Characteristics of the severe acute respiratory syndrome coronavirus 2 omicron BA.2 subvariant in Jilin, China from March to May 2022

Introduction

The novel coronavirus usually infects humans through the respiratory tract and causes damage to the respiratory system and various organs of the human body.^[1] Since the first outbreak at the end of 2019, the novel coronavirus is still raging worldwide, causing great economic and health burden. In the midst of the coronavirus disease 2019 (COVID-19) pandemic, new virus mutants continue to emerge, such as alpha, beta, gamma, delta, and omicron, some of which have stronger virulence or stronger immune escape ability.^[2] Presently, the most relevant variant worldwide is the omicron mutant.^{[2, 3, 4]}

In November 2021, Botswana reported the first case of omicron sequencing. A few days later, Hong Kong, China reported another case of omicron sequencing in a traveler from South Africa.^[5] In South Africa, COVID-19 cases increased by an average of 280 cases daily in the week before omicron was detected and further increased to 800 cases a day in the following week.^[6] Omicron has some deletions and more than 30 mutations.^[5] These deletions and mutations lead to increased transmissibility, stronger viral binding affinity, and higher antibody escape rates.^{[7, 8]} For governments and the public, the main concerns regarding omicron include whether it is more contagious or has more severe clinical manifestations and side effects and whether it can bypass vaccine protection.

Jilin is a province located in the northeast of China with a population of 27 million. In 2020, we performed a local epidemiological analysis involving all patients with COVID-19 in Jilin Province.^[9] In 2022, Jilin Province was again affected by the new coronavirus variant omicron. As of May 5, 2022, there have been more than 40,000 confirmed cases, with more than 39,000 cured cases and five cases of death in Jilin Province. In this study, we comprehensively analyzed 311 patients who recovered from the disease living in Jilin Province, China. We collected data on the patients’ demographic characteristics, COVID-19 illness, symptoms, lung computed tomography (CT), and peripheral blood laboratory results for statistics to identify the factors affecting the severity of COVID-19 and to provide insights into its spread and early indications. Hopefully, our experience will provide support to more areas worldwide in dealing with the new variant.

Methods

We conducted a retrospective study on the clinical characteristics of adult patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on symptoms and the positive nucleic acid amplification test (NAAT) result of nasal and/ or pharyngeal swab specimens. Data on 311 patients were randomly extracted from Changchun Infectious Disease Hospital and Hepatobiliary Disease Hospital of Jilin Province from March 10, 2022 to May 6, 2022. According to the interim guidance of the World Health Organization and the ninth edition of the Diagnosis and Treatment Protocol for COVID-19 Patients in China, the patients were divided into the mild (M) (n = 259) and moderate and severe (MS) (n = 52) groups. The inclusion criteria for the M group were as follows: (1) epidemiology history; (2) acute onset of fever, cough, fatigue, sore throat, headache, ageusia, diarrhea, and other symptoms; (3) age ≥ 18 years; (4) positive NAAT result for SARS-CoV-2; and (5) two successive negative NAAT results as a necessary condition for discharge. For the MS group, patients should meet at least one additional condition, as follows: (1) a typical CT image of viral pneumonia; (2) PaO2/FiO2 ≤ 300 mmHg; (3) oxygen saturation in resting state ≤ 93%; or (4) shortness of breath and/or respiratory rate ≥ 30 times per minute. This study was approved by the Ethics Committee of the First Hospital of Jilin University (Approval No. 22y050-001). This is a retrospective study and the informed consent was obtained.

Using a medical information system, the data on the clinical characteristics of the patients, including laboratory tests and demographic characteristics, were obtained from the database. For the laboratory assessments, data on leukocyte count, hemoprotein, platelet count (PLT), neutrophil count (NE), lymphocyte count, C-reactive protein (CRP), total bilirubin (TBIL), blood urea nitrogen (BUN), serum creatinine (SCR) were collected, and the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were calculated for analysis. Time to subsequent negative NAAT was defined as the duration between the dates of the first positive NAAT result and the second of the two successive negative NAAT results. The incubation period was defined as the time between exposure to the virus and the onset of clinical symptoms. Then, we analyzed the correlation among the clinical characteristics, COVID-19 illness, time to subsequent negative NAAT, and incubation period.

