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Shon Shmushkevich^{1, 2#}, Massimo Baudo^3#, Nagla Abdel Karim⁴, Mahmoud Morsi⁵, Mariam Khobsa⁶, Hala Aziz⁷, Maha Yahia⁷, Mohamed Emam⁷, Omnia Mohamed⁷, Hossameldin Abdallah⁷, Ahmed Abouarab⁸, Dina Mofed⁹, Mohamed Ismael¹⁰, Ayah A Hassan¹¹, Mostafa Rahouma¹², Mohamed Kamel¹³, Sherif Khairallah¹³, Ihab Saad¹³, Galal Ghaly¹³, Sherif Bahaa¹³, Rabab Gaafar⁷, Abdel Rahman Mohamed¹³, Mohamed Rahouma^13*#

¹Zanvyl Krieger School of Arts & Sciences, Johns Hopkins University, Maryland, USA

²Department of Cardiothoracic Surgery, Weill Cornell Medicine/New York Presbyterian Hospital, New York, USA

³Department of Cardiac Surgery, Spedali Civili di Brescia, Brescia, Italy

⁴Department of Hematology and Oncology, Medical College of Georgia, Augusta University, Augusta, USA

⁵Department of General Surgery, Montefiore Health System, New York, USA

⁶Department of Cardiology, Overlake Medical Center, Washington, USA

⁷Department of Medical Oncology, National Cancer Institute, Cairo University, Cairo, Egypt

⁸Department of Surgery, New York Presbyterian Hospital, New York, USA

⁹Department of Zoology, Faculty of science, Cairo University, Cairo, Egypt

¹⁰Department of Microbiology, High Institute of Public Health, Alexandria university, Qism Bab Sharqi, Egypt

¹¹Department of Pharmacognosy, Faculty of Pharmacy, Cairo University, Cairo, Egypt

¹²Department of Information Technology, National Cancer Institute, Cairo University, Cairo, Egypt

¹³Department of Surgical Oncology, National Cancer Institute, Cairo University, Cairo, Egypt

^*Corresponding Author: Mohamed Rahouma, Department of Surgical Oncology, National Cancer Institute, Cairo University, Egypt 1st Fom Elkhaleeg Square Masr ElKadema, Cairo, Egypt, E-mail: mhmdrahouma@gmail.com

# - Equally contributed to the work.

Received: 07 August 2020; Accepted: 19 August 2020; Published: 15 October 2020

Citation: Shon Shmushkevich, Nagla Abdel Karim, Massimo Baudo, Mahmoud Morsi, Mariam Khobsa, Hala Aziz, Maha Yahia, Mohamed Emam, Ahmed Abouarab, Dina Mofed, Mohamed Ismael, Ayah A Hassan, Mostafa Rahouma, Mohamed Kamel, Sherif Khairallah, Ihab Saad, Abdel Rahman Mohamed, Mohamed Rahouma. Effects of Cancer, Coronary Artery Disease and other Comorbidities on COVID-19 Related Mortality: A Meta-analysis and Meta-regression. Journal of Surgery and Research 3 (2020): 343-369.

Abstract

Keywords

COVID-19; Comorbidities; Meta-Analysis; Symptoms; Coronavirus; Mortality

Article Details

1. Introduction

Beginning in December 2019, pneumonia cases with unknown origin began to arise in Wuhan, Hubei, China. High-throughput sequencing from lower respiratory tract samples has revealed a novel coronavirus that was named 2019 novel coronavirus (2019-nCoV) and also named SARS Coronavirus-2 [1]. The 2019 coronavirus (COVID-19) pandemic has infected more than ten million people and caused more than five hundred thousand deaths (by the end of June 2020) [2]. 2019-nCoV targets the respiratory tract and shares many similar clinical symptoms with SARSCoV and Middle East respiratory syndrome

Coronavirus (MERS) [1]. Common symptoms include fever, fatigue, and dry cough [3-7]. Previous studies have shown a relationship between cardiovascular metabolic diseases, SARS, and MERS [2, 8]. One study provided that 637 MERS-CoV cases showed diabetes and hypertension as prevalent in 50% of patients and cardiac diseases as 30% of cases [2].

Even though our understanding of Covid-19 transmission is consistently growing, it is widely believed that SARS-CoV-2 is transmitted via droplets and close contacts with people carrying the virus [2].

