Clinical characteristics and outcomes of patients with COVID-19 and tuberculosis coinfection

Abstract

Background

Data on the coincidence of Tuberculosis (TB) and Coronavirus disease 2019 (COVID-19) are limited. We sought to investigate the clinical characteristics and outcomes of coinfected patients in Henan and identify whether TB disease is associated with an increased risk of intensive care unit (ICU) admission and mortality.

Method

We conducted a retrospective matched cohort study of COVID-19 inpatients involving 41 TB-positive patients with 82 patients without TB. Leveraging data was collected from electronic medical records.

Results

There were no significant differences in clinical manifestations, the need for mechanical ventilation and vasopressors, ICU admission, or in-hospital mortality between 2 groups. TB-positive patients had a lower lymphocyte counts (1.24 ± 0.54 vs. 1.59 ± 0.58, p = 0.01), B cells (99/µl vs. 201/µl, p < 0.01), CD4+ T cells (382/µl vs. 667/µl, p < 0.01), CD8+ T cells (243/µl vs. 423/µl, p < 0.01), NK cells (145/µl vs. 216/µl, p = 0.01), IL-2 (14.18 ± 11.23 vs. 31.86 ± 34.55, p < 0.01) and TNF-α (3.42 ± 2.93 vs. 5.62 ± 3.69, p < 0.01). Notably, the TB-positive group had a longer duration of SARS-CoV-2 shedding (67 days vs. 22 days, p < 0.01).

Conclusions

Concomitant TB does not significantly impact clinical outcomes of hospitalised patients with acute COVID-19. However, TB-positive patients had longer duration of SARS-COV-2-RNA positivity.

Keywords:

• Coronavirus disease 2019
• SARS-CoV-2
• tuberculosis
• in-patients
• viral shedding

Introduction

The Coronavirus disease-2019 (COVID-19), caused by the novel severe acute respiratory syndrome Coronavirus 2(SARS-CoV-2), was classified as a global pandemic by the World Health Organisation on 11 March 2020. The main reported risk factors include age > 65 years or having comorbid conditions such as diabetes mellitus, ischaemic heart disease, hypertension, chronic lung disease, and immune-suppression [1]. Tuberculosis (TB) and COVID-19 are both infectious diseases not only attack the respiratory systems and cause a broad spectrum but also targets other organ systems. The commonest symptoms of TB (fever and cough) are similar to those exhibited by COVID-19 patients, eliciting hyper inflammatory state in the lung, may share joint clinical presentations and risk factors. A recent data show that co-infection neared 3.6% in Africa, 1.5% in Asia,1.1% in America and between 0.37% and 4.47% in China [2]. Challenges and strategies need to be addressed to confront these dual diseases together [3,4]. However, Data on the coincidence of TB and COVID-19 are limited, which has led to polarised views on the course of infection with SARS-CoV-2 in patients with active TB [5–13]. Meta-analysis suggest that co-infection is associated with higher mortality rates, a doubling the maximum mortality estimated by Johns Hopkins for COVID-19 alone [14]. Reports from South Africa also indicate that co-infected individuals have a 2x mortality risk compared to patients with Mtb mono-infection [15]. While these reports highlight the negative effects of SARS-CoV-2 infection on TB resolution, experiments in mice suggest that prior Mtb infection is protective against SARS-CoV-2-mediated disease [16]. Nosocomial infection broke out in Henan Provincial Infectious Disease Hospital after the catastrophic flood in 20 July 2021, and some active TB inpatients were infected with SARS-CoV-2, with the first onset of COVID-19 was observed on 30 July 2021. To the best of our knowledge, data on clinical characteristics and natural history of COVID-19 among individuals with TB have not been well-characterized. Herein, in this ‘nature’ condition, we evaluated the demographic characteristics, clinical characteristics, laboratory test results, and hospital outcomes and compared them to a well-matched control without TB.

