Skip to main content
Download PDF

Clinical characteristics and outcome of Covid-19 illness and predictors of in-hospital mortality in Saudi Arabia

BMC Infectious Diseases volume 22, Article number: 950 (2022) Cite this article

Abstract

Background

Patients’ race and ethnicity may play a role in mortality from Covid-19. Studies in China, the US, and Europe have been conducted on the predictors of Covid-19 mortality, yet in the EMR countries, such studies are scarce. Therefore, we aimed to describe the hospitalization rate, ICU-admission, and in-hospital mortality of Covid-19 and predictors of in-hospital mortality in Saudi Arabia.

Methods

E-medical records were examined for all Covid-19 patients diagnosed in five tertiary hospitals affiliated with the Saudi-National Guard-Health Affairs during March 21, 2020, and September 12, 2021, based on a positive SARS-CoV-2 RT-PCR test, (n = 35,284). Data were collected on patients’ characteristics, comorbidities, laboratory findings, hospitalization, ICU admission, and in-hospital and overall mortality. Logestic regressions were used to identify the independent predictors of in-hospital mortality. The best laboratory parameters cut-off values to predict in-hospital mortality were identified using the area under the receiver operating characteristic curve (AUC). Significance was considered at p < 0.05.

Results

Of all 35,284 Covid-19 patients, 81.8% were adults and 21.7% were hospitalized. Compared to non-hospitalized patients, hospitalized patients were more of female gender (52.1% versus 47.3%, p < 0.001) and had higher mean age (p < 0.001), higher mean BMI (p < 0.001), and higher rates of: diabetes (p < 0.001), hypertension (p < 0.001), ischemic heart disease (p < 0.001), cancer (p < 0.001), COPD (p < 0.001) and asthma (p = 0.011). The study showed 3.1% overall case-fatality, 20.3% ICU admission rate, and 9.7% in-hospital mortality. Predictors of in-hospital mortality among adult patients were; patients’ age ≥ 70 years (OR = 6.93, 95% CI 1.94–24.79), ischemic heart disease (OR = 1.80, 95% CI 1.05–3.09), ICU admission (OR = 24.38, 95% CI 15.64–38.01), abnormal C-reactive protein “CRP” (OR = 1.85, 95% CI 1.08–3.16), abnormal D-dimer (OR = 1.96, 95% CI 1.15–3.36), lymphopenia (OR = 2.76, 95% CI 2.03–3.3.76), high neutrophil count (OR = 2.10, 95% CI 1.54–2.87), and abnormal procalcitonin (OR = 3.33, 95% CI 1.88–5.90). The best laboratory parameters cut-off values to predict in-hospital mortality were CRP > 72.25 mg/L (AUC = 0.64), d-dimer > 1125 µg/L (AUC = 0.75), neutrophils count > 5,745 × 10^9/L (AUC = 0.70), lymphocytic count < 1.10 × 10^9/L (AUC = 0.72), and procalcitonin > 0.18 ng/mL (AUC = 0.76).

Conclusions

Rates of hospitalization, ICU-admission, in-hospital mortality and overall case fatality were nearly comparable to the rates in western countries. Early interventions are necessary for high-risk Covid-19 patients, especially elderly patients and those with cardiac diseases.

Peer Review reports

Background

Coronavirus disease 2019 (COVID-19) is a respiratory transmissible disease caused by the virus of SARS-CoV-2. The spread of the virus began in late 2019, and as of January 2, 2022, over two hundred and eighty million people are infected, relating to more than five million deaths worldwide [1]. Many researchers have studied factors about the mortality of patients diagnosed with COVID-19. One of the most frequent predictors was age; multiple studies concluded that increased age was associated with higher mortality [2,3,4]. A meta-analysis of 49 articles and over 20,000 patients concluded that age 50 and above had a strong association with these patients’ mortality [5]. Moreover, comorbidities predicted higher mortality in moderate to severe COVID-19 patients [6]; included hypertension, diabetes mellitus, ischemic heart disease, cancer, chronic kidney disease, and liver injuries.

Specific laboratory parameters were associated with an increased risk of mortality. For instance, elevated C-reactive protein (CRP) and d-dimer levels were associated with increased mortality odds [6,7,8,9]. According to Leonidas Palaiodimos et al. [10], severe obesity with a high body mass index of more than 35 kg/m2 was associated with higher in-hospital mortality among COVID-19 patients.

Patients’ race and ethnicity may play a role in mortality from Covid-19. Mortality rates among Black Americans were 92.3 deaths/100,000 population and 74.3 deaths/100,000 among Hispanic Americans, higher than those of white Americans (45.2 deaths/100,000) or Asians (34.5 deaths/100,000) [11]. Economic and social conditions also may play a role [12, 13]. Living in heavily crowded areas may be a barrier to prevention measures like social distancing. In addition, economic status in different countries may influence the outcomes due to difference in resources needed for management [14,15,16]. Although a fair number of articles related to Covid-19 mortality predictors in Saudi Arabia were published [7, 9, 13, 17, 18], none of these studies was found with a large sample size that guarantees its validity. This study aimed to describe, in a large cohort of Saudi patients, the characteristics and outcomes of Covid-19 illness in children and adults, and to determine the predictors of in-hospital mortality in adult patients.

Methods

Study Design and study subjects

This is a retrospective cohort study of all patients diagnosed and treated in five tertiary hospitals affiliated with the Saudi Ministry of National Guard-health affairs (MNG-HA), from the start of the virus spread in Saudi Arabia on March 21, 2020, until September 12, 2021 (n = 35,284). All individuals with suspected Covid-19 symptoms, and those with history of a recent exposure to a Covid-19 case, in those five hospitals, were screened for Covid-19. Diagnosis was made based on a positive SARS-CoV-2 Reverse transcription-polymerase chain reaction (RT-PCR) from nasopharyngeal swabs.