In this study, the means ± standard deviations (SD) and/ or interquartile ranges were used to summarize continuous variables. Numbers and/or percentages were used to describe categorical and ordinal variables. The Wilcoxon rank sum test and t-test were performed for continuous variables. The chi-square test and Fisher’s exact test were applied to categorical variables. To calculate the optimal cutoff values for the laboratory tests, such as NLR and CRP, receiver operating characteristic (ROC) analysis was conducted. Logistic regression analysis was performed to identify the hazard ratios and 95% confidence intervals, which were used to evaluate the relative risk between COVID-19 illness and clinical characteristics. The Kaplan–Meier curve and Cox regression analyses were performed to determine the influence of clinical characteristics on the time to subsequent negative NAAT and incubation period. Statistical Package for the Social Sciences 19.0 (IBM Corp., Armonk, USA) was used to perform all statistical analyses, and P-values of less than 0.05 were used to indicate statistical significance.

Results

Multivariate analysis of the factors affecting COVID-19 illness

We performed the ROC analysis on PLT, NE, CRP, BUN, and NLR to calculate the optimal cutoff values. The ROC curves are shown in Figure 1. The optimal cutoff values for PLT, NE, CRP, BUN, and NLR were 294, 3.650, 4.785, 4.830, and 2.995, respectively. Furthermore, the areas under the ROC curve are described in Table 2. As the potential diagnostic biomarkers for MS COVID-19, PLT, CRP, and eligible demographic characteristics, including age, gender, vaccination, symptoms before admission (i.e., expectoration and dyspnea), and comorbidities (i.e., hypertension, COPD/chronic bronchitis/asthma, and stroke), were investigated through the logistic regression analysis to determine the predictive effect on COVID-19 illness. Table 3 shows that age (≥ 60 years), hypertension, COPD/ chronic bronchitis/asthma, and CRP (≥ 4.785 mg/L) were negatively correlated with mild COVID-19 (P < 0.05), and the odds ratios were 2.707, 3.285, 9.508, and 5.792, respectively. According to the results, the patients who had the aforementioned factors would be more likely to have moderate or severe COVID-19.

Figure 1

ROC analysis of PLT, NE, CRP, BUN and NLR for moderate and severe COVID-19 illness. The optimal cut-off values: PLT (294), NE (3.65), CRP (4.785), BUN (4.83), NLR (2.995). The area under curve: PLT (0.635), NE (0.580), CRP (0.737), BUN (0.574), NLR (0.580). PLT: platelet count; NE: neutrophil count; CRP: c-reactive protein; BUN: blood urea nitrogen; NLR: neutrophil-to-lymphocyte ratio; ROC: receiver operating characteristic.

Table 2

Area under curve and optimal cut-off value of PLT, NE, CRP, BUN, SCR and NLR

Variable	Area under curve	S.E.	95% CI	Optimal cut-off value	P-value
PLT	0.635	0.047	0.272–0.457	294	0.002
NE	0.580	0.045	0.491–0.669	3.65	0.069
CRP	0.737	0.034	0.671–0.804	4.785	< 0.001
BUN	0.574	0.047	0.482–0.665	4.83	0.093
NLR	0.580	0.045	0.491–0.669	2.995	0.069

1. PLT: platelet count; NE: neutrophil count; CRP: c-reactive protein; BUN: blood urea nitrogen; NLR: neutrophil-to-lymphocyte ratio; S.E.: standard error; CI: confidence interval; SCR: serum creatinine.

Table 3

Laboratory tests and demographic characteristics to predict COVID-19 illness

Variable	Multivariate analysis model 1				Multivariate analysis model 2
	OR	S.E.	95% CI	P-value	OR	S.E.	95% CI	P-value
Age (≥ 60 years)	2.249	0.406	1.016–4.980	0.046	2.707	0.371	1.309–5.599	0.007
Gender (Male)	1.425	0.431	0.302–1.633	0.411	-	-	-	-
No vaccination	1.942	0.424	0.225–1.182	0.118	-	-	-	-
Expectoration	2.511	0.521	0.904–6.970	0.077	-	-	-	-
Dyspnea	1.379	0.531	0.488–3.903	0.544	-	-	-	-
Hypertension	3.473	0.410	1.556–7.750	0.002	3.285	0.384	1.547–6.973	0.002
Stroke	1.294	0.743	0.302–5.552	0.728	-	-	-	-
COPD/chronic bronchitis/asthma	11.546	0.969	1.728–77.161	0.012	9.508	0.877	1.705–53.031	0.010
PLT (≥ 294×109/L)	1.034	0.575	0.335–3.190	0.954	-	-	-	-
CRP (≥ 4.785 mg/L)	4.189	0.405	1.893–9.270	< 0.001	5.792	0.377	2.768–12.120	< 0.001

1. COVID-19: coronavirus disease 2019; COPD: chronic obstructive pulmonary disease; PLT: platelet count; CRP: c-reactive protein; OR: odds ratio; S.E.: standard error; CI: confidence interval.