Materials and methods

Study design and participants

This study was designed to include all the reachable patients hospitalised in the designated COVID-19 hospital in Zhengzhou City, i.e. The First People’s Hospital of Zhengzhou City in Henan Province, China. We queried the database for (a) adults 18 years of age or older, (b) with a history of active TB and hospitalised during the period from July 23, 2021 to August 6, 2021, for COVID‐19 infection with follow‐up through January 10, 2022. All of the patients had laboratory-confirmed COVID-19 infection, which was defined as a positive result on a reverse-transcriptase-polymerase reaction (RT-PCR) tests for SARS-CoV-2RNA from nasopharyngeal swabs, using Novel Coronavirus[2019-nCoV] Nucleic Acid Detection Kit [PCR Fluorescence Probing], Shanghai Bio Germ Medical Biotchnology [17]. Diagnosis of TB in these patients was undertaken utilising WHO guidelines [18]. Extrapulmonary tuberculosis (EPTB) cases were mainly categorised by the disease site according to WHO [19], defined as those who have at least one EPTB disease site, including EPTB concurrent with PTB patients. Patients who did not test positive for COVID-19, who were never admitted to the hospital, and who had not yet completed their clinical course were excluded from the study.

We collected demographic data including age, sex, medical comorbidities, and data on presenting symptoms. For all patients, we collected absolute the following laboratory markers from electronic medical records: C-reactive protein (CRP), D-dimer, lactatede hydrogenase, cytokine, and cell subsets of lymphocyte overviews with Peripheral blood samples. TB-specific data included antituberculosis therapy regimens, most recent CD4+ T-cell count, CD8+ T-cell count, NK cell count, B cell count, and radiographic reports during hospitalisation.

Statistics

Greedy1:2 nearest-neighbor matching was employed using the gmatch SAS macro to generate 82 matched non-TB patients who were hospitalised during the same period for our comparison group. Matching was performed using gender and age. Primary stratification was between the TB positive group versus the non-TB group. Continuous variables were presented as mean ± SD or median and interquartilerange (IQR). Categorical data were summarised using frequency and percentage. A 2-tailed Student t test was used for parametric analysis, and a Mann–Whitney U test was used for nonparametric data analysis. A Pearson x2 test was used to compare categorical variables between groups, as appropriate. All analyses were performed using SPSS software package (version 25.0; SPSS, IBM, Chicago, IL, United States).

Results

Clinical characteristics

Our study included co-infected patients (n = 41) and a control group (n = 82) matched by age and sex, as shown in Table 1. All patients required admission. There were no significant differences between groups in Diabetes, Hypertension, Chronic liver diseases, Cardiovascular diseases, Respiratory system diseases, Chronic kidney disease, Cerebrovascular disease, Rheumatic diseases, Thyroid disease, Smoking. Presenting symptoms did not differ significantly between the 2 groups. The most common presenting symptoms across both groups were fever (41% in TB-positive and 48% in non-TB patients), dry cough (44% and 32%), Sore throat (27% and 16%), and Sputum production (24% and 17%) over a median duration of 3 days. Shortness of breath, Myalgia, headache and acute loss of smell and taste were uncommon in both groups. A greater percentage of non-TB patients had an SARS-CoV-2 vaccination rates than TB-positive patients [non-TB: 36(45%) vs. TB-positive: 11(27%)], although no statistical difference was seen between the groups.

Table 1. Demographic, clinical characteristics and outcomes comparison between TB-positive and non-TB controls.

