key: cord-1049063-pm02w02c
authors: Kamel, Fatemah O.; Magadmi, Rania M.; Alqutub, Sulafa T.; Badawi, Maha; Al-Sayes, Fatin; Badawi, Mazen; Madni, Tariq A.; Alhothali, Areej; Abozinadah, Ehab A.; Adam, Soheir
title: Clinical and hematologic presentations of adults with COVID-19 patients in Jeddah: A case control study
date: 2021-03-19
journal: J Infect Public Health
DOI: 10.1016/j.jiph.2021.03.007
sha: 6d60195d780cbd95b0ca7f224ee7787986849de5
doc_id: 1049063
cord_uid: pm02w02c

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), is associated with significant morbidity and mortality. The clinical features of COVID-19 were mentioned in previous studies. However, risk factors for COVID-19 are not fully recognized. The aim of this study is to characterize risk factors and clinical features of COVID-19 disease in Jeddah, Saudi Arabia. METHODS: A retrospective, chart-review, case-control study was conducted at King Abdulaziz University, Jeddah, Saudi Arabia. Demographic, clinical, radiological, and laboratory data on patients diagnosed between March 18 and May 18, 2020 were collected and analyzed. RESULTS: We reviewed medical records on 297 suspected cases of COVID-19. Of these, 175 (59%) tested positive for COVID-19 by polymerase chain reaction (PCR) and considered as cases, while 122 (41%) tested negative and considered as control. COVID-19 positive cases were more likely to be males, and non-health care providers. Hypertension (15%), diabetes (10%) and two or more concurrent comorbidities (54.4%) were more prevalent among COVID-19 patients. Patients presented with fever, cough, and loss of taste/smell were more likely to test positive for COVID-19 (P = 0.001, 0.008, 0.008; respectively). Radiological evidence of pneumonia was associated with confirmed COVID-19 disease (P = 0.001). Shortness of breath and gastrointestinal symptoms were not associated with the risk of COVID-19 at presentation. On admission, white blood cells, neutrophils, lymphocytes, eosinophils, basophils, and platelets were significantly lower among COVID-19 patients compared with controls. Surprisingly, D-Dimer levels were lower among COVID-19 positive patients when compared with controls. CONCLUSION: Male gender, hypertension, and diabetes are the most commonly observed risk factors associated with COVID-19 disease in Jeddah, Saudi Arabia. COVID-19 patient had significantly lower lymphocyte and neutrophil counts.

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) was initially recognized in December 2019 in Wuhan, China [1] . Within the ensuing weeks, SARS-CoV-2 spread across China and around the world, resulting in a global pandemic. The World Health Organization (WHO) declared that the outbreak was a Public Health Emergency of International Concern on the 30 th of January 2020 [2] . On February 12, 2020, WHO officially renamed the SARS-CoV-2 infection Coronavirus Disease-19 (COVID-19) [3] . This highly contagious virus spread at an alarming rate around the world, and the first case of COVID-19 in Saudi Arabia (SA) was identified on March 2, 2020 [4] . Subsequently, many cases have been confirmed in different regions of SA. Various demographic and clinical factors have been associated with the risk of severe disease [5] [6] [7] .

Rapid diagnosis and case isolation are crucial for slowing the spread and for outbreak containment [8] . Case recognition can rely on a series of clinical presentations that increase the likelihood of a positive diagnosis, including shortness of breath, cough, sore throat, and fever.

However, these symptoms are also common in other respiratory infections, including bacterial pneumonia, influenza virus infections, or rhinovirus infections [9] .

The gold standard method for the presence of COVID-19 is the real-time polymerase chain reaction (RT-PCR) for detecting the presence of the virus in nasopharyngeal swab.

However, this test can be time consuming and delay the diagnosis. Subsequently, patient isolation may be delayed, which results in further spread of the infection.

Previous studies reported significant changes in various laboratory parameters, including lymphopenia, high C reactive protein levels, ferritin, and D-dimer among COVID-19 cases [7, 9] . Another study has reported a significant leukopenia in around 30% of 24 asymptomatic COVID-19 cases [10] . However, most of the studies were conducted on a relatively small population.. Therefore, this retrospective case-control study identify the clinical and laboratory predictors of positive COVID-19 patients and risk factors for severity and outcomes. The results of this project will foster the prediction of disease and provide a great opportunity to explore novel diagnostic and prognostic factors to identify and treat the disease effectively.