Kaplan–Meier curves and Cox regression analysis of the factors affecting the incubation period

To investigate the factors that influence the incubation period, the Kaplan-Meier curve analysis was performed involving potential factors. The eligible demographic characteristics, including age, gender, vaccination, and comorbidities (i.e., hypertension, COPD/chronic bronchitis/asthma, and stroke) were included to identify their effects on the incubation period. The incubation period of the M group was 3.07 ± 2.59 days, ranging from 0 to 13 days, and that of the MS group was 2.94 ± 2.81 days, ranging from 0 to 11 days. No significant difference was observed between the two groups (P = 0.78). Table 4 and Figure 2 show that older patients (≥ 60 years) were more likely to undergo a longer incubation period than younger one (< 60 years) (P < 0.05).

Figure 2

Kaplan-Meier curve analysis of age for incubation period. The elder patients (≥ 60 years) were more likely to have a longer incubation period than the younger (< 60 years). P < 0.05.

Table 4

Demographic characteristics to predict incubation period

Variable	Mean	S.E.	95% CI	P-value
Age
< 60 years	2.891	0.161	2.575–3.207	0.033
≥ 60 years	3.451	0.325	2.814–4.087
Gender
Male	2.708	0.223	2.270–3.146	0.062
Female	3.272	0.197	2.887–3.657
Vaccination
Yes	3.105	0.164	2.784–3.427	0.681
No	2.859	0.353	2.168–3.551
Hypertension
Yes	2.972	0.331	2.323–3.620	0.937
No	3.079	0.167	2.752–3.406
COPD/chronic bronchitis/asthma
Yes	3.125	1.125	0.920–5.330	0.810
No	3.053	0.150	2.758–3.347
Stroke
Yes	3.727	1.010	1.748–5.707	0.387
No	3.030	0.150	2.736–3.324

1. COPD: chronic obstructive pulmonary disease; S.E.: standard error; CI: confidence interval.

Kaplan–Meier curves and Cox regression analysis of the factors affecting the time to subsequent negative NAAT

To identify the factors affecting the time to subsequent negative NAAT, the Kaplan–Meier curve analysis was performed involving potential factors. The eligible demographic characteristics, including age, gender, vaccination, symptoms before admission (i.e., expectoration and dyspnea), comorbidities (i.e., hypertension, COPD/ chronic bronchitis/asthma, and stroke), therapeutic strategy (i.e., traditional Chinese medicine and Lianhua Qingwen capsules), and PLT, NE, CRP, BUN, and NLR were analyzed to clarify their effects on the time to subsequent negative NAAT. Similarly, no significant differences were observed between the two groups (P = 0.055). The time to subsequent negative NAAT was 14.91 ± 6.98 days, ranging from 2 to 36 days in the M group, and that in the MS group was 16.96 ± 7.09 days, ranging from 5 to 32 days. Table 5 and Figure 3 show that male, CRP (≥ 4.785 mg/L), and NLR (≥ 2.995) were correlated with a longer time to subsequent negative NAAT (P < 0.05). Then, a multivariate analysis of male, CRP, and NLR was performed. Table 6 reveals that only CRP (≥ 4.785 mg/L) was an independent risk factor for a longer time to subsequent negative NAAT (P < 0.05).

Figure 3

Kaplan-Meier curve analysis of gender (A), CRP (B) and NLR (C) for time to subsequent negative NAAT. Male, elevated values of CRP (≥ 4.785mg/L) and NLR (≥ 2.995) were more inclined to undergo a longer time to subsequent negative NAAT than female, lowered values of CRP (< 4.785mg/L) and NLR (< 2.995). (P < 0.05). CRP: c-reactive protein; NLR: neutrophil-to-lymphocyte ratio; NAAT: nucleic acid amplification test.