Characteristics	TB-Positive, n = 41, N (%) or Median (IQR)	Non-TB, n = 82, N (%) or Median (IQR)	P value
Gender
male	28	55	0.89
femal	13	27
Age, years	47 (23.8-60)	47 (23.5-61)	0.93
Chronic comorbidity
Diabetes	8 (20)	13 (16)	0.61
Hypertension	7 (17)	17 (21)	0.63
Chronic liver diseases	6 (15)	5 (6)	0.12
Cardiovascular diseases	2 (5)	7 (9)	0.46
Respiratory system diseases	1 (2)	4 (5)	0.52
Chronic kidney disease	1 (2)	3 (4)	0.72
Cerebrovascular disease	2 (5)	2 (2)	0.47
Rheumatic diseases	3 (7)	2 (2)	0.20
Thyroid disease	1 (2)	0 (0)	0.16
Smoking^#	5 (12)	13 (16)	0.59
Symptom of COVID-19: New Onset or exacerbation of existing symptoms
Fever	17 (42)	39 (48)	0.52
Dry Cough	18 (44)	26 (32)	0.18
Sore throat	11 (27)	13 (16)	0.15
Sputum production	10 (25)	14 (17)	0.33
Fatigue	8 (20)	11 (13)	0.11
Headache	5 (12)	8 (10)	0.68
Nausea	3 (7)	4 (5)	0.34
Shortness of breath	3 (7)	2 (2)	0.20
Acute loss of smell and taste	2 (5)	11 (13)	0.15
Myalgia	2 (5)	6 (7)	0.60
Diarrhea	2 (5)	3 (4)	0.75
Abdominal pai	1 (2)	2 (2)	1.00
Chest CT imaging: Typical Multiple mottling/ground-glass (new onset)
Bilateral infiltrates	25 (61)	40 (49)	0.11
Unilateral infiltrates	9 (22)	13 (16)
No infiltrates	7 (17)	29 (35)
Laboratory parameters^a
White blood cell count,10^9/L	4.41 ± 1.90	5.09 ± 1.55	0.09
Haemoglobin,g/L	130.25 ± 32.15	135.45 ± 25.43	0.43
platelets count,10^9/L	149.8 ± 66.53	198.2 ± 58.05	＜0.01
Lymphocytes count,10^9/L	1.24 ± 0.54	1.59 ± 0.58	0.01
Neutrophils count,10^9/L	2.68 ± 1.63	2.99 ± 1.41	0.370
B cells count, /ul	99(78-143),m^b=6	201(141-253),m = 19	＜0.01
CD4+ cells count, /ul	382(288-522),m = 6	667(465-799),m = 19	＜0.01
CD8+ cells count, /ul	243(140-334),m = 6	423(381-503),m = 19	＜0.01
NK cells count,/ul	145(103-224),m = 6	216(201-286),m = 19	0.01
Alanine aminotransferase, U/L	36.50 ± 30.66	26.61 ± 24.96	0.12
Creatinine phosphokinase, U/L	61.51 ± 41.75	58.29 ± 33.94	0.71
C-reactive protein, mg/L	16.98 ± 30.68	12.04 ± 21.19	0.41
Ferritin, ng/ml	98.69(67.96-174),m = 2	90.14(52-157.9),m = 8	0.76
Lactate dehydrogenase, U/L	211.5(184.3-247.5)	178.5(150.3-212.5),m = 8	0.11
D-dimer, ug/ml	0.32(0.26-0.85)	0.31(0.18-0.66),m = 11	0.37
Interleukins-2, pg/ml	14.18 ± 11.23,m = 2	31.86 ± 34.55,m = 11	＜0.01
Interleukins-4, pg/ml	10.17 ± 8.65,m = 2	9.45 ± 6.53,m = 11	0.70
Interleukins −6, pg/ml	60.86 ± 30.97,m = 2	48.98 ± 15.51,m = 11	0.05
Interleukins −10, pg/ml	8.33 ± 5.31,m = 2	7.88 ± 5.28,m = 11	0.72
TNF-a, pg/ml	3.42 ± 2.93,m = 2	5.62 ± 3.69,m = 11	＜0.01
TNF –r, pg/ml	48.03 ± 2.72,m = 2	47.05 ± 11.84,m = 11	0.64
Interleukins −17a, pg/ml	149.32 ± 2.46,m = 2	141.60 ± 40.89,m = 11	0.28
Interleukins −12p70, pg/ml	1.31 ± 0.81,m = 2	3.56 ± 8.07,m = 11	0.11
Duration of viral-RNA positivity (days)^c	67, 143	24, 40	＜0.01
Oxygen requirements
Room air	29 (71)	63 (82)	0.75
Nasal canula	11 (27)	17 (21)
Invasive mechanical ventilation	2 (5)	2 (2)
Need for vasopressors	1 (2)	0 (0)	0.16
ICU admission	2 (5)	3 (4)	0.75
Death	1 (2)	0 (0)	0.16
Covid-19 Vaccination
YES	11(272)	36(45)	0.07
NO	30(73)	46(55)