Study setting

MNG-HA provides healthcare services to national guard service members and their dependents through large medical cities located in the three most densely populated regions of Saudi Arabia, namely the Central (one hospital), Western (two hospitals), and Eastern (two hospitals) regions. All facilities have been Joint Commission International (JCI)-accredited since 2006. During the COVID-19, and following the first reported case in Saudi Arabia, MNG-HA has taken drastic infection control measures that included the reduction of elective surgeries, stopping in person outpatient services, and introducing ER workflow to minimize Covid19 cases flow through the main ER [19]. Standard treatment of patients infected with COVID-19 includes symptoms treatment, supportive measures such as; supplemental oxygen, and approved drugs to treat the infection per the MOH and MNGHA treatment guidelines [20]. Recommendations for adult patients admitted to the ICU include antiviral therapy, Immunomodulation therapy, and corticosteroids. Dexamethasone over other corticosteroids is also suggested [21].

Data collection

The collected variables included routine demographic variables such as age and sex, and location, Comorbidities included Diabetes, Hypertension, Cancer, Chronic obstructive pulmonary disease, Asthma, and Ischemic heart disease, biometric measurements, i.e., Body-Mass-Index, Laboratory data including C-reactive protein (CRP), D-dimer, Ferritin, Lactate Dehydrogenase (LDH), Lymphocytes, Neutrophils, and Procalcitonin, and outcome variables including hospitalization, intensive care unit (ICU) admission, mortality status, in-hospital mortality, and time from diagnosis to death.

Data was extracted from the health system’s electronic Datawarehouse, which host all medical information for patient care. Based on the study inclusion criteria, the data was extracted using a search engine within the Electronic Health Record (EHR) and collated in a structured format including ICD codes, lab values and health utilization (i.e., ICU admission). All the data obtained are documented by the healthcare professionals who provided those services to patients. All hospitals, within the MNG-HA, provide uniform healthcare services across the organization. Therefore, data collected are consistent across hospitals. Additional validation for mortality status was done by ensuring other information are documented, such as date and time of death, and the presence of a death notice.

Ethical issues

The study was approved by the Institutional Review Board (IRB) of the Ministry of National Guard- Health Affairs (MNG-HA) by study number NRC21R/445/10. The IRB of the MNG-HA waived the requirement for informed consent because of the retrospective nature of the study. All methods were carried out in accordance with relevant guidelines and regulations.

Data analysis

Data were analyzed using the (SPSS version 26.0; IBM Corporation, Armonk, NY, USA). We used descriptive statistics such as mean (SD), median (IQR), and range. Frequencies (%) and their corresponding 95% confidence intervals (CIs) were estimated. Student t test was used to compare numerical data, while the chi-square test and Fisher exact test were applied for categorical data. Odds ratios (ORs) and their corresponding 95% CIs were calculated, and logistic regression analysis was applied to adjust for confounders of the association between different variables and the in-hospital case fatality of Covid-19. Kolmogorov–Smirnov and Shapiro–Wilk tests were applied to test for normality of age and BMI variables before deciding which statistical test to use. Receiver Operator Characteristic curve (ROC) was applied to allocate the optimum cut-off values for age and different laboratory parameters in the prediction of in-hospital mortality from Covid-19. For every parameter optimum cut-off, accuracy was determined, in terms of sensitivity and specificity, with the corresponding 95% confidence intervals, and the area under the curve (AUC). Significance was considered at p ≤ 0.05.

The primary outcomes were the characteristics and outcome of Covid-19 illness. The secondary outcome was predictors of in-hospital mortality to help for proper and timely treatment of patients with Covid-19 illness.

Results

Hospitalization & In-hospital mortality rate among Covid-19 patients

Of all 35,284 Covid-19 patients, 28,876 patients (81.8%) were adults 18 years or more and 6408 patients (18.2%) were children less than 18 years. A total of 7484 patients (21.7%) were hospitalized [24.3% of adult patients and 9.7% of children with Covid-19, χ2 = 664.15, p < 0.001]. Of all hospitalized patients, 1552 patients (20.3%) were admitted to ICU [21.1% of hospitalized adult patients and 11.5% of hospitalized children, χ2 = 32.61, p < 0.001]. The overall case fatality rate was 3.1% [3.7% of adults and 0.3% of children, χ2 = 206.97, p < 0.001]. In-hospital mortality rate was 9.7% of all hospitalized patients [10.5% of hospitalized adults and 1.1% of hospitalized children, χ2 = 57.00, p < 0.001], Table 1.

Table 1 Distribution of all Covid-19 patients (n = 35,284) diagnosed in the Ministry of National Guard Hospitals, Saudi Arabia [March 21, 2020 until September 12, 2021] according to age, hospitalization, ICU admission and in-hospital mortality
Full size table

Personal characteristics and comorbidities among adult Covid-19 patients

Of all hospitalized patients, 51.5% were males and 48.5% were females. Most patients (43.0%) were 30 to < 50 years of age, and 30.9% of patients were ≥ 50 years of age, with a mean age of 42.4 ± 16.7 years. The majority of patients had a BMI of ≥ 30 kg/m2. The most common comorbidities were diabetes (14.4%) and hypertension (11.8%). Hospitalized patients were significantly older (54.4 ± 4 years versus 38.5 ± 14.1 years, p < 0.001) and with higher BMI (p < 0.001). Concerning comorbidities, hospitalized patients showed a significantly higher rate of diabetes (p < 0.001), hypertension (p < 0.001), ischemic heat disease (p < 0.001), cancer (p < 0.001), COPD (p < 0.001), and Asthma (p = 0.011). There was a significant sex difference between hospitalized and non-hospitalized patients, with higher proportion of females among hospitalized patients (p < 0.001), Table 2.

Table 2 Distribution of hospitalized and non-hospitalized adult Covid-19 patients according to personal characteristics and comorbidity
Full size table

Predictors of in-hospital mortality

In bivariate analysis, the rate of in-hospital mortality was significantly higher among male patients (p < 0.001), patients ages 30 to < 50 years (p = 0.006), 50 to < 70 years (p < 0.001) and ≥ 70 years of age (p < 0.001). Obese patients of ≥ 30 kg/m2 showed significantly lower in-hospital mortality (p = 0.001). However, after adjusting for confounders, old age of 70 or more was the only significant personal factor associated with in-hospital mortality among hospitalized Covid-19 patients (OR = 6.93, 95% CI 1.94–24.79, p = 0.003), Table 3.

