ORIGINAL RESEARCH article

Front. Med., 12 May 2021

Sec. Pulmonary Medicine

Volume 8 - 2021 | https://doi.org/10.3389/fmed.2021.670195

Clinical and Laboratory Findings of COVID-19 in High-Altitude Inhabitants of Saudi Arabia

  • 1. Alameen Hospital, Taif, Saudi Arabia

  • 2. Mansoura Nephrology and Dialysis Unit, Internal Medicine Department, College of Medicine, Mansoura University, Mansoura, Egypt

  • 3. College of Medicine, Taif University, Taif, Saudi Arabia

  • 4. Internal Medicine Department, King Abdulaziz Hospital, Jeddah, Saudi Arabia

  • 5. General Department of Medical Services, Security Forces Hospital, Mecca, Saudi Arabia

  • 6. Anesthesiology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt

  • 7. Radiology Department, Faculty of Medicine, Cairo University, Giza, Egypt

  • 8. Albbassia Chest Hospital, Cairo, Egypt

  • 9. Department of Physiology, Faculty of Veterinary Medicine, Kafrelsheikh University, Kafrelsheikh, Egypt

Abstract

Background: SARS-CoV-2, the causative agent of COVID-19, continues to cause a worldwide pandemic, with more than 147 million being affected globally as of this writing. People's responses to COVID-19 range from asymptomatic to severe, and the disease is sometimes fatal. Its severity is affected by different factors and comorbidities of the infected patients. Living at a high altitude could be another factor that affects the severity of the disease in infected patients.

Methods: In the present study, we have analyzed the clinical, laboratory, and radiological findings of COVID-19-infected patients in Taif, a high-altitude region of Saudi Arabia. In addition, we compared matched diseased subjects to those living at sea level. We hypothesized that people living in high-altitude locations are prone to develop a more severe form of COVID-19 than those living at sea level.

Results: Age and a high Charlson comorbidity score were associated with increased numbers of intensive care unit (ICU) admissions and mortality among COVID-19 patients. These ICU admissions and fatalities were found mainly in patients with comorbidities. Rates of leukocytosis, neutrophilia, higher D-dimer, ferritin, and highly sensitive C-reactive protein (CRP) were significantly higher in ICU patients. CRP was the most independent of the laboratory biomarkers found to be potential predictors of death. COVID-19 patients who live at higher altitude developed a less severe form of the disease and had a lower mortality rate, in comparison to matched subjects living at sea level.

Conclusion: CRP and Charlson comorbidity scores can be considered predictive of disease severity. People living at higher altitudes developed less severe forms of COVID-19 disease than those living at sea level, due to a not-yet-known mechanism.

Introduction

Humans worldwide have been besieged by the relentless spread of the SARS-CoV-2 pandemic. As of this writing, SARS-CoV-2 laboratory-confirmed cases have exceeded 147 million globally. Patients infected with this newly discovered coronavirus can be affected by either asymptomatic or symptomatic forms of the disease. The latter includes fever, sore throat, cough, shortness of breath, and a loss of smell and taste sensation. Some patients develop severe pneumonia and acute respiratory distress syndrome with fatal consequences (1). Older people and those with other health problems such as diabetes, cancer, lung diseases, and heart disease are the most vulnerable groups afflicted by the severe form of COVID-19. Early diagnosis and treatment of infected patients are important to avoid a complicated form of the disease and to prevent further spread of the virus to uninfected subjects (2).

Lymphopenia, elevated inflammatory markers such as high C-reactive protein (CRP), ferritin, and elevated D-dimer, in association with abnormal radiological findings in both chest X-rays and CT scans, are suggestive of COVID-19 (3). The COVID-19 Reporting and Data System (CO-RADS) is used as an indicator of the pulmonary engagement of COVID-19 (4). However, nucleic acid testing on respiratory tract specimens is the only laboratory confirmation of SARS-CoV-2. The angiotensin-converting enzyme 2 (ACE2) receptor has been recognized as the binding site of SARS-CoV-2 in the target cells (5). ACE2 plays a major role in patients with hypertension, CVD, obesity, and type 2 diabetes by opposing the harmful effects of the renin–angiotensin–aldosterone system (RAAS); therefore, the ACE/ACE2 ratio could be a good indicator of SARS-CoV-2 disease progression. There is a direct proportional relationship between COVID-19 infectivity and virulence that depends on the level of ACE2 expression on target cells (6, 7).

High-altitude-linked hypoxia has been reported to cause systemic inflammation associated with hypercoagulability and promotes venous thromboembolism development (8–10). Exposure to high altitudes has been reported to predispose patients to a series of thromboembolic events, due to increased blood hypercoagulability (11, 12). Platelet counts have been observed to rise within 1 week of exposure to high altitudes (13). Moreover, exposure to high altitudes for 1 year has been shown to increase plasma fibrinogen concentration by as much as 61% (14). In addition, a recent study showed that the mean platelet count is higher among high-altitude dwellers compared to low-altitude dwellers in Saudi Arabia, predisposing them for hypercoagulability (15).

The development of coagulation test abnormalities seen in COVID-19-infected patients is most likely a result of the profound inflammatory response caused by the virus and its associated coagulopathy (16). Based on these data, the combined causes of hypoxia and increased platelet counts in high-altitude dwellers may leave these people prone to more severe attacks of disseminated intravascular coagulation if they develop a COVID-19 infection. However, a recent study has reported less severe manifestations of the disease in people living in high-altitude regions (17).

