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Risk prediction and early intervention strategies for persistent SARS-CoV-2 infection in patients with non-Hodgkin lymphoma: a retrospective cohort study

BMC Pulmonary Medicine volume 25, Article number: 122 (2025) Cite this article

Abstract

Background

Patients with non-Hodgkin lymphoma (NHL) face heightened mortality and accelerated disease progression when persistently infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This critical situation underscores the urgent need to identify risk factors and establish early intervention strategies tailored to this vulnerable population. The primary aim of this study was to investigate the risk factors associated with persistent SARS-CoV-2 infection in NHL patients during the COVID-19 pandemic.

Methods

A retrospective cohort study was conducted using data from January 2020 to June 2024, obtained from the Aerospace Center Hospital’s database, electronic health records, and laboratory archives. Inclusion criteria comprised patients with confirmed NHL and SARS-CoV-2 infection, with persistence defined as positive viral test results beyond 14 days after initial diagnosis. Patients with incomplete medical records or loss of follow-up were excluded. Predictive models were developed and refined using logistic regression and random forest algorithms. The models incorporated data on demographics, comorbidities, laboratory findings, and imaging results. Model performance was evaluated using accuracy, precision, and the area under the receiver operating characteristic curve (AUC-ROC). Validation was conducted on an independent dataset to ensure generalizability, and the best-performing model guided the development of a prediction tool for early risk assessment and intervention.

Results

Key risk factors for persistent SARS-CoV-2 infection in NHL patients included advanced age, hypertension, diabetes, immunosuppressed status, low lymphocyte count, elevated C-reactive protein, high body mass index, anemia, reduced CD4 + cell count, and the presence of lung lesions. The random forest model demonstrated superior predictive performance, achieving an AUC of 0.93. The study further highlighted that prompt antiviral therapy, adjustments to immunosuppressive regimens, and enhanced monitoring significantly reduced infection persistence.

Conclusions

This study identifies critical risk factors for persistent SARS-CoV-2 infection in NHL patients and underscores the importance of early intervention strategies. These findings may guide clinical decision-making to improve outcomes in this high-risk population.

Peer Review reports

Background

Non-Hodgkin’s lymphoma (NHL) is a malignancy of the lymphatic system, and its incidence has been increasing globally [1]. Treatment of NHL includes chemotherapy, immunotherapy, and targeted therapy. However, owing to the immunosuppressive nature of the disease itself and its treatment, patients become more susceptible to infections, particularly viral infections [2]. The global outbreak of the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has significantly increased the health risks faced by NHL patients [3]. Therefore, understanding and predicting the risk factors for persistent infection in NHL patients during the COVID-19 pandemic and developing effective early intervention strategies are crucial for these patients [4, 5].

Although several studies have explored the impact of COVID-19 on cancer patients, critical gaps persist, particularly in relation to NHL. For instance, Bravaccini et al. examined the clinical outcomes of COVID-19 in cancer patients but did not delve deeply into NHL-specific implications [6]. Similarly, Yang et al. focused on the general characteristics and outcomes of cancer patients with COVID-19, but NHL-specific concerns were not addressed comprehensively [7, 8]. This study aimed to test the hypothesis that patient characteristics, such as impaired immune function and complications at different NHL stages, are important risk factors for persistent SARS-CoV-2 infection in NHL patients.

This retrospective cohort study endeavored to systematically assess the risk factors for persistent infection in NHL patients during the COVID-19 pandemic and propose targeted early intervention strategies. Our specific objectives included identifying the primary risk factors for persistent infection, evaluating the effects of various intervention measures, and furnishing a scientific rationale for clinical decision-making aimed at minimizing COVID-19-related risks in NHL patients. By addressing these research questions, our findings have the potential to influence clinical practice guidelines and public health policies for managing COVID-19 in immunocompromised patients and ultimately enhance the quality of life and survival rates of NHL patients during the ongoing pandemic.

Methods

Study details

We assembled an expert team comprising specialists in respiratory and critical care medicine, infectious diseases, and statistics. Utilizing a retrospective cohort study design, our objectives were to evaluate the clinical characteristics and identify risk factors for persistent SARS-CoV-2 infection among patients with NHL. Furthermore, we aimed to construct and validate predictive models based on these factors. To achieve these goals, we analyzed historical clinical data and patient follow-up records. The data for this study were sourced from the electronic medical record system of the Aerospace Center Hospital, which encompasses over 10 million outpatients and inpatients who visited the hospital between January 2020 and June 2024.