Study design and data collection J o u r n a l P r e -p r o o f This retrospective case-control study was conducted in King Abdulaziz University Hospital (KAUH), in Jeddah, SA. All the case and control participants were identified from hospital admissions. Demographic and clinical data of participants tested for SARS-CoV-2 by nasopharyngeal swab between March 18, 2020, to May 18, 2020 were extracted from the hospital's electronic medical information system. The recorded data included the participants' baseline demographic characteristics including: age, gender, nationality, and occupation. The clinical symptoms and signs on presentation, laboratory parameters, and underlying comorbidities, as well as the admission course and patients' outcome were obtained for analysis.

Patients of < 18 years and those with incomplete data were excluded. The protocol for this study was approved by the Institutional Ethics Committee and followed the ethical standards of the bioethics and research committee of the King Abdulaziz University. Ethics Reference No 271-20 on June 6, 2020.

The COVID-19 Score is a visual triage scoring system that has been released by the Saudi Ministry of Health (MoH) for the early prediction of patients with acute respiratory illness in the emergency departments [11] . The visual triage form consists of a nine-item scoring system divided into two parts. The first part is related to signs and symptoms at presentation, and the second part is related to the potential risk of exposure to COVID-19.

COVID-19 diagnosis was confirmed by RT-PCR using nasopharyngeal swabs performed at the institute central lab. Laboratory investigations included: 1) complete blood count (CBC), differential white cell count, and blood group typing, 2) coagulation parameters, such as Ddimer, prothrombin time (PT), activated partial thromboplastin time (aPTT), and fibrinogen, and J o u r n a l P r e -p r o o f 3) inflammatory markers, such as C-reactive protein (CRP), ferritin, and N-terminal pro b-type natriuretic peptide (NT-proBNP).

Open epi info (CDC, Atlanta) was used to calculate the required sample size for the case-control study. The calculated sample size was 360 participants who underwent COVID-19 PCR test ( Figure 1 ). A total of 63 participants were excluded. Forty-three of them were excluded because of missing laboratory data, and 20 participants were excluded for being under the age of 18.

Among the 297 participants, 175 (59%) patients were confirmed positive for COVID-19 cases by RT-PCR testing (the case group) and 122 (41%) participants tested negative for COVID-19 (the control group). Patients from control group were symptomatic and admitted for other medical conditions. With the identified sample size of 297, the calculated power is 90.98%. 175 cases with 60% of exposure and 122 controls with 40% of exposure. The minimum detectable OR is 2.2.

The socio-demographic results, clinical presentations, and comorbidities of patients are represented using descriptive statistics. Frequencies and percentages were reported for categorical variables. Continuous data were reported as mean ± standard deviation. Differences between groups were analyzed using Pearson's chi-square and odd ratio (OR) with 95% confidence interval (CI) to test categorical variables, and t-test for continuous outcome variables.

Significance (P-value) was set at 0.05. Statistical analysis was performed with Social Sciences Statistical Package (SPSS) software version 21 (IBM, US).

To assess the significance of the relevant demographical, clinical, and hematological parameters as predictors in diagnosing COVID-19 patients, two machine learning algorithms, namely logistic regression and naïve Bayes, were implemented. Both were trained on patients' characteristics to predict their COVID-19 test results (i.e., positive, negative). The machine learning approaches are implemented on 18 characteristics that show statistical relevance (Pvalue < 0.07) in the t-test and chi-square test (Supplement 1). Both machine learning approaches were trained and tested using cross-validation techniques, and a grid-search algorithm was performed to choose the best hyper-parameters (regularization penalty: l1, solver: liblinear, and C:10) for the logistic regression model.

Demographic data for COVID-19 positive and negative groups are presented in Table 1 . Most COVID-19 positive cases (28%) were between 50-59 years of age, while most COVID-19 negative participants (71.1%) were younger than 50 years old. However, there was no statistically significant difference in age (P = 0.29) between both groups. COVID-19 positive cases were more likely to be males (68.6%) compared to (31.4%) COVID-19 female positive cases (P = 0.001). Conversely, COVID-19 positive cases were less likely to be health care providers (90.9%) compared with control group (P = 0.02).

Of 175 COVID-19 positive cases, 77 (44%) were known to have history of chronic diseases (Table 2) . Almost half of this group (43 patients), had more than two comorbidities. The most common single comorbidity in COVID-19 positive cases was hypertension (15.6%), followed by diabetes (10.4%). Table 3 shows the most common clinical presentations of COVID-19 cases and control groups.

Patients who presented with a fever between 37.3-39 o C were significantly more likely to be COVID-19 positive than COVID-19 negative (P = 0.001). Likewise, patients who presented with cough or loss of taste/ smell were significantly more likely to be COVID-19 positive than negative (All P = 0.008).

As expected, evidence of pneumonia on chest x-ray showed a significant association with COVID-19 positive cases compared with control group (P = 0.001) ( Table 3) .

