from the early phase of hospitalisation (days 0–3) with the later phase (days 4–13) in keeping with the intention to predict outcome early on before signs of critical illness may become clinically overt. We did however plot the data over time to document the patterns as this may be useful in the future to assess response to specific COVID-19 therapies.

The prognostic score correctly identified 70.5% of these patients during first 3 days after hospital admission, with similar performance confirmed in the validation cohort (72.0%). Prognostic score trends over 14 days confirmed a stable clinical course in NC patients and disease progression in CI patients, peaking on day 6 (sensitivity 93%). In the development cohort there was a distinct progressive upward trend of prognostic score values from day 0, peaking on day 6 in the CI group. In the validation cohort, the score also peaked on day 6, but the day 0 score value started relatively high, dropping on day 1, giving the appearance of convergence with the NC group on day 1. The validation cohort was comprised of patients from only two hospitals. Study enrolment at these two hospitals took place at different times of the pandemic, during which time ICU capacity was ramped up significantly. In this regard we speculate that ICU access differed at the two hospitals, hence patients with a similar degree of disease severity may have been admitted to ICU at one hospital and remained on the general ward at the other. Furthermore, all day 0 patients in the CI group were from a single hospital, and numbers were small relative to the development cohort. Our ethics approval did not permit us to review the individual patient clinical records of six patients in the CI group that already had very high (10–16 points) prognostic score values on day 0, four of whom also had no further follow-up samples which would have brought the score value averages on the days following admission, upwards. Any such inter-hospital variability would have been masked in the development cohort, because of the much larger sample size and representation from nine hospitals, compared to the validation cohort where such differences may have been more readily exposed. We therefore speculate that the high average day 0 score value patients in the validation cohort are outliers, and that the day 1 data is more representative of the expected prognostic score values in the validation cohort data set.

As has been widely reported in the literature, our data too shows that males were predominantly affected, and that disease severity was associated with increasing age and presence of comorbidities in general (Table 1, Figure 2). However, not all young patients had a mild course, and not all old patients with comorbidities were critical. Systemic inflammation is an important factor driving disease severity. Our prognostic score, incorporating the activation status of immune cells, may therefore have additional value, especially on an individual patient level, over classical risk factors such as age, gender and comorbidities in discriminating between NC and CI patients, (Table 6, Figure 9). As such, our prognostic score may assist in identifying any patients at risk for severe disease progression, being young or old, male or female, with or without comorbidity and by doing so, support individualised treatment decisions with objective data.

Notably, the mortality rate was relatively high in patients on the general ward, comparable to that of patients admitted to ICU, possibly due to ICU bed shortages or unfamiliarity with COVID-19 at that time. Whatever reason, we assumed that the need for more intensive treatment should have been considered for all patients that died in the general ward. Once a patient is overtly critically ill, clinical judgement will suffice to prioritise intensive care for such a patient. Importantly, our score strives to identify the deranged immune response as a harbinger of criticality (organ failure) before such organ failure is clinically evident. Our data (Figure 8B) shows that prognostic score values of patients with a critical clinical course (CI group) were indistinguishable based on outcome, namely whether they recovered or died, confirming that the clinical applicability of our score is to predict on an individual patient level who is likely to have a critical clinical course, but not to predict mortality.

In our study, the prognostic score was calculated retrospectively using the haemocytometric data from each individual sample measurement exported into Microsoft Excel (Microsoft Corp., USA). For future clinical practice, we envisage that the prognostic score will be automatically calculated by the laboratory information system and that the result is given on request, together with complete blood count analysis. Besides serving as a risk stratification tool for clinical decision making early on, we postulate that the prognostic score, which provides a snapshot in time of the phase of an individual’s immune response, may be promising as an aid to patient selection for future clinical trials exploring therapeutic options for COVID-19.

Most previous risk score development studies were limited by small sample size, single centre, univariate analysis, and lack of a validation cohort. In contrast, the 4C mortality score, developed and validated by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC), incorporating patient demographic information (age, sex, comorbidities), clinical observations (respiratory rate, peripheral oxygen saturation, level of consciousness) and blood parameters (C-reactive protein (CRP), urea level), incorporated more than 35,000 and 22,000 patients into the development and validation cohorts respectively, and showed better performance at predicting mortality in COVID-19 patients than all previously published scores (Knight et al., 2020). The ISARIC investigators conclude that their score is easy to use as commonly available at hospital admission. Our score has several advantages: firstly, only objective measurements of a haematology analyser are used whilst the ISARIC 4C mortality score uses clinical observation parameters which may be subject to interpretation; secondly, our score is aimed to be automatically generated whilst 4C mortality score needs to be calculated; thirdly, different laboratory measurements (oxygen saturation, CRP, urea) are required to calculate the 4C mortality score, whilst our score uses the globally most commonly requested laboratory examination for patients attending health facilities; fourthly, haematology analysers are widely available globally more so than CRP measurements; and fifthly, the 4C mortality score is aimed at patients admitted to the hospital, whilst our score was developed for patients presenting at the hospital (of whom some were never admitted).