Sample selection criteria

The inclusion criteria were as follows: Patients who met the NHL diagnostic criteria and were confirmed to have SARS-CoV-2 infection between January 2020 and June 2024, with persistent PCR positivity that was not attributed to Paxlovid rebound (specifically, excluding those who showed symptom recurrence or PCR positivity between 2 and 8 days post-Paxlovid treatment completion). The exclusion criteria were as follows: Patients with other severe illnesses that could affect their expected survival.

Sample grouping

  • Persistent infection group: NHL patients confirmed with persistent SARS-CoV-2 infection, defined as virus RNA detected ≥ 14 days after symptom onset or initial positive test result. During the survey, samples were collected weekly for the first 2 weeks of enrollment.

  • Non-persistent infection group: NHL patients were confirmed to have SARS-CoV-2 infection but did not develop a persistent infection.

Data collection

Basic patient information, clinical characteristics, laboratory test results, imaging findings, treatments, and follow-up records were extracted. Based on recent literature related to persistent SARS-CoV-2 infection in oncology patients and expert panel recommendations, variables specifically associated with SARS-CoV-2 infection were prioritized, including age, sex, underlying diseases (e.g., hypertension and diabetes), immunosuppressive status, lymphocyte count, C-reactive protein (CRP) level, body mass index (BMI), anemia status, CD4 + cell count, and lung lesions. CD4 + cell count was determined using flow cytometry, IgG levels were measured using ELISA, and viral load was quantified using real-time quantitative PCR (RT-qPCR).

Data preprocessing

  • Missing data and outliers were addressed and corrected.

  • Continuous variables were standardized.

  • Categorical variables were encoded for analysis.

Research design

Clinical characteristics and risk factor analysis

  • Data were collected from eligible NHL patients between January 2020 and June 2024.

  • Random forest feature importance analysis was employed to identify the primary risk factors contributing to persistent SARS-CoV-2 infection.

Prediction model development and validation

  • After expert panel discussions and literature review, approximately 600 patients were deemed necessary for the study based on the prevalence of persistent SARS-CoV-2 infection in NHL patients and sample size calculations. Consequently, 660 eligible subjects from January 2020 to June 2023 were included, with 110 and 550 cases of persistent and non-persistent infections, respectively, for model training and initial validation.

  • From July 2023 to June 2024, an additional 450 NHL patients infected with SARS-CoV-2 were recruited for model validation.

  • Multiple machine learning algorithms (e.g., logistic regression and random forest) were utilized to model and train the dataset.

  • Model performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC-ROC) in the training set. The best-performing model was further assessed in the validation set to ensure its robustness to the new data.

Intervention strategy development

Based on the feature importance analysis and clinical expert input, personalized intervention strategies for high-risk NHL patients were formulated, encompassing intensified monitoring, preventive treatments, lifestyle modifications, AI-assisted health management, and multidisciplinary collaboration.

Statistical analyses

SPSS 25.0 (IBM Corp., Armonk, NY, USA), or R software version 3.2.2 (version 3.2.2; R Foundation for Statistical Computing, Vienna, Austria), was used for statistical analyses. Continuous variables were analyzed using t-tests or Mann–Whitney U tests, and categorical variables were analyzed using chi-square or Fisher’s exact tests. Statistical significance was set at p < 0.05. After conducting a thorough power analysis, we have determined that, to achieve the desired statistical power (set at 0.8) and given significance level (0.05), and taking into account the expected effect size (i.e., the difference in the risk of persistent SARS-CoV-2 infection among NHL patients) as well as the heterogeneity of the patient population, approximately 50 to 300 subjects are needed per group. This sample size is intended to ensure that we can accurately assess and predict the risk of persistent SARS-CoV-2 infection in NHL patients, while also proposing effective early intervention strategies. This sample size is based on currently available information and assumptions and may need to be adjusted appropriately according to specific circumstances in actual research.

Ethical considerations

This study was approved by the Ethics Committee of Aerospace Central Hospital, the Ethical Mention Approval Number: Jinghang Medical Ethics Review 2022 No. (075). Given that it is a retrospective study, after comprehensively considering the nature, purpose, potential risks and benefits of this research, as well as the rights and privacy of patients, informed consent is exempted. Strict confidentiality measures were implemented to protect patient privacy, and all data were used solely for this study.

Expert consultation and discussion

Experts in relevant fields were consulted and engaged in discussions to validate and assess the study findings and offer suggestions for optimizing the diagnostic and treatment strategies.

Results

Basic characteristics and treatment regimens

Table 1 outlines the basic patient characteristics and treatment plans for persistent and non-persistent SARS-CoV-2 infections in the experimental cohort.