Table 3 In-hospital mortality rate (%) in adult hospitalized Covid-19 patients according to some personal characteristics, comorbidities and laboratory findings
Full size table

In-hospital mortality rate was significantly higher among patients with diabetes (p = 0.001), hypertension (p < 0.001), IHD (p < 0.001), cancer (p = 0.018), and COPD (p = 0.006). After adjusting for possible confounders, of all comorbidities studied, IHD was only combidity significantly associated with in-hospital mortality (OR = 1.80, 95% CI 1.05–3.09, p = 0.034). Patients admitted to ICU showed significant association with in-hospital mortality (p < 0.001). Even after controlling for possible confounders, ICU admission remained a significant predictor of in-hospital mortality (OR = 24.38, 95% CI 15.64–38.01, p < 0.001), Table 3.

As for laboratory findings, after adjusting for possible confounders, in-hospital mortality was significantly associated with abnormal values of CRP (OR = 1.85, 95% CI 1.08–3.16, p = 0.024), d-dimer (OR = 1.96, 95% CI 1.15–3.36, p = 0.014), procalcitonin (OR = 3.33, 95% CI 1.88–5.90, p < 0.001), low lymphocyte count (OR = 2.76, 95% CI 2.03–3.3.76, p < 0.001), and high neutrophil count (OR = 2.10, 95% CI 1.54–2.87, p < 0.001), Table 3.

The ROC curves for age, CRP, D-dimer, procalcitonin, lymphocyte count and neutrophil count were applied to better predict in-hospital mortality. The best cut-off point for age was 64 years with a sensitivity of 64% and specificity of 60%, and AUC = 69%. The best laboratory findings cut-off points for prediction of in-hospital mortality were 72.25 mg/L for CRP, > 1125 µg/L for d-dimer, > 5,745 for neutrophils count, < 1.10 for the lymphocytic count and > 0.18 for procalcitonin, Table 4.

Table 4 Accuracy of predictors of in-hospital mortality in Covid-19 adult hospitalized patients as determined by the receiver operating characteristic curve (ROC)
Full size table

Discussion

Worldwide, a case fatality rate of 3.5% was estimated for Covid-19 [22], and this rate was similar to that of Spanish influenza (2–3%) but much higher than that of seasonal influenza (0.1%) [23]. Variable mortality rates for Covid-19, ranging from 18.9% to 0.1% [24], have been reported in different countries. The overall case fatality rate was high in Belgium [25], in US [26], and in UK [27]. Our study showed an overall case fatality rate of 3.1%, a figure that is comparable to figures in EMR countries (3.8%, 95% CI 3.8–3.9%) [24], yet much higher than the figures reported for the GCC countries (0.6%, 95% CI 0.55–0.65%) [28]. It was also comparable to rates in China [29] and Italy [30]. Worldwide, over 80% of COVID-19 cases have mild symptoms; however 10 and 20% of COVID-19 cases proceed to a severe stage [31]. This pattern was similar to the situation shown in our study, where 24.3% of all patients with Covid-19 were hospitalized because of moderate to severe symptoms, while 75.7% of cases did not need hospitalization, probably because of their mild symptoms.

Reported mortality rates of COVID-19 patients are in the range of 20–40% for hospitalised patients and 30–88% for critically-ill or ICU patients with substantial differences between countries and regions [32]. This diversity was attributed to the different strategies of referral to hospital, admission strategies and decisions on treatment withdrawal [32]. In-hospital mortality rate among hospitalized cases, in our study, was 9.7%. The incidence of fatalities in many developing countries appeared to be low in the early stages of the pandemic, due to their relatively younger age structure, poor vital statistics systems leading to underreporting of COVID-19 deaths, and lower access to good-quality healthcare [33]. However, this figure, in our study, is high when compared with the counterpart figure of 1.63% in Abu Dhabi, UAE [34].

ICU utilization may be viewed as an indicator of the quality of healthcare delivered. The global rates of 20–30% of Covid-19 ICU admissions were reported [35]. The overall rate of ICU admission in KSA in all regions for all COVID-19 patients who were admitted to hospital based on MOH criteria is almost was 50% [36]. In our study, this rate of ICU admission was 20.3% of all hospitalized patients. It was high compared to a rate of only 7.68% patients in Abu Dhabi. ICU admission rate is a sensitive indicator for the severity of the illness. In our study, the rate of in-hospital mortality among ICU admitted cases was 38.7%, a rate which is nearly two times the rate of 19.56% in Abu Dhabi. This difference might be attributed to the difference in the strategy of hospitalization in the two countries, where in Abu Dhabi, initially, all people with a positive PCR were admitted regardless of clinical presentation for isolation and monitoring, a strategy that Saudi Arabia didn’t practice.

Patients’ characteristics, comorbidities and laboratory values have been reported and considered in the recent guidelines for the management of Covid-19 [35, 37,38,39]. Age was reported as the most significant predictor of mortality in patients with Covid-19 [40,41,42,43,44,45,46,47,48,49,50]. In our study, the old age of 70 or more was the only significant personal factor associated with in-hospital mortality among hospitalized Covid-19 patients. Older patients showed higher tendency to progress to severe Covid-1 9 illness than relatively younger patients [21], and this was attributed to functional defects in immune cells, leading to inability to suppress viral replication, as age advances [45,46,47]. In our study, age of > 64 was the significant cut-off age for the prediction of in-hospital mortality from Covid-19. This finding was in agreement with the finding of a cut-off value of ≥ 65 years reported in a previous study [51].

Some studies reported that obesity was a significant predictor of mortality from Covid-19 [52,53,54]. However, in our research, adjusting for other possible confounders, obesity was not amongst the predictors of mortality. This finding was in agreement with the results of Stefan et.al [25]. The association of obesity with lower mortality could be attributed to the possible natural protective effect of adipose tissue [55, 56], or to the lower levels of proinflammatory cytokines in obese subjects.

Hypertension, diabetes, and coronary heart disease were the most common comorbidities among Covid-19 patients, in our study. These findings are similar to the common comorbidities observed in the US, Italy, China, and UAE [34, 57, 58]. The following comorbidities were reported as key predictors for Covid-cases progression: Diabetes mellitus [59,60,61], hypertension [60,61,62,63], COPD [59], and Ischemic heart disease [43]. In our study, of all these comorbidities, ischaemic heart disease was the only significant predictor of in-hospital mortality, and Covid-19 cases who suffered from ischemic heart disease were two times more likely to end with in-hospital mortality. In a nested case–control study to evaluate the risk of pre-existing comorbidities on Covid-19 mortality [64], in-hospital mortality was three times more likely to occur among cases with pre-existing cardiac disease than those without. Early medical intervention is necessary for Covid-19 patients with pre-existing comorbidities, especially cardiac disease.

