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Abstract
Rationale: The coronavirus disease (COVID-19) pandemic is now a global health concern.
Objectives: We compared the clinical characteristics, laboratory examinations, computed tomography images, and treatments of patients with COVID-19 from three different cities in China.
Methods: A total of 476 patients were recruited from January 1, 2020, to February 15, 2020, at three hospitals in Wuhan, Shanghai, and Anhui. The patients were divided into four groups according to age and into three groups (moderate, severe, and critical) according to the fifth edition of the Guidelines on the Diagnosis and Treatment of COVID-19 issued by the National Health Commission of China.
Measurements and Main Results: The incidence of comorbidities was higher in the severe (46.3%) and critical (67.1%) groups than in the moderate group (37.8%). More patients were taking angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers in the moderate group than in the severe and critical groups. More patients had multiple lung lobe involvement and pleural effusion in the critical group than in the moderate group. More patients received antiviral agents within the first 4 days in the moderate group than in the severe group, and more patients received antibiotics and corticosteroids in the critical and severe groups. Patients >75 years old had a significantly lower survival rate than younger patients.
Conclusions: Multiple organ dysfunction and impaired immune function were the typical characteristics of patients with severe or critical illness. There was a significant difference in the use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers among patients with different severities of disease. Involvement of multiple lung lobes and pleural effusion were associated with the severity of COVID-19. Advanced age (≥75 yr) was a risk factor for mortality.
Coronavirus disease (COVID-19) is posing an unprecedented threat to global healthcare systems. A number of observational studies have described clinical characteristics of patients with COVID-19 in single centers. However, details regarding the clinical features of patients in different age groups with varying disease severities remain limited.
In our study, we found that adults ≥75 years of age with COVID-19 had poor outcomes, and the in-hospital mortality rate among critical patients was 41.1%. Involvement of multiple pulmonary lobes and pleural effusion were associated with higher disease severity, whereas antihypertensive medication use was not. These clinical features should help clinicians identify high-risk patients.
Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread throughout the world, posing a critical threat to global health. The novel betacoronavirus, an enveloped RNA virus, was first identified by high-throughput sequencing (1). SARS-CoV-2 has a receptor-binding domain structure similar to that of SARS-CoV, as shown by homology modeling (1). Zhou and colleagues found that 96% of SARS-CoV-2 is 96% identical at the whole-genome level to a bat coronavirus (2). COVID-19 was declared a public health emergency by the World Health Organization, and 4,006,257 laboratory-confirmed infections had been reported globally by May 12, 2020 (3).
Several studies have described the clinical characteristics and epidemiology of COVID-19 (1, 3–6). These studies confirmed human-to-human transmission of COVID-19, and that SARS-CoV-2 infection could result in severe and even fatal acute respiratory distress syndrome. Three published studies on COVID-19 cases were conducted in Wuhan, Hubei Province (1, 4, 5). Two recent studies summarized the findings regarding a large number of laboratory-confirmed SARS-CoV-2 infections in 31 provinces/provincial municipalities (6, 7). The Guidelines on the Diagnosis and Treatment of COVID-19 (fifth edition) published by the National Health Commission of China was issued on February 8, 2020. These guidelines classified SARS-CoV-2 infections into four groups (mild type, moderate type, severe type, and critical type). Herein, we compare clinical features, laboratory examinations, computed tomography (CT) images, and use of therapies (including antiviral, antibacterial, and antifungal agents; corticosteroids; and antihypertensive medications) among three of the four groups (moderate type, severe type, and critical type) and four age groups of 476 patients with COVID-19 in three cities (Shanghai, Wuhan in Hubei Province, and Tongling in Anhui Province). We also summarize the dynamic changes observed in CT images of improved patients to characterize the evolution of the disease.
Patients were recruited for this multicenter retrospective study from three hospitals designated for the treatment of COVID-19, namely, Jinyintan Hospital in Wuhan, Shanghai Public Health Clinical Center in Shanghai, and Tongling People’s Hospital in Anhui Province, China. The recruitment period was from January 1, 2020, to February 15, 2020. All patients enrolled in this study had received a diagnosis of COVID-19 according to the diagnostic criteria from the fifth edition of the Guidelines on the Diagnosis and Treatment of COVID-19 by the National Health Commission of China. The study was approved by the Shanghai Public Health Clinical Center Ethics Committee, the Jinyintan Hospital Ethics Committee, and the Tongling People’s Hospital Ethics Committee.
According to the fifth edition of the Guidelines on the Diagnosis and Treatment of COVID-19 by the National Health Commission of China, COVID-19 severity is classified as follows:
| 1. | Mild type: The clinical symptoms are mild, with no abnormal radiological findings. | ||||||||||||||||||||||
| 2. | Moderate type: Fever, cough, and other symptoms are present with pneumonia on chest CT. | ||||||||||||||||||||||
| 3. | Severe type: The disease is classified as severe if one of the following conditions is met:
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| 4. | Critical type: One of the following conditions has to be met:
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Based on the clinical information collected until February 15, 2020, the final date of enrollment, we classified our patients with COVID-19 into three groups (moderate, severe, and critical) in this study.
Medical records of patients with COVID-19 were reviewed and epidemiological, demographic, clinical, laboratory examination, and outcome data were collected by the research team from Ruijin Hospital. As of February 15, collection of clinical data was completed. Additional information was collected from attending doctors and immediate family members of patients. Two of the authors (Y.F. and M.Z.) from Ruijin Hospital cross-checked the data. The Chinese Center for Disease Control and local Center for Disease Control labs made a definite diagnosis of COVID-19 by examining throat-swab specimens from the upper respiratory tract. Real-time RT-PCR assays were used to confirm COVID-19 (8) and exclude other viral infections. All patients underwent a chest CT scan. BAL fluid, bronchial aspirates, and sputum were sent for bacterial and viral examinations. Two radiologists were invited to interpret all chest CT scans independently and were blinded to the clinical information of each patient. In the case of discordance, the opinion of a third radiologist was sought to reach a final decision. Data regarding prognosis and treatment were updated on March 21, 2020.
