Retrospective Study Open Access
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jan 16, 2021; 9(2): 344-356
Published online Jan 16, 2021. doi: 10.12998/wjcc.v9.i2.344
Risk factors associated with acute respiratory distress syndrome in COVID-19 patients outside Wuhan: A double-center retrospective cohort study of 197 cases in Hunan, China
Xing-Sheng Hu, Chun-Hong Hu, Ping Zhong, Ya-Jing Wen, Xiang-Yu Chen
Xing-Sheng Hu, Chun-Hong Hu, Department of Oncology, the Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China
Ping Zhong, Department of Dermatology, Nanchong Central Hospital, Nanchong 637000, Sichuan Province, China
Ya-Jing Wen, Department of Clinical Medicine, Chengdu Medical College, Chengdu 610000, Sichuan Province, China
Xiang-Yu Chen, Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China
ORCID number: Xing-Sheng Hu (0000-0002-5325-4691); Chun-Hong Hu (0000-0003-3857-4598); Ping Zhong (0000-0003-2888-0727); Ya-Jing Wen (0000-0003-2884-0143); Xiang-Yu Chen (0000-0002-4233-8822).
Author contributions: Hu XS designed the study, acquired and analyzed the data, and wrote the paper; Hu CH designed the research and contributed to the data analysis; Zhong P and Wen YJ contributed to the analysis and interpretation of the data, and drafted the article; Chen XY designed the research, revised the paper, and supervised the report; All authors made critical revisions related to important intellectual content of the manuscript and gave final approval of the version of the article to be published.
Supported by The Natural Science Foundation of Hunan Province, No. 2019JJ40435.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Second Xiangya Hospital of Central south university (2020-017).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent. Written informed consent was waived by the Ethics Committee of the designated hospital.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Xiang-Yu Chen, MD, Associate Professor, Department of Radiology, the Second Xiangya Hospital of Central South University, No. 139 Renmin Road Central, Changsha 410011, Hunan Province, China. chenxiangyu@csu.edu.cn
Received: October 16, 2020
Peer-review started: October 16, 2020
First decision: October 27, 2020
Revised: October 30, 2020
Accepted: November 12, 2020
Article in press: November 12, 2020
Published online: January 16, 2021

Abstract
BACKGROUND

There have been few reports on the risk factors for acute respiratory distress syndrome (ARDS) in coronavirus disease 2019 (COVID-19), and there were obvious differences regarding the incidence of ADRS between Wuhan and outside Wuhan in China.

AIM

To investigate the risk factors associated with ARDS in COVID-19, and compare the characteristics of ARDS between Wuhan and outside Wuhan in China.

METHODS

Patients were enrolled from two medical centers in Hunan Province. A total of 197 patients with confirmed COVID-19, who had either been discharged or had died by March 15, 2020, were included in this study. We retrospectively collected the patients’ clinical data, and the factors associated with ARDS were compared by the χ² test, Fisher’s exact test, and Mann-Whitney U test. Significant variables were chosen for the univariate and multivariate logistic regression analyses. In addition, literature in the PubMed database was reviewed, and the characteristics of ARDS, mortality, and biomarkers of COVID-19 severity were compared between Wuhan and outside Wuhan in China.

RESULTS

Compared with the non-ARDS group, patients in the ARDS group were significantly older, had more coexisting diseases, dyspnea, higher D-dimer, lactate dehydrogenase (LDH), and C-reactive protein. In univariate logistic analysis, risk factors associated with the development of ARDS included older age [odds ratio (OR) = 1.04), coexisting diseases (OR = 3.94), dyspnea (OR = 17.82), dry/moist rales (OR = 9.06), consolidative/mixed opacities (OR = 2.93), lymphocytes (OR = 0.68 for high lymphocytes compared to low lymphocytes), D-dimer (OR = 1.41), albumin (OR = 0.69 for high albumin compared to low albumin), alanine aminotransferase (OR = 1.03), aspartate aminotransferase (OR = 1.02), LDH (OR = 1.02), C-reactive protein (OR = 1.04) and procalcitonin (OR = 17.01). In logistic multivariate analysis, dyspnea (adjusted OR = 27.10), dry/moist rales (adjusted OR = 9.46), and higher LDH (adjusted OR = 1.02) were independent risk factors. The literature review showed that patients in Wuhan had a higher incidence of ARDS, higher mortality rate, and higher levels of biomarkers associated with COVID-19 severity than those outside Wuhan in China.

CONCLUSION

Dyspnea, dry/moist rales and higher LDH are independent risk factors for ARDS in COVID-19. The incidence of ARDS in Wuhan seems to be overestimated compared with outside Wuhan in China.

Key Words: Acute respiratory distress syndrome, COVID-19, Risk factor, Mortality, Severity, Dyspnea

Core Tip: Some of the risk factors associated with the incidence of acute respiratory distress syndrome in coronavirus disease 2019 include older age, coexisting diseases, dyspnea, dry/moist rales, consolidative/mixed opacities, lower lymphocytes/albumin, higher D-dimer, alanine aminotransferase/aspartate aminotransferase, lactate dehydrogenase, C-reactive protein, and procalcitonin. Logistic multivariate analysis showed that dyspnea, dry/moist rales, and higher lactate dehydrogenase were three independent risk factors. The incidence of acute respiratory distress syndrome in coronavirus disease 2019 was higher in Wuhan than outside Wuhan in China, which may be due to a lack of sufficient medical resources in the early period of the epidemic in Wuhan.