In terms of laboratory results, the following abnormal laboratory findings were reported as key predictors for Covid-19 cases progression; d-dimer [59, 60, 65,66,67,68], CRP [59, 64, 67,68,69,70,71], LDH [60, 72,73,74,75,76], lymphocytes [59, 60, 62, 68,69,70, 73, 74, 77], and procalcitonin levels [78]. In our study, in-hospital mortality was significantly associated with abnormal values of CRP, with a cut-off of 72.25 mg/L (AUC = 0.64). CRP has been reported as a significant predictor of severe Covid-19 illness [79], and an early marker of infection and inflammation [80]. In our study, d-dimer was a significant predictor of in-hospital mortality, with a cut-off value of > 1125 µg/L (AUC = 0.75). Increased d-dimer levels was associated with microthrombi formation, resulting from viral management of vascular endothelial cells [81]. Increased procalcitonin levels was associated with severe bacterial infection, and progression to severe condition in Covid-19 patients [78]. In our study, abnormal procalcitonin level was a predictor of, with a cut-off value of > 0.18 ng/mL (AUC = 0.76).

The pathogenesis of highly pathogenic human coronavirus is still not completely understood. Cytokine storm and viral evasion of cellular immune responses are thought to play important roles in disease severity [82]. Low lymphotic count was a significant predictor of in-hospital mortality, with a cut-off value of < 1.10 × 10^9/L (AUC = 0.72). Lymphocytes were reported to help clear SARS-CoV, and a suboptimal T-cell response was found to cause pathological changes observed in mice with SARS-CoV [83]. High neutrophil count was a significant predictor of in-hospital mortality as well, with a cut-off value of > 5,745 × 10^9/L (AUC = 0.70). Neutrophils are the main source of chemokines and cytokines. The generation of cytokine storm can lead to ARDS, which is a leading cause of death in patients with severe acute respiratory syndromer and Middle East respiratory syndrome [84]. However, further studies are needed to characterize the role of the neutrophil and lymphocyte response in SARS-CoV-2 infection. These indicators would help in early prediction and guide for proper, timely treatment.

Limitations

This study has some limitations. First, it is a retrospective one, and the causal relationship between abnormal clinical/ laboratory indicators and death is not guaranteed. Second, the data were collected from hospitals affiliated with one institution (MNG-HA) in Saudi Arabia. However, these hospitals are five large tertiary, JCI-accredited hospitals across the whole Saudi Arabia. Moreover, the strategy of managing Covid-19 patients is the same in these tertiary hospitals, which would avoid the competing mortality risk caused by insufficient healthcare resources. Third, because not all patient profiles were complete, however, with a large cohort of more than 35 thousand patients in this study, we assumed that missing data are missing at random. Lastly, different clinical features or mechanisms for the disease progression might be promoted with new mutants SARS-coV-2. Thus, further prospective studies are needed to validate the present study’s findings.

Conclusion

This study is one of the first observational studies in the EMR to determine the outcomes of Covid-19 disease and identify the key predictors of in-hospital mortality, with a large cohort of Covid-19 patients. The rates of hospitalization, ICU admission, in-hospital and overall mortality are nearly comparable to the counterpart rates in western countries. Older patients are more likely to die from Covid-19 illness than those of younger age. A pre-existing history of ischemic heart disease was the only comorbidity that predicts in-hospital mortality from Covid-19. Early interventions are required for Covid-19 patients with comorbidities, especially cardiac diseases. Careful monitoring of laboratory parameters in Covid-19 illness is necessary, with special attention to CRP, D-dimer, procalcitonin values, and lymphocyte and neutrophil counts.

Availability of data and materials

Most of the data supporting our findings is contained within the manuscript, and all others, excluding identifying/confidential patient data, will be shared upon request, by contacting the corresponding author [Mostafa Abolfotouh, mabolfotouh@gmail.com].

Abbreviations

RT-PCR:

_Reverse transcription-polymerase chain reaction

JCI:

Joint Commission International

ER:

Emergency department

MOH:

Ministry of Health

ICUL:

Intensive care unit

CRP:

C-reactive protein

LDH:

Lactate Dehydrogenase

IQR:

Interquartile range

CI:

Confidence interval

OR:

Odds ratio

COPD:

Chronic obstructive pulmonary disease

ROC:

Receiver operating characteristic curve

AUC:

Area under the curve

EMR:

Eastern Mediterranean Region

GCC:

Gulf Cooperation Council

UAE:

United Arab Emarates

KAMC:

King Abdulaziz Medical city

MNG-HA:

Ministry of National Guard-Health Affairs

Covid-19:

Coronavirus disease 2019

SARS-CoV-2:

Severe Acute Respiratory Syndrome Coronavirus-2

WHO:

World Health Organization

IHD:

Ischemic heart disease

CFR:

Case fatality rate

ER:

Emergency department

ICU:

Intensive care unit

IRB:

Institutional Review Board

References

  1. World Health Organization. Coronavirus [Internet]. [cited 2022 Jan 4]. https://www.who.int/health-topics/coronavirus#tab=tab_1

  2. Moledina SM, Maini AA, Gargan A, Harland W, Jenney H, Phillips G, et al. Clinical characteristics and predictors of mortality in patients with COVID-19 infection outside intensive care. Int J Gen Med. 2020;13:1157–65.

    Article  CAS  Google Scholar 

  3. Yadaw AS, Li Y-C, Bose S, Iyengar R, Bunyavanich S, Pandey G. Clinical predictors of COVID-19 mortality. medRxiv. 2020. https://doi.org/10.1101/2020.05.19.20103036.

    Article  Google Scholar 

  4. Lippi G, Wong J, Henry BM. Hypertension in patients with coronavirus disease 2019 (COVID-19): a pooled analysis. Polish Arch Intern Med. 2020;130(4):304–9.

