In this study, we aimed to analyze the clinical characteristics of mixed infections involving different SARS-CoV-2 variants and other respiratory pathogens in patients with acute COVID-19 in central China. Participants’ samples were obtained from the PCR department of the Henan Provincial People’s Hospital. Other respiratory pathogens included bacteria, viruses, mycoplasma and fungi. Information on patients’ infections with other pathogens is obtained from the hospital’s medical record information system. The identification of mycoplasma pneumoniae is carried out using immunological antibody methods, bacterial identification is done using bacterial culture and identification card, and the identification of viruses and fungi is conducted to bronchoalveolar lavage fluid by using metagenome next-generation sequencing.

Overall, 172 throat swab samples were collected from 152 patients with confirmed COVID-19 between July 1, 2024, and February 1, 2025. This was because some patients provided more than once sample during their hospital stay. We conducted SARS-CoV-2 variant identification on all these samples, However, during our analysis, we classified and analyzed the data based on the 152 patients, thus excluding the duplicate samples, as shown in Tables 1, 2.

The Ethics Committee of the Henan Provincial People’s Hospital approved this study (Approval No. 241225). Written informed consent was obtained from all participants. All procedures adhered to the guidelines of the Declaration of Helsinki.

Nested polymerase chain reaction (PCR) was used to identify SARS-CoV-2 variants and their primers. The annealing temperature, amplified fragment length, and variant identification criteria are presented in Supplementary Tables 1, 2. High-throughput sequencing technology, a metagenomic next-generation sequencing (NGS) from Zhengzhou Autodiag Company (Zhengzhou, China) and Shanghai Sangon Biotech (Shanghai, China), was used to validate the identification of SARS-CoV-2 variants. UDG (Uracil- DNA Glycosylase) enzyme was added to the PCR reaction mixture to prevent contamination during the amplification process, Flu A, RSV, and mycobacterium tuberculosis had been validated the specificity, more validation details were shown in our previous study (Yuan et al., 2025).

Among the 172 throat swabs collected, whole-genome sequencing was performed for 5 SARS-CoV-2 variants through NGS technology at Antu Company. Additionally, the whole-genome sequence (WGS) information for 23 strains belonging to Alpha, Beta, Delta, Gamma, and Omicron variant strains was downloaded from GASAID, Genebase, or the National Center for Biotechnology Information websites (GISAID, 2025; GenBase, 2024; National Library of Medicine, 2025) and aligned using SnapGene (version 6.02; GSL Biotech, Chicago, IL, USA). Furthermore, molecular evolutionary genetics analysis (MEGA) software (version 12.0.8; MEGA, USA, https://www.megasoftware.net/) was employed to construct an evolutionary tree using the best-fit substitution model of evolution and maximum likelihood method, incorporating variants currently prevalent in China. More details including accession number, nations, year, variants classification and strain name regarding WGS are summarized in Supplementary Table 3.

The clinical data of 132 hospitalized patients and 20 outpatients treated in a fever clinic with confirmed SARS-CoV-2 infection, including seven patients co-infected with different variants, were retrieved from the hospital’s electronic medical record system (Hospital Information System). We collected variables including age, sex, city of residence in Henan Province, duration from admission to time of positive SARS-CoV-2 detection, underlying comorbidities, symptoms, cycle threshold (Ct) values for ORF1a and N genes, hospitalization duration, computed tomography (CT) features, treatment outcome, and the first laboratory detection data, which corresponded to the most recent date of positive SARS-CoV-2 infection. These data were analyzed to characterize the clinical differences between patients with respiratory mixed infection pathogens and those with a single SARS-CoV-2 infection. Mixed infection refers to both co-infection with multiple SARS-CoV-2 variants and co-infection with other non-SARS-CoV-2 respiratory pathogens.

In traditional Chinese culture, the patient’s family members do not want the patient to die in the hospital and hope that the patient dies at home. The family members, especially those in rural areas, usually take the patient home prior to death; thus, the patient’s death record is unclear in the medical records. Therefore, we defined the outcomes described in the patient’s medical record prior to death: treatment failure was defined as voluntary discharge and discontinuation of treatment, whereas treatment success was defined as treatment response leading to discharge. Additionally, we classified the patients as mild and severe according to the latest Chinese guidelines of COVID-19 diagnostic and treatment criteria (Jin et al., 2020).

