A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study

COVID-19 induces haemocytometric changes. Complete blood count changes, including new cell activation parameters, from 982 confirmed COVID-19 adult patients from 11 European hospitals were retrospectively analysed for distinctive patterns based on age, gender, clinical severity, symptom duration, and hospital days. The observed haemocytometric patterns formed the basis to develop a multi-haemocytometric-parameter prognostic score to predict, during the first three days after presentation, which patients will recover without ventilation or deteriorate within a two-week timeframe, needing intensive care or with fatal outcome. The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723–0.781) increasing to 0.875 (95% CI 0.806–0.926) on day 3, was superior to any individual parameter at distinguishing between clinical severity. Findings were confirmed in a validation cohort. Aim is that the score and haemocytometry results are simultaneously provided by analyser software, enabling wide applicability of the score as haemocytometry is commonly requested in COVID-19 patients.


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including measurement of immune cell activation (29, 30), which has shown promise in 15 screening for infectious diseases (31). The aim of this study is to develop and validate a 16 prognostic score using only haemocytometric data for COVID-19 patients presenting at 17 hospitals, to predict within three days of hospital admission, who will deteriorate and require 18 intensive care unit (ICU) transfer within 14 days of admission. Importantly, our intended purpose 19 of this score is to assist with objective risk stratification to support patient management decision 20 making early on, and thus facilitate timely interventions, such as need for ICU or not, before 21 symptoms of severe illness become clinically overt, with the intention to improve patient 22 outcomes, and not to predict mortality.

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Methods 25 26 Study design, sample size and participants

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Whilst it was not possible to calculate an appropriate sample size due to the rapid escalation of 29 the COVID-19 pandemic and concomitant resource contraints experienced by the study centres 30 during the time of planned data collection, the study team set a minimum target of 500 patients, 31 of which at least 250 were admitted to intensive care, and that the study would remain open for 32 enrolment of patients until 6 April 2020 to increase the patient numbers as much as possible .

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In this explorative multicentre study patients were prospectively enrolled into a prognostic score 35 development cohort from 21 February to 6 April 2020, with follow-up to document clinical 36 outcome until 9 June 2020, from 7 hospitals in the Netherlands and 1 each in Italy and Belgium.

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Data analysis and prognostic score development was performed retrospectively from 9 to 29

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Two management/outcome groups were defined, assuming that all patients that died would 34 have needed ICU admission, and would have been admitted to ICU, had an ICU bed been 35 available: 1) non-critical (NC) group comprising patients classified as mild, moderate or severe, 36 and 2) the CF group comprised of critical (C) and fatal (F) outcome patients.

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Day 0 refers to the day of first presentation at hospital, and the day of admission for those 38 patients requiring hospitalisation. Day 1 refers to one day after the day of admission, or 39 alternatively, one hospital bed night. Day 2, refers to 2 days, and so on.

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The prognostic score development process is outlined in Figure 1. Haemocytometric data were 46 grouped according to clinical severity, management/outcome, symptom duration and days of 47 hospitalisation, and analysed up to day 14 and compared with healthy controls, to identify 48 specific patterns and trends. Haemoglobin values were adjusted for age and gender.

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In brief, each parameter was analysed univariately with regard to critical/fatal outcome and 51 backward-selection multiple logistic regression analysis was conducted to select parameters for 52 inclusion in the prognostic score, using the following criteria: 1) significant difference on days 0- is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . https://doi.org/10.1101/2020.09.27.20202168 doi: medRxiv preprint    is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020.  Abbreviations: NC, non-critical patient group; CF, critical/fatal patient group; ROC, receiver operating characteristics (curve); AUC, area under the curve.