INTRODUCTION

Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)[1], which emerged in Wuhan, China in December 2019, and rapidly spread to every province in China. Hunan Province, with the closest geographical location to Wuhan, became the second most affected area. Almost 2 mo later, COVID-19 was identified in South Korea, Japan, Europe, and United States, and then worldwide. According to the World Health Organization (WHO), through October 15, 2020, more than 38 million people had been infected and more than 1 million people had died worldwide, and these figures are still soaring[2].

During the COVID-19 outbreak, there was an increase in the number of reports regarding its clinical characteristics, and prevention and control, but few reports on the risk factors for ARDS. These risk factors are very important in predicting if critically ill patients may rapidly progress to ARDS and even death[3]. More importantly, when reviewing the literature and analyzing our data, we found that there were obvious differences in ARDS incidence, mortality rates, and intensive care unit (ICU) admission rates between Wuhan and non-Wuhan studies in China. One article published in JAMA Internal Medicine[4] showed that the incidence of ARDS in COVID-19 was 41.80% and the mortality rate was 21.9% in Wuhan, whereas in our study, the incidence of ARDS was only 6.6%. Furthermore, we reviewed the literature and found higher ARDS and mortality rates in Wuhan studies compared to non-Wuhan studies in China. It appears that COVID-19 has different features between epidemic areas and unaffected areas.

The aim of this retrospective study was to investigate the risk factors associated with ARDS of COVID-19 outside Wuhan in China, and review the literature to determine the different features of ARDS in Wuhan and in non-Wuhan areas of China.

MATERIALS AND METHODS
Study design

The first objective of this retrospective study was to identify the risk factors for ARDS in COVID-19 patients; the second objective was to compare the different characteristics of ARDS between Wuhan and non-Wuhan studies in China. Patients were enrolled from two medical centers: Changsha Public Health Treatment Center (Hunan, China) and Xiangtan Central Hospital (Hunan, China). The inclusion criteria were as follows: Inpatients with laboratory-confirmed COVID-19; and available data regarding epidemiological, clinical, and laboratory findings, especially ARDS findings. The inclusion criteria for literature review were: Inpatients with laboratory-confirmed COVID-19 according to the diagnosis and treatment protocol for COVID-19 by China[5] or the WHO[6,7]; available data on the incidence of ARDS, and/or mortality rate, ICU admission rate, discharge rate, routine blood examination, liver function, D-dimer, lactate dehydrogenase (LDH), C-reactive protein (CRP), computed tomography (CT) findings, and treatment regimens; and publication year and language regardless of the retrospective/randomized study (but excluding case reports).

This work was carried out in accordance with the Code of Ethics of the World Medical Association, and was approved by the Institutional Review Board and the Ethics Committee of the Second Xiangya Hospital (2020-017). Written informed consent was waived by the Ethics Committee of the designated hospital for the nature of retrospective analysis and the newly emerged infectious disease.

Data collection

We retrospectively collected COVID-19 patient data from the two medical centers mentioned above. The first date of patient admission to hospital was January 24, 2020, and the last date of admission was February 16, 2020. The first date of discharge from hospital was February 4, 2020, and the last date of discharge was March 15, 2020. The reviewed data included the basic demographic, epidemiological, clinical, laboratory, imaging, therapy and outcome data.

In the literature review, the key word “COVID-19” was used to search relevant studies in the PubMed database from the onset of COVID-19 to April 10, 2020. Relevant studies were screened and analyzed according to the PRISMA statement guidelines 2009[8]. Two reviewers (Xing-Sheng Hu and Ping Zhong) independently reviewed the literature and the incidence of ARDS, mortality, and biomarkers of disease severity were extracted.

We used the Cochrane Handbook Version 6.0 (2019) “Assessing risk of bias in a non-randomized study”[9] to assess the risk of bias within studies, based on the following four domains: Confounding bias, selection bias, information bias, and reporting biases.

Patient diagnosis

COVID-19 was diagnosed on the basis of the Diagnosis and Treatment Protocol for Novel Coronavirus Infection-Induced Pneumonia version 7 (trial)[5]. Diagnosis was confirmed based on two aspects: real-time reverse-transcriptase–polymerase-chain-reaction assay of nucleic acid from respiratory or blood specimens was positive; and high-throughput gene sequencing was highly homologous with SARS-CoV-2 in respiratory or blood specimens. The real-time reverse-transcriptase–polymerase-chain-reaction assay was performed in accordance with the protocol established by the WHO[6].