The primary outcomes were discharge or death. The data included clinical characteristics and symptoms on admission, comorbidities, laboratory findings, immunological findings, treatments and outcomes, and chest CT scan findings.
Continuous variables were expressed as median with interquartile range (IQR), and categorical variables were reported as frequency and percentage. According to the latest Chinese guidelines, patients were divided into three groups: moderate, severe, and critical. Single-factor ANOVA or the Kruskal-Wallis H test were used as appropriate to assess differences among the three groups. Categorical data were analyzed either by Pearson’s chi-squared test or by Fisher’s exact test. Two-tailed tests were performed two-sided to determine significance at the 5% level. Bonferroni correction was used for pairwise comparisons. All data analyses were performed using IBM SPSS Statistics (version 25.0) and R software (version 3.6.0).
As of February 15, 2020, data from the 476 patients with COVID-19 who had been admitted by then to the three selected hospitals had been collected to be included in this study. As shown in Table 1, the median age of the patients was 53 years (IQR, 40–64 yr). Patients in the critical and severe groups were older than those in the moderate group. The critical group had a higher percentage of patients aged ≥75 years than the moderate group. Male patients accounted for 56.9% of all patients, and 89.3% of patients had “Wuhan-related exposures.” The median number of days from the onset of illness (the first date of presenting COVID-19–related symptoms, such as fever, cough, diarrhea, etc.) to diagnosis was 4 days (IQR, 2–7 d). The median number of days from illness onset to admission was 6 days (IQR, 4–10 d). Patients from the moderate and severe groups had lower CURB-65 (confusion, urea, respiratory rate, and blood pressure at age 65 years or older) scores than those from the critical group, and 48.6% of critical patients had a CURB-65 score of 0. Patients from the moderate group presented with lower MuLBSTA (multilobular infiltrates, lymphocyte, bacterial coinfection, smoking, hypertension, and age) scores than both the severe and critical groups. Among clinical symptoms, including fever, cough, sputum production, dry cough, pharyngalgia, chest pain, shortness of breath, hemoptysis, muscle pain, digestive symptoms, and neurological symptoms, fever was the most common (85.9%), followed by dry cough (59.4%). The percentage of patients with fever or shortness of breath was significantly higher in the severe group than in the moderate group.
| Characteristics | All (N = 476) | Disease Severity | P Value | ||
|---|---|---|---|---|---|
| Moderate (n = 352) | Severe (n = 54) | Critical (n = 70) | |||
| Median age, yr (IQR) | 53 (40–64) | 51 (37–63) | 58 (48–67) | 61 (49–68) | <0.0001 |
| Age group, no./total no. (%) | <0.001 | ||||
| <40 yr | 118/476 (24.8) | 107/352 (30.4)*† | 5/54 (9.3) | 6/70 (8.6) | — |
| 40–64 yr | 240/476 (50.4) | 172/352 (48.9) | 29/54 (53.7) | 39/70 (55.7) | — |
| 65–74 yr | 84/476 (17.6) | 56/352 (15.9) | 15/54 (27.8) | 13/70 (18.6) | — |
| ≥75 yr | 34/476 (7.1) | 17/352 (4.8)* | 5/54 (9.3) | 12/70 (17.1) | — |
| Sex, no./total no. (%) | 0.064 | ||||
| Male | 271/476 (56.9) | 190/352 (54) | 33/54 (61.1) | 48/70 (68.6) | — |
| Female | 205/476 (43.1) | 162/352 (46) | 21/54 (38.9) | 22/70 (31.4) | — |
| Wuhan-related exposure, no./total no. (%)‡ | 425/476 (89.3) | 312/352 (88.6) | 48/54 (88.9) | 65/70 (92.9) | 0.578 |
| Days from illness onset to diagnosis confirmed, median (IQR) | 4 (2–7) | 4 (2–7) | 4 (2–6) | 2 (0–7) | 0.024 |
| Days from illness onset to admission, median (IQR) | 6 (4–10) | 6 (3–10) | 7 (4–10) | 9 (7–13) | 0.0001 |
| CURB-65 on admission, median (IQR) | 0 (0–1) | 0 (0–0)* | 0 (0–1)* | 1 (0–1) | <0.001 |
| 0 | 351/474 (74.1) | 280/350 (80.0) | 37/54 (68.5) | 34/70 (48.6) | <0.001 |
| 1–2 | 118/474 (24.9) | 70/350 (20.0) | 17/ (31.5) | 31/70 (44.3) | — |
| 3–4 | 5/474 (1.0) | 0 | 0 | 5/70 (7.1) | — |
| MuLBSTA on admission, median (IQR) | 7 (5–9) | 7 (5–9)*† | 9 (7–11) | 11 (7–13) | <0.001 |
| Habits | |||||
| Smoking, no./total no. (%) | 44/454 (9.7) | 27/333 (8.1) | 7/53 (13.2) | 10/68 (14.7) | 0.161 |