The current study aimed to analyze the clinical characteristics, laboratory findings, risk factors, associated comorbidities, radiological findings, and outcomes in patients admitted to the hospital with a confirmed COVID-19 infection in Taif, Saudi Arabia. In addition, we also aimed to investigate the effect of high altitude on the severity of the disease, based on the clinical outcomes and disease severity in the hospitalized COVID-19 patients from one high-altitude city, Taif, and a sea-level city, Jeddah.

Methods

Patients and Time Frame

An observational retrospective study was conducted on a total of 760 COVID-19 laboratory-confirmed cases between April and September 2020 in Taif, Saudi Arabia. All patients were at least 18 years old and had confirmed positive polymerase chain reaction (PCR) results for COVID-19. Data from subjects were collected from the Alameen and King Faisal Hospitals, in Taif. An additional 208 subjects were recruited from Jeddah (which is at sea level), from patients at King Abdulaziz Hospital. The Jeddah data (n = 208) were compared to matched patients from Taif (n = 272/760). Matching patients' data from Taif were selected from the original 760 subjects based on patients' ages and comorbidities.

Clinical and Laboratory Investigations

Initial investigations included complete blood count, highly sensitive CRP, and chest X-rays. CT chest scans were used as a routine investigation for most of the Taif patients during the early months of the epidemic. X-rays were performed on all patients, while the CT chest scans were only applied to 722/760 (95%) of the patients. Other investigations, such as D-dimer, ferritin, alanine aminotransferase, aspartate aminotransferase (AST), creatinine, international normalized ratio, prothrombin time, and partial thromboplastin time were also conducted. Supplemental oxygen therapy was used if patients' oxygen levels were <90%, to keep these levels between 92 and 96%. Patients who were admitted to an intensive care unit (ICU) were closely monitored for adequate hemodynamic support and fluid therapy, depending on both dynamic and static parameters. Patients who required supplemental oxygen were also given dexamethasone (6 mg daily), either orally or via intravenous injection.

The Charlson Comorbidity Index

The relationships among the severity of COVID-19, ICU admission, disease outcome, and associated comorbidities were assessed in patients from Taif. Twenty-nine points were assigned to different comorbidities: a history of myocardial infarction (one point), congestive heart failure (one point), peripheral vascular disease (one point), dementia (one point), chronic pulmonary disease (one point), rheumatologic disease (one point), peptic ulcer disease (one point), liver disease (three points), diabetes (two points), hemiplegia or paraplegia (two points), renal disease (two points), malignancy (six points), and HIV/AIDS (six points). The Charlson comorbidity index was used as previously described (18–20).

CO-RADS Classification

CO-RADS classification was used to classify patients from Taif according to their CT results (4). Those in the CO-RADS 1 group had a normal CT or showed non-infectious radiological changes, while those in CO-RADS 2 had a CT that showed changes relevant to non-COVID-19 infectious diseases. Those described as CO-RADS 3 showed an intermediate suspension of COVID-19, including widespread bronchopneumonia, lobar pneumonia, and ground-glass opacities (GGO) or septic emboli. Patients in CO-RADS 4 were cases in which COVID-19 was highly suspected. Unilateral GGO and multifocal consolidations without any other typical findings may be present in these patients' CT scans. A CO-RADS 5 classification is highly indicative for COVID-19, with CT showing multifocal GGO and consolidation. CO-RADS 6 was used to describe patients with a positive PCR with bilateral GGO.

Statistical Analysis

Data are reported as means and standard error of mean (SEM) or counts and percentages, as appropriate. Comparisons among groups were made using t tests, the Mann–Whitney U test, chi-square, or Fisher exact tests, as dictated by data type and distribution. A multiple regression analysis using independent variables determined the predictors of ICU admission. One-way analysis of variance (ANOVA) was used to test differences among more than two groups. Pearson's or Spearman's correlations were used to test the correlations among variables. A p < 0.05 was considered significant for all statistical analyses in this study. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 24 for Windows (SPSS, Inc., Chicago, IL, USA).

Results

Demographic Data of Patients From Taif

Healthcare workers accounted for 6.4% of all patients. The detected comorbidities included diabetes (16.2%), hypertension (12%), chronic obstructive pulmonary disease (3%), chronic kidney disease (2.6%), peripheral vascular disease (2.6%), coronary artery disease (1.8%), congestive heart failure (1.4%), and pulmonary embolism (PE) (3.8%). The symptoms included fever (84.7%), cough (58.2%), shortness of breath (45.3%), GIT symptoms (25.4%), sore throat (10.8%), anosmia (20.9%), and loss of taste sensation (15.7%). Sixty-four (8.5%) patients were admitted to the ICU, and 13 (1.7%) patients died. Patients were classified into three groups: (i) recovered patients without ICU admission, 695 (91.5%); (ii) recovered patients after ICU admission 52 (6.8%) patients; and (iii) deceased patients, 13 (1.7%) patients (Table 1).