Treatment strategy

Antiviral drugs were administered to the patients with confirmed COVID-19. Arbidol was given at a dose of 200 mg every 8 h, lopinavir (400 mg)/ritonavir (100 mg) (LPV/r) orally every 12 h, interferon-alpha 5 MIU was added to 2 mL normal saline and inhaled every 12 h, and novaferon 20 μg was injected intramuscularly every 12 h. All patients received the best supportive care and symptomatic treatment, if necessary, such as supplemental oxygen, noninvasive and invasive ventilation, extracorporeal membrane oxygenation, antibiotic agents, corticosteroids, gamma globulin, continuous renal replacement therapy and convalescent plasma therapy. Clinical and laboratory monitoring was carried out routinely.

Outcomes

ARDS was defined according to the WHO interim guidance[7]. The patients’ discharge criteria and clinical classifications were evaluated according to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection-Induced Pneumonia version 7 (trial)[5].

Statistical analysis

The statistical methods used in this study were reviewed by Ya Zheng from Lanzhou University (Gansu, China). Continuous variables are expressed as medians (interquartile range, IQR) and were compared using the Mann-Whitney U test; categorical variables are expressed as a number (%) and were compared using the χ² test or Fisher’s exact test. Significant variables in the univariate analysis were chosen and entered into the univariate logistic regression model and multivariate regression model (measurement data were entered as continuous variables) to calculate the odds ratio (OR) and independent risk factors, using forward logistic regression methods. Statistical analyses were performed using SPSS 25.0 (IBM), and P < 0.05 was considered statistically significant.

RESULTS
Demographic and clinical characteristics

A total of 197 patients were included in this study. The median age of the 93 male and 104 female patients was 45 years. Patients who traveled to Wuhan accounted for 33.8%, and imported cases accounted for 41.5%. The most common clinical manifestations were cough (75.6%), expectoration (38.6%), fever (65.5%), fatigue (35.5%) and dyspnea (19.8%). The most common abnormal laboratory findings were low white cell count (36.0%) and low lymphocyte count (23.9%), high D-dimer (26.4%), and CRP (53.3%), and less common factors were elevated creatine kinase (CK) (9.9%), creatine kinase-MB (CK-MB) (6.2%), alanine aminotransferase (ALT) (16.2%), aspartate aminotransferase (AST) (12.2%), and LDH (12.7%). Common characteristic CT findings were bilateral lung involvement (82.8%), ground glass opacities (86.7%), involvement of two lobes on the left (38.6%), and involvement of three lobes on the right (35.7%). The clinical characteristics of these patients are presented in Table 1.