    Google Scholar 

  5. Figliozzi S, Masci PG, Ahmadi N, Tondi L, Koutli E, Aimo A, et al. Predictors of adverse prognosis in COVID-19: a systematic review and meta-analysis. Eur J Clin Invest. 2020;50(10):e13362. https://doi.org/10.1111/eci.13362.

    Article  CAS  Google Scholar 

  6. Aksel G, İslam MM, Algın A, Eroğlu SE, Yaşar GB, Ademoğlu E, et al. Early predictors of mortality for moderate to severely ill patients with Covid-19. Am J Emerg Med. 2021;45:290–6.

    Article  Google Scholar 

  7. Albalawi O, Alharbi Y, Bakouri M, Alqahtani A, Alanazi T, Almutairi AZ, et al. Clinical characteristics and predictors of mortality among COVID-19 patients in Saudi Arabia. J Infect Public Health. 2021;14(8):994–1000.

    Article  Google Scholar 

  8. Tian W, Jiang W, Yao J, Nicholson CJ, Li RH, Sigurslid HH, et al. Predictors of mortality in hospitalized COVID-19 patients: a systematic review and meta-analysis. J Med Virol. 2020;92(10):1875–83. https://doi.org/10.1002/jmv.26050.

    Article  CAS  Google Scholar 

  9. Alharthy A, Aletreby W, Faqihi F, Balhamar A, Alaklobi F, Alanezi K, et al. Clinical characteristics and predictors of 28-Day mortality in 352 critically ill patients with COVID-19: a retrospective study. J Epidemiol Glob Health. 2021;11(1):98–108.

    Article  Google Scholar 

  10. Palaiodimos L, Kokkinidis DG, Li W, Karamanis D, Ognibene J, Arora S, et al. Severe obesity, increasing age and male sex are independently associated with worse in-hospital outcomes, and higher in-hospital mortality, in a cohort of patients with COVID-19 in the Bronx, New York. Metabolism. 2020;108:154262.

    Article  CAS  Google Scholar 

  11. NYC Health. Age-adjusted rates of lab confirmed COVID-19 non-hospitalized cases, estimated non-fatal hospitalized cases, and patients known to have died 100,000 by race/ethnicity group as of April 16, 2020. https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-deaths-race-ethnicity-04162020-1.pdf. Accessed 15 May 2020

  12. Centers for Disease Control and Prevention. COVID-19 in Racial and Ethnic Minority Groups: Centers for Disease Control and Prevention. 2020. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/racial-ethnic-minorities.html. Accessed 4 May 2020.

  13. Alwafi H, Naser AY, Qanash S, Brinji AS, Ghazawi MA, Alotaibi B, et al. Predictors of length of hospital stay, mortality, and outcomes among hospitalised COVID-19 patients in Saudi Arabia: a cross-sectional study. J Multidiscip Healthc. 2021;14:839.

    Article  Google Scholar 

  14. Foster HM, Ho FK, Mair FS, Jani BD, Sattar N, Katikireddi SV, Pell JP, Niedzwiedz CL, Hastie CE, Anderson JJ, Nicholl BI. The association between a lifestyle score, socioeconomic status, and COVID-19 outcomes within the UK Biobank cohort. BMC Infect Dis. 2022;22(1):1–3.

    Article  Google Scholar 

  15. Mena GE, Martinez PP, Mahmud AS, Marquet PA, Buckee CO, Santillana M. Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile. Science. 2021;372(6545):eabg5298.

    Article  CAS  Google Scholar 

  16. Faramarzi A, Javan-Noughabi J, Mousavi SA, Bahrami Asl F, Shabanikiya H. Socioeconomic status and COVID-19-related cases and fatalities in the world: a cross-sectional ecological study. Health Sci Rep. 2022;5(3): e628. https://doi.org/10.1002/hsr2.628.

    Article  Google Scholar 

  17. Abohamr SI, Abazid RM, Aldossari MA, Amer HA, Badhawi OS, Aljunaidi OM, et al. Clinical characteristics and in-hospital mortality of COVID-19 adult patients in Saudi Arabia. Saudi Med J. 2020;41(11):1217.

    Article  Google Scholar 

  18. Alswaidi FM, Assiri AM, Alhaqbani HH, Alalawi MM. Characteristics and outcome of COVID-19 cases in Saudi Arabia: review of six-months of data (March–August 2020). Saudi Pharm J SPJ. 2021;29(7):682.

    Article  CAS  Google Scholar 

  19. Abolfotouh MA, Almutairi AF, BaniMustafa AA, et al. Perception and attitude of healthcare workers in Saudi Arabia with regard to Covid-19 pandemic and potential associated predictors. BMC Infect Dis. 2020;20:719. https://doi.org/10.1186/s12879-020-05443-3.

    Article  CAS  Google Scholar 

  20. Saudi MoH Protocol for Patients Suspected of/Confirmed with COVID-19 Supportive care and antiviral treatment of suspected or confirmed COVID-19 infection. [cited 2022 Jan 5]; Available from: https://covid19.cdc.gov.sa/pr

  21. Alhazzani W, Alshahrani M, Alshamsi F, Aljuhani O, Eljaaly K, Hashim S, et al. The Saudi Critical Care Society practice guidelines on the management of COVID-19 in the ICU: Therapy section. J Infect Public Health. 2021. https://doi.org/10.1016/j.jiph.2021.10.005.

    Article  Google Scholar 

  22. Chang MC, Park YK, Kim BO, Park D. Risk factors for disease progression in COVID-19 patients. BMC Infect Dis. 2020;20(1):445. https://doi.org/10.1186/s12879-020-05144-x.

    Article  CAS  Google Scholar 

  23. Gates B. Responding to Covid-19—a once-in-a-century pandemic? N Engl J Med. 2020;382(18):1677–9.

    Article  CAS  Google Scholar 

  24. Johns Hopkins University & Medicine. Mortality Analyses. 2020. https://coronavirus.jhu.edu/data/mortality. Accessed 2 May 2020.

  25. Stefan N, Birkenfeld AL, Schulze MB, Ludwig DS. Obesity and impaired metabolic health in patients with COVID-19. Nat Rev Endocrinol. 2020;16(7):341–2. https://doi.org/10.1038/s41574-020-0364-6.

