July27Unitedkingdom  2021 

*WearMask,KeepSanitizeYourHands*

Abstract Volume: 3 Issue: 1 ISSN:

Syndromic Surveillance of 167 COVID-19 Patients in a Tertiary Hospital, Riyadh, Saudi Arabia

Oluwasegun Albert, DHSc, MPH, RRT-NPS-ACCS *1, Mohammed Turki Al Dawsari, BS, RT 1, Shamsudeen Faisal Fagbo, DVM, MSc 2,3, Abdulraheem Abdullah Al Zahrani, MSRT, RRT-NPS-ACCS-RPFT 1, Mohammed Al Maani, MD 1, Kamardeen Alao, MD 4,
 Yasir Ameen Qasem, MD 1

 

1.King Fahad Medical City, Riyadh; Saudi Arabia.

2.Center for Disease Control and Prevention, Ministry of Health, Riyadh, Saudi Arabia.

3.Department of Public Health, Nigerian Institute of Medical Research, Lagos Nigeria.

4.Temple University, Philadelphia, Pennsylvania, United States.


Corresponding Author: Oluwasegun Albert, King Fahad Medical City, Respiratory Care Administration P.O. Box 59046, Riyadh, 11525, Kingdom of Saudi Arabia.


Copy Right: © 2021 Oluwasegun Albert. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Received Date: June 10, 2021

Published date: July 01, 2021


Abstract

Background: The Coronavirus Disease 19 (COVID-19) that emerged in China in late December 2019 has rapidly evolved into a pandemic, placing health systems and economies worldwide under unprecedented burdens. The objective of this study is to evaluate clinical characteristics for hospitalized COVID-19 patients in a large tertiary hospital in Saudi Arabia.

Methods: The study setting was King Fahad Medical City, a 1200-bed tertiary hospital, in Riyadh, Saudi Arabia; it included all RT-PCR confirmed COVID-19 cases, occurring between May 1 and June 13, 2020. Clinical and demographic data were retrieved from electronic health records and analyzed.  

Results: One hundred and sixty-seven patients were included; all but three were managed in the ICU. Male patients, (n=148; 88.6%) had a median age of 54; IQR of 16 (0-94), and SD =15.78 compared with female with a median age of 57; IQR of 24 (28-75) and SD =15.15. The median age for survivors (n= 144; 86.2%) was 53; IQR 16 (0-88), and SD 15.35.  The mean age between survivors and non-survivors was statistically significant; p = 0.029. Patients with comorbidities (n= 84; 50.3%) had a median age of 59.60; IQR, 19 (0-88); and SD of 17.50. The mean age between patients with and without comorbidities was statistically significant; p = 0.012. 

Conclusion: In this cohort of hospitalized COVID-19 patients, younger patients are less likely to have comorbidities irrespective of gender. Further, younger patients are likely to survive COVID-19 than older patients.

Keywords: COVID-19, SARS COV2, mortality, Survivals, Non-survivals, Comorbidities, risk factors, biomarkers, Saudi Arabi

Syndromic Surveillance of 167 COVID-19 Patients in a Tertiary Hospital, Riyadh, Saudi Arabia

Introduction:

In late December 2019, the World Health Organization (WHO) was notified of a cluster of respiratory illnesses observed in Wuhan, China [1]. In January 2020, the causal pathogen was identified to be a newly emerged beta coronavirus similar to severe acute respiratory syndrome Coronavirus (SARS CoV) and Middle East respiratory syndrome Coronavirus (MERS CoV); the latter remains endemic in Saudi Arabia. 