Table 1 Basic characteristics and treatment regimens in patients with NHL and persistent vs. non-persistent SARS-CoV-2 infections
Full size table

Comparison of clinical symptoms, disease course, and laboratory and imaging findings between persistent and non-persistent NHL patients with SARS-CoV-2 infections

Table 2 compares the differences in clinical symptoms, disease course, and laboratory and imaging findings between NHL patients with persistent and non-persistent SARS-CoV-2 infections. The results indicated no significant differences in clinical symptoms between the groups (p > 0.05). The listed symptoms reflect the main symptoms observed throughout the patients’ entire illness, rather than just those prior to hospital admission. Additionally, the table presents detailed information on the disease course and complications, as well as significant differences in CD4 + T cell counts and incidence of lung lesions based on laboratory tests.

Table 2 Clinical characteristics, disease course, complications, and laboratory/imaging findings in NHL patients with persistent vs. non-persistent SARS-CoV-2 infections
Full size table

Univariate analysis

Univariate analysis identified age, hypertension, diabetes, immunosuppression, lymphocyte count, CRP level, BMI, anemia, CD4 + cell count, and lung lesions as significant risk factors for persistent SARS-CoV-2 infection.

Multivariate regression analysis

Multivariate logistic regression analysis further confirmed hypertension, immunosuppression, lymphocyte count, and lung lesions as independent risk factors (Table 3).

Table 3 Independent risk factors for persistent SARS-CoV-2 infection in NHL patients
Full size table

Regression equation and risk-scoring table

A regression equation and risk-scoring table (Table 4) were constructed to predict persistent SARS-CoV-2 infection based on significant risk factors:

Table 4 Risk scoring table
Full size table

Log (\(\:\frac{p}{1-p}\)) = − 0.7838 + 1.053 Hypertension + 1.288 Immunosuppression+ (− 0.å755) Lymphocyte count + 1.å391 Lung lesions

Nomogram for predicting persistent SARS-CoV-2 infection in patients with NHL

As shown in Fig. 1, we created a forest plot based on the results of the multivariate logistic regression analysis, illustrating the associations between individual independent risk factors and persistent SARS-CoV-2 infection.

Fig. 1
figure 1

Independent risk factors for persistent SARS-CoV-2 infection in patients with NHL. NHL, non-Hodgkin’s lymphoma; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 In the forest plot, the boxes for each risk factor are all positioned to the right of 1, and their horizontal lines do not cross 1, indicating that these factors significantly increase the risk of persistent SARS-CoV-2 infection. The box for lymphocyte count is positioned to the left of 1, indicating a negative correlation with persistent SARS-CoV-2 infection, and its confidence interval also does not cross 1, demonstrating statistical significance

Full size image

AUC curve

We assessed the predictive accuracy of the multivariate logistic regression model by plotting the ROC curve and calculating the AUC. The results are shown in Fig. 2.

Fig. 2
figure 2

ROC curve for predicting the risk of persistent SARS-CoV-2 infection in NHL patients NHL, non-Hodgkin’s lymphoma; ROC, Receiver Operating Characteristic; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2

Full size image

Model’s performance in predicting persistent SARS-CoV-2 infections in patients with NHL

We evaluated the model’s performance using a validation set of 450 patients. The model had 85% accuracy, 78% sensitivity, 88% specificity, and AUC value of 0.93.

Discussion

Recent studies hint at a higher susceptibility and severity of COVID-19 among NHL patients but do not provide definitive answers to several pertinent questions specific to NHL patients [8]. Key unanswered questions include the following. How does NHL impact the susceptibility and severity of COVID-19? Does COVID-19 accelerate NHL progression or affect treatment effectiveness? What is the mortality rate among NHL patients with COVID-19? What are the long-term health consequences? What are best practices for managing NHL in patients with COVID-19? Understanding and accurately predicting the risk factors for persistent SARS-CoV-2 infection in patients with NHL will facilitate the formulation of effective early intervention strategies, which can significantly mitigate adverse clinical outcomes. Our study not only provides a scientific foundation for clinical treatment decisions but also serves as pivotal support for the development of public health prevention and control strategies.

In this innovative study, we employed a multifaceted approach, integrating various research methods to comprehensively analyze the clinical characteristics and risk factors associated with persistent SARS-CoV-2 infection in NHL patients. This comprehensive analysis enabled us to construct a robust and reliable prediction model that facilitates early identification and intervention in high-risk NHL patients.

