key: cord-0839309-17b6p59v
authors: Alsagaby, Suliman A.; Aljouie, Abdulrhman; Alshammari, Talal H.; Ahmad Mir, Shabir; Alhumaydhi, Fahad A.; Al Abdulmonem, Waleed; Alshaalan, Hesham; Alomaish, Hassan; Daghistani, Rayyan; Alsehawi, Ali; Alharbi, Naif Khalaf
title: Haematological and radiological-based prognostic markers of COVID-19
date: 2021-09-30
journal: J Infect Public Health
DOI: 10.1016/j.jiph.2021.09.021
sha: 8c1acf7871d34bb7af4ccce1188314e47372950a
doc_id: 839309
cord_uid: 17b6p59v

BACKGROUND: Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged in 2019 and caused a global pandemic in 2020, manifesting in the coronavirus disease 2019 (COVID-19). The majority of patients exhibit a mild form of the disease with no major complications; however, moderate to severe and fatal cases are of public health concerns. Predicting the potential prognosis of COVID-19 could assist healthcare workers in managing the case and controlling the pandemic in an effective way. METHODS: Here, clinical data of COVID-19 patients admitted to two large centers in Saudi Arabia between April and June 2020 were retrospectively analysed. The objectives of the study were to search for biomarkers associated with COVID-19 mortality and predictors of the overall survival (OS) of the patients. RESULTS: More than 23% of the study subjects with available data have died, enabling the prediction of mortality in our cohort. Markers that were significantly associated with mortality in our study were older age, increased D-dimer in the blood, higher counts of WBCs, higher percentage of neutrophil, and a higher chest X-ray (CXR) score. The CXR scores were also positively associated with age, D-dimer, WBC count, and percentage of neutrophil. This supports the utility of CXR scores in the absence of blood testing. Predicting mortality based on Ct values of RT-PCR was not successful, necessitating a more quantitative RT-PCR to determine virus quantity in samples. Our work has also identified age, D-dimer concentration, leukocyte parameters and CXR score to be prognostic markers of the OS of COVID-19 patients. CONCLUSION: Overall, this retrospective study on hospitalised cohort of COVID-19 patients presents that age, haematological, and radiological data at the time of diagnosis are of value and could be used to guide better clinical management of COVID-19 patients.

A novel coronavirus has recently emerged into human populations, which was first identified in December 2019 in Wuhan, China and has spread globally causing a global pandemic [1] [2] [3] . This virus is named Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and cause the coronavirus disease 2019 (COVID-19) [1] [2] [3] [4] [5] ; it has infected more than 173 million in the pandemic with more than three million deaths in more than 200 countries worldwide [6] [7] [8] .

The clinical manifestation of COVID-19 includes fever, non-productive cough, hypoxia, runny nose, sore throat, fatigue and dyspnea [9] [10] [11] . Other less common symptoms reported to be vomiting, diarrhoea, nausea, abdominal pain and [9, 12, 13] . The virus transmission occurs through breathing droplets carrying the virus from cough or sneezing via close contact [14, 15] especially from symptomatic patients [16] , and contaminated items or surfaces can also be a source of COVID-19 infection [4, 16] .

Laboratory diagnosis is achieved mainly by reverse transcription-polymerase chain reaction (RT-PCR) tests to detect parts of the viral genes in respiratory samples [17] . Other samples such as stool or saliva could also be used but with less reliability and accuracy [18] [19] [20] . RT-PCR is designed to target parts of different viral genes such as those encoded for spike, nucleocapsid, and envelope proteins [17] . [4, 21] . COVID-19 was found to be associated with abnormal readings in haematological, biochemical, inflammatory, and immunological tests [22] . Interestingly, some of these abnormalities were proposed to distinguish between mild and severe diseases [22] . For example, lymphopenia was observed in 80% of COVID-19 patients with critical conditions [23] , while only 20% of mild cases showed lymphopenia, suggesting low count of lymphocytes could serve as a poor prognostic marker [24, 25] . In addition, percentages of basophils, eosinophils and monocytes, were reported to be lower in severe cases of COVID-19 compared to non-severe cases [26] . Inflammatory proteins such as C-reactive protein (CRP) and main constituents of plasma proteins like albumin were significantly associated with the severe form of the disease [21, 22] . Taken together, these studies suggested that routine test performed in a standard diagnostic lab (e.g., haematology, serology and biochemistry) hold valuable information that may help to predict the prognosis of the disease.