| Smoking years | 20 (10–30) | 20 (10–30) | 30 (20–40) | 23 (18–30) | 0.119 |
| Alcohol consumption, no./total no. (%) | 37/454 (8.1) | 20/333 (6)* | 6/53 (11.3) | 11/68 (16.2) | 0.014 |
| Symptoms, no./total no. (%) | |||||
| Fever | 390/454 (85.9) | 277/337 (82.2)*† | 49/51 (96.1) | 64/66 (97) | <0.0001 |
| Shivering | 24/374 (6.4) | 17/300 (5.7) | 2/35 (5.7) | 5/39 (12.8) | 0.25 |
| Sputum production | 161/453 (35.5) | 100/336 (29.8)* | 20/50 (40) | 41/67 (61.2) | <0.0001 |
| Dry cough | 269/453 (59.4) | 220/336 (65.5)* | 28/50 (56) | 21/67 (31.3) | <0.0001 |
| Pharyngodynia | 35/433 (8.1) | 26/330 (7.9) | 3/45 (6.7) | 6/58 (10.3) | 0.83 |
| Chest pain | 21/440 (4.8) | 13/335 (3.9) | 5/47 (10.6) | 3/58 (5.2) | 0.13 |
| Shortness of breath | 109/447 (24.4) | 50/335 (14.9)*† | 14/48 (29.2)* | 45/64 (70.3) | <0.0001 |
| Hemoptysis | 5/435 (1.1) | 2/332 (0.6) | 1/45 (2.2) | 2/58 (3.4) | 0.089 |
| Myalgia | 55/438 (12.6) | 38/333 (11.4) | 4/46 (8.7) | 13/59 (22) | 0.054 |
| Digestive symptoms | 49/446 (11) | 39/336 (11.6) | 6/48 (12.5) | 4/62 (6.5) | 0.47 |
| Neurological symptoms | 47/440 (10.7) | 35/334 (10.5) | 6/46 (13) | 6/60 (10) | 0.84 |
Various comorbidities, including hypertension, cardiovascular disease, diabetes, malignancy, cerebrovascular disease, immunosuppression, chronic obstructive pulmonary disease, and chronic nephropathy, were investigated in this study. Among the patients included in the study, 205 (43.1%) had comorbidities (Table 2). The percentage of comorbidities was significantly different among the three groups (P < 0.001). The percentage of comorbidities was higher in the critical group than in the moderate group (67.1% vs. 37.8%; P < 0.05). There were more patients with hypertension in the critical group than in the moderate group (35.7% vs. 20.7%; P < 0.05). In addition, we assessed the use of antihypertensive drugs in patients with COVID-19 and hypertension. The moderate group had a higher percentage of patients receiving either angiotensin II receptor blockers (ARB) or angiotensin-converting enzyme inhibitors (ACEI/ARB) than the severe and critical groups.
| All (N = 476) | Disease Severity | P Value | |||
|---|---|---|---|---|---|
| Moderate (n = 352) | Severe (n = 54) | Critical (n = 70) | |||
| Any comorbidity | 205/476 (43.1) | 133/352 (37.8)* | 25/54 (46.3) | 47/70 (67.1) | <0.001 |
| Hypertension | 113/476 (23.7) | 73/352 (20.7)* | 15/54 (27.8) | 25/70 (35.7) | 0.02 |
| ACEI | 8/113 (7.1) | 7/8 (87.5) | 1/8 (12.5) | 0/8 (0) | 0.279 |
| ARB | 27/113 (23.9) | 23/27 (85.2) | 2/27 (7.4) | 2/27 (7.4) | 0.035 |
| ACEI or ARB | 33/113 (29.2) | 29/33 (87.9)* | 2/33 (6.1) | 2/33 (6.1) | 0.004 |
| Other regimens | 62/113 (54.9) | 35/62 (56.5) | 12/62 (19.4) | 15/62 (24.3) | 0.064 |
| Cardiovascular disease | 38/476 (8) | 21/352 (6)* | 5/54 (9.3) | 12/70 (17.1) | 0.007 |
| Diabetes | 49/476 (10.3) | 32/352 (9.1)* | 11/54 (20.4) | 6/70 (8.6) | 0.035 |
| Malignancy | 12/476 (2.5) | 5/352 (1.4)* | 1/54 (1.9) | 6/70 (8.6) | 0.002 |
| Cerebrovascular disease | 17/476 (3.6) | 8/352 (2.3)* | 1/54 (1.9) | 8/70 (11.4) | 0.001 |
| Immunosuppression | 7/476 (1.5) | 2/352 (0.6)* | 0/54 (0) | 5/70 (7.1) | 0.002 |
| COPD | 22/476 (4.6) | 8/352 (2.3)* | 3/54 (5.6) | 11/70 (15.7) | <0.001 |
| Chronic nephropathy | 4/476 (0.8) | 2/352 (0.6) | 1/54 (1.9) | 1/70 (1.4) | 0.279 |
| Others | 103/476 (21.6) | 63/352 (17.9)* | 17/54 (31.5) | 23/70 (32.9) | 0.004 |
The normal range of laboratory parameters are shown in Table E1 in the online supplement. Further analysis of the laboratory findings (Table 3) indicated that levels of C-reactive protein, alanine aminotransferase, aspartate aminotransferase, total bilirubin, lactate dehydrogenase, myohemoglobin, and D-dimer were much higher in the severe group and the critical group than in the moderate group. The critical group had a significantly higher percentage of patients showing elevated troponin, and higher levels of serum creatine kinase–myocardial band, procalcitonin, and brain natriuretic peptide than the moderate group. Other indexes, including lymphocyte count, serum albumin, and serum calcium, were significantly lower in the severe and critical groups. Patients with moderate disease had a higher estimated glomerular filtration rate than those with critical disease.