Data are represented as median (IQR), mean 6 SD, or N (%). Sample size is reported where it differed due to laboratory tests not performed. P values were calculated using a 2-sided t test for parametric variables and a Mann–Whitney U test for nonparametric continuous variables. A Pearson x2 test was used for categorical comparisons. p＜0 .05 (in bold) was deemed significant.

^aAbsolute White blood cell count, Haemoglobin, platelets count, Lymphocytes count, Neutrophils count and Alanine aminotransferase were collected by the initial value upon hospitalisation. C-reactive protein, Creatinine phosphokinase, Ferritin, Lactate dehydrogenase, D-dimer, and lactate dehydrogenase were collected as peak values of all available values within 7 days of hospitalisation. B cells count, CD4+ cells count, CD8+ cells count, NK cells count, Interleukins-2, Interleukins-4, Interleukins -6, Interleukins -10, TNF-a, TNF –r, Interleukins -17a and Interleukins -12p70 were collected within 5 days of hospitalisation.

IQR, interquartile range.

^bmissing data.

^cdescribed as: median, range.

TB characteristics

In this study, 41 active TB hospitalised patients were infected with COVID‐19. 27 (68%) patients had pulmonary tuberculosis (PTB), 13 had EPTB (Table 2). Drug resistance tuberculosis was detected in 3 (8%) patients. The average duration of anti-tuberculosis treatment before the diagnosis of COVID-19 infection was 6 (IQR 3-12) months. All regimens were continued anti-tuberculosis treatment during hospitalisation (Table 2). Additionally, lumbar puncture with intrathecal injection of isoniazid 0.1 g, dexamethasone 2 mg (1 or 2 times a week) in tuberculosis meningitis patients. All the patients were tested for HIV at admission, while no positive.

Laboratory and radiographic findings

Active-TB and non-TB groups did not differ statistically on Haemoglobin, neutrophils count, ALT, CRP, Creatinine phosphokinase, Ferritin, Lactate dehydrogenase, and D-dimer. TB-positive patients had a higher IL-6 and lower platelets counts, lymphocyte counts, B cells, CD8+ T cells, NK cells, IL-2 and TNF-α(p < 0.05) on initial laboratory test results than non-TB patients (Table 1).

Clinical outcomes

All of patients were given traditional Chinese herbal medicine, symptomatic therapy or oxygen therapy. One TB and liver cirrhosis patient (65 years) died due to acute bleeding from the upper gastrointestinal tract.122 patients were recovered and discharged. Notably, we found that the active-TB group had a longer SARS-CoV-2 shedding period [(63,143) days vs. (24, 40) days, p＜0.01] (Table 1). One Pulmonary TB patients with pleural TB, Central nervous system TB and Thoracic spinal TB stayed in the hospital for 156 days and discharged home uneventfully on January 9, 2022.

Discussion

COVID-19 and tuberculosis are both infectious diseases that most commonly affect the lungs, but can damage any tissue [1]. While Mtb and SARS-CoV-2 have unique features, both pathogens thrive in the lungs and can elicit similar hyperinflammatory syndromes, immune dysregulation, and extensive lung damage [20]. Data on the coincidence of TB and COVID-19 are limited [5–13]. The first cohort analysis to assess the relationship between TB and COVID-19 was prepared through an international collaboration and included 49 cases of coinfection identified in 8 countries and revealed a higher mortality rate among the elderly with a history of tuberculosis [7]. Chen et al. reported that tuberculosis increased susceptibility to COVID-19 and exacerbated its symptoms [9]. Similarly, Italian researchers have suggested that coinfection may be more severe in the elderly or in patients with comorbidities, but that it is a clinically manageable condition [6]. Another study carried out in the Philippines confirmed a negative role of TB in the course of COVID-19,and linked coinfection with a higher risk of morbidity and mortality [10]. Substantial evidence for the impact of TB on COVID-19 outcomes was obtained in a South African study that compared data on more than 3 million patients treated in public healthcare institutions, with or without COVID-19, and with other comorbidities, including TB and HIV. It demonstrated that a history of tuberculosis, active tuberculosis, and tuberculosis coexisting with HIV infection all increase the risk of death in COVID-19 patients [11]. Contrasting conclusions were reached in another two studies, which found no direct relationship between tuberculosis and the deterioration of COVID-19 symptoms [12,13].