The novelty of our study lies in its comprehensive and integrated approach, which distinguishes it from previous studies. By leveraging a diverse array of research methods and analyzing a wide range of clinical data, we have developed a prediction model that holds promise for significantly improving patient outcomes and advancing our understanding of persistent SARS-CoV-2 infection in the NHL patient population.

Since its first report in Wuhan, China, at the end of 2019, SARS-CoV-2 has spread rapidly, leading to a global COVID-19 pandemic. While the virus is typically cleared from the respiratory tract within 9 days of symptom onset, viral RNA can be detected for an average of 17 days [9]. However, some case reports have indicated that viral RNA can persist in the upper respiratory tract and feces for several months [9]. This abnormally prolonged viral presence, known as “persistent infection,” is generally defined as the detection of viral RNA ≥ 14 days after symptom onset or the first positive test [9]. SARS-CoV-2 not only directly attacks the respiratory mucosa but can also increase the severity and duration of infection in patients with NHL by disrupting their immune system. As recently reported, immunosuppressive therapy, severity of underlying diseases, and viral load may be associated with persistent SARS-CoV-2 infection [10]. Furthermore, early intervention measures, such as intensified monitoring, antiviral therapy, and personalized management strategies, have been shown to reduce persistent infections and improve patient outcomes [11].

In patients with NHL, persistent SARS-CoV-2 infection can lead to more severe consequences, including higher hospitalization rates, intensive care requirements, and mortality [12]. NHL is a highly heterogeneous hematological malignancy, and its incidence increases with age. Using univariate analysis, we identified age, hypertension, diabetes, immunosuppressive status, lymphocyte count, CRP level, BMI, anemia, CD4 + cell count, and lung lesions to be the main risk factors for persistent SARS-CoV-2 infection, consistent with the findings of previous studies [13]. The immune function of patients with NHL is often suppressed by the disease itself or the treatment, making them more susceptible to SARS-CoV-2 infection and more likely to develop a persistent infection [14]. As reported, 70% of patients with NHL receiving immunosuppressive therapy experience immune suppression, which increases the risk of persistent infection [15]. Additionally, underlying conditions such as hypertension and diabetes also increase the risk of severe disease progression after infection. In patients with NHL, persistent infections have been reported in 65% of those with hypertension and in 30% with diabetes [16]. Chronic inflammatory states, anemia, low lymphocyte counts, and high CRP levels are all closely associated with the occurrence of persistent infection [17].

Patients with persistent SARS-CoV-2 infection have significantly lower lymphocyte counts (averaging 0.8 ± 0.5 × 109/L) [18], significantly higher CRP levels (averaging 50.4 ± 15.2 mg/L) [19], and significantly reduced CD4 + cell counts (averaging 450 ± 110 cells/µL) [20] when compared to those in patients without persistent infection.

Among patients with persistent infections, 85.5% presented with lung lesions [21]. Based on the multivariate logistic regression analysis, we identified hypertension, immunosuppressive status, low lymphocyte count, and lung lesions as independent risk factors for persistent SARS-CoV-2 infection in patients with NHL. Our findings are consistent with those of recent studies. For example, a 2021 systematic review indicated that hypertension is associated with increased severity and mortality in COVID-19 [22]. Immunosuppressive status, as seen in patients receiving chemotherapy and biological agents, has also been confirmed as an important risk factor for persistent COVID-19 and poor prognosis [23]. A low lymphocyte count, reflecting impaired immune function, was also reported to be a poor prognostic factor in COVID-19 patients in 2022 [24]. Lung lesions, as markers of COVID-19 severity, have been proven by multiple studies to correlate with long-term viral load [25, 26].

We constructed a prediction model for persistent SARS-CoV-2 infection in patients with NHL using a random forest algorithm, based on a comprehensive dataset encompassing clinical information, laboratory test results, and treatment records. The model’s efficacy was evaluated using a validation set of 450 patients, yielding impressive results.

The validation process showed that the model demonstrated an accuracy of 85%, correctly predicting persistent infection in 383 out of 450 patients, sensitivity of 78%, indicating its ability to accurately identify patients with actual persistent infection; and specificity of 88%, indicating its ability to correctly distinguish patients without persistent infection. The area under the ROC curve was 0.93, further confirming the model’s robust performance in differentiating high- and low-risk patients.

These findings highlight the potential of our model as a valuable decision-support tool for clinicians. By precisely identifying NHL patients more prone to developing persistent SARS-CoV-2 infection, the model could enable timely implementation of aggressive treatments or preventive measures, ultimately improving patient outcomes. The seamless integration of this model into electronic medical record systems or clinical decision-support systems could facilitate real-time automated risk assessments. Additionally, its alignment with existing clinical guidelines and treatment protocols offers a scientific foundation for personalized treatment plans.