 Genetically, it is more closely related to SARS CoV (79%) than MERS CoV (50%) [2]. It was named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS COV2) [3] and the disease Coronavirus Disease 19 (COVID-19) [4]. The COVID-19 is similar to SARS and MERS: they all present with acute respiratory distress syndrome (ARDS). Yet, SARS COV2 infected persons can be asymptomatic, mild, or severe. Infected individuals present with fever, dry cough, dyspnea, sore throat, headache, and pneumonia about five to six days post-infection [2]. Although transmission is thought to be droplet-mediated, there persist debates as to the possibility of aerosol-mediated airborne transmission. Currently, there are no approved vaccines or therapies [2]

Consequently, early case detection and management coupled with public health surveillance remain essential to mitigate the spread and impact of COVID-19. However, SARS COV2 has proven to be more infectious than SARS and MERS. By March 2020, its global spread compelled the World Health Organization to declare COVID-19 a pandemic [2]. As of August 9, 2020, Saudi Arabia has conducted 3,745,562 tests and has confirmed 287,262 cases of which 250,440 recovered with 3,130 associated deaths [5]. Riyadh has the highest number of confirmed COVID-19 cases in the country at 54,029 [5]

Killerby et al. [6] demonstrated that being male, black, smoking, having cancer, and diabetic was associated with hospitalization in 220 COVID-19 patients from Atlanta, Georgia in the US. Other similar studies suggested that advanced age and comorbidities, such as hypertension, diabetes, cancer, and respiratory illnesses are risk factors for COVID-19 [6, 21-27]. There is a paucity of data on the clinical characteristics of COVID-19 at the patient level in the Middle East. A recent retrospective study in Saudi Arabia analyzed aggregated national surveillance data [7] while in Oman workers have published the results of a limited case series (n=63) study [8]

Therefore, the purpose of this study is to investigate the baseline clinical characteristics and outcomes of COVID-19 patients in a tertiary hospital in Riyadh, Saudi Arabia. 

 

Methods 

Study population

The study was carried out at King Fahad Medical City (KFMC), Riyadh, a 1200-bed tertiary referral hospital in Riyadh, Saudi ArabiaAll hospitalized laboratory-confirmed real-time polymerase chain reaction (RT-PCR) COVID-19 positive patients were retrospectively included in the study between May 1 and June 13, 2020. Nasopharyngeal swabs from patients were tested for the presence of SARS-COV-2 using real-time RT-PCR. This study was approved as an exempted study by the institutional review board of King Fahad Medical City under IRB #20-332.

Data collection 

The patient’s baseline clinical characteristics, demographics, symptoms, comorbidities, chest radiographic, and laboratory findings were collected from electronic medical records into Excel (Microsoft Seattle), de-identified, and cleaned. The collected data were organized in excel by two clinicians (OA and MT) and data entries were revalidated by two clinical epidemiologists, OA and SF for consistency and accuracy.

Laboratory and Radiographic Studies

Four pediatric COVID-19 were separated from the initial laboratory findings because of the different criteria for laboratory ranges for the pediatric population. Baseline laboratory values were collected, which included routine hematology, biochemistry, ferritin, C-reactive protein (CRP), and lactate dehydrogenase (LDH). Further, high-sensitivity cardiac troponin I and D-dimer were collected for some of the patients on admission. However, depending on the attending physicians, some of the laboratory values were not investigated at the time of admission to the emergency room. Baseline chest radiographs were requested on a case-by-case basis by the attending physician.  

Definition

Acute respiratory distress syndrome (ARDS) was defined according to the Berlin definition as the ratio of partial pressure of oxygen to fractioned of inspired oxygen ≤ 300 mm Hg [9] or as documented in the patient’s medical history by the physician. Successful extubation was defined as a patient not needing reintubation after removal of endotracheal tube for 72 hours [10]. Acute kidney injury was defined using the clinical practice guidelines for Kidney Disease: Improving Global Outcomes (KDIGO) [11]. The cardiac injury was classified by correlating with high sensitivity serum Troponin I levels that are higher than the 99th percentile of the upper limit [12]