In this retrospective cohort study, we present cases of COVID-19 infection in Henan, including 41 patients with active TB and 82 non –TB patients contracted a SARS-CoV-2 infection, detected shortly after the outbreak of catastrophic flood and nosocomial infection. Our data does not show that active TB was associated with an increase the risk of ICU, severity and mortality in COVID-19 patients. All of the case achieved good clinical outcome. Early diagnosis (3.5 days range:1-8days), early started and well underway in full-course anti-TB treatment, traditional Chinese and interventions medicine may be the key influencing factors to avoid the development of severe disease or death in patients with tuberculosis complicated by COVID-19 infection. This may be due to the small number of samples used in the analysis. Further investigation is needed to validate this result in the future (Table 2).

Table 2. Baseline demographic and clinical manifestations of TB-positive patients (n = 41).

Variable	Distribution
Sex(M/F)	28/13
Age (years)	47 (23.8,60)
TB localisation(n(%))
pulmonary tuberculosis only	27 (66)
lymph node TB	4 (10)
lumbar spinal TB (L2-L3)	1 (2)
Pleural TB + Pulmonary TB	2 (5)
Central nervous system TB + Pulmonary TB	3 (7)
lumbar spinal(L3-L5)+ Pulmonary TB	1 (2)
Lymphadenitis + Pulmonary TB	2 (5)
Pleural TB + Central nervous system TB + Thoracic spinal(T9-T10) TB + Pulmonary TB	1 (2)
Radiological involvement of TB(n(%))
Unilateral cavities	5 (12)
Bilateral cavities	7 (17)
Unilateral nodules	8 (20)
Bilateral nodules	6 (15)
Miliary nodules	2 (5)
Unilateral thickenings	2 (5)
Bilateral thickenings	5 (12)
Unilateral infiltrates	4 (10)
Bilateral infiltrates	2 (5)
Calcific lesions	1 (2)
Pleural effusion	2 (5)
Thoracic collapse	1 (2)

Data are represented as median (IQR) or N (%).

However, we observed that TB-positive patients had a greater percentage of an abnormal finding of infiltrate on initial chest CT imaging than non-TB patients (TB-positive 83% vs. non-TB 65%), although no statistical difference was seen between the 2 groups. Furthermore, TB may be having a negative impact on the clinical course of COVID-19, reflecting in a longer duration of viral-RNA positivity (67 days vs. 22 days, p＜0.01). Our study found a maximum SARS-COV-2 RNA shedding duration of 159 days (July 30, 2021 to January 5, 2022), the longest reported to date, compared with Li et al. described the longest SARS-CoV-2 RNA shedding duration of 83 days [21]. In several studies, prolonged viral RNA shedding has been variously associated with male sex, advanced age, disease severity, delayed hospital admission/therapy start after symptoms onset and comorbidities [22–24]. However, our study did not find significant difference between the two groups among the above factors.