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Step 5: Step 6: Step 7: Step 8: Trend analysis of individual haemocytometric parameters over 14-day period grouped by defined disease severity classifications Step 1: Step 2: All parameters All sample from days 0 -13 Step 3: Univariate analysis with regard to critical or fatal outcome and backward-selection multiple logistic regression analysis was conducted to select parameters for inclusion in the prognostic score, using the following criteria: 1) significant difference on days 0-3 (P≤0.0001); 2) >20% outside reference range (CF group; 3) only dominant parameter selected if ≥2 eligible parameters are interdependent.
Step 4: Parameters identified in step 2. All samples from days 0-3 ROC curve analysis of individual parameters. AUC values used to select best cut-off value to discriminate between NC and CF groups.
Points per parameter assigned based on fulfilment of 1) precondition 2) optimal cut-off value 3) with extra points for increasing specificity and likelihood ratio (Range 0-4 points per variable) Prognostic Score Determination of prognostic score cut-off value to best discriminate between NC and CF during first 3 days of hospitalisation (using all full profile (CBC-DIFF-RET) samples from days 0-3) Prognostic Score Validation of prognostic score and cut-offs using an independent patient cohort Prognostic score 14-day time horizon plot (using all full profile samples from days 0-13) Identification of additional parameters to improve discrimination between CF and NC groups (using all samples from days 0-3) (Maximum 1 point per additional parameter identified) Prognostic Score All parameters All samples from days 0 -3 Prognostic score parameters 1 calculation of optimal cut-off values associated with sensitivity and specificity and used as the 2 baseline for points assignment, with further increments for increasing specificity and likelihood 3 ratio in discriminating between NC and CF groups (maximum 4 points). The score represents 4 the sum of individual parameter points.

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Score values were calculated for day 0-3 measurements to determine the cut-off value that best 7 discriminate between the NC and CF groups. To further enhance score sensitivity without 8 compromising specificity, additional parameters were included (maximum 1 point each) if they 9 were significantly abnormal from day 4 onwards with >95% specificity.

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The 14-day prognostic score time horizon was plotted using all available measurements and its 12 performance in predicting disease severity validated in an independent patient cohort.

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Only haemocytometry parameters were used as predictors in the prognostic score 15 development. As these data are genearted from automated haematology analysers, and do not 16 rely on interpretation, predictors for the prognostic score were automatocally blinded.

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Also, the outcome groups were defined in advance of patient enrolment, and patients were 19 classified according to objective data (length of hospital stay, general ward or ICU, recovered 20 and discharged from hospital or died) providing by the enrolling study centre prior to the 21 commencment of data analysis and score development. In this regard, the authors involved in 22 score developed had no influence over assessment of prediction of disease severity of 23 individual patients, and hence assessment of outcome was deemed to have been blinded.

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Missing data

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Those samples that did not have a RET channel measurement were excluded from the trend 43 analysis of all parameters measured or derived from this measurment channel (Table 1)   is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . https://doi.org/10.1101/2020.09.27.20202168 doi: medRxiv preprint

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Patient characteristics

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In total 999 patients were enrolled in the development cohort ( Figure 2). Seventeen patients 5 with underlying haematological malignancies or currently undergoing chemotherapy, were 6 subsequently excluded. Nine hundred eighty-two patients with 2587 haematology 7 measurements (day 0-13), were included to analyse temporal haemocytometric data trends 8 and for performing steps 1 and 2 of prognostic score development (Figure 1). Median age was 9 71 years (range 18-96) and 68% of the patients were male. Patient distribution by sex, clinical 10 severity and comorbidities is shown in Table 2. After excluding 59 patients with missing day 0-3 11 CBC-Diff data, the remaining 923 patients with 1587 measurements were used to complete 12 prognostic score development (step 3 to 6).

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Patients who died or were critically ill, were significantly older than those less severely ill 19 (median age 74 vs 65 years, p<0.001). Although males outnumbered females (631; 292) in 20 patients that had a severe disease progression, mortality rates were independent of sex.

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Characteristics of the 923 patients are presented in Table 3. Distribution of clinical severity by  is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. Footnote. a) Details of how the validation patient cohort patients were selected are provided in Figure 11, b) the exclusion criteria for the validation cohort were the same as for the development patient cohort   1 Footnote: a) Occurrence of comorbidities is shown as a relative frequency expressed as a percentage (patients with a comorbidity 2 divided by total number of patients in whom the presence or absence of comorbidities was recorded) as 2 of the participating study 3 centres did not document the presence or absence of comorbidities.   to document obesity as a comorbidity. As this study was undertaken in the early phase of the pandemic, it may not have been 8 common knowledge to all attending physicians that obesity is significant contributor to adverse outcomes in COVID-19.  is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . Page 11 of 36  In NC and CF groups, lymphopenia is present for 7 and 10 days respectively and normalises 16 thereafter, as lymphocyte numbers have a tendency to increase after 5 days in both groups.