    Article  CAS  Google Scholar 

  26. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, the Northwell COVID-19 Research Consortium, Barnaby DP, Becker LB, Chelico JD, Cohen SL, Cookingham J, Coppa K, Diefenbach MA, Dominello AJ, Duer-Hefele J, Falzon L, Gitlin J, Hajizadeh N, Harvin TG, Hirschwerk DA, Kim EJ, Kozel ZM, Marrast LM, Mogavero JN, Osorio GA, Qiu M, Zanos TP. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA. 2020;323(20):2052–9. https://doi.org/10.1001/jama.2020.6775. Erratum in: JAMA. 2020 May 26;323(20):2098.

    Article  CAS  Google Scholar 

  27. Tomlins J, Hamilton F, Gunning S, Sheehy C, Moran E, MacGowan A. Clinical features of 95 sequential hospitalised patients with novel coronavirus 2019 disease (COVID-19), the first UK cohort. J Infect. 2020;81(2):e59–61. https://doi.org/10.1016/j.jinf.2020.04.020. (Epub 2020 Apr 27).

    Article  CAS  Google Scholar 

  28. BaHammam AS, Bindayna KM, Joji RM, Jahrami H, Faris MAE, Bragazzi NL. Outcomes of COVID-19 in the Eastern Mediterranean Region in the first 4 months of the pandemic. Saudi Med J. 2020;41(9):907–15. https://doi.org/10.15537/smj.2020.9.25320.

    Article  Google Scholar 

  29. Fu L, Wang B, Yuan T, Chen X, Ao Y, Fitzpatrick T, Li P, Zhou Y, Lin YF, Duan Q, Luo G, Fan S, Lu Y, Feng A, Zhan Y, Liang B, Cai W, Zhang L, Du X, Li L, Shu Y, Zou H. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: a systematic review and meta-analysis. J Infect. 2020;80(6):656–65. https://doi.org/10.1016/j.jinf.2020.03.041. (Epub 2020 Apr 10).

    Article  CAS  Google Scholar 

  30. Giacomelli A, Ridolfo AL, Milazzo L, Oreni L, Bernacchia D, Siano M, Bonazzetti C, Covizzi A, Schiuma M, Passerini M, Piscaglia M, Coen M, Gubertini G, Rizzardini G, Cogliati C, Brambilla AM, Colombo R, Castelli A, Rech R, Riva A, Torre A, Meroni L, Rusconi S, Antinori S, Galli M. 30-day mortality in patients hospitalized with COVID-19 during the first wave of the Italian epidemic: a prospective cohort study. Pharmacol Res. 2020;158: 104931. https://doi.org/10.1016/j.phrs.2020.104931. (Epub 2020 May 22).

    Article  CAS  Google Scholar 

  31. Wang Y, Wang Y, Chen Y, Qin Q. Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. J Med Virol. 2020;92(6):568–76.

    Article  CAS  Google Scholar 

  32. Rieg S, von Cube M, Kalbhenn J, Utzolino S, Pernice K, Bechet L, Baur J, Lang CN, Wagner D, Wolkewitz M, Kern WV, Biever P, COVID UKF Study Group. COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany. PLoS ONE. 2020;15(11):e0242127. https://doi.org/10.1371/journal.pone.0242127.

    Article  CAS  Google Scholar 

  33. Levin AT, Owusu-Boaitey N, Pugh S, et al. Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications. BMJ Glob Health. 2022;7: e008477. https://doi.org/10.1136/bmjgh-2022-008477.

    Article  Google Scholar 

  34. Al Harbi M, Al Kaabi N, Al Nuaimi A, Abdalla J, Khan T, Gasmelseed H, Khan A, Hamdoun O, Weber S. Clinical and laboratory characteristics of patients hospitalised with COVID-19: clinical outcomes in Abu Dhabi, United Arab Emirates. BMC Infect Dis. 2022;22(1):136. https://doi.org/10.1186/s12879-022-07059-1.

    Article  CAS  Google Scholar 

  35. Nicola M, O’Neill N, Sohrabi C, et al. Evidence based management guideline for the COVID-19 pandemic—review article. Int J Surg. 2020;77:206–16.

    Article  Google Scholar 

  36. Alharbi AA, Alqassim AY, Gosadi IM, Aqeeli AA, Muaddi MA, Makeen AM, Alhazmi AH, Alharbi AA. Regional differences in COVID-19 ICU admission rates in the Kingdom of Saudi Arabia: a simulation of the new model of care under vision 2030. J Infect Public Health. 2021;14(6):717–23. https://doi.org/10.1016/j.jiph.2021.04.012. (Epub 2021 May 12).

    Article  Google Scholar 

  37. Guan WJ, Liang WH, Zhao Y, Liang HR, Chen ZS, Li YM, et al. Comorbidity and its impact on 1590 patients with Covid-19 in China: a Nationwide analysis. Eur Respir J. 2020;55:2001227.

    Article  Google Scholar 

  38. Gao Y, Li T, Han M, et al. Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J Med Virol. 2020;92(7):791–6.

    Article  CAS  Google Scholar 

  39. World Health Organization. 2020. www.who.org.https://apps.who.int/iris/ bitstream/handle/10665/332196/WHO-2019-nCoV-clinical-2020.5-eng. pdf

  40. Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46:846–8.

    Article  CAS  Google Scholar 

  41. Porcheddu R, Serra C, Kelvin D, Kelvin N, Rubino S. Similarity in case fatality rates (CFR) of COVID-19/SARS-COV-2 in Italy and China. J Infect Dev Ctries. 2020;14(2):125–8.

    Article  CAS  Google Scholar 

  42. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, Gong W, Liu X, Liang J, Zhao Q, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China. JAMA Cardiol. 2020;5(7):802–10.

    Article  Google Scholar 

  43. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, et al. Clinical course and risk factors for mortality of adult inpatients with COVID19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62.

    Article  CAS  Google Scholar 

  44. Casqueiro J, Casqueiro J, Alves C. Infections in patients with diabetes mellitus: a review of pathogenesis. Ind J Endocrinol Metab. 2012;16(Suppl 1):S27-36.