| All (N = 476) | Disease Severity | P Value | |||
|---|---|---|---|---|---|
| Moderate (n = 352) | Severe (n = 54) | Critical (n = 70) | |||
| C-reactive protein, mg/L | 18.8 (5.23–57) | 12 (4.17–37.37)*† | 36.7 (15.75–74.58)* | 83.4 (28.8–126.8) | <0.0001 |
| ≥10 mg/L, no./total no. (%) | 266/415 (64.1) | 169/307 (55)*† | 38/45 (84.4) | 59/63 (93.7) | <0.0001 |
| White blood cell count, ×109/L | 5.29 (4.22–7.02) | 5.15 (4.17–6.54)* | 5.42 (3.69–8.17)* | 7.19 (4.61–11.19) | <0.0001 |
| >10 × 109/L, no./total no. (%) | 49/475 (10.3) | 23/351 (6.6)* | 7/54 (13) | 19/70 (27.1) | <0.0001 |
| <4 × 109/L, no./total no. (%) | 91/475 (19.2) | 67/351 (19.1) | 17/54 (31.5)* | 7/70 (10) | — |
| Neutrophil count, ×109/L | 3.56 (2.61–5.42) | 3.39 (2.5–4.64)* | 3.6 (2.59–5.99)* | 5.99 (3.47–9.55) | <0.0001 |
| Lymphocyte count, ×109/L | 1.03 (0.7–1.45) | 1.13 (0.79–1.53)*† | 0.78 (0.52–1.08) | 0.82 (0.49–1.08) | <0.0001 |
| <1.0 × 109/L- no./total no. (%) | 225/476 (47.3) | 136/352 (38.6)*† | 39/54 (72.2) | 50/70 (71.4) | <0.0001 |
| Hemoglobin, g/L | 132 (121–144) | 133 (121–144) | 132 (123–144) | 131 (118–143) | 0.704 |
| Platelet count, ×109/L | 184 (145–238) | 185 (146–238) | 184 (138–216) | 181 (135–246) | 0.666 |
| ALT > 40 μ/L | 26 (16–41) | 23 (15–38)*† | 32 (21–47) | 35 (25–53) | <0.0001 |
| AST > 40 μ/L | 28 (21–39) | 25 (19–34)*† | 34 (26–53) | 39 (30–54) | <0.0001 |
| Total bilirubin, μmol/L | 10.1 (7.5–14) | 9.5 (7.3–13.3)*† | 11.9 (8.9–15.6) | 12.2 (8.6–16.7) | <0.0001 |
| Direct bilirubin, μmol/L | 4 (3.1–5.5) | 3.9 (3–5.5) | 4.5 (3.4–6.7) | 4.1 (3.1–5.5) | 0.216 |
| Albumin, g/L | 37.87 (32.8–41.84) | 39.14 (35.15–42.7)*† | 35.93 (32.05–39.56)* | 32.25 (27.88–34.35) | <0.0001 |
| Urea, mmol/L | 4.8 (3.67–5.89) | 4.6 (3.6–5.59)* | 4.8 (3.96–5.84) | 5.65 (4.3–7.73) | <0.0001 |
| Creatinine, μmol/L | 66.77 (53.66–78.6) | 65.46 (52.96–76.66) | 70.9 (54.67–84.1) | 67.95 (55.23–81.28) | 0.237 |
| eGFR, ml/min/1.73 m2‡ | 106 (87–125) | 108 (92–128)* | 102 (89–118) | 96 (76–120) | 0.001 |
| Sodium, mmol/L | 139 (137–141) | 139 (137–141) | 140 (137–141) | 140 (137–142) | 0.574 |
| Potassium, mmol/L | 3.9 (3.6–4.2) | 3.9 (3.6–4.1)* | 4 (3.5–4.2) | 4 (3.7–4.6) | 0.046 |
| Calcium, mmol/L | 2.04 (1.96–2.15) | 2.05 (1.98–2.16)*† | 2.03 (1.89–2.07) | 1.95 (1.87–2.06) | <0.0001 |
| LDH, μ/L | 259 (202–356) | 236 (192–314)*† | 307 (228–401)* | 378 (275–523) | <0.0001 |
| Creatine kinase, μ/L | 82 (55–148) | 80 (55–138) | 98 (57–154) | 93 (52–246) | 0.468 |
| CK-MB, μ/L | 13 (10.49–16.74) | 12.75 (10.07–15.95)* | 14.11 (11.31–19.25) | 15.5 (11.75–23) | 0.001 |
| Myohemoglobin, ng/ml | 18.85 (4.8–51.48) | 11.7 (3.65–40.2)*† | 28.04 (10.07–51.5)* | 52.05 (29.8–107.63) | <0.0001 |
| Troponin increased, no./total no. (%) | 86/384 (22.4) | 59/296 (19.9)* | 10/41 (24.4) | 17/47 (36.2) | 0.044 |
| PCT, μg/L | 0.05 (0.02–0.08) | 0.04 (0.02–0.06)* | 0.06 (0.02–0.13) | 0.07 (0–0.18) | 0.006 |
| ESR, mm/h | 48 (30–80) | 48 (27–83) | 45 (33–79) | 58 (39–72) | 0.7 |
| BNP, pg/ml | 40.85 (21.64–79.37) | 34.53 (21.15–67.1)* | 52.5 (16.93–113.3) | 49.9 (34.45–120.4) | 0.049 |
| Lactic acid, mmol/L | 2.75 (2.23–3.27) | 2.73 (2.22–3.22) | 3.09 (2.37–3.62) | 2.5 (2.15–3.34) | 0.308 |
| Fibrinogen, g/L | 4.4 (3.65–5.41) | 4.31 (3.55–5.33)† | 4.78 (4.33–5.74) | 4.71 (3.89–5.74) | 0.021 |
| D-dimer, μg/L | 0.58 (0.35–1.48) | 0.51 (0.32–1.08)*† | 0.89 (0.44–2.33) | 1.11 (0.51–4) | <0.0001 |
Total T-lymphocyte counts and T-cell subset values differed among the three groups. CD3 counts were significantly lower in the severe and critical groups than in the moderate group. CD4 counts (174; IQR, 122–285) were lower in the critical group than in the moderate group (449; IQR, 312–659). CD8 counts were lower in the critical (125; IQR, 59–213) and severe groups (179; IQR, 106–286) groups than in the moderate group (266; IQR, 165–414). The percentages of CD3 and CD4 cells followed the same trend. There was no difference in IgG and IgA levels among the three groups, but there was a trend toward decreased levels of IgM in the severe and critical groups (Table 4).