The strength of our study is the inclusion of a relatively large group of confirmed COVID-19 cases from multiple centres and countries, including a validation cohort, Furthermore, we believe that an advantage of our prognostic score is that all input data required to calculate the score value are generated from a single haematology profile test, which is the most common routinely requested baseline blood test in all patients globally.

A limitation of our study is its retrospective nature, as data retrieved from hospital records were sometimes incomplete. Our study was performed at a time that COVID-19 was a new disease entity that constrained the health care in many of the participating centres which may have affected management decisions and therefore study outcome parameters. Data from out-patient settings including more mild cases, and from nursing homes that usually accommodate high risk patients, are needed. Furthermore, clinical data collection was limited, including comorbidity affecting COVID-19 susceptibility and ICU admission decisions, notably as the demand for ICU beds was greater than the availability at the time of our study. Importantly, the conditions of our fast-tracked ethics clearance to facilitate rapid study initiation did not permit data collection about bacterial superinfections and medication (antibiotics, corticosteroids), whilst these factors may affect outcome and haemocytometric parameters.

Finally, our prognostic score includes Sysmex unique parameters. This is a limitation as the score is not universally applicable to all haematology analysers, although the concept is transferable (see Supplementary file 2 for parameters available on other manufacturer haematology platforms). However, it is the very ability to quantify blood cell activation, a reflection of the general immune response status of an individual, that has rendered our prognostic score (which incorporates cell activation parameters such as reactive monocytes and antibody-synthesising lymphocytes amongst others) better than using only standard parameters, such as neutrophil-to-lymphocyte ratio, which are universally available on all systems, at least in our patient dataset.

All data analysed during this study are included in the manuscript and supporting files. Source data files have been provided for figures 2, 3, 4, 5, 6, 7, 8, 9 and 11, and tables 1, 2, 4, 5, and 6.

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Author details

1. Joachim Linssen

   Sysmex Europe GmbH, Hamburg, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   is a permanent employee of Sysmex Europe GMBH who provided free of charge study reagents to the study centres.

2. Anthony Ermens

   Amphia Hospital, Breda, Netherlands

   Contribution

   Conceptualization, Investigation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

3. Marvin Berrevoets

   Elisabeth-Tweesteden Hospital, Tilburg, Netherlands

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Michela Seghezzi

   Hospital Papa Giovanni XXIII, Bergamo, Italy

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Giulia Previtali

   Hospital Papa Giovanni XXIII, Bergamo, Italy

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Simone van der Sar-van der Brugge

   Amphia Hospital, Breda, Netherlands

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6462-075X

7. Henk Russcher

   Erasmus MC, University Medical Center, Rotterdam, Netherlands

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

8. Annelies Verbon

   Erasmus MC, University Medical Center, Rotterdam, Netherlands

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

9. Judith Gillis

   Leiden University Medical Center, Leiden, Netherlands

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

10. Jürgen Riedl

    Albert Schweitzer Hospital, Dordrecht, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

11. Eva de Jongh

    Albert Schweitzer Hospital, Dordrecht, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

12. Jarob Saker

    Sysmex Europe GmbH, Hamburg, Germany

    Contribution

    Data curation, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

    Competing interests

    is a permanent employee of Sysmex Europe GMBH who provided free of charge study reagents to the study centres.

13. Marion Münster

    Sysmex Europe GmbH, Hamburg, Germany

    Contribution

    Formal analysis, Validation, Visualization, Writing - original draft, Writing - review and editing

    Competing interests

    is a permanent employee of Sysmex Europe GMBH who provided free of charge study reagents to the study centres.

14. Imke CA Munnix

    Canisius Wilhelmina Hospital, Nijmegen, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

15. Anthonius Dofferhof

    Canisius Wilhelmina Hospital, Nijmegen, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

16. Volkher Scharnhorst

    Catharina Hospital, Eindhoven, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

17. Heidi Ammerlaan

    Catharina Hospital, Eindhoven, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

18. Kathleen Deiteren

    University Hospital Antwerp, Antwerp, Belgium

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

19. Stephan JL Bakker

    University Medical Center Groningen, University of Groningen, Groningen, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

20. Lucas Joost Van Pelt

    University Medical Center Groningen, University of Groningen, Groningen, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5538-1806

21. Yvette Kluiters-de Hingh

    Elisabeth-Tweesteden Hospital, Tilburg, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

22. Mathie PG Leers

    Zuyderland Medical Center, Sittard-Geleen, Netherlands

    Contribution

    Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

23. Andre J van der Ven

    Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Conceptualization, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    andre.vanderven@radboudumc.nl

    Competing interests

    has an ad hoc consultancy agreement with Sysmex Europe GMBH who provided free of charge study reagents to the study centres.

    "This ORCID iD identifies the author of this article:" 0000-0003-1833-3391

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