Given the heterogeneous clinical outcomes of COVID-19, identification of biomarkers of the disease that can be measured at the time of infection (baseline) is useful and urgently needed to enable clinicians to predict the disease prognosis. Early knowledge of the disease prognosis is likely to direct early actions, leading to reduced complications and mortality rate. Here, clinical data of COVID-19 patients from Saudi Arabia were retrospectively analyzed in order to search for (i) biomarkers associated with the mortality of the disease, and (ii) predictors of the OS of the patients. (IRB approval number: 1442-651036), respectively. Chest X-ray scans of those patients were collected from the hospital picture archiving and communication system and annotated based on the presentation of ground glass opacity/consolidation by four radiologists (experience 3-15 years). For viral load, the data are stored in a narrative text, so we create in-house python script to extract S, N1, N2 and E genes Ct values using regular expression algorithm.

Similar to Toussie et al. [27] , we divided the chest X-ray into 12 regions (6 for each lung) to score upper, middle, and lower lung zones. For each region the score ranges between 0-2, where 0 represents no manifestation of GOO/consolidation, and 2 represent severe opacity. The 12 lung zones severity values are then added to generate one score between 0-24 for each patient as a measure of lungs GOO/consolidation severity.

Excel software and coefficient of determination test (R 2 ) were used to determine the correlation degree between the Ct values of the diagnostic genes of COVID19. Unpaired student t-test was applied using Prism Graph Pad software to calculate the statistical significance of biomarkers associated with mortality of COVID-19. One-Way ANOVA test with Prism Graph Pad software were employed to determine the statistical significance of biomarkers associated with CXR score. Kaplan-Meier curve and log-rank test were used for the OS analysis.

J o u r n a l P r e -p r o o f 3. Results

In the present work, a retrospective study on adult hospitalized patients with COVID-19 between 2/April/2020 and 16/September/2020 in two regions of Saudi Arabia (SA); Riyadh City (central region of SA; National Guard Hospital) and Hafar Al-Batin city (North region of SA; King Khalid General Hospital) was conducted. The total number of patients was 6026 of whom 3226 (53%) were females and 2799 (47%) were males. The median age of the patients was 38 years with the range being from 15 years to 106 years.

One of the most common diagnostic tools of COVID-19 is the detection and relative quantification of SARS-CoV-2 genes, such as spike gene (gene S), envelop gene (gene E), nucleocapsid genes (N1 and N2 genes), using RT-PCR test. The data set used in this study contained viral loads of COVID-19 patients based on the following gene targets: gene E data for 5592 patients, gene S data for 5480 patients, gene N1 for 200 patients and gene N2 for 285 patients. Coefficient of determination test (R 2 ) was applied to examine the correlation between the Ct values of the target genes. The analysis showed a positive correlations of the Ct values of gene S, gene N1 and gene N2 with that of gene E. The highest correlation was observed for gene S with gene E (R 2 = 0.94, n = 5119; Figure 1A ). Gene N2 and gene E showed a lower degree of correlation (R 2 = 0.83, n = 274; Figure 1B ). The lowest correlation was between gene N1 and gene E (R 2 = 0.50, n = 139), which could be due to outlier samples that needs further investigations; Figure 1C . Given the high degree of correlation between gene S and E, it seems reliably practical to select one of these two genes for the diagnosis of Correlation analysis of the diagnostic target genes of COVID-19. The analysis was conducted using Coefficient of determination test (R 2 ).