Statistical analysis 

Descriptive statistics were used for continuous variables in respect to median, mean, interquartile range (IQR), and standard deviation (SD). Age was used as a continuous variable for ethnicity, gender, survivals, non-survivals, presence or absence of comorbidities. The median was used as descriptive for the age distribution because it is the most robust statistic because of the presence of outliers (n=4; pediatric patients) in the sample size. Baseline laboratory values at the time of admission to the emergency room were presented as continuous variables in respect to median, IQR, and SD. Categorical variables, such as symptoms, patients with or without comorbidities, and chest radiographic findings were presented as proportions and percentiles. Independent t-test was used to compare mean age in determining the level of significance among groups (i.e., Saudis vs. non-Saudis; Female vs. males; survivors vs. non-survivals; comorbidities vs. without comorbidities; males with comorbidities vs. females with comorbidities; males without comorbidities vs. females without comorbidities; male survivors vs. female survivors; and male non-survivors vs. female non-survivors). Confidence intervals (CI) were set at 95% across groups. Two-sided P values < 0.05 among age groups were classified as statistically significant. All data were analyzed using the statistical package SPSS version 26 (IBM).

 

Results

Table 1: Demography and baseline Characteristics of Hospitalized COVID-19 at Presentation.

Demography and baseline characteristics

A total of 167 RT-PCR confirmed COVID-19 patients were included in the study. Table 1 summarizes their demographic profiles (age, sex, and ethnicity) and baseline characteristics (survivals, non-survivals, patients with or without comorbidities). The median age was included in the study because it was the most robust parameter to deal with the variation in age from 0 to 98 years. Males (n=148; 88.6%), median, IQR (range); standard deviation (SD); median age of 54 years [IQR, 16 (0-94), SD=15.78] were in the majority compared with females (n=19; 11.4%), median age of 57 years [(IQR, 24 (28-75), SD=15.15]. The mean age between the male and female was not statistically significant with a P-value = 0.441. Of the 167 patients, n=51 (30.5%) were Saudis, median age of 61 years (IQR, 27 [0-94], SD = 20.85) compared with non-Saudis with a median age of 53 years (IQR, 13 [0-76], SD = 11.71). The mean age between Saudis and non-Saudis was not statistically significant with a P-value = 0.332. The difference in age of eight years may be indicative of the younger expatriate population working in Saudi Arabia.

 

Table 2: Clinical Symptoms on admission, comorbidities, and chest radiograph findings.

Of the total study population, n=144 (86.2%) survived with a median age of 53 years [IQR, 16 (0-88), SD =15.35] compared with non-survivals with a median age of 61 years [IQR, 25 (30-94), SD =16.23]. The mean age between survivors and non-survivors was statistically significant with a P-value = 0.029. More than half (50.3%) of the study population have one or more comorbidities [Table 2; median =2, IQR; 2 (1-5), SD = 1.02] with a median age of 59.60 years [IQR, 19 (0-88), SD =17.50] compared with patients without comorbidities with a median age of 50 years [IQR, 12 (0-94), SD =13.02]. The mean age between patients with comorbidities and those patients without comorbidities was statistically significant with a P-value = 0.012. 

Male patients with comorbidities was n=69 (46.6%) with a median age of 59 years [IQR, 16 (0-98), SD =18.03] compared with female patients (n=15; 78.9%) with a median age of 64 years [IQR, 19(28-75), SD =14.63]. The mean age between male and female patients with comorbidities was not statistically significant with a P-value = 0.388. Male patients without comorbidities was n=79 (53.4%) with a median age of 51 years [IQR, 13 (0-94), SD = 13.12] compared with female patients without comorbidities (n=4; 21.1%) with a median age of 46.50 years [IQR, 13 (30-47), SD =8.35]. The younger age difference may be suggestive of the smaller sample size of the female population in the study period. The mean age between males and females without comorbidities was not statistically significant with a P-value = 0.139. Male non-survivors were n=20 (13.5%) with a median age of 61.50 years [IQR, 18 (30-94), SD = 15.40] compared with female non-survivors (n=3; 15.8%) with a median age of 40 years (range, 36-75; SD = 21.46). The age difference of more than 20 years may not have been representative of the study population because there were only three non-survivors for the female population; and the mean age between male and female non-survivors was not statistically significant with a P-value = 0.443.  