    Google Scholar 

  45. Bektas A, Schurman SH, Sen R, Ferrucci L. Human T cell immunosenescence and inflammation in aging. J Leukoc Biol. 2017;102(4):977–88.

    Article  CAS  Google Scholar 

  46. Chung HY, Kim DH, Lee EK, Chung KW, Chung S, Lee B, Seo AY, Chung JH, Jung YS, Im E, et al. Redefining chronic inflammation in aging and agerelated diseases: proposal of the Senoinflammation concept. Aging Dis. 2019;10(2):367–82.

    Article  Google Scholar 

  47. Salam N, Rane S, Das R, Faulkner M, Gund R, Kandpal U, Lewis V, Mattoo H, Prabhu S, Ranganathan V, et al. T cell ageing: effects of age on development, survival & function. Indian J Med Res. 2013;138(5):595–608.

    CAS  Google Scholar 

  48. Aggarwal S, Garcia-Telles N. Aggarwal G et al Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): early report from the United States. Diagnosis. 2020;7(2):91–6.

    Article  CAS  Google Scholar 

  49. Fu L, Wang B, Yuan T, Chen X, et al. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: a systematic review and meta-analysis. J Infect. 2020;80(6):656–65.

    Article  CAS  Google Scholar 

  50. Covino M, De Matteis G, Della Polla DA, Santoro M, Burzo ML, Torelli E, Simeoni B, Russo A, Sandroni C, Gasbarrini A, Franceschi F. Predictors of in-hospital mortality AND death RISK STRATIFICATION among COVID-19 PATIENTS aged≥ 80 YEARs OLD. Arch Gerontol Geriatr. 2021;1(95): 104383.

    Article  Google Scholar 

  51. Du R-H, Liang L-R, Yang C-Q, Wang W, Cao T-Z, Li M, et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J. 2020;55:2000524. https://doi.org/10.1183/13993003.00524-2020.

    Article  CAS  Google Scholar 

  52. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet. 2020;20:669–767.

    Article  CAS  Google Scholar 

  53. Dietz W, Santos-Burgoa C. Obesity and its implications for COVID-19 mortality. Obesity. 2020;28:1005.

    Article  CAS  Google Scholar 

  54. Schetz M, De Jong A, Deane AM, Druml W, Hemelaar P, Pelosi P, Pickkers P, Reintam-Blaser A, Roberts J, Sakr Y, Jaber S. Obesity in the critically ill: a narrative review. Intensive Care Med. 2019;45(6):757–69. https://doi.org/10.1007/s00134-019-05594-1. (Epub 2019 Mar 19).

    Article  Google Scholar 

  55. Nie W, Zhang Y, Jee SH, Jung KJ, Li B, Xiu Q. Obesity survival paradox in pneumonia: a meta-analysis. BMC Med. 2014;10(12):61. https://doi.org/10.1186/1741-7015-12-61.

    Article  Google Scholar 

  56. Stapleton RD, Dixon AE, Parsons PE, Ware LB, Suratt BT, NHLBI Acute Respiratory Distress Syndrome Network. The association between BMI and plasma cytokine levels in patients with acute lung injury. Chest. 2010;138(3):568–77. https://doi.org/10.1378/chest.10-0014. (Epub 2010 Apr 30).

    Article  CAS  Google Scholar 

  57. Lippi G, Mattiuzzi C, Sanchis-Gomar F, et al. Clinical and demographic characteristics of patients dying from COVID-19 in Italy vs China. J Med Virol. 2020;92(10):1759–60.

    Article  CAS  Google Scholar 

  58. Myers L, Parodi S, Escobar G, et al. Characteristics of hospitalized adults with COVID-19. JAMA. 2020;323(21):2195–8.

    Article  CAS  Google Scholar 

  59. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62. https://doi.org/10.1016/S0140-6736(20)30566-3.

    Article  CAS  Google Scholar 

  60. Liu J, Li S, Liu J, Liang B, Wang X, Wang H, et al. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARSCoV-2 infected patients. EBioMedicine. 2020;55:102763. https://doi.org/10.1016/j.ebiom.2020.102763.

    Article  Google Scholar 

  61. Muniyappa R, Gubbi S. COVID-19 pandemic, coronaviruses, and diabetes mellitus. Am J Physiol Endocrinol Metab. 2020;318:E736–41.

    Article  CAS  Google Scholar 

  62. Li J, Li M, Zheng S, Li M, Zhang M, Sun M, et al. Plasma albumin levels predict risk for nonsurvivors in critically ill patients with COVID-19. Biomark Med. 2020;14(10):827–37. https://doi.org/10.2217/bmm-2020-0254. (Epub 2020 Jun 3).

    Article  CAS  Google Scholar 

  63. Covino M, De Matteis G, Burzo ML, Santoro M, Fuorlo M, Sabia L, Sandroni C, Gasbarrini A, Franceschi F, Gambassi G, Gemelli Against COVID-19 Group. Angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers and prognosis of hypertensive patients hospitalised with COVID-19. Intern Med J. 2020;50(12):1483–91.

    Article  CAS  Google Scholar 

  64. Gu T, Chu Q, Yu Z, Fa B, Li A, Xu L, Wu R, He Y. History of coronary heart disease increased the mortality rate of patients with COVID-19: a nested case-control study. BMJ Open. 2020;10(9): e038976. https://doi.org/10.1136/bmjopen-2020-038976.

    Article  Google Scholar 

  65. Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844–7.

    Article  CAS  Google Scholar 

  66. Zhang L, Yan X, Fan Q, Liu H, Liu X, Liu Z, et al. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J Thromb Haemost. https://www.onlinelibrary. Accessed 25 Apr 2020

  67. 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.

    Article  CAS  Google Scholar 

  68. Yang W, Cao Q, Qin L, Wang X, Cheng Z, Pan A, et al. Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-centre study in Wenzhou city, Zhejiang, China. J Infect. 2020;80(4):388–93. https://doi.org/10.1016/j.jinf.2020.02.016.

    Article  CAS  Google Scholar 

  69. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, et al. Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis. 2020;27(15):762–8. https://doi.org/10.1093/cid/ciaa248.

    Article  CAS  Google Scholar 

  70. Liu F, Li L, Xu M, Wu J, Luo D, Zhu Y, et al. Prognostic value of interleukin6, creactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 2020;127(April): 104370.