With regard to COVID-19 score, the most common reported score among COVID-19 positive cases in this study was 7 (31%), followed by 6 (27%). Patients with scores 7 or 6 were more likely to be COVID-19 positive (P < 0.001).

J o u r n a l P r e -p r o o f Table 4 shows the comparison of hematological parameters for both the COVID-19 positive cases and control groups. On admission, COVID-19 positive cases showed a significantly higher in hemoglobin (Hb) levels compared with the COVID-19 negative cases (P = 0.031).

On the other hand, COVID-19 positive cases showed a significant decrease in mean white blood cells (WBC) count, as well as basophil, neutrophil, lymphocyte, and eosinophil counts compared with the controls (All P < 0.05). However, all parameters in COVID- 19 

The machine learning models used in this study show an accuracy of 82% and 81% for diagnosing COVID-19 using Naïve Bayes and logistic regression, respectively. As shown in the Most COVID-19 positive cases in this study were 50-59 years old. This observation is in agreement with previous international studies that showed the average age of COVID-19 positive cases was 47-62 years [6, 12] . Similarly, males were more likely to be COVID-19 positive, as in previous reports [6, 12] . Such figures may be because of sex-based immune disparities or may be because of smoking habits and prevalence [13] . Another explanation of male gender prevalent in this study could be the fact that more females stayed at home during the law of curfew than males, which made males more exposed to the infection.

In global efforts against COVID-19, health care providers' shortages are significant. Awareness of the consequences of this shortage leads to other interventions considered extreme under normal circumstances [14] . About 41% of the 138 health-care providers reported acquired hospital infections in a single-center study in Wuhan [7] . Approximately half of the emergency room workforce tested positive in the Royal Gwent Hospital, Newport, Wales [15] . In contrast, only 13.13% of COVID-19 positive patients were health care providers in this study. However, Alsofayan et al. [5] reported that health care providers in SA represented 12.5% of the cases (n = 190). This emphasizes the implementation of strict infection control measures which provided adequate protection within health care setting in SA. In addition, these researchers underlined the importance of early screening for those providers to prevent disease transmission and avoid unnecessary staff depletion.

During the early phase of the COVID-19 epidemic, diagnosing the disease in suspected cases based on symptomatology was increasingly difficult because of the complexity of the symptoms and the similarity with common respiratory infections. Consistent with previous reports [5, 9, 16] , symptoms associated with COVID-19 infection were cough and loss of taste and smell. At the other end of the spectrum, others [17] [18] [19] demonstrated that in the vast majority of symptommanifesting cases, the clinical characteristics of COVID-19 are like those seen in classic SARS-CoV cases. Variations in the main symptomatology were present for fever and cough in Viral Tropism from SARS-CoV, MERS-CoV, and influenza [20] .

The most common co-morbid medical conditions were HTN and DM in COVID-19 positive cases, while 54.4% of patients had more than two co-morbid conditions in positive cases group as compared to the COVID-19 negative group. These results were like previous reports [5] .

Similarly, HTN, DM, and cardiovascular disease were the most common comorbidities in a meta-analysis of eight studies performed in China with 46,248 patients diagnosed with COVID-19 [21] . Other research [22] also shows that patients with HTN and/or DM exhibit an increased risk of COVID-19 complications, including acute respiratory distress. The mechanism remains under-research, and it is still unclear whether unregulated blood pressure patients have a worse COVID-19 result compared to controlled blood pressure patients.

Since the earliest COVID-19 epidemiological studies were done in China [1] , WBC counts emerged as a possible predictor for the risk of COVID-19 infection. The main hematologic findings reported in the current study support this observation. Lymphocytopenia was reported to be associated with a more severe form of COVID-19 [17, 23] . Yang et al. provided a plausible explanation for lymphocytopenia in COVID-19 [17] . The group attributes lymphopenia to the destruction of lymphocytes by the virus and subsequent cell death. Notably, lymphocytopenia is also common in patients with Middle East Respiratory Syndromes (MERS) because of lymphocyte apoptosis [24] . Therefore, previous research corroborates the results in this study, indicating that lymphocyte count could be an indicator of disease severity.

Changes in eosinophil counts were not reported in early studies on COVID-19, because of the unclear functions of eosinophils in infections and their relatively small number among WBCs [1, 12, 19] . However, the current study showed that the number of eosinophils in COVID-19 positive patients was significantly lower compared to the COVID-19 negative group. This might indicate a role for eosinophils, as an early sign of COVID-19 infection. Further research to produce a detailed overview of this mechanism may be necessary. Up to this point, it is hypothesized that the viral assault on bone marrow blocks entry into peripheral circulation of eosinophils, or penetration into certain bodies (such as lungs) [9, 25] .