Table 1 Clinical characteristics and factors associated respiratory distress syndrome.
Demographic characteristics
All patients (n = 197)
Non-ARDS (n = 184)
ARDS (n = 13)
P value
Ages (yr)45.0 (34.0-58.5)42 (34-57)58 (48-65)0.010
Sex
Male93/197 (47.2%)85/184 (46.2%)8/13 (61.5%)0.284
Female104/197 (52.8%)99/184 (53.8%)5/13 (38.5%)
Body mass index23.42 (21.39-25.69)23.29 (21.29-25.49)26.03 (21.50-26.89)0.170
Smoking11/171 (6.4%)10/159 (6.3%)1/12 (8.3%)0.5621
Travelling to Wuhan45/133 (33.8%)40/125 (32.0%)5/8 (62.5%)0.167
Imported cases76/183 (41.5%)73/184 (39.7%)3/12 (25.0%)0.481
Cluster exposure history132/197 (67.0%)127/184 (69.0%)5/13 (38.5%)0.050
Coexisting disease
Any49/197 (24.9%)42/184 (22.8%)7/13 (53.8%)0.030
Heart disease8/197 (4.0%)6/184 (3.3%)2/13 (15.4%)0.0901
Hypertension27/197 (13.7%)24/184 (13.0%)3/13 (23.1%)0.549
Diabetes13/197 (6.6%)12/184 (6.5%)1/13(7.7%)0.600
Other25/197 (12.7%)22/184 (12.0%)3/13 (23.1%)0.464
Clinical manifestations
Fever
37.3–39.0 °C115/197 (58.4%)107/184 (58.2%)8/13 (61.5%)0.308
> 39.0 °C17/197 (8.6%)14/184 (7.6%)3/13 (23.0%)
Non-fever68/197 (34.5%)65/184 (35.3%)3/13 (23.0%)0.551
Fever129/197 (65.5%)119/184 (64.7%)10/13 (76.9%)
Cough147/197 (74.6%)137/184 (74.5%)10/13 (76.9%)1.000
Expectoration76/197 (38.6%)71/184 (38.6%)7/13 (53.8%)0.277
Dyspnea39/197 (19.8%)29/184 (15.8%)10/13 (58.8%)< 0.001
Diarrhea27/197 (13.7%)26/184 (14.1%)1/13 (7.7%)0.814
Nausea/vomit17/197 (8.6%)16/184 (8.7%)1/13 (7.7%)1.000
Fatigue70/197 (35.5%)64/184 (34.8%)6/13 (46.2%)0.597
Sore throat18/197 (9.1%)18/184 (9.8%)0/13 (0.0%)0.493
Headache19/197 (9.6%)18/184 (9.8%)1/13 (7.7%)1.000
Muscular soreness15/197 (7.6%)14/184 (7.6%)1/13 (7.7%)1.0001
Total complications21/197 (10.7%)14/184 (7.6%)7/13 (53.8%)< 0.001
Dry/moist rales11/162 (6.8%)8/153 (5.2%)3/9 (33.3%)0.016
CT imagings
Single lung involvement23/169 (13.6%)23/150 (%)0/13 (%)0.268
Bilateral lung involvement140/169 (82.8%)127/15013/13 (100.0%)
Ground glass opacities143/165 (86.7%)137/145 (94.5%)6/9 (66.7%)0.0181
Consolidative/mixed opacities11/165 (6.7%)8/145 (5.5%)3/9 (33.3%)
Number of lobe involvement
Single left lobe58/158 (36.7%)55/111 (49.5%)3/8 (37.5%)0.770
Double left lobe61/158 (38.6%)56/111 (50.5%)5/8 (62.5%)
Single or double right lobe65/157 (41.4%)62/113 (54.9%)3/8 (37.5%)0.558
Triple right lobe56/157 (35.7%)51/113 (45.1%)5/8 (62.5%)
Laboratory findingsP value
White cell count (× 109/L)4.75 (3.44-5.91)4.75 (3.44-5.89)4.51 (3.06-7.05)0.990
< 471/197 (36.0%)66/184 (35.9%)5/13 (38.5%)0.293
4-10122/197 (61.9%)115/184 (62.5%)7/13 (53.8%)
> 104/197 (2.0%)3/184 (1.6%)1/13 (7.7%)
Neutrophil (× 109/L)2.89 (2.16-3.72)2.88 (2.15-3.65)3.31 (2.16-5.46)0.260
< 240/197 (20.3%)37/184 (20.1%)3/13 (23.1%)0.325
2-7152/197 (77.2%)143/184 (77.7%)9/13 (69.2%)
> 75/197 (2.5%)4/184 (2.2%)1/13 (7.7%)
Lymphocyte (× 109/L)1.20 (0.81-1.66)1.21 (0.88-1.69)0.70 (0.60-0.94)< 0.001
< 0.847/197 (23.9%)38/184 (20.7%)9/13 (69.2%)< 0.001
Hemoglobin (g/L)130.00 (119.00-141.00)130.00 (119.25-140.75)127.50 (103.25-148.00)0.511
< 110 g/L18/197 (9.1%)16/184 (8.7%)2/13 (15.4%)0.756
Blood platelet173.00 (139.00-230.00)178.50 (139.00-229.50)148.00 (91.25-225.25)0.174
< 100, × 109/L12/197 (6.1%)10/184 (5.4%)2/13 (15.4%)0.1821
Prothrombin time (s)11.5 (10.90-12.35)11.55 (10.90-12.30)11.40 (10.60-12.75)0.964
> 16 s21.1 (%)1/184 (0.5%)1/13 (7.7%)0.128
APTT (s)32.20 (29.80-34.75)32.40 (30.20-34.57)29.70 (26.90-35.90)0.212
< 223/1971.5 (%)2/184 (1.0%)1/13 (%)0.186
CK (U/L)64.10 (41.97-93.87)63.85 (41.17-91.85)83.20 (47.00-187.30)0.195
> 170 U/L19/192 (9.9%)15/182 (8.2%)4/10 (40.0%)0.010
CK-MB (U/L)9.10 (5.90-12.05)8.60 (5.60-11.90)14.10 (10.43-30.50)0.005
> 2312/193 (6.2%)19/183 (10.4%)4/10 (40.0%)0.021
D-dmier (mg/L)0.26 (0.13-0.58)0.26 (0.12-0.56)1.17 (0.26-8.57)0.001
> 0.552/165 (31.5%)44/153 (28.8%)8/12 (66.7%)0.016
Albumin (g/L)38.28 (35.35-41.08)38.52 (35.78-41.59)29.90 (27.86-34.88)< 0.001
< 3538/197 (19.3%)26/155 (16.8%)9/13 (69.2%)< 0.001
ALT (U/L)20.13 (14.12-30.29)19.72 (13.91-28.75)37.41 (23.93-78.65)< 0.001
> 4032/197 (16.2%)26/184 (14.1%)6/13 (46.2%)0.008
AST (U/L)23.38 (19.14-31.28)23.12 (18.98-30.49)33.24 (21.47-68.61)0.029
> 4024/197 (12.2%)18/184 (9.8%)6/13 (46.2%)0.001
Total bilirubin (μmol/L)10.80 (7.89-15.12)10.67 (7.82-14.86)13.26 (8.81-23.31)0.114
> 17.140/197 (20.3%)36/184 (19.6%)4/13 (30.8%)0.539
Creatinine (μmol/L)64.10 (41.98-93.88)51.25 (40.39-64.65)46.17 (36.79-111.57)0.684
> 1336/197 (3.0%)4/184 (2.2%)2/13 (15.4%)0.052
LDH (U/L)161.15 (135.80-208.88)157.80 (133.85-205.97)313.60 (183.55-352.50)< 0.001
> 250 U/L25/197 (12.7%)17/184 (9.2%)8/13 (61.5%)< 0.001
CRP (mg/L)12.79 (3.55-28.50)12.47 (3.49-25.52)45.70 (13.30-72.08)0.003
> 1010/105 (53.3%)96/184 (52.2%)9/13 (69.2%)0.064
Procalcitonin (μg/L)0.08 (0.06-0.20)0.70 (0.05-0.18)0.80 (0.60-71.83)0.117
> 0.54/187 (2.1%)1/175 (0.6%)2/12 (16.7%)0.0111
Blood glucose (mmol/L)161/197 (81.7%)5.32 (4.73-6.66)6.03 (5.01-12.97)0.169
> 731/161 (19.3%)28/154 (18.2%)3/7 (42.9%)0.259
Treatments
Oxygen therapy
Mechanical ventilation4/164 (2.0%)0/155 (0.0%)4/9 (44.4%)< 0.0011
Nasal cannula151/164 (92.1%)146/155 (94.2%)5/9 (55.6%)
Did not oxygen therapy9/164 (5.5%)9/155 (5.8%)0/9 (0.0%)
Antiviral therapy161/162 (99.4%)153/154 (99.4%)8/8 (100.0%)1.0001
Antibiotic therapy67/153 (43.8%)62/147 (42.2%)5/6 (83.3%)0.116
Corticosteroid40/161 (24.8%)30/151 (19.9%)10/10 (100.0%)< 0.001
Convalescent plasma4/197 (2.0%)0/184 (0.0%)4/13 (30.8%)< 0.0011
Gamma globulin39/157 (24.8%)32/150 (21.3%)7/7 (100.0%)< 0.001
Treatment and outcome