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( Figure 4A) The NLR increases in the CF group compared to the NC group, and then gradually 18 decreases again. The differences between the groups remain significant over time (p<0.001)

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( Figure 4B). Neutrophil counts are normal and remain stable in the NC group, whereas values 20 are mildly elevated and continue to rise over time in the CF group ( Figure 5A). This increase in 21 neutrophils is accompanied by a mildly elevated NEUT-RI level in the CF group ( Figure 5B).

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Absolute differences in baseline IG#, although statistically significant (p<0.001), are small. After 23 day 2, there is a marked rise in IG in the CF but not in the NC group ( Figure 5C). The immature 24 granulocyte-to-lymphocyte ratio (IGLR) trend mirrors that of IG ( Figure 5D)

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In contrast, in the CF group monocyte activation increases up to days 3 to 4, with values 34 returning to within the reference range after a week ( Figure 6B). is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020.   The number of sample measurements available per day for the trend analysis for the parameters plotted per patient group are shown 10 in the table below:   11   Day  0  1  2  3  4  5  6  7  8  9  10  11  12  13  NC  469  139  146  129  95  65  59  42  31  25  18  12  11  17  CF  321  122  134  90  94  71  54  58  45  44  33  33  25  17 12 . CC-BY-NC 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020.  In all patients, there is a gradual drop in HGB, also after adjusting for age and sex ( Figure 7A), 2 with differences between the groups becoming increasingly wider from day 5 onwards. After day 3 7, HGB continues to decline only in the CF-group. RET# remain low in both groups despite 4 dropping HGB in the first week but RET# shows a consistent rise thereafter in the CF group, 5 towards the upper limit of the normal reference range ( Figure 7B).

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The Delta-He is negative and remains relatively stable in the NC group ( Figure 7C). In contrast, 7 Delta-He drops progressively in the CF group, reaching its nadir at about day 7, and then rises 8 towards zero, primarily due to an improvement in the RET-He values (data not shown). NRBC# 9 is almost zero in the NC group (within normal range) but rise sharply and progressively at about

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The number of sample measurements available per day for the trend analysis for the parameters plotted per patient group are 20 shown below: is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . https://doi.org/10.1101/2020.09.27.20202168 doi: medRxiv preprint 1 PLT findings 2 PLT are largely within the normal range but show a progressive upward trend over time for both 3 groups, with patients in the CF group manifesting with mild thrombocytosis from about day 10 4 onwards ( Figure 8A). The IPF# initially is within the normal reference range for both groups but 5 over time the CF group shows a gradual increase, exceeding the upper limit of the reference 6 range in parallel to PLT# ( Figure 8C), whereas IPF(%) remains within normal limits throughout 7 for both groups ( Figure 8D). The platelet-to-lymphocyte ratio (PLR) is abnormally elevated for 8 both groups throughout, with values slightly higher in the CF group, but only until day 5, after 9 which the NC and CF groups overlap ( Figure 8B).    NC  469  139  146  129  95  65  59  42  31  25  18  12  11  17  CF  321  122  134  90  94  71  54  58  45  44  33  33  25  17  IPF#,  IPF   NC  275  55  91  69  53  37  36  22  10  14  7  6  33  7  CF  183  57  74  64  60  56  44  48  38  38  28  25  22  14   21  22  23  24  25  26  27  28  29  30  31 . CC-BY-NC 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. HEe(p<0.0001, 85,7%), and NRBC (p<0.0001, 46.0%). Other parameters also fulfilled these 10 criteria but these were not selected as they are interdependent on others already included.  Table 4.