| All (n = 253/476)* | Disease Severity | P Value | |||
|---|---|---|---|---|---|
| Moderate (n = 214/352) | Severe (n = 26/54) | Critical (n = 13/70) | |||
| CD3+ cell counts, cells/μl | 712 (482–1,036) | 764 (513–1,069)†‡ | 538 (277–860) | 323 (186–512) | <0.0001 |
| CD4+ cell counts, cells/μl | 418 (273–636) | 449 (312–659)† | 327 (160–587) | 174 (122–285) | <0.0001 |
| CD8+ cell counts, cells/μl | 247 (155–388) | 266 (165–414)†‡ | 179 (106–286) | 125 (59–213) | <0.0001 |
| CD3+ cell percentage | 68 (60–75) | 69 (62–76)† | 65 (55–74) | 56 (40–64) | 0.001 |
| CD4+ cell percentage | 40 (33–47) | 41 (35–47)† | 33 (28–46) | 29 (23–39) | <0.0001 |
| CD8+ cell percentage | 24 (19–30) | 25 (19–30) | 19 (17–34) | 22 (13–29) | 0.258 |
| IgG, g/L | 11.8 (10.2–13.6) | 11.8 (10.3–13.6) | 12.4 (9.3–14.15) | 10.9 (9.97–13.1) | 0.726 |
| IgA, g/L | 2.38 (1.81–3.14) | 2.46 (1.82–3.1) | 2.36 (1.56–3.47) | 2.24 (1.91–2.99) | 0.954 |
| IgM, g/L | 0.93 (0.69–1.2) | 0.94 (0.7–1.21) | 0.86 (0.63–1.18) | 0.68 (0.55–0.99) | 0.051 |
A total of 286 patients (60.1%) received antiviral therapy within the first 4 days. The antivirals used included lopinavir and tonavir, arbidol, darunavir, corbicostat, and chloroquine. Most patients (67.0%) received antibacterial therapy, including moxifloxacin, ceftriaxone, and azithromycin. Eight patients (1.7%) received antifungal therapy. More patients received antiviral agents within the first 4 days in the moderate group than in the severe group, and more patients received antibiotics and corticosteroids in the critical and severe groups (Table 5). It was found that in the moderate and severe groups, patients who received antibiotics or corticosteroids had longer hospital stays than those who did not (Table E2). In the critical group, giving early antiviral treatment within the first 4 days and not giving corticosteroids throughout the hospitalization period were associated with good prognosis; however, neither of these therapies had any association with disease progression to death or mechanical ventilation (Table E3). All patients with moderate disease were given only oxygen via nasal cannula or no oxygenation support at all. In the severe group, 24 patients (44.4%) received high-flow oxygen treatment. In the critical group, 4 patients (5.7%) received extracorporeal membrane oxygenation rescue therapy and 39 (55.7%) were given invasive mechanical ventilation. As of March 21, 2020, 403 patients (84.7%) had been discharged, 38 (8%) had died, 23 (4.8%) were still in the hospital, and 12 (2.5%) were lost to follow-up owing to transfer to other facilities or loss of contact. Critical patients had a higher percentage of bacterial coinfections and a higher mortality rate than patients with severe or moderate disease, and they also had longer hospital stays than patients from the moderate group. The patients were divided into four age groups for Kaplan-Meier survival curve analysis: <45 years, 45–64 years, 65–74 years, and ≥75 years. The group of patients ≥75 years of age had a significantly lower survival rate than the other three groups (Figure E1). In our multivariate cox regression model (Table E4), age ≥75 years (hazard ratio, 6.07 [95% confidence interval (CI), 1.65–22.35]; P = 0.007), creatine kinase (hazard ratio, 1.01 [95% CI, 1.01–1.02]; P = 0.032), and lactate dehydrogenase (hazard ratio, 1.002 [95% CI, 1–1.004]; P = 0.044) were associated with a higher risk of in-hospital mortality.