Of the 6026 patients, the follow up data (vital status; death or survival with full recovery) were available for 1683; with a severe disease in 398 patients resulting in death. On the other hand, 1285 patients exhibited mild symptoms followed by complete recovery. In order to search for biomarkers that identify COVID19 patients at high-risk of death, clinical data concerning patient age, viral load, haematological parameters, CXR score were studied in the deceased and survived patients. No significant differences between the two groups of patients was recorded for the red blood cells (RBCs) count, haemoglobin (Hb) concentration, mean cell volume (MCV), mean cell Hb (MCH), mean cell Hb concentration (MCHC), haematocrit, platelets count or viral load, which was determined based on viral genes of S, E, N1 and N2. In contrast, age appeared to be an important determinant of the disease prognosis; the mean age was 61.4 years in the deceased patients compared to 53 years in the survived patients (Figure 2A, p˂0.0001 ). In addition, a significant association of D-dimer with the mortality was found; the D-dimer level was 3.3 μg/ml in fatal cases versus 2μg/ml in survived cases ( Figure 2B ; p = 0.003). White blood cells (WBCs) count and the neutrophil percentage were associated with poor prognosis of the disease. The WBCs count was 9. Figure 1D , p < 0.0001; Figure 1F , p < 0.0001; Figure 1G , p = 0.03). CXR score appeared to associate with mortality of COVID-19; the CXR score was 5.4 in the fatal cases as opposed to 2.8 in the survived cases ( Figure 1H , p < 0.0001). Taken together, these findings revealed informative roles of patient age, CBC data and CXR score in the prognosis of COVID-19. 

CXR score has been proposed to serve as a monitoring tool for the clinical course of COVID-19. Given the significant association of CXR score with the mortality of COVID-19

shown in figure 1H and by others [28, 29] , association between CXR score and patient age, 

The potential of the above clinical data to predict the OS of COVID-19 patients was evaluated. The OS data were available for 1084 patients. Initially, the median value of the clinical data was used to divide patients into two groups (high group (HG) > median value;

and low group (LG) < median value). Next, the OS data of the two groups were compared for each clinical parameter. RBCs count, Hb concentration, MCV, MCH, MCHC, haematocrit and viral load (genes S, E, N1 and N2) did not significantly predict the OS of COVID-19

patients. Nevertheless, age, D-dimer, leukocyte parameters and CXR score were significantly informative of the patients' OS (Table 1 ). Next, an effort was made to improve the ability of the previous parameters to predict the OS of COVID-19 patients by using the "Cutoff Finder", which is an online tool that searches for the optimal cutoff value to generate the most significant p value and hazard ration (HR) using log-rank test [30] . The cut-off values produced by "Cutoff Finder" significantly enhanced the capability of the parameters to predict the OS (Figure 4) Figure 4A ). Among the markers, the best predictors of short OS appeared to be increased age ( Figure 4A ) and high neutrophil % (HR of HG versus LG = 3.4; p < 0.0001; Figure 4D ). To the contrary, elevated monocyte % (HR of HG versus LG = 0.3; p < 0.0001; Figure 4E ) and raised lymphocyte % (HR of HG versus LG = 0.45; p < 0.0001; Figure 4C ) were found to be the best markers for long OS.

J o u r n a l P r e -p r o o f Median value of the markers was used to divide the patients into two groups; high group (HG) > median value and low group (LG) < median value. Next, Kaplan-Meier curve with long-rank test was used to compare the OS in the two groups. OS: overall survival; CXR: chest X-ray.

J o u r n a l P r e -p r o o f Figure 4 : Prognostic markers of OS in COVID-19 patients. Kaplan-Meier curve and log-rank test show that age (A), leukocyte parameters (B-F) and CXR score significantly predict the OS of COVID-19 patients. The cut-off value that was used to segregate patients into high and low groups was generated by "Cutoff Finder" for each clinical parameter. [31] [32] [33] [34] . While the CDC suggests testing N1, N2, and RdRp as primary targets, the WHO support using the E as primary target and RdRp as confirmatory [35, 36] ; however, more targets and assays are widely used in various countries by different diagnostic providers. Our analysis supports the utility of the E and S targets in a sequential testing method where E serve as the primary target and S as the confirmatory target.