Table 5

Laboratory tests and demographic characteristics to predict time to subsequent negative NAAT

Variable	Mean	S.E.	95% CI	P-value
Age
< 60 years	15.195	0.455	14.303–16.088	0.587
≥ 60 years	15.396	0.805	13.818–16.973
Gender
Male	16.333	0.721	14.920–17.746	0.013
Female	14.576	0.459	13.676–15.476
Vaccination
Yes	15.166	0.429	14.325–16.007	0.367
No	15.594	1.010	13.615–17.572
Expectoration
Yes	16.500	1.320	13.912–19.088	0.194
No	15.091	0.416	14.276–15.906
Dyspnea
Yes	13.613	1.016	11.622–15.604	0.082
No	15.436	0.427	14.599–16.273
Hypertension
Yes	15.479	0.869	13.776–17.182	0.692
No	15.188	0.449	14.308–16.067
COPD/chronic bronchitis/asthma
Yes	16.250	3.087	10.200–22.300	0.686
No	15.228	0.402	14.441–16.015
Stroke
Yes	17.364	2.487	12.488–22.239	0.354
No	15.177	0.403	14.387–15.967
Traditional Chinese medicine
Yes	15.469	0.562	14.368–16.570	0.773
No	14.897	0.724	13.479–16.315
Lianhua Qingwen capsule
Yes	15.433	0.616	14.225–16.640	0.577
No	15.064	0.640	13.809–16.319
PLT
< 294×109/L	15.399	0.422	14.571–16.226	0.345
≥ 294×109/L	14.114	1.203	11.757–16.472
NE
< 3.65×109/L	15.355	0.448	14.477–16.233	0.949
≥ 3.65×109/L	14.962	0.856	13.285–16.640
CRP
< 4.785 mg/L	13.632	0.482	12.688–14.577	< 0.001
≥ 4.785 mg/L	17.635	0.627	16.406–18.864
BUN
< 4.83 mmol/L	15.725	0.495	14.755–16.694	0.105
≥ 4.83 mmol/L	14.317	0.665	13.015–15.620
NLR
< 2.995	14.828	0.420	14.006–15.651	0.021
≥ 2.995	17.531	1.120	15.335–19.726

1. NAAT: negative nucleic acid amplification test; COPD: chronic obstructive pulmonary disease; PLT: platelet count; NE: neutrophil count; CRP: c-reactive protein; BUN: blood urea nitrogen; NLR: neutrophil-to-lymphocyte ratio; S.E.: standard error; CI: confidence interval.

Table 6

Laboratory tests and demographic characteristics to predict time to subsequent negative NAAT

Variable	Multivariate analysis model 1				Multivariate analysis model 2
	OR	S.E.	95% CI	P-value	OR	S.E.	95% CI	P-value
Gender (Male)	1.145	0.124	0.897–1.460	0.276	-	-	-	-
CRP (≥ 4.785 mg/L)	1.493	0.126	0.523–0.858	0.001	1.616	0.117	0.492–0.778	< 0.001
NLR (≥ 2.995)	1.220	0.163	0.596–1.128	0.222	-	-	-	-

1. NAAT: negative nucleic acid amplification test; CRP: c-reactive protein; NLR: neutrophil-to-lymphocyte ratio; OR: odds ratio; S.E.: standard error; CI: confidence interval.

Discussion

COVID-19 is an infectious disease caused by SARS-CoV-2.^{[10, 11, 12, 13]} Most people infected with the virus will experience mild-to-moderate respiratory illness.^{[14, 15]} However, some will become seriously ill and require medical attention.^{[16, 17, 18, 19]} Older individuals and those with underlying medical conditions, such as cardiovascular disease, chronic respiratory disease, diabetes, and cancer, are more likely to have serious illnesses.^{[20, 21, 22, 23]} Anyone can get sick with COVID-19 and become seriously ill or die at any age.^{[24, 25, 26]} The virus can spread from an infected person’s mouth or nose via small liquid particles when they cough, sneeze, speak, sing, or breathe. These particles range from larger respiratory droplets to smaller aerosols.^[10,11,27]

Ever since scientists from Botswana and South Africa warned the world that the SARS-CoV-2 variant omicron could spread quickly,^[2,28,29] researchers worldwide have been racing to understand the threat it poses to the world. The main concern about this variant is that it has high transmissibility. In contrast to the epidemic in 2020, when there were fewer than 100 confirmed cases in Jilin Province, China,^[9] the current pandemic has reached more than 40,000 cases, as underlined by its rapid spread in different countries.^[30] The epidemic has affected many cities in China and even had a huge impact on first-tier cities, such as Beijing and Shanghai.