Of the included patients, 99.4% received antiviral therapy, and the most commonly used antiviral drugs were interferon, arbidol, and LPV/r. A single antiviral drug was administered in 24.5%, two antiviral drugs in 44.3%, three antiviral drugs in 23.4% and four antiviral drugs in 3.8% of patients. And 43.8% of patients received antibiotic therapy (86.6% were treated with moxifloxacin, 10.4% with levofloxacin, 0.6% with piperacillin-tazobactam, and 0.6% with ceftriaxone), 24.8% received gamma globulin therapy, 24.8% received corticosteroid therapy, 3.6% received convalescent plasma therapy, and 2.0% received mechanical ventilation (0.5% patients received invasive mechanical ventilation) therapy.

On March 15, 2020, the incidence of ARDS was 6.6%, the ICU admission rate was 8.6%, the rate of severe disease was 11.2%, the rate of critical disease was 3.6%, and the mortality rate was 1.5% (3 patients). All remaining patients were discharged from hospital.

Comparison of risk factors between the ARDS and non-ARDS groups

Compared to the non-ARDS group, patients in the ARDS group were significantly older (median 58 years vs 42 years), had more coexisting diseases (53.8% vs 22.8%), more dyspnea (58.8% vs 15.8%), dry/moist rales (33.3% vs 5.2%) and consolidative/mixed opacities on CT (33.3% vs 5.5%); higher inflammation-related indicators such as CRP (median 45.70 mg/L vs 12.47 mg/L) and PCT (16.7% vs 0.6%) (P < 0.05); higher tissue injury indicators such as CK (40.0% vs 8.2%), CK-MB (median 14.1 U/L vs 8.6 U/L), ALT (median 37.41 U/L vs 19.72 U/L), AST (median 33.24 U/L vs 23.12 U/L), LDH (median 313.60 U/L vs 157.80 U/L); higher coagulation function levels including D-dimer (median 1.17 mg/L vs 0.26 mg/L), and a lower median level of lymphocytes (median 0.70 × 109/L vs 1.20 × 109/L) and albumin (median 29.90 g/L vs 38.52 g/L) (P < 0.05). The risk factors associated with ARDS are presented in Table 1.

Logistic regression analysis for odds ratio values

Univariate logistic regression analysis showed that older age [odds ratio (OR) = 1.04], coexisting diseases (OR = 3.94), dyspnea (OR = 17.82), dry/moist rales (OR = 9.06), consolidative/mixed opacities (OR = 2.93), lymphocytes (OR = 0.68 for high lymphocytes compared to low lymphocytes), CK (OR = 2.02), D-dimer (OR = 1.41), albumin (OR = 0.69 for high albumin compared to low albumin), ALT (OR = 1.03), AST (OR = 1.02), LDH (OR = 1.02), CRP (OR = 1.04) and PCT (OR = 17.01) were all risk factors for ARDS (P < 0.05) (measurement data were entered as continuous variables). Multivariate logistic regression analysis showed only three significant independent risk factors: dyspnea (adjusted OR = 27.10), dry/moist rales (adjusted OR = 9.46) and higher LDH (adjusted OR = 1.02) (P < 0.05). The logistic regression analysis results are presented in Table 2.