| All (N = 476) | Disease Severity | P Value | |||
|---|---|---|---|---|---|
| Moderate (n = 352) | Severe (n = 54) | Critical (n = 70) | |||
| Administration of antiviral, no./total no. (%)* | 286/476 (60.1) | 199/352 (56.5)† | 40/54 (74.1) | 47/70 (67.1) | 0.021 |
| Administration of antibiotics, no./total no. (%) | 319/476 (67) | 209/352 (59.4)†‡ | 45/54 (83.3) | 65/70 (92.9) | <0.001 |
| Administration of antifungal agent, no./total no. (%) | 8/476 (1.7) | 2/352 (0.6)‡ | 0/54 (0) | 6/70 (8.6) | <0.001 |
| Administration of corticosteroids, no./total no. (%) | 127/476 (26.7) | 47/352 (13.4)†‡ | 28/54 (51.9)‡ | 52/70 (74.3) | <0.001 |
| Oxygen therapy, no./total no. (%) | <0.001 | ||||
| Nasal cannula or no oxygen therapy | 368/476 (77.3) | 352/352 (100) | 15/54 (27.8) | 1/70 (1.4) | — |
| High-flow nasal cannula | 31/476 (6.5) | 0/352 (0) | 24/54 (44.4) | 7/70 (10) | — |
| Noninvasive mechanical ventilation (i.e., face mask) | 34/476 (7.1) | 0/352 (0) | 15/54 (27.8) | 19/70 (27.1) | — |
| Invasive mechanical ventilation | 39/476 (8.2) | 0/352 (0) | 0/54 (0) | 39/70 (55.7) | — |
| ECMO | 4/476 (0.8) | 0/352 (0) | 0/54 (0) | 4/70 (5.7) | — |
| Prognosis, no./total no. (%) | <0.001 | ||||
| Discharge from hospital | 403/476 (84.7) | 334/352(94.9)†‡ | 46/54 (85.2)‡ | 23/70 (32.9) | — |
| Death | 38/476 (8) | 6/352 (1.7)‡ | 3/54 (5.6)‡ | 29/70 (41.4) | — |
| Remained in hospital | 23/476 (4.8) | 6/352 (1.7)† | 4/54 (7.4) | 13/70 (18.6) | — |
| Lost to follow-up | 12/476 (2.5) | 6/352 (1.7)‡ | 1/54 (1.9) | 5/70 (7.1) | — |
| Secondary bacterial infection, no./total no. (%)§ | 35/410 (8.5) | 12/307 (3.9)‡ | 4/48 (8.3)‡ | 19/55 (34.5) | <0.001 |
| Length of hospital stay, d, median (IQR) | 16 (12–24) | 15 (12–22)†‡ | 20 (15–27) | 21 (12–48) | <0.001 |
On admission, chest CT scans were performed to estimate the patients’ condition and degree of lung involvement (Table 6). In the severe and critical groups, most patients showed involvement of multiple lung lobes (5 lung lobes; IQR, 5–5). More patients had pleural effusions in the critical group than in the moderate group (18% vs. 3.1%; P < 0.05). To monitor the changes observed in CT images during the whole process, we examined the dynamic changes in CT images of a patient in the severe group from the Shanghai Public Health Clinical Center from onset to improvement of the disease. As shown in Figure 1, the patient had ground-glass opacities on chest CT in the early stage of the disease. Consolidation was noted on chest CT during disease progression. Finally, the patient had linear opacities on Day 29 from onset of illness.
| All (N = 476) | Disease Severity | P Value | |||
|---|---|---|---|---|---|
| Moderate (n = 352) | Severe (n = 54) | Critical (n = 70) | |||
| Bilateral lungs involved | 373/442 (84.4) | 266/327 (81.3)* | 53/54 (98.1) | 54/61 (88.5) | 0.04 |
| Lung lobes involved, median (IQR) | 5 (3–5) | 5 (3–5) | 5 (5–5) | 5 (5–5) | <0.001 |
| Consolidation | 87/442 (19.7) | 68/327 (20.8) | 13/54 (24.1) | 6/61 (9.8) | 0.098 |
| Ground-glass opacity | 425/442 (96.2) | 311/327 (95.1) | 53/54 (98.1) | 61/61 (100) | 0.137 |
| Linear opacity | 129/442 (29.2) | 88/327 (26.9) | 19/54 (35.2) | 22/61 (36.1) | 0.206 |
| Pleural effusion | 25/442 (5.7) | 10/327 (3.1)† | 4/54 (7.4) | 11/61 (18) | <0.001 |
| Pleural thickening | 238/442 (53.8) | 176/327 (53.8) | 32/54 (59.3) | 30/61 (49.2) | 0.567 |
Figure 1. Cross-sectional, unenhanced chest computed tomography images of a 30-year-old male patient with severe coronavirus disease (COVID-19) in different stages. (A–C) In the early stage, bilateral, peripheral, patchy ground-glass opacities (GGO) and consolidations were noticed on admission (A), and denser GGO (B) and predominant consolidation with an inside air bronchogram sign (C) occurred within 2 weeks after illness onset. (D and E) The lesions were gradually absorbed later from Day 19 (D) to Day 25 (E). (F) Linear opacities still remained within GGO that previously manifested as consolidation at the end of our observation.
[More] [Minimize]In our study, 300 patients were admitted in hospitals outside of Hubei, and 176 patients were from a hospital in Hubei (Table E5). The percentages of critical patients in hospitals outside of and inside Hubei were 5% and 31.3%, respectively. Compared with patients in the Wuhan hospital, patients in hospitals outside of Hubei were younger and less likely to present with shortness of breath on admission and had shorter lengths of time from onset of illness to the time when the diagnosis was confirmed or they were admitted (Figure E2). Patients outside of Hubei also had fewer comorbidities. In terms of treatment, antibiotics and corticosteroids were prescribed less frequently to patients in hospitals outside of Hubei (53% vs. 90.9%; P < 0.001 and 19% vs. 39.8%; P < 0.001). Patients in hospitals outside of Hubei had lower mortality rates in each severity group than those in the Wuhan hospital (Table E6). In hospitals outside of Hubei, patients with moderate disease had shorter hospital stays than those with severe or critical disease.
As shown in Table E7, there was no difference in sex distribution among the four age groups. The ≥75 years group had a higher percentage of patients with critical disease, and a higher percentage of comorbidities and death. The percentage of patients with chronic obstructive pulmonary disease increased with age. There was a significant difference in smoking history among the four age groups (P = 0.014). The distribution of alcohol consumption among the four groups had no statistical difference. The levels of lymphocytes and IgM, as well as the percentage of patients presenting with a lymphocyte count of <1 × 109/L showed significant differences among the four age groups. In the <45 years group, patients had higher lymphocyte counts and IgM levels, and fewer patients had decreased lymphocyte counts. The ratio of bilateral lung involvement, the number of involved lung lobes, and the presence of consolidation, linear opacity, and pleural effusion on CT scans among the four age groups differed significantly.