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Prognostic score performance in the 923 patients using 3 as the cut-off, correctly identified  is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . https://doi.org/10.1101/2020.09.27.20202168 doi: medRxiv preprint 1 2

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The number of measurements for each day of hospitalisation that were available per patient group are shown in the is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020.  3 Note: For the primary variables, 1 point = value above the cut-off value for the best AUC; 2 points = value above the cut-off value for the best AUC and ≥80% specificity; 3 points = value above 4 the cut-off value for the best AUC and >90% specificity; and 4 points = value above the cut-off value for the best AUC and >95% specificity. The cut-off values for the secondary variables 5 were chosen exclusively based on observed extremes of values in critical disease, with the maximum award of 1 point per variable.

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The prognostic score values were calculated automatically using a pre-set algorithm, using the above cut-off values to assign points per individual parameter. The aim is to have the formula for calculation of the 7 prognostic score incorporated inot the Laboratory Information System software in use in indivodual laboratories.

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Page 20 of 36 1 2 Hospitalised with admission to a general ward on day of initial presentation (non-ICU) Hospitalised with admission directly to ICU on day of initial presentation day Mild Median score a n Recovered Median score a n Died Median score a n Recovered Median score a n Died Median score a n    For the initial period of hospitalisation (<4 days) and for the 14-day prediction time-horizon, the prognostic score was better than NLR at differentiating 14 clinical severity, with a higher AUC at all time points (Table 6). Notably, the cut-off value that determined the best AUC was consistent for the prognostic     In investigating if the score can predict severity independent of the classical risk factors such as 1 age and presence of comorbidities, using a Mann-Whitney test, it was found that the prognostic 2 score was significantly higher in the CF group than the NC group across all age groups and for 3 all age groups segregated by the presence or absence of comorbidities, with the exception of 4 patients 84 years and older with with reported comorbidities (Table 7 and Figure 10). The 5 median difference in prognostic score values between the NC and CF groups ranged from 2-7 6 points. Table 7. Mann-Whitney test for significance of the difference in prognostic score between critical/ 10 fatal (CF) and non-critical (NC) patients.

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A) Box and whisker plots of prognostic score values for NC and CF groups segregated by age. The prognostic score can predict 27 severity independent of age, therefore potentially assisting in identifying young patients at risk for severe disease progression as  is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020.

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The number of measurements for each day of hospitalisation that were available per patient group are shown in the table below and 11 indicate that overall, there were relatively few measurements per day for the NC group which has contributed to greater variance per 12 time point.

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We showed that SARS-CoV-2 infection is accompanied by haemocytometric changes over time 20 and that distinct early haemocytometric parameters, combined in a COVID-19 prognostic score,

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can be used early on to identify those patients likely to deteriorate therafter and thus may 22 benefit from ICU admission. Moreover, our data suggest that parameters reflecting the 23 activation or functional status of blood cells are better disease severity indicators than traditional 24 parameters, such as lymphocyte or platelet counts.

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In COVID-19, lymphopenia has been assigned a key role based on a higher incidence and

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In contrast to lymphocytes, neutrophils, including precursors, have a tendency to increase in is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . https://doi.org/10.1101/2020.09.27.20202168 doi: medRxiv preprint neutrophil counts and neutrophil activity (NEUT-RI) were dissimilar, unlike observations in 1 bacterial infections (40). RE-MONO%/M is abnormal in critical/fatal cases only, in line with a 2 previous report attributing a key role for monocytes and macrophages in severe COVID-19 (41).

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Erythropoietic changes have been reported, mostly showing low HGB levels (22,34,42). We 5 found that whilst HGB levels decrease in COVID-19 patients, the erythropoietic response to 6 anaemia, indicated by RET and reticulocyte production index (data not shown), were mostly 7 normal. Haemoglobinisation of reticulocytes, as indicated by negative DELTA-He levels, is 8 however significantly compromised, specifically in more severe cases, possibly due to ongoing 9 inflammation (43). NRBCs are absent in peripheral blood of healthy adults. Their presence, 10 without reticulocytosis, in severe COVID-19 cases indicates haematopoietic stress, probably 11 due to prolonged hypoxia or inflammation (44). Furthermore, NRBCs, were reported as a 12 marker of disease severity in ARDS patients, indicating a higher risk of death (45).