Additionally, it is also reported that the virus transmits through various surfaces, gastrointestinal transmission [9], and airborne exposures [2, 10]. Although our knowledge of transmission and at-risk populations has significantly increased, our understanding of effective therapeutic interventions has been limited. There are several published studies that describe the epidemiological and clinical characteristics of recovered and mortality cases affected by COVID-19. In this report, we will comprehensively evaluate patient demographics, comorbidities, symptoms, mortality, and length of hospital stay.

2. Methods

2.1 Study design and sample selection

This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement [11] and the PRISMA flow diagram is presented in Supplementary figure 1. A systematic review was performed to identify studies reporting on patients affected with COVID-19. Pubmed, Ovid’s version of MEDLINE (In-Process & Other Non-Indexed Citations and Ovid MEDLINE January 2020 to Present), Ovid EMBASE (January 2020 to present), and The Cochrane Library (Wiley) were searched. Literature search was terminated on April 1^st, 2020. The inclusion criteria were adult patients > 18 years, English-language and full-length articles about early outcomes of patients with COVID-19. In addition, references of recent meta-analyses and reviews on this topic were searched for potential additional studies (i.e. backward snowballing). In case of studies from the same or overlapping cohorts reporting different outcomes, the largest series was included for each outcome. Studies with patients <18 years and editorial or reviews were excluded.

2.2 Data Extraction

Studies were independently screened by 2 investigators (S.S. and M.B.). In case of any discrepancy, a consensus was reached with the aid of a third author (M.R.). Microsoft Office 365 Excel software (Microsoft, Redmond, Washington) was used for data extraction. The following variables were included: study demographics as sample size, number of centers, name of center, publication year, and country. Patient demographics and comorbidities variables on age, sex, smoking, diabetes mellitus, hypertension, chronic liver disease, cancer history, coronary artery disease (CAD) and chronic obstructive pulmonary disease (COPD) and cardiac comorbidity were abstracted. Symptoms presentation variables such as fever, cough, sore throat, headache, diarrhea, chest pain fatigue and vomiting were retrieved (Supplementary Table 1). Continuous variables were reported as mean and standard deviation, while categorical variables were reported as counts and percentages. The quality of the included observational studies was assessed using the Newcastle- Ottawa Quality Assessment Scale (NOS) for cohort studies[12]. The comparison evaluation points were excluded, as the studies were analyzed as single arm. Thus, 6 stars was the highest possible score out of the 9 (Supplementary Table 2).

2.3 Outcomes

The primary outcome was the pooled all-cause short-term mortality. The secondary outcomes were the length of hospital stay and symptom presentation.

2.4 Data synthesis and statistical analysis

This is a single arm meta-analysis. The binary outcomes and continuous outcomes were reported as pooled event rate (PER) and pooled event means (PEM) with 95% confidence interval (CI) respectively were calculated using the generic inverse variance method with logit transformation and log transformed mean, respectively. The DerSimonian-Laird method was used as between-study estimator [13]. Statistical significance will be set at P <0.05. Heterogeneity will be reported as low (I² = 0%-25%), moderate (I² = 26%-50%), high (I² > 50%), consistent with guidelines [14]. Individual study inference analysis was performed through a “leave-one-out” sensitivity analysis. Publication bias was analyzed by funnel plot visual assessment and Egger regression test. All statistical analyses were performed using “meta” and “metaphor” packages in R (version 3.6.2 R Project for Statistical Computing) within RStudio.

2.5 Meta-regression

Univariable meta-regression was performed to investigate the effect of different collected variables on the primary outcome including age, gender, hypertension, diabetes, COPD, smoking, chronic liver disease, cancer history, coronary artery disease, cardiac comorbidities. Studies were weighted by the inverse of the variance of the estimate for that study, and between-study variance was estimated with DerSimonian-Laird estimator. The results were reported as regression coefficient (i.e., beta).

3. Results

The literature search identified 2735 studies. No additional articles were identified through backward snowballing. 2598 studies were excluded due to title and abstract screening. 137 studies received full text screening. Fifty-six articles met our inclusion criteria with a total of 9074 patients, Supplementary Figure 1. 48 studies were from China, 3 from Korea, 2 from France, 1 from each of USA, Europe and Italy. Female percent range for included studies was 26.8% to 66.6%. The mean age ranged from 29.2 to 77 in included studies.