This study summarizes the clinical characteristics, laboratory tests, dynamic changes in CT images, treatments, and prognoses of patients with COVID-19 in two eastern China cities and in the city of disease onset, Wuhan. Patients with COVID-19 were divided into three groups (moderate, severe, and critical) according to the criteria set in the fifth edition of the Guidelines on the Diagnosis and Treatment of COVID-19 issued by the National Health Commission of China.
Patients in the severe and critical groups had more comorbidities, especially diabetes and hypertension. ACEIs and ARBs were commonly used antihypertensive drugs. ACE2 (angiotensin-converting enzyme 2) is a component of the renin-angiotensin system that is expressed in the heart and plays an important role in cardiac function. ACE2 is the host receptor of SARS-CoV-2 (9, 10). It was reported that ACE2 is also the receptor of SARS and NL63 (11–13). COVID-19 has a higher affinity than SARS-CoV (14) for ACE2. Recently, using a single-cell RNA-sequencing technique, Zhao and colleagues showed that ACE2 virus receptor expression was concentrated in a small population of type II alveolar cells (15). It was reported that ACE inhibitor therapy could increase cardiac ACE2 mRNA expression, and losartan increased cardiac ACE2 activity (16). Compared with other antihypertensive drugs, whether ACEI/ARB would aggravate COVID-19 is not clear. In this study, the use of antihypertensives in patients with COVID-19 was evaluated for the first time. The proportion of patients taking antihypertensives was higher in the moderate group. There were more patients taking ACEI/ARB in the moderate group. More case studies are needed in the future to further extend our preliminary conclusion. The mechanism and relationship between antihypertensives and the severity of COVID-19 remain to be studied.
In this study, we demonstrated that systemic organ indexes, including levels of T lymphocytes, D-dimer, C-reactive protein, aspartate aminotransferase, myohemoglobin, CD3+, CD4+, and CD8+, were associated with COVID-19 severity. These laboratory findings demonstrated that patients with COVID-19 also had impaired cardiac, liver, hematological, and cellular immune system function, as previously reported (7). Previous studies showed that CD8+ T cells protect against and depletion of macrophages exacerbates Middle East respiratory syndrome coronavirus–induced pathology and clinical symptoms of disease (17). SARS-CoV–specific memory CD8+ T cells protect susceptible hosts from lethal SARS-CoV infection (18). Dramatic losses of CD4+ T (∼90–100% of patients) and CD8+ T cells (∼80–90% of patients) were found in patients with SARS infection compared with healthy control individuals (19–21). We also found that CD3+, CD4+, and CD8+ T cells were significantly reduced in patients with severe or critical COVID-19, but immunoglobulins were less affected, as previously reported (22).
CT scans showed dynamic changes from ground-glass opacification to consolidation, and then absorption of the lesions or change to a linear opacity. In this study, for the first time, CT images of COVID-19 were observed and recorded in real time. We found that more lung lobes were involved in the severe and critical groups than in the moderate group, which was consistent with other research results (7). We also demonstrated for the first time that the percentage of patients with pleural effusion was significantly higher in the severe and critical groups than in the moderate group. Previous studies also showed that pleural effusion was a poor prognostic indicator in H5N1 infection (23).
Previous reports showed that none of the scoring systems used to assess severity of illness, such as the pneumonia severity index or CURB-65, have a good predictive ability in influenza pneumonia (24, 25). Our results showed that CURB-65 scores were associated with the severity of COVID-19, but the difference in scores among the three groups was small. The variance in MuLBSTA scores (4, 26), an early warning model for predicting mortality in viral pneumonia, among the three groups is significant and therefore may have a better predictive value.
A comparison of patients inside and outside of Hubei showed that early isolation, early diagnosis, and early management might contribute to a decrease in the spread and progression of COVID-19. Our study also stratified patients with COVID-19 based on age. Patients >75 years old had more severe disease and a higher risk of death. Age >75 years was also an important index contributing to the mortality risk. These results were consistent with previous studies (4, 27, 28).
Several limitations need to be addressed in further research. First, given the limited number of cases, some of our conclusions are preliminary, especially regarding the influence of the antihypertensive drugs ACEI/ARB on COVID-19. These results need to be further validated with more patients. Second, although data regarding outcomes of prognosis and treatment have been updated, the effects of antiviral agents and corticosteroids require further validation. Prospective studies should be performed to obtain more accurate results. Third, we only analyzed dynamic changes in CT images of a patient with marked improvement. More cases need to be analyzed to obtain more information.
In conclusion, this multicenter, retrospective study demonstrated that patients with severe or critical disease were older and had more comorbidities. Multiple organ dysfunction and immune dysfunction were characteristic of patients with severe or critical disease. The proportion of patients who were taking antihypertensives was higher in the group with moderate disease, and more patients received ACEI/ARB in the moderate group. Patients with severe or critical disease had more lung lobes involved and pleural effusion. These clinical features are helpful for the diagnosis and treatment of COVID-19.