Importantly, compared with COVID-19 infection patients, the counts of lymphocyte, B cells, CD4+ T cells, CD8+ Tcells and NK cells were decreased significantly in co-infected patients in our study. M. tuberculosis and SARS-CoV-2 may act synergistically when they share the same host. Despite pressures from host immunity, M. tuberculosis, an intracellular pathogen, evolved myriad strategies to evade and subvert immune responses in order to persist within a host [25]. In humans and animal models, persistent M. tuberculosis antigen stimulation impaired cytokine production by T Cells, caused a decrease in the production of IFN-γ and IL-2, and decreased the Number of Antigen Specific CD8+ T Cells [26]. Similarly, the host immune response to COVID-19 as well as being a major determinant of recovery orchestrated by coordinated B and T cell responses [27]. Immunologically, COVID-19 severity has been associated with major systemic alterations of the host immune system, including profound lymphopenia, skewed distribution and activation of T cell subpopulations, disruption of the B cell compartment, and elevated plasma concentrations of proinflammatory cytokines [28]. Pre-existing lymphopenia impairs the immune response to SARS-CoV-2, and current TB reduces the polyfunctional potential of SARS-CoV-2–specific CD4 + T cells. SARS-CoV-2 itself can block the host immune defense by suppressing T cell functions by inducing their programmed cell death e.g. by apoptosis. A growing body of evidence suggests that decreased numbers of T cells strongly correlated with the severity of the acute phase of SARS disease in humans. In a systematic review of 61 articles, symptomatic adult coronavirus disease 2019 (COVID-19) cases consistently showed peripheral T-cell lymphopenia positively correlating with increased disease severity, duration of viral-RNA positivity, and non-survival [29]. Further evidence of the importance of T-cell responses for COVID-19 recovery can be observed in patients with B-cell defiencies, who exhibit prolonged and severe COVID-19(29). Le Bert et al. noted increased IFN-g and interleukin-2 (IL-2) production in asymptomatic patients compared with symptomatic patients, despite similar overall frequencies of T-cells in both groups [7]. Cytotoxic lymphocytes (CTLs) and natural killer (NK) cells are important for the control of viral infection, and the functional exhaustion of cytotoxic lymphocytesis may increase the severity of diseasesCOVID-19 [30]. Mild cases of SARS-CoV-2 infection are associated with early, highly specific T-cell responses, whereas severe COVID-19 cases are associated with late, disproportionate secretion of pro-inflammatory cytokines (tumor necrosis factor-a, IL-6, IL-1b),a phenomenon which has also been observed in no survivors and is suggestive of a cytokine storm [31]. Following Mtb engulfment, the phagocytes produce a plethora of roinflammatory cytokines, including TNF-α, IL-1β, IL-6, IL-12, IL-18, IL-23, and IFN-γ, and chemokines [32]. Some of these molecules, including IL-6, IL-12, IL-1β and IL-18, also constitute the inflammatory cytokine storm seen in severe COVID-19 cases [33]. These factors may together account or the delayed development of the adaptive immune response and prolonged virus clearance in TB and SARS-CoV-2co-infection. IL-2 is the most important cytokine that regulates the differentiation of T cells. Recombinant IL-2 could enhance T cell survival, reverse T cell energy, and maintain virus-specific CD8+ T cell numbers and function during persistent viral infection, such as HIV and LCMV [34,35]. Moreover, IL-2 could reverse T cell dysfunction induced by persistent M. tuberculosis infection such as in MDR-TB [36]. Combining IL-2 therapy maybe is a novel strategy for patients coinfected with TB and COVID-19 in future.

Interestingly, in this study of 123 patients hospitalised for COVID-19, the SARS-CoV-2 vaccination rates were 27% (8/11 one dose, 3/11 two dose) and 4% (14/36 one dose, 22/36 two dose) for active-TB group and non-TB group, respectively. They received inactivated whole-virion SARS-CoV-2 vaccines (CoronaVac, BBIBP-CorVorWIBP-CorV) respectively. It is thought that these variations is due to dose-dependent (1 dose vs. 2 doses), effectiveness of the inactivated vaccine and whether induce a potent and persistent immune response exposuring to certain M. tuberculosis. Given the high risk of serious health consequences of SARS-CoV-2 infection in patients with TB, the potential benefits of the vaccine, both to patients and to healthcare systems, are likely to outweigh the risks associated with vaccination. We therefore recommend that patients who had or have active TB should do their upmost to avoid getting COVID-19 and should be prioritised suitable vaccination when possible [37]. Finally, since the effectiveness of vaccination may be lower in these patients, immunisation against SARS-CoV-2 should be recommended to household members and healthcare professional scaring for these patients to reduce exposure to SARS-CoV-2.