More than 23% of the study subjects with available data have died, but this does not reflect the mortality rate as data were not available for large number of the subjects. In addition, this study focused on hospitalised patients who have mainly moderate to severe conditions, this is similar to other studies that focused on hospitalised cases where mortality rate is high [37] . Markers that were associated with mortality in our study were older age, increased D-dimer in the blood, higher counts of WBCs, higher percentage of neutrophil, and a higher CXR score. The CXR scores were also positively associated with age, D-dimer, WBC count, and percentage of neutrophil. This supports the utility of CXR scores in the absence of blood testing. More than thirty studies have previously reported the potential of neutrophil and D-dimer as well as other markers such as CRP, platelet, and J o u r n a l P r e -p r o o f CLEAN (without corrections) Manuscript neutrophil/lymphocyte ratio as strong predictors of COVID-19 prognosis to severe conditions and fatal outcomes [38] . Neutrophil counts in an earlier study [26] were observed to be as 4.3x10 9 cells/L in those who prognosed to severe cases whereas in our cohort neutrophil was 73% of the total count of 9.3x10 9 cells/L in those who passed away, meaning neutrophil counts was on average around 6.7x10 9 cells/L. The higher count of neutrophil in our subjects could be because our cohort was based on hospitalised patients with mainly moderate to severe manifestation of the disease.

Our data did not show significant differences between deceased and survivors in RBCs count, Hb concentration, MCV, MCH, MCHC, or platelets count. Although platelet counts and RBC distribution width (RDW) were previously suggested to be associated with COVID-19 mortality, these reports have monitored the increase of biomarkers over the course of the infection and not only at the time of diagnosis [39, 40] .

The utility of viral load in predicting mortality has been reported previously [41] ;

however, our study is based on Ct values, which do not directly implies the magnitude of viruses in the samples; and a further quantitative RT-PCR would be required to evaluate the precise viral load and its association with COVID-19 outcomes. There have been contradicting reports on the utility of Ct values in predicting the outcome of COVID-19 [42] [43] [44] [45] . One report showed that higher viral RNA load in plasmas could predict mortality [46] , but plasma viremia data were not available for our cohort.

Previous studies successfully predicted the OS based on clinical signs, co-morbidities, ICU length of stay, and socioeconomic conditions [47] [48] [49] [50] . Adding to this, our work identified age, D-dimer concentration, leukocyte parameters and CXR score to be prognostic markers of OS in COVID-19 patients. For example, older age appeared in our study to be the most significant predictor of short OS, whereas increased percentage of monocyte was the most significant indicator of long OS.

J o u r n a l P r e -p r o o f

Ideally, a biomarker serves well when it can be assessed at time of diagnosis (baseline) to predict future clinical course of a disease. Interestingly, the data (D-dimer, CBC and viral load) studied here were generated at the time of diagnosis. Therefore, the biomarkers of mortality and OS, proposed here, could be of benefit for COVID-19 patients as they provide an early prediction of prognosis, which allows clinical decisions to be taken on time and provides better clinical management.

Overall, this retrospective study investigated a cohort of hospitalised COVID-19

patients and presented that age, haematological, and radiological data are of value and could be used to guide better clinical management of COVID-19 patients.

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The authors would like to thank the National Guard Hospital in Riyadh city and the King Khalid General Hospital in Hafar Al-Batin city for the provision of the clinical data.

No funding was required.

The authors declare no conflict of interest.

The study was approved by the IRB at KAIMRC for the study RC/357/R and the IRB at the health ministry for the study 1442-651036.J o u r n a l P r e -p r o o f