Table 2 Logistic regression analysis for risk odds of acute respiratory distress syndrome.
Logistic univariate regression
Variables
OR (95%CI)
P value
Ages1.05 (1.00-1.09)0.017
Dyspnea17.82 (4.62-68.71)< 0.001
Dry/moist rales9.06 (1.91-43.04)0.006
Consolidative/mixed opacities2.93 (1.34-6.38)0.007
Lymphocyte0.68 (0.01-0.43)0.004
Creatine kinase8.00 (2.02-31.72)0.003
Creatine kinase-MB/0.255
D-dmier1.41 (1.12-1.78)0.004
Albumin0.69 (0.59-0.82)< 0.001
Alanine amino-transferase1.03 (1.01-1.04)0.001
Aspartate amino-transferase1.02 (1.00-1.03)0.048
Lactate dehydrogenase1.02 (1.01-1.03)< 0.001
C-reactive protein1.04 (1.02-1.06)0.001
Coexisting disease3.94 (1.26-12.38)0.019
Procalcitonin17.10 (2.18-134.31)0.007
Logistic multivariate regression
VariablesOR (95%CI)P value
Dyspnea26.89 (1.77-407.72)0.018
Dry/moist rales9.42 (1.02-87.08)0.048
Lactate dehydrogenase1.02 (1.00-1.03)0.014
Comparison of the clinical characteristics of ARDS between Wuhan and non-Wuhan studies

We screened 3267 reports, and 9 conformed to our inclusion criteria (6 reports in Wuhan and 3 reports outside Wuhan in China); all of them were retrospective studies. The flow chart is shown in Figure 1 and individual studies are shown in Tables 3 and 4. After assessing the studies’ bias using the Cochrane Handbook, we found that six studies of Wuhan had confounding bias, and the final follow-up date was earlier than studies outside Wuhan. At the beginning of the epidemic, the disease prevention and control and medical resources were not sufficient, which may lead to higher rates of severe disease and mortality[10]. The selection bias in Wuhan and outside Wuhan’ studies were similar; the proportions of patients still in the hospital were 23.5% and 21.3%, respectively. The information bias were also similar; two studies (Chen et al[11] and Cao et al[12]) in Wuhan and one study (Yang et al[13]) outside Wuhan did not report ARDS definition. None of the studies had obvious report bias.

Figure 1
Figure 1 Flow chart of reviewed literature was included.
Table 3 Comparison clinical characteristics of acute respiratory distress syndrome between Wuhan and outside Wuhan.
Ref. (n)
Final follow-up date
ARDS rate (%)
ICU rate (%)
Death rate (%)
Still in hospital (%)
Median age (yr)
Dyspnea (%)
WBC (4-10 × 109/L) median, elevated rate
Lymphocyte (0.8-4 × 109/L) median, elevated rate
ALT (0-40 U/L) median, elevated rate
Chen et al[11], (99)25 January1732115656317.5, 24%0.9, 35% (< 1.1)39, 28% (> 50)
Huang et al[19], (41)22 January2932151749556.2, 30%0.8, 63% (< 1.0)32 /
Zhou et al[14], (191)31 January312628.3056/6.2, 21%1.0, 40%30, 31%
Wang et al[17], (138)3 February19.626.14.361.65631.24.5, /0.8, 70.3%24 /
Wu et al[4], (201) 13 February41.826.421.96.55139.85.9, 23.4% (> 9.5)0.9, 64.0% (< 1.1)31, 21.7% (> 50)
Cao et al[12], (102)15 February19.617.616.7054//,/0.9, 3.7% (< 1.1)23, 24.8%
Total median/mean26.326.716.223.553.739.56.2 (5.2-6.8)0.9 (0.8-0.9)31.0 (27.0-35.5)
Outside Wuhan
Guan et al[20], (1099)131 January3.451.493.64718.74.7, 5.9%1.0, 83.2% (< 1.5)/, 21.3%
Chen et al[16], (249)25 February3.28.80.812.8517.64.7, 28.9%1.1, 47.4%23.0 /
Yang et al[13], (149)15 February00051.0451.344.6, 1.34%1.2, 35.6% (< 1.1)20, 12.1%
This study (197)15 March6.68.61.504519.84.8, 2.0%1.2, 23.9%20, 16.2%
Total median/mean3.35.60.921.34711.94.7 (4.6-4.8)1.2 (1.0-1.2)20 (20-/)
Table 4 Comparison clinical characteristics of acute respiratory distress syndrome between Wuhan and outside Wuhan.
Ref. (n)
AST (0-40 U/L) median, elevated rate
D-dimer (0-0.5 mg/L) median, elevated rate
LDH (0-250 U/L) median, elevated rate
CRP (0-10 mg/L) median, elevated rate
CT bilateralpneumonia (%)
Antiviral rate (%)
Antibiotic rate (%)
Corticost-eroid rate (%)
Mechanical ventilation rate (%)
Chen et al[11], (99)34, 35%0.9, 36% (> 1.5)336, 76%51.3, 86% (> 5)7576 (oseltamivir)1 711920
Huang et al[19], (41)34, 37%0.5, /286, 73% (> 245)/9893 (oseltamivir)1002229
Zhou et al[14], (191)/0.8, 68%300, 67% (> 245)/5921 (LPV/r)953031
Wang et al[17], (138)31, /0.20, /261, 39.9% (> 243)/, //89.9 (oseltamivir)Many544.926
Wu et al[4], (201)33, 29.8%23.3% (> 1.5)308, 98% (> 150)42.4, 85.6% (> 5)9584.6 (oseltamivir)29830.833
Cao et al[12], (102)/, /0.19, 20.8%/, /24.8, 51%70.698.0 (oseltamivi)3995019.6
Total median/mean33.5 (31.5-34.0)0.65 (0.27-0.87)300 (273-322)42 (25-/)8377.192.632.826.4
Outside Wuhan
Guan et al[20], (1099)6/, 22.2%/, 46.4%/, 41.0%/, 60.7%51.835.8 (oseltamivir)5818.66.10
Chen et al[16], (249)25.0, //229, /12.0, 50%81.5Unknown (LPV/r, arbidol)/12.9/
Yang et al[13], (149)23, 18.2%0.2, 14.1%210, 30.2%7.3, 55.0%/93.9 (interferon)233.01.0
This study (197)23, 12.2%0.3, 26.4%161, 12.7%12.8, 53.3%8.2899.4 (arbidol, LPV/r)44424.82.0
Total median/mean23 (23-/)0.25 (0.20-/)210 (161-/)12 (7.3-/)72.376.441.514.93.0