    Article  CAS  Google Scholar 

  71. Ferrari D, Motta A, Strollo M, Banfi G, Locatelli M. Routine blood tests as a potential diagnostic tool for COVID-19. Clin Chem Lab Med (CCLM). 2020;58(7):1095–9. https://doi.org/10.1515/cclm-2020-0398.

    Article  CAS  Google Scholar 

  72. Ji W, Bishnu G, Cai Z, Shen X. Analysis clinical features of COVID-19 infection in secondary epidemic area and report potential biomarkers in evaluation. medRxiv 2020; 2020.03.10.20033613

  73. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA J Am Med Assoc. 2020;323(11):1061–9. https://doi.org/10.1001/jama.2020.1585.

    Article  CAS  Google Scholar 

  74. Feng C, Huang Z, Wang L, Chen X, Zhai Y, Zhu F, et al. A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics. medRxiv. 2020. https://doi.org/10.1101/2020.03.19.20039099v1.

    Article  Google Scholar 

  75. Mo P, Xing Y, Xiao Y, Deng L, Zhao Q, Wang H, Xiong Y, Cheng Z, Gao S, Liang K, Luo M, Chen T, Song S, Ma Z, Chen X, Zheng R, Cao Q, Wang F, Zhang Y. Clinical characteristics of refractory coronavirus disease 2019 in Wuhan, China. Clin Infect Dis. 2021;73(11):e4208–13. https://doi.org/10.1093/cid/ciaa270.PMID:32173725;PMCID:PMC7184444.

    Article  CAS  Google Scholar 

  76. Luo W, Lin Y, Yao X, Shi Y, Lu F, Wang Z, et al. Clinical findings of 35 cases with novel coronavirus pneumonia outside of Wuhan. 2020, https://doi.org/10.21203/rs.3.rs-22554/v1.

  77. Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, et al. Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19). Front Immunol. 2020;11:827. https://doi.org/10.3389/fimmu.2020.00827.

    Article  CAS  Google Scholar 

  78. Liu W, Yang C, Liao YG, Wan F, Lin L, Huang X, Zhang BH, Yuan Y, Zhang P, Zhang XJ, She ZG, Wang L, Li H. Risk factors for COVID-19 progression and mortality in hospitalized patients without pre-existing comorbidities. J Infect Public Health. 2022;15(1):13–20. https://doi.org/10.1016/j.jiph.2021.11.012. (Epub 2021 Nov 18).

    Article  Google Scholar 

  79. Wang G, Wu C, Zhang Q, et al. C-reactive protein level may predict the risk of COVID-19 aggravation. Open Forum Infect Dis. 2020. https://doi.org/10.1093/ofid/ofaa153.

    Article  Google Scholar 

  80. Ali N. Elevated level of C-reactive protein may be an early marker to predict risk for severity of COVID-19. J Med Virol. 2020;92(11):2409–11. https://doi.org/10.1002/jmv.26097. (Epub 2020 Jun 9).

    Article  CAS  Google Scholar 

  81. Varga Z, Flammer AJ, Steiger P, Haberecker M, Andermatt R, Zinkernagel AS, et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet. 2020;395:1417–8. https://doi.org/10.1016/S0140-6736(20)30937-5.

    Article  CAS  Google Scholar 

  82. Wong RS, Wu A, To KF, Lee N, Lam CW, Wong CK, Chan PK, Ng MH, Yu LM, Hui DS, Tam JS. Haematological manifestations in patients with severe acute respiratory syndrome: retrospective analysis. BMJ. 2003;326(7403):1358–62.

    Article  Google Scholar 

  83. Liu X, Zhang R, He G. Hematological findings in coronavirus disease 2019: indications of progression of disease. Ann Hematol. 2020;99(7):1421–8.

    Article  CAS  Google Scholar 

  84. Li T, Qiu Z, Han Y, Wang Z, Fan H, Lu W, Xie J, Ma X, Wang A. Rapid loss of both CD4+ and CD8+ T lymphocyte subsets during the acute phase of severe acute respiratory syndrome. Chin Med J. 2003;116(07):985–7.

    Google Scholar 

Download references

Acknowledgements

This study was initiated by King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. The final draft of the manuscript was English language edited by Macmillan Science Communication.

Funding

None.

Author information

Authors and Affiliations

  1. King Abdullah International Medical Research Center, Mail Code 3533, Riyadh, Saudi Arabia

    Mostafa A. Abolfotouh, Abrar Musattat, Maha Alanazi, Suliman Alghnam & Mohammad Bosaeed

  2. King Saud Bin-Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia

    Mostafa A. Abolfotouh, Suliman Alghnam & Mohammad Bosaeed

  3. King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, POB 22490, Riyadh, 11426, Saudi Arabia

    Mostafa A. Abolfotouh & Mohammad Bosaeed

Authors
  1. Mostafa A. Abolfotouh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abrar Musattat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maha Alanazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suliman Alghnam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Bosaeed
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MAA and MB contributed to concept development, manuscript preparation and final writing, SA, AM and MA contriputed to concept development and data collection, MAA and MB contributed to concept development statistical analysis and manuscript finalization, and MB, SA and MAA contributed to research proposal writing, analysis and interpretation. MAA, MB, SA, AM and MA contributed to manuscript drafting. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mostafa A. Abolfotouh.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the institutional review board (IRB) of the Ministry of National Guard-Health Affairs (MNG-HA) in Riyadh, Saudi Arabia (NRC21R/445/10). Participation in this study was voluntary. The requirement for informed consent was waived by the IRB of the MNG-HA, because of the retrospective nature of the study. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abolfotouh, M.A., Musattat, A., Alanazi, M. et al. Clinical characteristics and outcome of Covid-19 illness and predictors of in-hospital mortality in Saudi Arabia. BMC Infect Dis 22, 950 (2022). https://doi.org/10.1186/s12879-022-07945-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12879-022-07945-8

Keywords

  • SARS-CoV-2
  • Disease severity
  • ICU-admission
  • Hospitalization
  • Key parameters
  • Laboratory findings
  • Comorbidities
  • Case fatality
  • Risk factors
  • AUC

BMC Infectious Diseases

ISSN: 1471-2334

Contact us