Clinical symptoms on admission, comorbidities, and chest radiograph findings 

Table 2 summarizes the clinical symptoms, comorbidities, and chest radiographic findings at the time of admission to the emergency room. Most of the symptoms (n=163; 98.2%) at presentation were shortness of breath, tachypnea, and desaturation, which was followed by cough (n=100; 59.9%) and fever (n=83; 49.7%). Frequent comorbidities at presentation was diabetes mellitus (n=60; 71.4%) and hypertension (n=53; 63.1%) with both comorbidities occurring together or in isolation. More than half (n=88; 52.7%) of the chest radiograph showed ground glass appearance with air-bronchograms and pulmonary infiltrates (n=67; 40.1%).


Interventions, complications, and clinical outcomes 

Table 3: Interventions, complications, and outcomes in hospitalized Patients.

Table 3 summarizes the interventions, complications, and clinical outcomes. The majority of the COVID-19 patients (n=136; 81.4%) were treated with oxygen therapy. Fifteen (9%) of these patients were mechanically ventilated on admission while 13 (7.8%) were received mechanically-ventilated from other hospitals. Of the 167 COVID-19 patients, n=164 (98.2%) presented with acute respiratory distress syndrome (ARDS). Few of these patients were treated with advanced interventions, such as extracorporeal membrane oxygenation (n=2; 1.2%) and continuous renal replacement therapy (n=5; 3%) at the time of admission to the emergency room. Of the 28 (16.8%) who were mechanically ventilated, 14 (50%) were successfully extubated during the study period. 

 

Laboratory findings

Table 4: Initial Laboratory Results of Patients Hospitalized with COVID-19

(Please refer to the attached pdf to view all tables)

Table 4 summarizes baseline laboratory values. Of note, there are huge dispersions in some values because of outliers (extreme values); although, normal median values were maintained. Notably, outliers are observed in white blood cell counts, neutrophils, platelet counts, alanine aminotransferase, creatinine, urea, high sensitivity Troponin I, and alkaline phosphate. The presence of these outliers in some patients is suggestive of infective processes, thrombocytosis, pancreatic, kidney, liver, and cardiac involvement. However, initial laboratory values were high for some patients. Lymphopenia (159; 97.5%) was reported in most patients [median, IQR (range), 0.92*10^9/L, 1*10^9/L (0-3)]. Biomarkers of infective and inflammatory processes were high, such as C-reactive protein (CRP), D-dimer, and ferritin in the study population. The CRP (n=98; 60.1%) median was 119.50 mg/l and IQR of 121.20 mg/l (3.83-479); D-dimer (n=22; 13.5%) median was 1.82 ug/l and IQR of 2.27 ug/l (0.50-32.59); and ferritin (n=17; 10.4%) median was 1354.60 ug/l and IQR of 1820.90 ug/l (402.70-7734.70). 

The coagulation profile, such as the prothrombin time (n=127; 77.9%) median was 14.30 seconds (s) was extended with an IQR of 3s (11-32) and activated partial thromboplastin time (n=124; 76.1%) median was 30.90s and IQR of 4.98s (24-60.40). Lactate dehydrogenase (LDH), a marker of tissue damage, was elevated in the study population with a median of 657.00 U/l and IQR of 297 U/l (372-1047). In addition, in the pediatric population (n=4), elevated values were shown in total bilirubin, Troponin I, CRP, and Alkaline phosphate. The low sample size of the pediatric population may best be explained by the angiotensin-converting enzyme 2 (ACE2) protein. ACE2 plays a key role in the transmission of SARS-COV 2 in the adult population; however, ACE2 is not as profuse in the lower respiratory tract of children [13].  