For univariate analyses, summary statistics are presented as medians with interquartile ranges (IQRs) or means with standard errors, depending on the distribution. Statistical significance was assessed using the Kruskal–Wallis one-way analysis of variance test with Bonferroni adjustments for continuous variables. Categorical variables were analyzed using Pearson’s χ² or Fisher’s exact tests. Variables with p values <0.1 in the univariate analyses were included in the multivariate logistic regression analysis. Backward stepwise logistic regression was used to evaluate the risk factors for COVID-19 in patients with mixed respiratory infections and treatment failure. Eighty-day survival curves were constructed using the Kaplan–Meier method. Event was defined as treatment outcome of patient including treatment success or treatment failure during 80 days of hospitalization. Moreover, censoring defined situations where, at the end of the study or during follow-up, a patient has not experienced the event we defined, or for other reasons, we can no longer obtain complete follow-up information for that patient. Log-rank tests were performed using Prism (version 8.0; GraphPad, La Jolla, CA, USA) software. The significance threshold for group differences was set at p < 0.05. All analyses were performed using SPSS (version 25.0; IBM Corp., Armonk, NY, USA) and GraphPad Prism.

When identifying the new coronavirus variants using nested PCR, we identified cases of mixed infection with different variants. Among 172 throat swab samples from 152 patients, 15 had at least two consecutive samples collected. Among these 15 patients, 7 had different SARS-CoV-2 variants detected by nested PCR and NGS in samples collected on different dates from the same patient (Table 3, Figure 1, 2). The detection rate of mixed infection with different SARS-CoV-2 variants was 46.7% (7/15) among all patients with more than two samples. Among the seven patients infected with different variants, five had two positive samples, one had three positive samples, and one patient had four positive samples. We designated patients as patient 1 through 7 (Figure 1). Among them, patient 1 was a 71-year-old male who died after staying in the intensive care unit (ICU) for 22 days due to a mixed infection of respiratory Flu B, Acinetobacter baumannii, and Candida albicans. He had a cerebral infarction and was diagnosed with COVID-19 after spinal surgery on 1 August 2024. A positive SARS-CoV-2 throat swab sample was documented on the 7^th and 17^th of August 2024. However, these two positive samples were identified as the JN.1.18.2 and XDV.1 variants using nested PCR and NGS, respectively. To treat acute COVID-19, oral antiviral drugs oseltamivir and monoravir were administered for 10 d. However, the patient died due to severe comorbidities. Therefore, the mortality rate was 14.3% (1/7) among seven patients co-infected with different SARS-CoV-2 variants, which was higher than the average mortality rate of all patients with COVID-19 (3.3%, 5/152; p < 0.001). The other six patients had at least two positive SARS-CoV-2 samples, each containing different variants identified as JN.1.16, JN.1.18.2, or XDV.1, within the same host on different dates (Figures 1, 2). As shown by the phylogenetic tree constructed for the WGS results of five variant strains and downloaded sequences of other 23 SARS-CoV-2 variants, the five selective and representative variant strains from Henan Province and same Omicron variants from Thailand, the United States, and England, were clustered together (Figure 3). These are the Omicron variants currently circulating in the world. Furthermore, the phylogenetic tree showed that the XDV.1 variant was closer to the Wuhan prototype, whereas JN.1.16 and JN.1.18.2 had a longer evolutionary distance (Figure 3). Overall, patients with mixed COVID-19 infections of different variants had distinct clinical characteristics compared to those without; they were older [73(66,85)] and had more underlying diseases, such as hypertension, diabetes, chronic heart disease, and cerebral infarction. Moreover, in relation to other patients, these patients had a higher mixed infection rate with other respiratory pathogens (85.5% vs 54.6%) and higher severe COVID-19 rate in accordance with previous guidelines (42.8% [3/7] vs. 17.1% [26/152]; p < 0.001] (Jin et al., 2020)). Common respiratory pathogens in patients with acute COVID-19 included M. pneumoniae, Klebsiella pneumoniae, and Aspergillus. Additionally, 71.4% (5/7) of patients with mixed infection variants were treated with antiviral drugs such as nirmatrelvir, ritonavir, or molnupiravir (Table 3).

The clinical characteristics of patients co-infected with other respiratory pathogens and those without such mixed infections were further investigated in the univariate analysis (Table 1). Among the 152 patients with COVID-19, 83 had co-existing respiratory pathogen infections, resulting in a total respiratory pathogen mixed infection rate of 54.6%. The most common co-existing respiratory pathogen was M. pneumoniae, with a mixed infection rate of 29.6% (45/152), followed by the group with mixed infections of multiple pathogens consisting of at least two or more types of mycoplasmas, bacteria and viruses, and the group infected with either bacteria or viruses, with an infection rate of 17.1% (26/152) and 7.9% (12/152), respectively. Significant differences in the mixed infection rates were observed among patients infected with different pathogens (p < 0.001).