Table 1

ParametersTotal patients (760)Recovered without ICU (695) (91.5%)Recovered after ICU (1) (6.8%)Passed away 13 (1.7%)p
Age mean (SEM) years41.4 (0.53)40.3 (0.6)ab52 (2.3)a57.3 (5.6)b0.0001
Gender
Male n (%)578 (76.1)530 (76.3)40 (76.9)8 (61.5)0.468
Female n (%)182 (23.9)165 (23.7)12 (23.1)5 (38.5)
Nationality
Saudi n (%)283 (37.2)254 (36.5)25 (48.1)4 (30.8)0.225
Others n (%)477 (62.8)441 (63.5)27 (51.9)9 (69.2)
Clinical presentation
Fever n (%)644 (84.7)590 (84.9)42 (80.8)12 (92.3)0.58
Cough n (%)442 (58.2)401 (57.7)33 (63.5)8 (61.5)0.735
Shortness of breath n (%)344 (45.3)312 (44.9)24 (46.2)8 (61.5)0.492
GIT symptoms n (%)193 (25.4)182 (26.2)8 (15.4)3 (23.1)0.227
Chronic diseases n (%)197 (25.9)155 (28.3)31 (59.6)11 (84.6)0.0001
History of contact n (%)178 (23.4)171 (24.6)6 (11.5)1 (7.7)0.041
Health care workers n (%)49 (6.4)47 (6.8)1 (1.9)1 (7.7)0.354
Sore throat n (%)82 (10.8)79 (11.4)1 (1.9)2 (15.4)0.046
Loss of smell sensation n (%)159 (20.9)153 (21)5 (9.6)1 (7.7)0.046
Loss of taste sensation n (%)119 (15.7)116 (16.7)3 (5.8)00.033
Associated co-morbidities and chronic diseases:
Hypertension n (%)91 (14)64 (9.2)20 (38.5)7 (53.8)0.0001
DM n (%)123 (16.2)93 (13.4)21 (40.4)9 (69.2)0.0001
CKD n (%)20 (2.6)11 (1.6)5 (9.6)4 (30.8)0.0001
CLD n (%)7 (0.9)5 (0.7)1 (1.9)1 (7.7)0.052
CHF n (%)11 (1.4)8 (1.2)3 (5.8)00.067
CVA n (%)10 (1.3)7 (2)3 (5.8)00.052
Peripheral vascular disease n (%)20 (2.6)10 (1.4)7 (13.5)3 (23.1)0.0001
COPD n (%)23 (4)17 (2.4)5 (9.6)1 (7.7)0.015
CAD n (%)14 (1.8)8 (1.2)3 (5.8)3 (23.1)0.0001
Dementia n (%)7 (0.9)5 (0.7)2 (3.8)00.114
Rheumatological diseases n (%)6 (0.8)6 (0.9)001
Peptic ulcers n (%)23 (4)21 (3%)2 (3.8)00.78
Malignancy n (%)4 (0.5)2 (0.3)02 (15.4)0.004
PE n (%)29 (3.8)7 (2)12 (23.1)10 (76.9)0.0001
DVT n (%)4 (0.5)3 (0.4)1 (1.9)00.301
Duration of admission (Days) (mean±SE)6.9 ± 0.236.2 ± 0.2ab11.4 ± 1.6a13.2 ± 2.1b0.0001
Charlson comorbidity score (mean±SE)1 ± 0.050.57 ± 0.05 ab1.7 ± 0.3 a3.9 ± 0.9 b0.0001

Clinical and demographic criteria of the studied groups of patients.

Means followed by the same superscript is not statistically significant.

Age is a major factor in the severity of COVID-19, as greater ages were associated with increased ICU admission and mortality rates, with p < 0.0001. We found no significant differences between the patient subgroups for gender and nationality. Patients who were admitted to the ICU and/or who died showed significant histories of hypertension, diabetes, chronic kidney disease, peripheral vascular disease, and coronary artery disease, with a p value of 0.0001 for all. We observed no significant differences in most of the initial clinical presentations among the patient subgroups, though patients with a history of ICU admission had a longer duration of hospital length of stay, with a p value of < 0.0001. Charlson's comorbidity scores were significantly higher in patients admitted to the ICU and/or who died, with a p < 0.0001 (Table 1).

Patients' mean oxygen saturation percentage at the time of admission was 94.6%, and 45.1% of our patients showed absolute lymphocytopenia. The mean level of high-sensitivity CRP was 23.6 (3.8) mg/L, the mean ferritin level was 540 (47.7) ng/ml, and the mean D-dimer level was 0.7 (0.1) mcg/ml. Patients with a history of ICU admission and/or death showed significant hypoxia, with a p < 0.001. Leukocytosis and neutrophilia were found to be significant among patients admitted to the ICU and/or who died, in addition to significantly higher D-dimer, ferritin, and highly sensitive CRP levels, with a p < 0.0001 for all patients (Table 2).

Table 2

ItemTotal patientsRecovered without ICURecovered after ICUPassed Awayp
Number760695 (91.5%)52 (6.8%)13 (1.7%)
O2 saturation mean (%) (SEM)94.6 (0.13)94.8 (0.14)ab90.6 (0.43)ac85.2 (2.4)bc0.0001
Total WBCs count mean (SEM)/ μl7.1 (0.13)6.8 (0.11) ab8.3 (0.6) ac12.8 (1.6) bc0.0001
Neutrophil count mean (SEM)/ μl4.9 (0.11)4.65 (0.1)6.1 (0.5)9.9 (1.4)0.0001
Lymphocyte count mean (SEM)/ μl1.7 (0.04)1.7 (0.04)1.5 (0.1)1.75 (0.3)0.186
Hb mean (SEM)gm/dl14.2 (0.23)14.4 (0.3)13.8 (0.5)13.5 (0.5)0.137
PLT mean (SEM) μl278.2 (10.4)284.9 (13.5)270.8 (17.6)246.5 (27.3)0.671
INR mean (SEM)1.04 (0.01)1.05 (0.01)0.97 (0.02)1.1 (0.05)0.283
PT mean (SEM)/sec.13.6 (0.12)13.7 (0.13)13.1 (0.19)14.45 (0.6)0.563
PTT mean (SEM)/sec.35.6 (0.7)35.5 (0.6)35.2 (1.03)36.6 (5.5)0.903
D-Dimer CRP mean (SEM) mcg/mL0.7 (0.1)0.48 (0.06)ab0.34 (0.05)ac2.5 (0.88)bc0.0001
High CRP mean (SEM) mg/L23.6 (3.8)10.7 (2.1) ab48.3 (13.1)a65.7 (14.9)b0.0001
Ferritin mean (SEM) ng/ml540 (47.7)416 (44.7)ab745.9 (117.4)a1003 (197.4)b0.0001
ALT mean (SEM) IU/L39 (5.4)30.4 (2.7)64.8 (25.5)44.2 (7.9)0.08
AST mean (SEM) IU/L39 (5.1)29.2 (1.8)58.4 (23.2)67.8 (15.9)0.0001
Creatinine mean (SEM) μmol/L78.2 (3.6)78.4 (4.1)102.75 (17.8)70 (8.3)0.258