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Contrary to other studies (15, 23, 46, 47), PLT at presentation were similar between CF and NC 15 cases, mostly within normal limits with no sign of increased platelet consumption as IPF also 16 remained normal. PLT, and IPF#, tended to increase with disease severity. Higher PLT have 17 been previously reported in severe COVID-19 (48). So thrombocytosis, more than 18 thrombocytopenia may be linked to severe COVID-19 which is in contrast to guidelines to 19 identify severe pneumonia (49).

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A recent meta-analysis (50) concluded that severe COVID-19 patients had higher neutrophil 22 counts and NLR, and lower lymphocyte counts than those with non-severe COVID-19, and that 23 these basic parameters might help clinicians to predict the severity and prognosis of COVID-19.

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Although our findings concur with their observed clinical severity-based WBC differences, the 25 discriminating power, early on during hospitalisation and thus value to determine prognosis, was 26 insufficient. A previous report about the prognostic value of NLR (51) is also not supported by 27 our data. Altogether, our findings indicate that new parameters, reflecting functional status of 28 blood cells, are more frequently outside reference ranges in COVID-19 than classical 29 parameters such as lymphocytes, neutrophils or platelets. However, none of the measured 30 parameters, traditional or novel, alone could discriminate patients based on disease severity.

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The prognostic score we developed used multiple parameters, representing the three

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The prognostic score correctly identified 70.5 % of these patients during first 3 days of hospital 38 admission, with similar performance confirmed in the validation cohort (72.0%). Prognostic 39 score trends over 14 days confirmed a stable clinical course in NC patients and disease 40 progression in CF patients, peaking on day 6 (sensitivity 93%). In the development cohort there 41 was a distinct progressive upward trend of prognostic score values from day 0, peaking on day 42 6 in the CF group. In the validation cohort, the score also peaked on day 6, but the day 0 score 43 value started relatively high, dropping on day 1, giving the appearance of convergence with the 44 NC group on day 1. The validation cohort was comprised of patients from only two hospitals.

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Study enrolment at these two hospitals took place at different times of the pandemic, during  is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . outliers, and that the day 1 data is more representative of the expected prognostic score values 1 in the validation cohort data set.

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As has been widely reported in the literature, our data too shows that males were predominantly 4 affected, and that disease severity was associated with increasing age and presence of 5 comorbidites in general (Table 2, Figure 3). However, not all young patients had a mild course, 6 and not all old patients with comorbidities were critical. Systemic inflammation is an important 7 factor driving disease severity. Our prognostic score, incorporating the activation status of 8 immune cells, may therefore have additional value, especially on an individual patient level, 9 over classical risk factors such as age, gender and comorbidities in discriminating between NC 10 and CF patients, (Table 7, Figure 10). As such, our prognostic score may assist in identifying 11 any patients at risk for severe disease progression, being young or old, male or female, with or 12 without comorbidity and by doing so, support individualised treatment decisions with objective 13 data.

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Notably, the mortality rate was relatively high in patients on the general ward compared to ICU, 16 possibly due to ICU bed shortages or unfamiliarity with COVID-19 at that time. Whatever 17 reason, we assumed that the need for more intensive treatment should have been considered 18 for all patients that died in the general ward. Once a patient is overtly critically ill, clinical 19 judgement will suffice to prioritise intensive care for such a patient. Importantly, our score strives

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In our study, the prognostic score was calculated retrospectively using the haemocytometric 28 data from each individual sample measurement exported into Excel. For future clinical practice, 29 we envisage that the prognostic score will be automatically calculated by the laboratory score is aimed to be automatically generated while 4C mortality score needs to be calculated; 49 thirdly, different laboratory measurements (oxygen saturation, CRP, urea) are required to 50 calculate the 4C mortality score, while our score uses the globally most commonly requested 51 laboratory examination for patients attending health facilities; fourthly, haematology analyser are 52 widely available globally more so than CRP measurements; and fifthly, the 4C mortality score is 53 aimed at patients admitted to the hospital, while our score was develop for patients presenting 54 at the hospital (of whom some were never admitted).