Discussion

This study investigates clinical characteristics and outcomes of COVID-19 patients during a surge crisis in a tertiary hospital, which represents the largest hospital in Saudi Arabia. To our knowledge, there are no studies that have explored COVID-19 clinical characteristics during a surge crisis in Saudi Arabia. Our study adds to the body of knowledge by exploring the demographic variables, clinical characteristics, comorbidities, symptoms, and laboratory findings at the time of admission to the emergency room before hospitalization.

Of note, almost 98.2% (n=164) of the case-patients presented at the emergency room with tachypnea, desaturation, and shortness of breath, which includes cough (59.9%) and fever 49.7%). Similar findings have already been reported elsewhere [14, 23-24]. Interestingly, more than 50% of the subjects had abnormal chest radiographic findings characterized by diffused bilateral opacification, air bronchograms (suggestive of infective and inflammatory processes) with pneumonic changes, and ground glass appearance, which further corroborates other similar investigations [22-23].

The median age of 57 years among female case-patients irrespective of ethnicity was similar to that of African-American females (57.5 years) in another study [14]. Guan et al. [15] have already concluded a similar finding when they reported a mean age of 48.9 years among 686 COVID-19 confirmed positive Asian females. Although these findings may not be generalizable, it does provide some insights into the clinical management and outcomes of COVID-19 among different ethnicities: there is evidence associating poor outcomes, including mortality with being black, Asian, or another minority group [16]

The mean age of survivors was 53.3 years compared with 60.6 years for non-survivors, which is statistically significant (P = 0.029). This adds to the body of knowledge [7] that younger age is protective for surviving COVID-19; this addition however comes from the Middle East with various ethnicities involved. Further, a recent study has already corroborated our study that having comorbidities and old age (≥65 years) is associated with death in COVID-19 patients [17].

By comparison, the median ages for patients with and without comorbidities were 59.50 and 50 years, respectively. Of the 51 Saudi patients, 42 (29.2%) survived; 9 (39.1%) did not survive; and 7 (41.2%) of these nine case patients had one or more comorbidities. The observation that having comorbidities was strongly associated with non-survival among our Saudi case-patients corroborates our earlier experiences with MERS [18] and, more recently, that of others with COVID-19 [6, 19-21]. It adds to the growing body of knowledge that comorbidity is a risk factor for COVID-19 patients [22-28]. Indeed, 17 (73.9%) of the 23 non-survivors had one or more comorbidities. Of note, 84 (50.3%) of our case-patients had one or more [Median, IQR (range); 2, 2 (1-5)] of the following comorbidities: diabetes mellitus (71.4%), hypertension (63.1%), ischemic heart disease (7.1%) or combination of these comorbidities. The Saudi subset of our study population had a median age of 61 years and IQR of 27 years (0-94): this may be related to the improved life expectancy enjoyed in the Kingdom following years of continuously enhanced health care delivery and modernization. From a public health perspective, it provides additional data supporting the preventive intervention measures targeting the elderly. 

Although our study population did not have “thromboembolic and hemorrhagic” [29] crisis (except in some few outlier cases) that is customary with most COVID-19 patients, the initial prothrombin time was slightly extended with a median of 14.30 s with an IQR of 3 s (11-32) while activated partial thromboplastin time remained within normal limits at the time of presentation to the emergency room. This result may be attributable to the initial routine monitoring and prophylaxis treatment of abnormal coagulation profiles (n=127) of subjects in the study population at the time of presentation to the emergency room. Given the clinical effectiveness of thromboprophylaxis management, about 97.5% (n=159) were presented with lymphopenia at the time of presentation to the emergency room with median lymphocytes of 0.92*10^9/L and IQR of 1 *10^9/L (0-3). Lymphopenia is synonymous with COVID-19 patients and has already been reported as an important biomarker for predicting the severity of illness [29]. Similar to other studies [29, 31-34] that inflammatory and tissue biomarkers, such as C-reactive protein (CRP), D-dimer, LDH, and ferritin are prognostic indicators of interleukin and cytokine storm and disease progression in COVID-19 patients. Indeed, CRP (n=98), D-dimer (n=22), LDH (n=17), and ferritin (n=17) were significantly elevated in our study population at the time of presentation to the emergency room. Similar to this study, “extrapulmonary” [35] multi-system organ characterizations as indicated in some laboratory values, such as mild thromboembolic, kidney, liver, hepatic, and cardiac involvements were seen in our patient population. This finding is further evidence that the initial evaluation of extrapulmonary involvement, such as inflammatory and tissue biomarkers should be an important assessment, diagnostic, and prognostic indicator in the management of COVID-19 patients.