3.1 Meta-analysis

The PER for hospital mortality was 11.17% (95% CI, 6.67% - 17.89%), Figure 1. Among the analyzed studies, high heterogeneity (I² = 94%) was detected. Visual inspection of the funnel plot and Egger test did not reveal significant asymmetry for hospital mortality (Egger test p-value = 0.1308). The leave-one-out analysis is depicted in Supplementary Figure 2. Patients characteristics were as follows: the PEM for age was 49.6 years (95% CI 46.8-52.6); the PER for female gender was 46.79% (95% CI 44.48% - 49.11%); the PER for smoking was 10.96% (95% CI 7.35% - 16.02%); the PER for hypertension was 24.47% (95% CI 19.85% - 29.77%); the PER cardiac comorbidities was 20.30% (95% CI 9.43% - 38.40%); the PER for diabetes was 12.34% (95% CI 9.96% - 15.20%); the PER for CADwas 5.44% (95% CI 3.50% - 8.38%); the PER for COPD was 3.96% (95% CI 2.09% - 6.42%); the PER for history of cancer was 3.75% (95% CI 2.17% - 6.41%) and the PER for chronic liver disease was 3.08% (95% CI was 2.12% - 4.47%).

Patients characteristics are summarized in Supplementary Table 3 (forest plots can be seen in the Appendix Supplementary figures 3-6). As far as presenting symptoms concern, the PER for fever 79.29% (95% CI 73.56 – 84.04%); the PER for cough was 56.48% (95% CI 50.63% – 62.15%); the PER for sore throat was 11.10% (95% CI 6.94% - 17-28%); the PER for headache was 8.16% (95% CI 6.60% – 10.05%); the PER for diarrhea was 11.32% (95% CI 5.37% - 22.34%); the PER for chest pain was 13.43% (95% CI 7.58% - 22.70%); the PER for fatigue was 27.72% (95% CI 21.97% - 34.32%); the PER for vomiting was 11.98% (95% CI 2.87% - 38.54%) and the PEM for LOS was 10.9 days (95% CI 7.3 – 16.1 days). Symptom presentations are summarized in Table 1, Figure 1 and Supplementary Figures 7-10.

3.2 Meta-regression

Hospital mortality was significantly and positively associated with cardiac comorbidity (Beta=0.0981, p=0.0009), coronary artery disease (Beta =0.0806, p=0.0270) and COPD (Beta =0.4581, p=0.0218). Age and cancer history were not associated with higher hospital mortality. See Figure 2 for bubble-plots and Table 2 for details.

Figure 1: Forest Plot of hospital mortality.

Table 1: Meta-analysis outcomes summary.

Figure 2: Meta-regression of cardiac comorbidity, CAD and COPD for hospital mortality bubble plot Figure A) Coronary Artery Disease (CAD), B) Chronic Obstructive Pulmonary Disease (COPD), C) Cardiac comorbidities on hospital mortality. Hospital mortality is significantly and positively associated with CAD (Beta=0.0806, p=0.0270), COPD (Beta =0.4581, p=0.0218) and cardiac comorbidity (Beta =0.0981, p=0.0009).

CAD = Coronary Artery Disease; COPD = Chronic Obstructive Pulmonary Disease

Table 2: Meta-regression of different variables on hospital mortality.

4. Discussion

In this meta-analysis, we examined 56 studies with a total of 9,074 patients. This comprehensive analysis focused on numerous morbidities such as hypertension, cardiac comorbidities, COPD, coronary artery disease, diabetes, cancer, and chronic liver disease. The symptoms that were analyzed include fever, cough, sore throat, headache, diarrhea, chest pain, fatigue, and vomiting. Hospital mortality and length of stay were also analyzed. This meta-analysis indicated that the PER for hospital mortality was 11.17% (95% CI, 6.67% - 17.89%) which lies in the previously reported range of mortality rate, which is roughly 2.12%-18.9% [15-18]. Our analysis identified the most significant comorbidities being hypertension with PER of 24.47% (95% CI 19.85% - 29.77%), cardiac comorbidities which had a PER of 20.30% (95% CI 9.43% - 38.40%), and diabetes which had a PER of 12.34% (95% CI 9.96% - 15.20%). These results run parallel to previously conducted meta-analyses, which reported hypertension prevalence of about 20% and diabetes of about 10% [19]. A higher mortality rate of 11.17% can be attributed to a higher prevalence of significant comorbidities in our included studies. The most statistically significant presenting symptoms include fever and cough. Fever had a PER of 79.29% (95% CI 73.56 – 84.04%); while cough had a PER of 56.48% (95% CI 50.63% – 62.15%). Our results confirm that symptoms of fever and persistent cough are the most prevalent symptoms of COVID-19 worldwide [20].