As demonstrated in Tables 3 and 4, the total mean incidence of ARDS (26.3% vs 3.3%), ICU admission rate (26.7% vs 5.6%), and mortality rate (16.2% vs 0.9%) were higher in Wuhan, and the final follow-up date in Wuhan were earlier than those observed outside Wuhan. The laboratory findings showed that the total median white cell count (6.2 × 109/L vs 4.7 × 109/L), ALT (31 U/L vs 20 U/L), AST (33.5 U/L vs 23.0 U/L), D-dimer (0.65 mg/L vs 0.25 mg/L), LDH (300 U/L vs 210 U/L), CRP (42 mg/L vs 12 mg/L), and mean bilateral lung involvement rate (83% vs 72%) were higher in Wuhan than outside Wuhan in China. The rates of antibiotic use (92.6% vs 41.5%), corticosteroid use (32.8% vs 14.9%), and mechanical ventilation (26.8% vs 3.0%) were also higher in Wuhan than outside Wuhan in China. All the above factors indicated that the severity of disease in Wuhan exceeded that outside Wuhan in China. The most common antiviral drug used in Wuhan was oseltamivir, while interferon, arbidol, and LPV/r were more commonly used outside Wuhan in China, which indicated that more effective drugs were used outside Wuhan in the later period of the epidemic.

DISCUSSION

This study reported the clinical characteristics and risk factors associated with ARDS in COVID-19 patients. Older age and coexisting diseases increased the risk of developing ARDS, which were also factors associated with the poor prognosis of COVID-19. Previous reports have shown that they were also associated with more deaths[12,14,15] and ICU admission[16,17], and were associated with ARDS in the study by Wu et al[4]. The reason for this may be that older patients can experience a decline in lymphocyte function and excessive expression of type 2 cytokines, which leads to defects in control of the virus and prolonged proinflammatory responses[18]. A lower level of lymphocytes or albumin was associated with more severe/deceased COVID-19 patients[14,17,19] and a higher incidence of ARDS[4], which were important independent risk factors in our study. Dyspnea was the most obvious manifestation of ARDS, the proportion of COVID-19 patients with dyspnea was 18.7%-55%[19,20], and some studies have shown that dyspnea was associated with ICU admission[9,19], and ARDS[4]. In this study, dyspnea was an independent risk factor for ARDS, and increased the risk by 26.89-fold. The incidence of dry/moist rales in COVID-19 was low (6.8%), but in the ARDS group this percentage markedly increased to 33.3%, and it was also an independent risk factor, which increased the risk by 9.42-fold. More dry/moist rales and consolidative/mixed opacities in the lung indicated severe lung inflammation, and consolidative/mixed opacities were associated with ARDS. Some studies have shown that they increased the incidence of severe/critical COVID-19[21] and the mortality rate[14], and were late indicators of COVID-19[22].

Elevations in D-dimer, LDH, and CRP are very common in COVID-19, which were important factors for poor prognosis, and all of them were related to a strong inflammatory response and disease severity. A high D-dimer level indicates that the inflammatory factors have activated the coagulation system, which might cause the formation of small thromboses and ischemia in lung blood capillaries, which could block the exchange of gas and blood in the lung, trigger the occurrence of dyspnea and ARDS, and even cause disseminated intravascular coagulation. LDH is a tissue injury indicator, CRP is an inflammatory factor, and both of these factors were associated with death[4,11,14] and ICU admission[13,16,17] in the study by Wu et al[4] and with the risk of ARDS. LDH was also an important independent risk factor for ARDS in this study. Although elevated PCT is not common in COVID-19, its elevation is associated with a more serious inflammatory response.