Laboratory characteristic of the studied groups of patients.

Means followed by the same superscript is not statistically significant.

A logistic regression analysis for patients admitted to an ICU showed that the level of highly sensitive CRP was the most independent predictor for death, with a p value of 0.003 (Table 3). Radiological investigations of our confirmed cases showed that 74.5% had positive results in a chest x-ray. By contrast, 95% of our patients had CT chest affections and were classified according to the CO-RADS classification system (Figure 1); 18.2% were CO-RADS 1, 3.8% were CO-RADS 2, 1.6% were CO-RADS 3, 2.9% were CO-RADS 4, and 68.6% were CO-RADS 5. There was a significant difference among patient subgroups regarding the chest CT and chest X-ray results, with p-values of 0.03 and 0.0001, respectively (Table 4).

Table 3

VariableSig.Exp (B)95% C.I. for EXP(B)
LowerUpper
Age0.0591.0460.9981.095
DM0.6630.8060.3052.129
HTN0.6460.6660.1173.781
CKD0.8551.1960.1768.139
CAD0.5970.3250.00520.944
Charlson comorbidity index0.3161.3560.7472.460
WBCS0.7141.1050.6491.880
Neutrophil0.6181.1620.6432.101
CRP0.0031.0271.0101.045
D-dimer0.2851.2650.8221.949
Ferritin0.5181.0000.9991.002
Constant0.0000.002

Logistic regression analysis for independent predictors of ICU admission.

Figure 1

Table 4

ItemTotal patientsRecovered without ICURecovered after ICUPassed Awayp
Number (%)760 (100%)695 (91.5%)52 (6.8%)13 (1.7%)
X-ray findings
Positive n (%)566 (74.5)504 (72.5)49 (94.2)13 (100)0.0001
CT findings
CO-RADS 1 n (%)138 (18.2)129 (18.6)9 (17.3)00.03
CO-RADS 2 n (%)29 (3.8)29 (4.2)00
CO-RADS 3 n (%)12 (1.6)12 (1.7)00
CO-RADS 4 n (%)22 (2.9)20 (2.9)1 (1.9)1 (7.7)
CO-RADS 5 n (%)521 (68.6)468 (67.3)51 (98.1)12 (92.3)
Un-available n (%)38 (6)37 (5.3)1 (1.9)0

Radiological characteristics of the studied groups of patients.

Pearson's correlation was conducted between the different parameters with disease severity and radiological findings. Items that only showed significant correlation was presented in Table 5. Age, AST, D-dimer, ferritin, and CRP showed highly significant correlation (P > 0.01) with both disease severity and radiological findings.

Table 5

ItemDisease severityCT results
Ager0.249**0.164**
Sig.0.0000.000
Disease severityr10.080*
Sig.0.031
O2r−0.197-**−0.039-
Sig.0.0000.294
RBCsr−0.105-*−0.069-
Sig.0.0120.108
ESRr0.334**0.016
Sig.0.0000.763
HCTr−0.102-*−0.066-
Sig.0.0160.128
MCVr0.110**0.026
Sig.0.0090.553
WBCsr0.275**0.000
Sig.0.0000.995
Neutrophilsr0.319**0.041
Sig.0.0000.337
Lymphocyter−0.047-−0.091-*
Sig.0.2520.031
ALTr0.110**0.084*
Sig.0.0050.035
ASTr0.221**0.122**
Sig.0.0000.002
Urear0.118*−0.103-*
Sig.0.0130.033
D-dimerr0.225**−0.005-
Sig.0.0000.921
Ferritinr0.292**0.163**
Sig.0.0000.001
CRPr0.348**0.221**
Sig.0.0000.000

Correlation between clinical, laboratory, disease severity and CT findings.

*

Correlation is significant at the 0.05 level.

**

Correlation is significant at the 0.01 level.

Comparison of Matched Patients in High Altitude and Sea Level

Upon comparing the clinical data of matched patients from Taif and Jeddah, we noted that SARS-CoV-2 infection rates were found to be significantly higher in males than in females, and among subjects between the ages of 21–40 years and 41–60 years in both the high-altitude (Taif) and sea-level (Jeddah) regions (Table 6).

Table 6

Demographic factorsCategoriesJeddahTaif
No.%No.%
GenderMale13665.4018367.30
Female7234.608932.70
Total208100.0272100.0
Age (year)18–20115.3197.0
21–4010450.013248.5
41–607536.19434.6
61–80146.7259.2
81+41.920.7
Healthcare worker167.69207.35

Demographic data of Jeddah and Taif COVID-19 patients.