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The strength of our study is the inclusion of a relativey large group of confirmed COVID-19 57 cases from multiple centres and countries, including a validation cohort, Furthermore, we 58 . CC-BY-NC 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020. . https://doi.org/10.1101/2020.09.27.20202168 doi: medRxiv preprint believe that an advantage of our prognostic score is that all input data required to calculate the 1 score value are generated from a single haematology profile test, which is the most common 2 routinely requested baseline blood tests in all patients globally.

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A limitation of our study is its retrospective nature, as data retrieved from hospital records were 6 sometimes incomplete. Our study was performed at a time that COVID-19 was a new disease 7 entity that constrained the health care in many of the participating centers which may have 8 affected management decisions and therefore study outcome parameters. Data from out-patient 9 settings including more mild cases, and from nursing homes that usually accommodate high risk 10 patients, are needed. Furthermore, clinical data collection was limited, including comorbidity 11 affecting COVID-19 susceptibility and ICU admission decisions, notably as the demand for ICU 12 beds was greater than the availability at the time of our study. Importantly, the conditions of our 13 fast-tracked ethics clearance to facilitate rapid study initiation, did not permit data collection. 14 about bacterial superinfections and medication (antibiotics, corticosteroids), while these factors 15 may affect outcome and haemocytometric parameters.

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Finally, our prognostic score includes Sysmex unique parameters. This is a limitation as the 18 score is not universally applicable to all haematology analysers, although the concept is 19 transferable (see Table S1 for parameters available on other manufacturer haematology 20 platforms). However, it is the very ability to quantify blood cell activation, a reflection of the 21 general immune response status of an individual, that has rendered our prognostic score (which

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incorporates cell activation parameters such as reactive monocytes and antibody-synthesising 23 lymphocytes amongst others) better than using only standard parameters, such as neutrophil-24 to-lymphocyte ratio, which are universally available on all systems, at least in our patient 25 dataset.

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Our finding of potential usefulness of extended haemocytometry may be impactful as Sysmex 30 haematology analysers are widely available. Haematology blood profile requests are common, 31 inexpensive, quick, highly standardised, quality-controlled baseline tests. Furthermore, this 32 investigation is requested in febrile patients and those with non-febrile conditions. As the latter 33 patients are at higher risk for serious COVID-19, early recognition is important to provide 34 supportive care.

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We would like to thank all patients involved in this study, as well as doctors, nurses, and 39 researchers working together to fight against COVID-19. We also acknowledge Claudia   is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 28, 2020.

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Additional supporting information may be found online in the Supporting Information section at 15 the end of the article. Table S1. Novel parameters of different manufacturers in relation to possible adaptability of the 18 haemocytometric COVID-19 prognostic score 19 20            is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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The copyright holder for this this version posted September 28, 2020.

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Figure 10 -source data file 1. Impact of age on prediction of disease severity.

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Figure 10 -source data file 2. Impact of age on prediction of disease severity.     is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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The copyright holder for this this version posted September 28, 2020.  Note: Recent technological advancements in haematology analysers have produced many novel parameters to characterise blood cells (Ref). While some are common, each manufacturer has parameters exclusive 2 to their technology. The prognostic score could thus be adapted and trialled by using alternative parameters that provide similar assessment of cellular activation status, which is a core contributor to the 3 discriminatory power of the prognostic score.

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This table is not meant to reflect a comprehensive list of all non-standard parameters available on all models of haematology analysers from the manufacturers listed here. The information shared here is limited to 5 those parameter classes that in the authors opinion best match the parameters that make up the haemocytometric COVID-19-score. Parameters in black are diagnostic parameters, parameters in blue and in italics 6 are research parameters. The information provided in this table was extracted from the latest publicly accessible version of instructions for use of the analysers mentioned. Whilst every effort was made to ensure 7 that the data is correct, the authors do not take responsibility for any omissions or mistakes.