Despite the model’s strong performance, several limitations merit attention. These include the representativeness of the dataset, comprehensiveness of feature selection, and risk of model overfitting. Addressing these limitations requires further research and refinement. Future studies should aim to include larger sample sizes, explore additional biomarkers as predictive features, and develop dynamic prediction models to further enhance the model’s predictive accuracy and clinical applicability.

This study has some limitations. First, it had a relatively small sample size, which may have affected the external validity of the results. Second, the retrospective study design may have resulted in incomplete information, reporting bias, data bias, and omission of confounding factors, potentially affecting the universality and accuracy of the findings. Third, data derived from single-center or region-specific patients may limit the generalizability of the results. Finally, the inability to control for all potential confounding factors may have affected the interpretation of the results.

Conclusions

This study aimed to explore the key risk factors for persistent SARS-CoV-2 infection among patients with NHL. Through in-depth analysis, we successfully identified these risk factors, providing important insights into the mechanisms underlying persistent SARS-CoV-2 infection in NHL patients. The results of this study provide a reference for clinical practice and will help clinicians more accurately assess the infection risk of NHL patients and take corresponding preventive measures to reduce the incidence of infection. The risk factors identified in this study serve as a foundation for future research to further explore SARS-CoV-2 infection in NHL patients. Although we do not directly propose early intervention strategies, the identified risk factors provide a scientific basis for formulating targeted prevention and control measures. For NHL patients, especially those with high-risk factors, enhanced monitoring, personalized treatment recommendations, and health education should be provided to improve their self-management abilities and reduce the risk of infection.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AUC-ROC:

Area Under the Receiver Operating Characteristic Curve

BMI:

Body Mass Index

COVID-19:

Coronavirus Disease 2019

CRP:

C-Reactive Protein

Ct:

Cycle threshold

ELISA:

Enzyme-Linked Immunosorbent Assay

IgG:

Immunoglobulin G

NHL:

Non-Hodgkin’s Lymphoma

PCR:

Polymerase Chain Reaction

RT-qPCR:

Real-Time quantitative Polymerase Chain Reaction

SARS-CoV-2:

Severe Acute Respiratory Syndrome Coronavirus 2

SPSS:

Statistical Package for the Social Sciences

WBC:

White Blood Cell

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Acknowledgements

The authors would like to thank the patients; their families; and all the researchers, including physicians, nursing staff, and laboratory technicians, who participated in this study.

Funding

This study was supported by the National Natural Science Foundation of China (grant no. 82300089). This funding source played a crucial role in facilitating the research process for this study. Specifically, they provided essential resources for conducting the study, including financial support for data collection, analysis, and interpretation. However, the funding source had no direct involvement in the study design, writing of the report, or decision to submit the paper for publication. The authors maintained complete independence and autonomy in all aspects of the research and reporting process. Therefore, any conclusions or recommendations presented in this study are solely those of the authors and do not necessarily reflect the views or opinions of the funding source.

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Authors and Affiliations

  1. Department of Pulmonary and Critical Care Medicine, Aerospace Center Hospital, Beijing, 100049, China

    Wailong Zou, Jia Zhang, Yulin Li, Yuwei Cao, Jiaxin Li, Zhe Zhang, Xin Zhang, Chuan Song, Rui Yang, Yaxin Yan, Yumin Wang, Xinjun Zhang & Jichao Chen

  2. Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China

    Zhe Xu

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  1. Wailong Zou
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Contributions

WLZ, JZ, YLL, ZX, and JCC conceived the study and wrote the manuscript. YWC and JXL collected data and critically revised the manuscript. ZZ, XZ, and CS performed statistical analyses. YXY, YMW, XJZ, and RY collected data. All the authors have read and approved the final version of the manuscript. JZ, YLL, and CS visited and verified the data, and WLZ, ZX, JZ, and JCC were responsible for the decision to submit the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhe Xu or Jichao Chen.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board of the Aerospace Center Hospital (no. 2022-075) and was conducted according to the tenets of the Declaration of Helsinki. The need for informed consent was waived because of the retrospective nature of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Zou, W., Zhang, J., Li, Y. et al. Risk prediction and early intervention strategies for persistent SARS-CoV-2 infection in patients with non-Hodgkin lymphoma: a retrospective cohort study. BMC Pulm Med 25, 122 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-025-03524-0

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-025-03524-0

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BMC Pulmonary Medicine

ISSN: 1471-2466

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