Interestingly, we detected a significantly higher percentage of recovery in the high-altitude region, in comparison with those living at sea level. Importantly, the death rate in the sea level region was nearly threefold that reported in the high-altitude region (Table 7).

Table 7

VariableJeddah
(n=208)
Taif
(n=272)
χ2P-value
No.%No.%
FeverNo8741.8315456.6210.310.00**
Yes12158.1711843.38
CoughNo9847.1216359.937.790.00**
Yes11052.8810940.07
Shortness of breathNo11555.2919270.5911.960.00**
Yes9344.718029.41
Headache and sore throatNo18488.4624389.340.090.76
Yes2411.542910.66
SmellNo19794.7025292.600.830.36
Yes115.30207.40
Loss of TasteNo20397.6027199.633.960.04*
Yes52.4010.37
GIT symptomsNo18187.0223385.70.1830.669
Yes2712.983914.3
Chronic diseasesNo17584.1323887.51.110.29
Yes3315.873412.5
Diabetes mellitesNo16277.8821980.50.4980.48
Yes4622.125319.5
HypertensionNo16981.2022582.700.170.67
Yes3918.804717.30
AsthmaNo19493.3025192.300.170.67
Yes146.70217.70
DVTNo20598.6025694.106.110.01 *
Yes31.40165.90
PENo20598.6024489.7015.280.00**
Yes31.402810.30
MINo20699.0025995.205.670.01*
Yes21.00134.80
Ischemic strokeNo20297.1025995.201.110.29
Yes62.90134.80
ARDSNo18689.4225894.8550.02*
Yes2210.58145.15
Acute large vessels occlusionNo20799.5225995.227.690.00**
Yes10.48134.78
Coronary diseaseNo20397.6026095.591.390.23
Yes52.40124.41
TumorNo20598.562722053.940.04*
Yes31.4400
CKDNo20397.6027099.262.280.13
Yes52.4020.74
DeathNo3014.42134.7811.370.00**
Yes17885.5825995.22

Clinical, laboratory and deaths among COVID-19 ICU patients in Jeddah and Taif.

*

P-value refers to statistically significant result.

**

P-value refers to statistically highly significant result.

Contrary to the above results, significantly higher rates of deep vein thrombosis, PE, MI, and acute large vessel occlusion were reported in the high-altitude region, compared to the sea-level region. However, the incidence of fever, cough, shortness of breath, and acute respiratory distress syndrome (ARDS) during the course of the disease were higher in Jeddah than in Taif. Importantly, the incidence of comorbidities such as diabetes mellitus, hypertension, asthma, ischemic stroke, and coronary disease showed no significant differences between the high-altitude and sea-level regions (Table 7).

Discussion

In our study, as reported in other studies, patient age was a critical risk factor for ICU admission, with elderly patients being the most frequently admitted. In addition, comorbidities such as hypertension, diabetes mellitus, chronic kidney disease, ischemic heart disease, peripheral vascular disease, and malignancies were found to correlate with an increased probability of ICU admission and fatal consequences. Our findings also agree with previously available studies in this respect (21–25).

Patients who were admitted to the ICU and those who died showed significantly higher Charlson comorbidity scores than other patients did. These results were similar to previously published data from Denmark (26). Our results indicated that the Charlson comorbidity index is a valid indicator for COVID-19 patients with poor outcomes, in concordance with previous studies (27, 28).

In this study, we found no significant relationship between the presence of fever, cough, shortness of breath, GIT symptoms, and being a healthcare worker or with ICU admission or mortality. Our results did not match other studies (29, 30) that found a high fever to be associated with COVID-19 progression; we attribute this distance to differences in the studies' designs.

In contrast to the above, sore throat and a loss of smell and taste sensation were associated with a good prognosis among confirmed cases of COVID-19. Our results agree with a previous study that reported a correlation between smell loss and decreased hospitalization, intubation, and acute respiratory distress syndrome (31). In addition, another study also found that patients with a sore throat had better outcomes than others (32). However, there is no available explanation for these correlations.

Our study showed that low oxygen saturation at the time of admission was associated with poor outcomes. Hypoxia was found to be a powerful independent predictor of mortality in COVID-19 patients (33). Previous studies showed a strong relationship between hypoxia and both increasing the inflammatory response (34) and induction of viral replication (35, 36). It might also play a role in the pathogenesis of hypercoagulation (37).

In our study, we found that the presence of leukocytosis, neutrophilia, high levels of highly sensitive CRP, ferritin, D-dimer, and AST at the time of admission were associated with patients' deterioration, ICU admission, and death. Additionally, a significant correlation was found between such parameters and both the disease severity. These results were in line with previous studies that reported a significant association between disease progression and poor outcomes, with increased levels of inflammatory biomarkers and leukocytosis (38–40).

Logistic regression analysis showed that highly sensitive CRP was the most significant independent predictor of death among COVID-19 patients who were admitted to an ICU. This finding corresponds with previous reports that confirmed that CRP is the major determinant of disease severity in COVID-19 patients (41, 42). It may also highlight the role of secondary infection in the deterioration of patients' health status. Interestingly, the use of steroids was reported to be associated with secondary infection (43, 44).

In all, 18.2% of our patients showed normal chest CT appearance. Chest CTs were conducted early for most of our patients, and it is well-known that chest CTs continue to be normal in the first 4–5 days of infection (45). Meanwhile, among those who were admitted to the ICU and who later died, the chest CT results were significantly positive. At present, chest CT is not routinely ordered, and its use is limited to specific cases, especially in patients with false negative PCR (46, 47). However, we found a significant correlation of the radiological findings and the severity of the disease. This finding was also in accordance with recent findings that found similar correlation (48, 49).