This study is not without limitations. Firstly, it is a hospital-based study and results may not be representative of the community or nationwide epidemiological picture of COVID-19. Secondly, as it was a hospital-based study of critically ill patients, epidemiologic surveillance data, such as travel history, contact history, time of symptoms onset, and length of stay to discharge were not available when the study was conducted. Lastly, all confirmed positive PCR patients were included in the study period; however, clinical characteristics, treatment response time, physiology, and viral shedding for pediatric and adult populations are often expressed differently. 


Conclusion

The research team proposes some recommendations for future public health research. Targeted prevention strategies, such as community-based testing, early intervention, diagnosis, surveillance, and treatments of the older population with comorbidities. Biomarkers, such as lymphocytes, coagulation, liver, kidney, and hepatic profiles can serve as an early diagnostic tool for COVID-19 patients. Further, prognostic indicators, such as CRP, LDH, D-dimer, and ferritin can be used as a biomarker for severity and clinical outcomes for survivals and non-survivals for infected patients. Creating a clinical analytics center of excellence to capture population health data for early surveillance and prevention strategies would create an atmosphere in which comorbidity risks can be managed effectively. 

The use of information technology, such as clinical decision support systems that interface with all the hospitals in the Kingdom would further afford clinicians to work in an integrated health system, creating early clinical epidemiological and surveillance tracking systems for infected individuals to mitigate risks associated with COVID-19 infection.

 

Acknowledgments

The authors acknowledge the Pathology and Clinical Laboratory Medicine Administration, King Fahad Medical City for their support.

 

References

1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395(10223):497-506. https://doi:10.1016/S0140-6736(20)30183-5.

2. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterization and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020;395(10224):565-574. https://doi:10.1016/S0140-6736(20)30251-8.

3. Gorbalenya AE, Baker SC, Baric RS, de Groot RJ, Drosten, C, Gulyaeva AA et al. “Severe acute respiratory syndrome-related coronavirus: The species and its viruses–a statement of the Coronavirus Study Group”.

4. World Health Organization. Director-General's remarks at the media briefing on 2019-nCoV, 11 February 2020, https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020, [accessed 17 April 2020].

5. Ministry of Health. COVID 19, https://covid19.moh.gov.sa/, [ accessed 23 July 2020].

6. Killerby ME, Link-Gelles R, Haight SC, Schrodt CA, England L, Gomes DJ et al. “Characteristics associated with hospitalization among patients with COVID-19—Metropolitan Atlanta, Georgia, March-April 2020”. Morbidity and Mortality Weekly Report 2020;69(25):790-793.

7. Alsofayan YM, Althunayyan SM, Khan AA, Hakawi AM. Clinical characteristics of COVID-19 in Saudi Arabia: A national retrospective study. Journal of Infection and Public Health 2020;13:920-925. https://doi.org/10.1016/j.jiph.2020.05.026.

8. Khamis F, Al-Zakwani I, Al Naamani H, et al. “Clinical characteristics and outcomes of the first 63 adult patients hospitalized with COVID-19: An experience from Oman” [published online ahead of print, 2020 Jun 8]. J Infect Public Health 2020;S1876-0341(20)30496-2. doi:10.1016/j.jiph.2020.06.002

9. Ranieri VM, Rubenfeld GD, Thomson BT et al. “ARDS Definition Task Force. Acute respiratory distress syndrome: the Berlin Definition”. JAMA 2012;307(23):2526-2533.doi:10.1001/jama.2012.5669.