This study included 311 patients who were randomly extracted from two hospitals in Jilin Province, China. We conducted a retrospective study on the clinical characteristics of adult cases with confirmed infection of the SARS-CoV-2 omicron variant. The mean age of the novel coronavirus cases was 50.2 years after excluding patients aged < 18 years. Patients in the MS group were significantly older than those in the M group. The number of female patients was higher, which is consistent with the results of some other studies^{[31, 32, 33]}; however, there were slightly more men than women in the MS group. The incubation period in this study was approximately 3.05 days (range, 0–13 days), which was shorter than 4.2 days in Lee’s study and 3.6 days in Du’s study.^[32,34] In this study, unvaccinated patients are prone to further disease progression. However, scientists are likely to see reduced effectiveness of vaccines in preventing infection worldwide.^[35] This study revealed that the omicron variant may be more than 10 times as contagious as the original virus or twice as infectious as the delta variant and could be twice as likely to escape the current vaccine than the delta variant.^[36] We found that cough, fever, sore throat, muscle soreness, and expectoration were the top five symptoms. Therefore, fever is not the only symptom, and patients with upper respiratory symptoms should also be vigilant.^[37,38]

Peripheral inflammatory markers, such as leukocyte count, NE, NLR, PLR, and CRP levels, have been identified as independent predictors of prognosis in inflammatory diseases.^[22,39,40] Moreover, NLR and PLR have recently been found to be associated with the prognosis of noninflammatory diseases, such as amputation, acute mesenteric artery embolism, and thrombosis.^[41,42] Other peripheral blood indicators, including TBIL, BUN, and SCR, were also identified. In this study, although there were differences in PLT, NE, CRP, BUN, and NLR among the cases, we found that higher age, hypertension, or chronic lung disease and higher CRP levels were associated with MS COVID-19 after the multivariate analysis. This is similar to the results of a previous study,^[43] in which NLR values were significantly increased in patients with severe COVID-19, but is different from the findings that NLR was an independent risk factor for severe illness in patients with COVID-19.^{[44, 45, 46]} A pooled analysis by Lippi^[47] showed a 2.5-fold increased risk of death from hypertension in patients with COVID-19; however, no other studies have examined the association between disease severity classification and complications of the omicron variant infection.

Quantifying the length of the incubation period for COVID-19 is a key research issue, as this information can guide public health control measures for infectious diseases.^[48] Studies^[49,50] have shown a strong association between the incubation period of the virus and the severity of disease presentation. In this study, the incubation period of the omicron variant was only 3.05 days, which is consistent with the milder clinical symptoms that the omicron variant usually causes. Meanwhile, we found that advanced age was the only factor affecting the incubation period in this study. We also focused on the influence of different influencing factors on the time from nucleic acid positivity to continuous negative NAAT. Male, CRP > 4.785 mg/L, and NLR > 2.995 may prolong this time and increase the length of hospital stay. Of note, CRP is an independent factor that affects the time from nucleic acid positivity to continuous negative NAAT in our analysis. We did not find any similar results in other omicron-related studies.

We conducted a retrospective study on the clinical characteristics of adult cases with confirmed infection with the SARS-CoV-2 omicron variant. The data are relatively comprehensive and have certain local characteristics, which can also reflect the clinical characteristics of the SARS-CoV-2 omicron variant in China at this time. However, this study is limited by its retrospective nature. We did not obtain all the patients’ information; however, a certain number of patients were randomly selected for our cohort. We did not sequence the entire genome of all patients to determine if they were infected with the SARS-CoV-2 omicron variant. Furthermore, the study did not include the treatment and outcome of certain patients who were transferred to advanced hospitals because of exacerbation of their disease.

Conclusions

Based on this study, older patients (≥ 60 years) with hypertension and lung diseases were more likely to have moderate or severe COVID-19, and younger patients (< 60 years) might have a shorter incubation period. A male patient with high CRP (≥ 4.785 mg/L) and NLR (≥ 2.995) values might take more time to turn back negative in the NAAT. CRP (≥ 4.785 mg/L) was an independent risk factor for a longer time to subsequent negative NAAT. The SARS-CoV-2, which has already infected 500 million individuals and caused more than 6 million deaths worldwide, is prone to genetic evolution with the development of mutations over time. The pandemic remains a huge challenge for the world, and it will continue to be the focus of global attention.