We observed a lower mortality rate among people living at a high altitude, in comparison to those living at sea level. Another study has suggested a reduced severity of SARS-CoV-2 in high-altitude-dwelling patients (17); however, there are no current data explaining the mechanism involved in such an effect. The number of ACE2 receptors in target cells affects the susceptibility of such cells to SARS-CoV-2 infection. One speculation is the assumption that high-altitude inhabitants possess hypoxia-induced downregulation of ACE2. The RAAS is the expression of both ACE1 and ACE2 in negative feedback manner. During hypoxia, ACE2 is downregulated in the concomitant upregulation of ACE1 (50). Accordingly, decreased expressions of ACE2 in the pulmonary endothelial lining among high-altitude inhabitants is assumed to reduce the amplitude of virus replication (17). This speculation was argued strongly by Pun et al. (51), who presented conflicting results regarding ACE2. Their argument was augmented by many previous studies that reported the upregulation of ACE2 via hypoxia (52–54). Furthermore, deciphering hypoxia as a potential factor that alters the ACE2 expression is still obscure and needs further investigation. It is possible that some other, still-unknown mechanism renders high-altitude inhabitants more resistant to the severe consequences of COVID-19, compared to their sea-level-dwelling contemporaries.

Conclusion

Fever, cough, and shortness of breath are the main clinical presentations in COVID-19 patients. Age, multiple comorbidities, a high Charlson comorbidity score, leukocytosis, neutrophilia, higher levels of highly sensitive CRP, ferritin, D-dimer, and AST are associated with poor outcomes among COVID-19 patients. High-altitude inhabitants showed reduced disease severity, in comparison with sea-level-dwelling patients.

Statements

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics statement

The studies involving human participants were reviewed and approved by Research and Studies Department, Directorate of Health Affairs, Taif, Saudi Arabia (NO. 397 on 23/07/2020). The patients/participants provided their written informed consent to participate in this study.

Author contributions

AF, AR, MR, RMMA, and AAA collected and analyzed the data from Alamin hospital. RMA, SAA, TA, and SMA collected and analyzed data from Jeddah. MA, ZI, DN, ANA, and DA analyzed the data and prepared the initial draft. DN, DA, ASA-M and AAA conceived the study protocol and critically revised the manuscript. All authors shared in data analysis and discussing and approved the final form of the manuscript.

Acknowledgments

The researchers gratefully acknowledge the Taif University Researchers Supporting Program (TURSP-2020/233) of Taif University, Taif, Saudi Arabia.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  • 1.

    McIntoshKHirschMBloomA. Coronavirus Disease 2019 (COVID-19). Up-to-Date. (2020). Available online at: https://www.uptodate.com/contents/middle-east-respiratory-syndrome-coronavirus-treatment-and-preventionUpdatedFebruary24,2020. (accessed February 22, 2020).

  • 2.

    CarlosWGDela CruzCSCaoBPasnickSJamilS. Novel Wuhan (2019-nCoV) coronavirus. Am J Respir Crit Care Med. (2020) 201:P7–P8. 10.1164/rccm.2014P7

  • 3.

    GuanW-JNiZ-YHuYLiangW-HOuC-QHeJ-Xet al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. (2020) 382:1708–20. 10.1056/NEJMoa2002032

  • 4.

    ProkopMVan EverdingenWVan Rees VellingaTQuarles Van UffordHStögerLBeenenLet al. CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19-definition and evaluation. Radiology. (2020) 296:E97–104. 10.1148/radiol.2020201473

  • 5.

    WanYShangJGrahamRBaricRSLiF. Receptor recognition by the novel coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS coronavirus. J Virol. (2020) 94:e00127–20. 10.1128/JVI.00127-20

  • 6.

    FerreiraAJShenoyVQiYFraga-SilvaRASantosRAKatovichMJet al. Angiotensin-converting enzyme 2 activation protects against hypertension-induced cardiac fibrosis involving extracellular signal-regulated kinases. Exp Physiol. (2011) 96:287–94. 10.1113/expphysiol.2010.055277

  • 7.

    PatelVBZhongJCGrantMBOuditGY. Role of the ACE2/angiotensin 1-7 axis of the renin-angiotensin system in heart failure. Circ Res. (2016) 118:1313–26. 10.1161/CIRCRESAHA.116.307708

  • 8.

    Van VeenJJMakrisM. Altitude and coagulation activation: does going high provoke thrombosis?Acta Haematol. (2008) 119:156–57. 10.1159/000128045

  • 9.

    SabitRThomasPShaleDJCollinsPLinnaneSJ. The effects of hypoxia on markers of coagulation and systemic inflammation in patients with COPD. Chest. (2010) 138:47–51. 10.1378/chest.09-2764

  • 10.

    BrillASuidanGLWagnerDD. Hypoxia, such as encountered at high altitude, promotes deep vein thrombosis in mice. J Thromb Haemost. (2013) 11:1773–75. 10.1111/jth.12310

  • 11.

    SchreijerAJCannegieterSCRosendaalFRHelmerhorstFM. A case of thrombosis at high altitude. Thromb Haem. (2005) 94:1104–5. 10.1160/TH05-05-1104

  • 12.

    GuptaNAshrafMZ. Exposure to high altitude: a risk factor for venous thromboembolism?Semin Thromb Hemost. (2012) 38:156–63. 10.1055/s-0032-1301413

  • 13.