| 1. | Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020;395:565–574. |
| 2. | Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020;579:270–273. |
| 3. | World Health Organization. Coronavirus disease (COVID-19). Situation report - 112 [accessed 2020 May 12]. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200511-covid-19-sitrep-112.pdf?sfvrsn=813f2669_2. |
| 4. | Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507–513. |
| 5. | 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 2020;323:1061–1069. |
| 6. | Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA [online ahead of print] 24 Feb 2020; DOI: https://doi.org/10.1001/jama.2020.2648. |
| 7. | Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al.; China Medical Treatment Expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med [online ahead of print] 28 Feb 2020; DOI: https://doi.org/10.1056/nejmoa2002032. |
| 8. | 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;395:497–506. |
| 9. | Wan Y, Shang J, Graham R, Baric RS, Li F. 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. | Xu X, Chen P, Wang J, Feng J, Zhou H, Li X, et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci China Life Sci 2020;63:457–460. |
| 11. | Li W, Sui J, Huang IC, Kuhn JH, Radoshitzky SR, Marasco WA, et al. The S proteins of human coronavirus NL63 and severe acute respiratory syndrome coronavirus bind overlapping regions of ACE2. Virology 2007;367:367–374. |
| 12. | Wu K, Li W, Peng G, Li F. Crystal structure of NL63 respiratory coronavirus receptor-binding domain complexed with its human receptor. Proc Natl Acad Sci USA 2009;106:19970–19974. |
| 13. | He L, Ding Y, Zhang Q, Che X, He Y, Shen H, et al. Expression of elevated levels of pro-inflammatory cytokines in SARS-CoV-infected ACE2+ cells in SARS patients: relation to the acute lung injury and pathogenesis of SARS. J Pathol 2006;210:288–297. |
| 14. | Wrapp D, Wang N, Corbett KS, Goldsmith JA, Hsieh CL, Abiona O, et al. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 2020;367:1260–1263. |
| 15. | Zhao Y, Zhao ZX, Wang YJ, Zhou YQ, Ma Y, Zuo W. Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov [preprint]. bioRxiv; 2020 [accessed 2020 Feb 21]. Available from: https://www.biorxiv.org/content/10.1101/2020.01.26.919985v1. |
| 16. | Ferrario CM, Jessup J, Chappell MC, Averill DB, Brosnihan KB, Tallant EA, et al. Effect of angiotensin-converting enzyme inhibition and angiotensin II receptor blockers on cardiac angiotensin-converting enzyme 2. Circulation 2005;111:2605–2610. |
| 17. | Veit S, Jany S, Fux R, Sutter G, Volz A. CD8+ T cells responding to the Middle East respiratory syndrome coronavirus nucleocapsid protein delivered by vaccinia virus MVA in mice. Viruses 2018;10:718. |
| 18. | Channappanavar R, Fett C, Zhao J, Meyerholz DK, Perlman S. Virus-specific memory CD8 T cells provide substantial protection from lethal severe acute respiratory syndrome coronavirus infection. J Virol 2014;88:11034–11044. |
| 19. | Wong RS, Wu A, To KF, Lee N, Lam CW, Wong CK, et al. Haematological manifestations in patients with severe acute respiratory syndrome: retrospective analysis. BMJ 2003;326:1358–1362. |
| 20. | Cui W, Fan Y, Wu W, Zhang F, Wang JY, Ni AP. Expression of lymphocytes and lymphocyte subsets in patients with severe acute respiratory syndrome. Clin Infect Dis 2003;37:857–859. |
| 21. | Li T, Qiu Z, Zhang L, Han Y, He W, Liu Z, et al. Significant changes of peripheral T lymphocyte subsets in patients with severe acute respiratory syndrome. J Infect Dis 2004;189:648–651. |
| 22. | Wan SX, Yi QJ, Fan SB, Lv JL, Zhang XX, Guo L, et al. Characteristics of lymphocyte subsets and cytokines in peripheral blood of 123 hospitalized patients with 2019 novel coronavirus pneumonia (NCP) [preprint]. medRxiv; 2020 [accessed 2020 Feb 21]. Available from: https://www.medrxiv.org/content/10.1101/2020.02.10.20021832v1. |
| 23. | Qureshi NR, Hien TT, Farrar J, Gleeson FV. The radiologic manifestations of H5N1 avian influenza. J Thorac Imaging 2006;21:259–264. |
| 24. | Pereira JM, Moreno RP, Matos R, Rhodes A, Martin-Loeches I, Cecconi M, et al.; ESICM H1N1 Registry Steering Committee; ESICM H1N1 Registry Contributors. Severity assessment tools in ICU patients with 2009 influenza A (H1N1) pneumonia. Clin Microbiol Infect 2012;18:1040–1048. |
| 25. | Shi SJ, Li H, Liu M, Liu YM, Zhou F, Liu B, et al. Mortality prediction to hospitalized patients with influenza pneumonia: PO2/FiO2 combined lymphocyte count is the answer. Clin Respir J 2017;11:352–360. |
| 26. | Guo L, Wei D, Zhang X, Wu Y, Li Q, Zhou M, et al. Clinical features predicting mortality risk in patients with viral pneumonia: the MuLBSTA score. Front Microbiol 2019;10:2752. |
| 27. | Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med [online ahead of print] 13 Mar 2020; DOI: https://doi.org/10.1001/jamainternmed.2020.0994. |
| 28. | Niederman MS, Richeldi L, Chotirmall SH, Bai C. Rising to the challenge of the novel SARS-coronavirus-2 (SARS-CoV-2): advice for pulmonary and critical care and an agenda for research. Am J Respir Crit Care Med [online ahead of print] 23 Mar 2020; DOI: https://doi.org/10.1164/rccm.202003-0741ED. |
* These authors contributed equally to this work.
‡ These authors contributed equally to this work.
Supported by the National Natural Science Foundation of China (81630001), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Shenkang Hospital Development Center Clinical Science and Technology Innovation Project (SHDC12018102), and National Innovative Research Team of High-Level Local Universities in Shanghai.
Author Contributions: Y.F., Y. Ling, T.B., J.H., W.X., D.Y., R.C., F.L., Y. Lu, X. Liu, Y.C., X. Li, Y. Li, H. Lin, and J.Y. collected the epidemiological and clinical data. Y.X. and J.L. processed the statistical data. Y.F. and Y.X. drafted the manuscript. J.Q., H.D.S., and H. Lu revised the final manuscript. H. Lu and J.Q. were responsible for summarizing all data related to the virus. M.Z. was responsible for summarizing all epidemiological and clinical data.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.
Originally Published in Press as DOI: 10.1164/rccm.202002-0445OC on April 10, 2020
Author disclosures are available with the text of this article at www.atsjournals.org.