A recent study found that although the immune phenotype of COVID-19 is like other coronaviruses, marked differences exist [26] . Severe COVID-19 disease was found to be associated with a decreased number of lymphocytes, eosinophils, and basophils compared to less severe disease. The state of inflammatory cell depletion is strengthened in COVID-19 patients' recovery phase, but this continues or worsens in COVID-19 patients ' exacerbated process. They explained the reduced number of peripheral inflammatory cells by the migration of the J o u r n a l P r e -p r o o f neutrophils, eosinophils, and lymphocytes from peripheral blood to the lungs, leading to neutropenia, lymphopenia, and blood eosinopenia and concurrent respiratory distress [26] . An increase in peripheral blood inflammatory cells was found to be associated with the outcome of COVID-19. In the study carried out by Sun et al. [26] , several points mentioned were significant.

Severe type COVID-19 patients have decreased lymphocytes, eosinophils, and basophil counts compared to non-severe COVID-19 patients. The main risk factor for lymphopenia and eosinopenia is a highly severe clinical diagnosis. The state of inflammatory cell depletion is strengthened in COVID-19 patients' recovery phase, but this continues or worsens in COVID-19 patients' exacerbated process. They explained the reduction of peripheral inflammatory cells by the migration of the neutrophils, eosinophils, and lymphocytes from peripheral blood to the lungs, leading to neutropenia, lymphopenia, and eosinopenia which led to aggravate respiratory distress [26] .

Interestingly, previous studies reported significant changes in various laboratory parameters including lymphopenia, high C reactive protein levels, ferritin, and D-dimer among COVID-19 cases [7, 9] . Another study reported significant leukopenia present in around 30% of 24 asymptomatic COVID-19 cases [10] . However, all these were small studies and larger studies are needed to validate those findings. D-dimers are products of fibrin degradation and have been useful in a clinical decision for the diagnosis of pulmonary embolism, and deep vein thrombosis (DVT) [27] . The association between D-dimer and COVID-19 also was not fully reported with the level changes during disease development. In contrast to earlier findings [28] , the present study showed that there was a significantly decreased level of D-dimer in COVID-19 positive patients compared with COVID-19 negative group. However, other variables in this study may contribute to D-dimer findings, as half of the control group have comorbidities and chronic diseases (HTN, DM, heart diseases and malignancy) which influenced D-dimer level.

The D-dimer concentrations decreased concomitantly with inflammation followed by disease improvement, suggesting that it is not possible to produce an estimation of whether anticoagulation is only required in conjunction with D-dimer [28] . This could explain our findings of decreased in the CRP level in COVID-19 positive patients. The diagnostic value of D-dimer levels for thrombus formation in COVID-19 patients is unclear. Tang et al. [29] reported that patients with COVID-19 could have decreased blood viscosity because of high fever and heavy sweating and thus there may be no association with increasing D-dimer levels [29] . D-dimer elevation is not diagnostic of venous thromboembolism and is used mainly as an initial screening test because it has a negative predictive value.

Although group O is the most among ABO groups in the population of this study, as seen in daily practice and in the literature [30] , it was less represented in COVID-19 positive patients.

This finding was reported in a previous study [31] . Several theories to explain this exist, including a protective effect of anti-A in the serum of group O individuals [32] .

One of the most significant findings of this research is to highlight the impact of artificial intelligence in medicine. Artificial intelligence and machine learning techniques could help clinicians efficiently decide, evaluate alternatives, identifying changes, or predict outcomes. In the health care domain, in particular, artificial intelligence techniques can help to emulate human cognition in understanding, analyzing, and interpreting healthcare data such as patients' health records and medical images. This timely process is especially important in health emergencies and pandemics in controlling the spread of infectious diseases.

There is an urgent need for early diagnosis of COVID-19 and to identify factors associated with worse prognosis. The healthcare sector in SA is going through a major transformation, based on Saudi Vision 2030. The focus of this transformation is to achieve better preparedness and proficiency in dealing with economic changes, globalization, and pandemics while providing excellence in patient care. Artificial intelligence is one of the most important key enablers in this transformation to predict disease outcomes.

Predictors of clinical disease severity in COVID-19 patients and disease progression include lymphopenia. Hematological assessments could, therefore, become a COVID-19 sentinel and deserve consideration in the care process for COVID-19 patients. D-dimer levels in COVID-19 patients are associated with inflammation. Abnormal D-dimer changes and inflammatory factors may show that the treatment with anticoagulants is appropriate.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

The authors declared that there is no conflict of interest. 

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