    HudsonJGBowenALNaviaPRios-DalenzJPollardAJWilliamsDet al. The effect of high altitude on platelet counts, thrombopoietin and erythropoietin levels in young Bolivian airmen visiting the Andes. Int J Biometeorol. (1999) 43:85–90. 10.1007/s004840050120

  • 14.

    VijAG. Effect of prolonged stay at high altitude on platelet aggregation and fibrinogen levels. Platelets. (2009) 20:421–7. 10.1080/09537100903116516

  • 15.

    AlgahtaniFHAlqahtanyFSAl-ShehriAAbdelgaderAM. Features and incidence of thromboembolic disease: a comparative study between high and low altitude dwellers in Saudi Arabia. Saudi J Biol Sci. (2020) 27:1632–6. 10.1016/j.sjbs.2020.03.004

  • 16.

    ConnorsJMLevyJH. COVID-19 and its implications for thrombosis and anticoagulation. Blood. (2020) 135:2033–40. 10.1182/blood.2020006000

  • 17.

    Arias-ReyesCZubieta-DeuriosteNPoma-MachicaoLAliaga-RaduanFCarvajal-RodriguezFDutschmannMet al. Does the pathogenesis of SARS-CoV-2 virus decrease at high-altitude?Respir Physiol Neurobiol. (2020) 277:103443. 10.1016/j.resp.2020.103443

  • 18.

    CharlsonMEPompeiPAlesKLMackenzieCR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. (1987) 40:373–83. 10.1016/0021-9681(87)90171-8

  • 19.

    CharlsonMSzatrowskiTPPetersonJGoldJ. Validation of a combined comorbidity index. J Clin Epidemiol. (1994) 47:1245–51. 10.1016/0895-4356(94)90129-5

  • 20.

    QuanHLiBCourisCMFushimiKGrahamPHiderPet al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. (2011) 173:676–682. 10.1093/aje/kwq433

  • 21.

    ZhengZPengFXuBZhaoJLiuHPengJet al. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect81, e16–e25. 10.1016/j.jinf.2020.04.021

  • 22.

    HuangCWangYLiXRenLZhaoJHuYet al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. (2020) 395:497–506. 10.1016/S0140-6736(20)30183-5

  • 23.

    LiuWTaoZWWangLYuanMLLiuKZhouLet al. Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chin Med J (Engl). (2020) 133:1032–8. 10.1097/CM9.0000000000000775

  • 24.

    WangDHuBHuCZhuFLiuXZhangJet al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. Jama323, 1061–1069. 10.1001/jama.2020.1585

  • 25.

    ZhouFYuTDuRFanGLiuYLiuZet al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet395, 1054–1062. 10.1016/S0140-6736(20)30566-3

  • 26.

    ChristensenDMStrangeJEGislasonGTorp-PedersenCGerdsTFosbølEet al. Charlson comorbidity index score and risk of severe outcome and death in danish COVID-19 patients. J Gen Intern Med35, 2801–2803. 10.1007/s11606-020-05991-z

  • 27.

    Tuty KuswardhaniRAHenrinaJPranataRAnthonius LimMLawrensiaSSuastikaK. Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis. Diabetes Metab Syndr14, 2103–2109. 10.1016/j.dsx.2020.10.022

  • 28.

    ZhouWQinXHuXLuYPanJ. Prognosis models for severe and critical COVID-19 based on the Charlson and Elixhauser comorbidity indices. Int J Med Sci17, 2257–2263. 10.7150/ijms.50007

  • 29.

    ChangMCParkYKKimBOParkD. Risk factors for disease progression in COVID-19 patients. BMC Infect Dis. (2020) 20:445. 10.1186/s12879-020-05144-x

  • 30.

    RodJEOviedo-TrespalaciosOCortes-RamirezJ. A brief-review of the risk factors for covid-19 severity. Rev Saude Publica. (2020) 54:60. 10.11606/s1518-8787.2020054002481

  • 31.

    FosterKJJaureguiETajudeenBBishehsariFMahdaviniaM. Smell loss is a prognostic factor for lower severity of coronavirus disease 2019. Ann Allergy Asthma Immunol. (2020) 125:481–3. 10.1016/j.anai.2020.07.023

  • 32.

    KimSRNamSHKimYR. Risk factors on the progression to clinical outcomes of COVID-19 patients in south korea: using national data. Int J Environ Res Public Health. (2020) 17:1723884. 10.3390/ijerph17238847

  • 33.

    SomersVKKaraTXieJ. Progressive hypoxia: A pivotal pathophysiologic mechanism of COVID-19 pneumonia. Mayo Clin Proc95, 2339–2342. 10.1016/j.mayocp.2020.09.015

  • 34.

    EltzschigHKCarmelietP. Hypoxia and inflammation. N Engl J Med. (2011) 364:656–65. 10.1056/NEJMra0910283

  • 35.

    VassilakiNKalliampakouKIKotta-LoizouIBefaniCLiakosPSimosGet al. Low oxygen tension enhances hepatitis C virus replication. J Virol. (2013) 87:2935–48. 10.1128/JVI.02534-12

  • 36.

    López-RodríguezDMKirillovVKrugLTMesriEAAndreanskyS. A role of hypoxia-inducible factor 1 alpha in Murine Gammaherpesvirus 68 (MHV68) lytic replication and reactivation from latency. PLoS Pathog. (2019) 15:e1008192. 10.1371/journal.ppat.1008192

  • 37.

    PilliVSDattaAAfreenSCatalanoDSzaboGMajumderR. Hypoxia downregulates protein S expression. Blood. (2018) 132:452–5. 10.1182/blood-2018-04-841585

  • 38.