Infections involving multiple pathogens had a median hospitalization time of 14.5 d (IQR: 8.5–35.5 d), which was significantly longer than that of those without co-existing respiratory infection (median: 9.0 d, IQR: 4.5–14.0 d; p = 0.010, Figure 4A). Patients with mixed infections involving multiple pathogens (p = 0.010) and bacterium or virus (p = 0.023) had a longer duration from admission to positive SARS-CoV-2 sample detection than those without co-existing respiratory infections (Figure 4B). Additionally, patients infected with multiple pathogens had more elevated white blood cell (WBC) counts, procalcitonin (PCT), and D-dimer levels than those infected with M. pneumonia and without mixed infection (Figures 4C–E). Patients with mixed infections involving multiple pathogens were more likely to be male (Figure 4F); have a severe classification (Figure 4G); be hospitalized in the ICU (Figure 4H); have fever and cough (Figure 4I), double lung changes (Figure 5A), and treatment failure (Figure 5B) than those infected with M. pneumonia or without mixed infection. Multivariate analysis confirmed that ICU hospitalization (odds ratio [OR] = 3.65 (1.25-5.03), p = 0.031), number of hospitalization days (OR = 2.04 (1.2-3.02), p = 0.019), and days from admission to positive SARS-CoV-2 sample detection (OR = 2.05 (1.32-3.86), p = 0.032) were independent risk factors for acute COVID-19 mixed with different respiratory pathogens (Figure 5C). Survival analysis also indicated that patients with mixed infections involving multiple pathogens had a lower survival rate compared to those without mixed infection (log rank p < 0.001, Figure 5D).

No significant differences in age, SARS-CoV-2 variant distribution, geographic location, or underlying comorbidities were observed between patients infected with and without different pathogens. Similarly, no significant differences in laboratory detection results were found between patients with and without different pathogen mixed infections (Table 1).

To identify the clinical characteristics of patients with COVID-19 co-infected with different respiratory pathogens, we compared the characteristics of patients regarding treatment success and failure. Of 152 patients with COVID-19, 26 had severe infections according to the latest Chinese guidelines for COVID-19 treatment and diagnostic criteria (Jin et al., 2020), 19 had a record of treatment failure, reflecting a total treatment failure rate of 12.5% (Table 2).

Patients with treatment failure had a median hospitalization time of 45 d (IQR: 20.5–71.8 d), significantly longer than that of those with treatment success (median: 11 d, IQR: 5.5–24.5; p = 0.007; Supplementary Figure 1A). Patients with treatment failure had a longer duration from admission to positive SARS-CoV-2 sample detection than those with treatment success (p = 0.028; Supplementary Figure 1B). Additionally, patients with treatment failure had more elevated WBC counts (Supplementary Figure 1C), PCT (Supplementary Figure 1D), CRP (Supplementary Figure 1E), D-dimer (Additional File 4 (F)), and lactate dehydrogenase (Supplementary Figure 1H) than those with treatment success. Conversely, patients with treatment failure had fewer lymphocyte (p = 0.006; Supplementary Figure 1G) and platelet counts than those with treatment success (p = 0.007; Supplementary Figure 2A). Patients with treatment failure were mostly from the Eastern region of Henan Province, China (Supplementary Figure 2B). They had a greater incidence of fever and cough (Supplementary Figure 2C), double lung CT change (Supplementary Figure 2D), and cerebral fraction (Supplementary Figure 2E). Correspondingly, patients with treatment failure also had a greater number of underlying diseases than those with treatment success (Supplementary Figure 2F), with ICU hospitalizations (Supplementary Figure 2G) and more frequent severity classifications (Supplementary Figure 2H). Furthermore, multivariate analysis showed that mixed infection with multiple pathogens (OR = 3.58(1.34-6.67), p = 0.023), severity classification (OR = 29.58(8.65-44.1), p = 0.001), cerebral fraction (OR = 51.1(2.5-66.7), p = 0.038), and fever (OR = 13.8(2.23-25.6), p = 0.004) were independent risk factors for acute COVID-19 infection with treatment failure (Figure 5E). Survival analysis also indicated that patients with a severity classification had a lower survival rate than those with mild classifications (log rank p < 0.001, Figure 5F).

No significant differences in age, sex, SARS-CoV-2 variant distribution, and underlying comorbidities were observed between patients with treatment failure and success. No significant differences in laboratory parameters were noted between patients with treatment failure and success (Table 2).

We reported mixed infections with different SARS-CoV-2 variants in seven patients with acute COVID-19, identified from various samples and dates within the same host. These patients were older and had more underlying conditions. Due to longer hospitalization, they were more susceptible to developing severe cases of infection and were co-infected with another variant and additional pathogens. Early detection of laboratory parameters, including WBC count, PCT, and D-dimer concentrations, can help clinicians diagnose and treat patients with severe acute COVID-19 to reduce mortality. In the context of the SARS-CoV-2 pandemic, active variant surveillance and analysis of clinical data are essential to understand the relationship between mixed infection and disease progression (Alemán et al., 2025).