10. Yu H, Luo J, Ni Y, Hu Y, Liu D, Wang M, Liang B, Liang Z. Early prediction of extubation failure in patients with severe pneumonia: a retrospective cohort study. Bioscience Reports 2020;40(2):1-14. https://doi.org/10.1042/BSR20192435.

11. Kellum JA, Lameire N, Aspelin P, Barsoum RS, Burdmann EA, Goldstein SL et al. “Kidney disease: improving global outcomes (KDIGO) acute kidney injury work group”. KDIGO clinical practice guideline for acute kidney injury. Kidney international supplements 2012;2(1):1.doi:10.1038/kisup.2012.1.

12. Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, White HD et al. “Fourth Universal Definition of Myocardial Infarction (2018)”. Circulation 2018;13(4):e618-e651.

doi:10.1161/CIR.0000000000000617.

13. Rawat M, Chandrasekharan P, Hicar MD, Lakshminrusimha S. “COVID-19 in Newborns and Infants—Low Risk of Severe Disease: Silver Lining or Dark Cloud?” American journal of perinatology 2020;37(8):845-849. doi:10.1055/s-0040-1710512.

14. Suleyman G, Fadel RA, Malette KM, Hammond C, Abdulla H, Entz A, Demertzis Z, Hanna Z, Failla A, Dagher C, Chaudhry Z. “Clinical characteristics and morbidity associated with Coronavirus Disease 2019 in a series of patients in Metropolitan Detroit”. JAMA Network Open 2020;3(6):1-12.  https://doi:10.1001/jamanetworkopen.2020.12270.

15. Guan WJ, Liang WH, Zhao Y, Liang HR, Chen ZS, Li YM, Liu XQ, Chen RC, Tang CL, Wang T, Ou CQ. Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis. European Respiratory Journal 2020;55(5):1-14. https://doi:10.1183/13993003.00547-2020.

16. Ravi K. Ethnic disparities COVID-19 mortality: are comorbidities to blame? Lancet 2020: 1. https://doi:org/10.1016/S0140-6736(20)31423-9.

17. Yang X, Yu Y, Xu J, Shu H, Liu H, Wu Y, Zhang L, Yu Z, Fang M, Yu T, Wang Y. “Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study”. The Lancet Respiratory Medicine 2020;8(5):475-481. doi:10.1016/S2213-2600(20)30079-5.

18. Fagbo, SF, Skakni L, Chu DKW, Garbati MA, Joseph M, Peiris M, Hakawi AM. “Molecular epidemiology of hospital outbreak of Middle east respiratory syndrome, Riyadh, Saudi Arabia, 2014”. Emerging Infectious Diseases 2015;21(11):1981–1988. https://doi:10.3201/eid2111.150944.

19. Wyper GMA, Wyper GMA, Assunção R, Cuschieri S, Devleeschauwer B, Fletcher E, Haagsma JA et al. “Population vulnerability to COVID-19 in Europe: a burden of disease analysis”. Archives of Public Health 2020;78(47):1-8. https//doi.org//10.1186/s13690-020-00433-y.

20. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, Barnaby DP, Becker LB, Chelico JD, Cohen SL, Cookingham J. “Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area”. JAMA 2020;323(20):2052-2059. https//doi.org//10.100/jama.2020.6775.

21. Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, Ji R, Wang H, Wang Y, Zhou Y. “Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis”. International Journal of Infectious Diseases 2020;94:91-95.

https://doi.org//10.1016.j.ijid.2020.03.017.

22. Buckner FS, McCulloch DJ, Atluri V, Blain M, McGuffin SA, Nalla AK, Huang ML, Greninger AL, Jerome KR, Cohen SA, Neme S. “Clinical Features and Outcomes of 105 Hospitalized patients with COVID-19 in Seattle, Washington”. Clinical Infectious Diseases 2020: 1-24. https://doi.org//10.1093/cid/ciaa632.

23. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, Gong W, Liu X, Liang J, Zhao Q, Huang H. “Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China”. JAMA Cardiology 2020:E1-E8. https://doi.org//10.1001/jamacardio.2020.0950.

24. Jordan RE, Adab P, Cheng KK. “Covid-19: risk factors for severe disease and death”. Institute of Applied Health Research 2020: 1-2. https://doi.org//10.1136/bmj.m1198.

25. Lighter J, Phillips M, Hochman S, Sterling S, Johnson D, Francois F, Stachel A. “Obesity in patients younger than 60 years is a risk factor for Covid-19 hospital admission”. Clinical Infectious Diseases 2020: 1-2. https://doi.org//10.1093/cid/ciaa415.

26. Zhang J, Wang X, Jia X, Li J, Hu K, Chen G, Wei J, Gong Z, Zhou C, Yu H, Yu M. “Risk factors for disease severity, unimprovement, and mortality of COVID-19 patients in Wuhan, China”. Clinical Microbiology and Infection. 2020(26):767-772. https://doi.org//10.1016/j.cmi.2020.04.012.

27. Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, Li Q, Jiang C, Zhou Y, Liu S, Ye C. “Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis”. Journal of Infection. 2020;15(12):1-9. https://doi.org/10.1016/j.jinf.2020.04.021.

28. Chang MC, Park YK, Kim BO, Park D. “Risk factors for disease progression in COVID-19 patients”. BMC Infectious Diseases. 2020;20(1):1-6. https://doi.org/10.1186/s12879-020-05144-x.

29. Robba C, Battaglini D, Pelosi P, Rocco PR. “Multiple organ dysfunction in SARS-CoV-2: MODS-CoV-2”. Expert Review of Respiratory Medicine 2020;1-4. doi:10.1080/17476348.2020.1778470.

30. Zhao Q, Meng M, Kumar R, Wu Y, Huang J, Deng Y, Weng Z, Yang L. “Lymphopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A systemic review and meta-analysis”. International Journal of Infectious Diseases 2020;96:131-135. doi:10.1016/j.ijid.2020.04.086.

31.  Aloisio E, Chibireva M, Serafini L, Pasqualetti S, Falvella FS, Dolci A, Panteghini M. “A comprehensive appraisal of laboratory biochemistry tests as major predictors of COVID-19 severity”. Archives of pathology & laboratory medicine 2020;(Online-ahead-of-print):1-26.  doi:10.5858/arpa.2020-0389-SA.

32. Bonetti G, Manelli F, Patroni A, Bettinardi A, Borrelli G, Fiordalisi G, Marino A, Menolfi A, Saggini S, Volpi R, Anesi A. “Laboratory predictors of death from coronavirus disease 2019 (COVID-19) in the area of Valcamonica, Italy”. Clinical Chemistry and Laboratory Medicine (CCLM) 2020;58(7):1100-1105. doi:10.1515/cclm-2020-0459.

33. Ponti G, Maccaferri M, Ruini C, Tomasi A, Ozben T. “Biomarkers associated with COVID-19 disease progression”. Critical Reviews in Clinical Laboratory Sciences 2020;9(1):1-11.

doi:10.1080/10408363.2020.1770685.

34. Huang I, Pranata R, Lim MA, Oehadian A, Alisjahbana B. “C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis”. Therapeutic Advances in Respiratory Disease 2020;14:1-14. doi:10.1177/1753466620937175.

35. Gupta A, Madhavan MV, Sehgal K, Nair N, Mahajan S, Sehrawat TS, Bikdeli B, Ahluwalia N, Ausiello JC, Wan EY, Freedberg DE. “Extrapulmonary manifestations of COVID-19”. Nature Medicine 2020;26(7):1017-32. https://doi.org/10.1038/s41591-020-0968-3.