To determine the different characteristics in the incidence of ARDS in Wuhan and outside Wuhan in China, we reviewed the literature and compared the studies. The results showed that the studies in Wuhan commonly reported a higher incidence of ARDS, a higher mortality rate and higher biomarkers of COVID-19 severity than those outside Wuhan in China, accompanied by higher D-dimer, LDH, and CRP, which indicated more serious disease. To the best of our knowledge, there are two possible reasons that a higher incidence ARDS occurred in Wuhan. (1) Due to a lack of medical workers and material resources in the early period of the epidemic, many patients did not receive timely treatment; and (2) Due to a lack of experience related to effective therapeutic drugs in the early period, there were differences in the use of antiviral drugs in Wuhan and outside Wuhan in China. One study[10] showed that from the January 22, 2020 to March 2, 2020, the mortality rates in Wuhan declined continuously, while the mortality rates outside Wuhan in China were constant over time. This resulted from an increased number (as of March 1) of health workers who were dispatched from other provinces, increased number of acute care beds (as of February 24), and construction of temporary hospitals for admission of COVID-19 patients. However, the number of confirmed COVID-19 cases was high in the early period, and the number of cases declined rapidly in the later period. Therefore, it appears that the incidence of ARDS and the mortality rate in Wuhan seem to have been overestimated. As shown in Tables 3 and 4, the main drug used in the early period of the epidemic in Wuhan was oseltamivir, which is a common antiviral drug used in influenza, although other antiviral drugs were also used, such as arbidol or LPV/r, but these accounted for only a small proportion. Therefore, the administration of different antiviral drugs may result in a different prognosis. In short, the literature review showed that the incidence of ARDS in Wuhan was higher than outside Wuhan, accompanied by higher rates of mortality and severer disease. The final follow-up date of Wuhan was earlier than outside Wuhan, which was consistent with the reasons of shortage of medical resource in earlier stage of the epidemic. These findings may be helpful for medical workers and policy makers to accurately judge the state of COVID-19 and adopt earlier intervention and treatment measures.

This study had several limitations. (1) More cases and multicenter studies of ARDS in COVID-19 are required, which may reduce selection bias; (2) In a representative literature analysis of the characteristics of ARDS in Wuhan and outside Wuhan, we only screened the PubMed database; thus, more relevant databases should be included; and (3) Some patients in the reviewed studies were still in hospital at the final follow-up date, and the literature review was only performed till April 10, 2020, so the findings may not completely reflect the total ARDS or mortality rate.

CONCLUSION

We identified some risk factors for ARDS in COVID-19 dyspnea, dry/moist rales and higher LDH are the independent risk factors for ARDS in COVID-19. The ARDS incidence, mortality rate, and biomarkers of COVID-19 severity were higher in Wuhan than that outside Wuhan of China. These findings may provide references for the researchers and policy makers of COVID-19.

ARTICLE HIGHLIGHTS
Research background

There were few reports on the risk factors of acute respiratory distress syndrome (ARDS) in coronavirus disease 2019 (COVID-19), and the differences in ADRS incidence between Wuhan and outside Wuhan in China.

Research motivation

To identify the risk factors of ARDS in COVID-19, and determine whether the incidence of ADRS in Wuhan was overestimated compared to real world research.

Research objectives

The first objective of this study was to identify the risk factors for ARDS in COVID-19 patients, and the second objective was to compare the different characteristics of ARDS between Wuhan and non-Wuhan studies in China.

Research methods

We retrospectively collected the patients’ clinical data, and the factors associated with ARDS were compared using the χ² test, Fisher’s exact test, Mann-Whitney U test. Univariate and multivariate logistic regression was used to compute and adjust odds ratio value. The ARDS incidence, mortality rate, and biomarkers of COVID-19 severity were collected and compared between studies in and outside Wuhan after literature review.

Research results

Older age, coexisting diseases, lower lymphocytes/albumin, higher D-dimer and C-reactive protein levels all affected the incidence of ADRS, and dyspnea, dry/moist rales and higher lactate dehydrogenase level were three independent risk factors. The ARDS incidence, mortality rate, and biomarkers of COVID-19 severity were higher in Wuhan than outside Wuhan in China.

Research conclusions

There were some risk factors associated with ARDS in COVID-19. The higher ARDS rate in Wuhan may result from the shortage of medical resources in the early stage of the epidemic. These findings may provide references for the researchers and policy makers of COVID-19.

Research perspectives

Biomarkers of disease severity are important risk factors for ARDS in COVID-19. The incidence of the disease should be assessed comprehensively. Accurate estimation of the incidence of ARDS will be helpful to both health workers and policy makers to develop appropriate strategies for COVID-19.

ACKNOWLEDGEMENTS

We thank Ya Zheng of Lan Zhou University for reviewing statistical methods.

Footnotes

Manuscript source: Unsolicited manuscript

Specialty type: Medicine, research and experimental

Country/Territory of origin: China

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): C

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Ferreira LPS S-Editor: Zhang L L-Editor: Filipodia P-Editor: Wang LYT

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