COVID-19 can occur before, simultaneously or after the diagnosis of TB. The effects of COVID-19 on Mtb reactivation, latent TB, or anti-TB therapy are yet to be established. A recent work reported COVID-19 may be a predisposing factor for the conversion of latent TB to active TB and worsening of COVID-19 severity and progression of TB [38]. Further studies are needed to enable analysis of interactions and determinants of outcomes in patients with both diseases.

Limitations

Our study did not suggest that tuberculosis was associated with an increased risk of ICU admission, severity and mortality. This may be due to the small number of samples used in the analysis; another possible reason is that patients mostly were well beyond the initial TB treatment phase (first two months). Further analysis is needed to validate this result in the future. Our study also had some limitations. First, only active TB patients were included in our analysis. The results should be interpreted with caution. Recent evidence suggests that the suppression of the cell-mediated immunity caused by Coronavirus disease 2019 (COVID-19) induces the activation of latent TB [39]. A larger study is required to determine whether the trends we observed apply to all TB-positive patients. Second, due to limited data, we did not conduct subgroup analysis and assess the publication bias. As more evidence available, it will be interesting to assess whether the duration and type of tuberculosis are associated with increased complications in patients diagnosed with COVID-19. Finally, the duration of follow-up was limited, thus not allowing for assessment of longer-term outcomes which will be, however, assessed at a later time. A larger study is required to determine whether the trends we observed apply to all TB-positive patients.

Conclusion

In conclusion, active TB was not associated with an increase the risk of ICU, severity and mortality in COVID-19 patients. All patients had well-controlled TB infection except for one TB and liver cirrhosis patient died due to acute bleeding from the upper gastrointestinal tract. However, TB may be having a negative impact on the clinical course of COVID-19, reflecting in a longer duration of viral-RNA positivity. Based on the immunological mechanism involved, a shared dysregulation of immune responses in COVID-19 and TB has been found, suggesting combining IL-2 and anti-inflammatory drugs therapy maybe a novel strategy for patients coinfected with TB and COVID-19 treatments in future. As the science regarding the management of COVID‐19 evolves, more extensive studies are needed to understand better the prognosis of TB patients who present with COVID-19.

Ethics approval and consent to participate

The study was performed according to the ethics guidelines of the Declaration of Helsinki in 1975. Because this study constituted public health surveillance rather than research in human beings, ethical approval from institutional review boards was waived by the ethical Institutional Review Board of The First Affiliated Hospital of Zhengzhou University. Informed written consent was obtained from all patients.

Authors’ contributions

LJ and L-MY contributed equally to this work, drafting the article. LJ, Z-QL and Z-PF designed the study. WC and LH helped with manuscript preparation, edits, statistics, methods, and results. Z-MM, L-RJ and J-LQ helped with patient data collection and data entry. Z-QL and Z-PF also contributed to the statistical analyses and revise critically for important intellectual content of the article. All authors read and approved the final manuscript.

Acknowledgements

The authors thank all participants and their families in the study. We thank the following individuals for their assistance with this study, none of whom were compensated for their contributions: Yuxia Fan, Yanbing Sheng, Zhengxuan Gao (The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China), and Xia Luo (Department of Infectious Diseases, The Sixth People’s Hospital of Zhengzhou City, Zhengzhou, Henan Province, China) for data collection and study coordination; Zujiang Yu, Hongxia Liang and Yamin Qiao (The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China) for administrative support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Availability of data and materials

The datasets generated and analysed during the current study are not publicly available due to the fact that it contains personal information, but are available from the corresponding author on reasonable request.

Additional information

Funding

The study was supported by the National Natural Science Foundation of China (Nos. 81902470 and 82270629) and Key projects jointly built by Henan Province and Ministry of Finance (No. SBGJ2020002083).