Interestingly, the conducted meta-regression indicates that hospital mortality is significantly and positively associated with cardiac comorbidity (b=0.0981, p=0.0009), CAD (b=0.0806, p=0.0270), and COPD (b=0.4581, p=0.0218), but not with age. Based on this meta-regression, age is not significantly associated with hospital mortality, which opposes the current belief that is propagated in the medical community and media outlets. Recent studies portray that patients who are elderly carry a more significant risk factor for COVID-19 related mortality [18]. Against what we initially thought, in this meta-analysis, cancer did not affect overall mortality estimate. Immunocompromised patients tend to not respond normally to an infection due to an impaired or weakened immune system[21]. The inability to combat infection is attributed to numerous conditions including underlying disease (malignancy, organ or stem cell transplantation, systemic vasculitis, connective tissues diseases, etc.), associated conditions (diabetes, malnutrition, etc.) or drug-related immune suppression [21].

Up to our knowledge and compared to other meta-analysis conducted, this is the first meta-analysis to involve a meta-regression of numerous comorbidities effect on hospital mortality. Our results run in parallel with prior meta-analyses conducted, which link poor Covid-19 outcome with specific comorbidities. Our study is unique in that through a meta-regression we are able to add another dimension to statistical Covid-19 analysis, which depicted that hospital mortality was significantly and positively associated with cardiac comorbidity, CAD and COPD, while not being associated with patient age or cancer history. Other studies show that hypertension has a composite of poor outcome, comprising of mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care and disease progression [22]. Also, diabetes had a composite poor outcome, including mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care, and disease progression [23]. Yet, our study did not attribute higher incidence of mortality to these comorbidities. Another meta-regression showed that cardiovascular disease was associated with increased composite poor outcome, which our analysis agrees with [24].

This meta-analysis should aid policymakers by providing insight that age might not be the most significant factor in COVID-19 mortality. It is important to educate the public, which mostly believes that age is the sole factor driving Covid-19 deaths. With a potential second wave on the rise in the United States, many citizens are beginning to exercise less caution in social distancing protocols. We believe this is dangerous because many of those people may have significant comorbidities that could place them in a zone of tremendous risk - without being aware of it. Based on the evidence, it is important that policymakers provide strict guidance to the groups that are at increased risk for severe COVID-19 [19]. As previous meta-analyses have concluded, results from published work should aid in group selection for ongoing clinical trials, and inform policymakers as to which groups should be prioritized if a vaccination becomes available [19]. The increased mortality of COVID-19 in hypertension, cardiac comorbidities, and diabetes patient groups should direct future preventative therapies and vaccination programs for these particular groups while maintaining mitigating prevention strategies [19].

5. Conclusion

Fever and cough are the most common presenting symptoms with estimated PER of 79.29% and 56.48% respectively. Hospital mortality is significantly and positively associated with cardiac comorbidities, CAD, and COPD, while not being significantly associated with patient age or cancer.

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Supplementary Figure 1: Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA).

CAD: Coronary artery disease, COPD: Chronic obstructive pulmonary disease, NR: Not Reported

Supplementary Table 1: Patient demographic data.

Supplementary Table 2: The Newcastle-Ottawa Quality Assessment Scale of included studies.

Supplementary Figure 2: Leave-one-out analysis.

CAD = Coronary artery disease; COPD = Chronic obstructive pulmonary disease; yrs = years

Supplementary Table 3: Meta-analysis outcome summary of patient characteristics.

Supplementary Figure 4: Forest plot for A) Cardiac comorbidity and B) Diabetes.

Supplementary Figure 5: Forest plot for A) Coronary Artery Disease and B) Chronic Obstructive Pulmonary Disease.

Supplementary Figure 6: Forest plot for History of Cancer.

Supplementary Figure 7: Forest plot of A) Fever and B) Cough.

Supplementary Figure 8: Forest plot of A) Sore throat and B) Headache.

Supplementary Figure 9: Forest plot of A) Diarrhea and B) Chest Pain.

Supplementary Figure 10: Forest plot for Vomiting.

COPD: Chronic Obstructive Pulmonary Disease, CVD: cardiovascular disease, ICU: Intensive care unit, OR: Odds Ratio.

Supplementary Table 4: Results from the latest COVID-19 related published meta-analysis.

Supplementary Table 5: Prior meta-analysis meta-regression.