    CaoGLiPChenYFangKChenBWangSet al. A risk prediction model for evaluating the disease progression of COVID-19 pneumonia. Front Med (Lausanne). (2020) 7:556886. 10.3389/fmed.2020.556886

  • 39.

    LiuFLiLXuMWuJLuoDZhuYet al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. (2020) 127:104370. 10.1016/j.jcv.2020.104370

  • 40.

    TanCHuangYShiFTanKMaQChenYet al. C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early. J Med Virol. (2020) 92:856–62. 10.1002/jmv.25871

  • 41.

    MalikPPatelUMehtaDPatelNKelkarRAkrmahMet al. Biomarkers and outcomes of COVID-19 hospitalisations: systematic review and meta-analysis. BMJ Evid Based Med. (2020) bmjebm-2020-111536. 10.1136/bmjebm-2020-111536

  • 42.

    SharifpourMRangarajuSLiuMAlabyadDNahabFBCreel-BulosCMet al. C-Reactive protein as a prognostic indicator in hospitalized patients with COVID-19. PLoS One. (2020) 15:e0242400. 10.1371/journal.pone.0242400

  • 43.

    ObataRMaedaTDoDRKunoT. Increased secondary infection in COVID-19 patients treated with steroids in New York City. Jpn J Infect Dis. (2020). 10.7883/yoken.JJID.2020.884

  • 44.

    RipaMGalliLPoliAOltoliniCSpagnuoloVMastrangeloAet al. Secondary infections in patients hospitalized with COVID-19: incidence and predictive factors. Clin Microbiol Infect. (2020) 27:451–7. 10.1016/j.cmi.2020.10.021

  • 45.

    AdamsHJAKweeTCYakarDHopeMDKweeRM. Chest CT imaging signature of coronavirus disease 2019 infection: in pursuit of the scientific evidence. Chest. (2020) 158, 1885–95. 10.1016/j.chest.2020.06.025

  • 46.

    PanFYeTSunPGuiSLiangBLiLet al. Time course of lung changes at chest ct during recovery from coronavirus disease 2019 (COVID-19). Radiology. (2020) 295:715–21. 10.1148/radiol.2020200370

  • 47.

    RubinGDRyersonCJHaramatiLBSverzellatiNKanneJPRaoofSet al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the fleischner society. Chest. (2020) 158:106–16. 10.1016/j.chest.2020.04.003

  • 48.

    FranconeMIafrateFMasciGMCocoSCiliaFManganaroLet al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol. (2020) 30:6808–17. 10.1007/s00330-020-07033-y

  • 49.

    Al-SmadiASBhatnagarAAliRLewisNJohnsonS. Correlation of chest radiography findings with the severity and progression of COVID-19 pneumonia. Clin Imag. (2021) 71:17–23. 10.1016/j.clinimag.2020.11.004

  • 50.

    ZhangRWuYZhaoMLiuCZhouLShenSet al. Role of HIF-1alpha in the regulation ACE and ACE2 expression in hypoxic human pulmonary artery smooth muscle cells. Am J Physiol Lung Cell Mol Physiol. (2009) 297:L631–40. 10.1152/ajplung.90415.2008

  • 51.

    PunMTurnerRStrapazzonGBruggerHSwensonER. Lower incidence of COVID-19 at high altitude: facts and confounders. High Alt Med Biol. (2020) 21:217–22. 10.1089/ham.2020.0114

  • 52.

    HamplVHergetJBíbováJBanasováAHuskováZVanourkováZet al. Intrapulmonary activation of the angiotensin-converting enzyme type 2/angiotensin 1-7/G-protein-coupled Mas receptor axis attenuates pulmonary hypertension in Ren-2 transgenic rats exposed to chronic hypoxia. Physiol Res. (2015) 64:25–38. 10.33549/physiolres.932861

  • 53.

    OarheCIDangVDangMNguyenHGopallawaIGewolbIHet al. (2015). Hyperoxia downregulates angiotensin-converting enzyme-2 in human fetal lung fibroblasts. Pediatric Res. (2015) 77:656–62. 10.1038/pr.2015.27

  • 54.

    JoshiSWollenzienHLeclercEJarajapuYP. Hypoxic regulation of angiotensin-converting enzyme 2 and Mas receptor in human CD34(+) cells. J Cell Physiol. (2019) 234:20420–31. 10.1002/jcp.28643

Summary

Keywords

COVID–19, SARS-CoV-2, high-altitude, crp, CO-RADS classification, d-dimer

Citation

Abdelsalam M, Althaqafi RMM, Assiri SA, Althagafi TM, Althagafi SM, Fouda AY, Ramadan A, Rabah M, Ahmed RM, Ibrahim ZS, Nemenqani DM, Alghamdi AN, Al Aboud D, Abdel-Moneim AS and Alsulaimani AA (2021) Clinical and Laboratory Findings of COVID-19 in High-Altitude Inhabitants of Saudi Arabia. Front. Med. 8:670195. doi: 10.3389/fmed.2021.670195

Received

20 February 2021

Accepted

23 March 2021

Published

12 May 2021

Volume

8 - 2021

Edited by

Reza Lashgari, Institute for Research in Fundamental Sciences, Iran

Reviewed by

Jawhar Gharbi, King Faisal University, Saudi Arabia; Majid Marjani, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Iran

Updates

Copyright

*Correspondence: Ahmed S. Abdel-Moneim

This article was submitted to Pulmonary Medicine, a section of the journal Frontiers in Medicine

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Figures

Cite article

Copy to clipboard


Export citation file


Share article

Article metrics