The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model

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However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD. Purpose The aim of this study to investigate the value of the deep learning and radiomics features to evaluated the severity and predict the nucleic acid turning-negative time in COVID-19 patients with COPD including two phenotype of chronic bronchitis predominant patients and emphysema predominant patients. Methods A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema groups of 94 patients, COVID-19 with chronic bronchitis groups of 92 patients. All patients underwent chest scans and recorded clinical data. The U-net network was trained to segment the infection regions on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation between the data and visualize it through a heatmap. Then we respectively establish a deep learning model using original CT image and a fusion model combined deep learning with radiomics features to predict the time for nucleic acid turning-negative. Results COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time to nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that the fusion model achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time. Conclusion The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice. COVID-19 with COPD Pulmonary involvement Nucleic acid turning-negative time Deep learning method Radiomics features Figures Figure 1 Figure 2 Figure 3 Introduction COVID-19 is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [ 1 ]. Mudatsir et al. demonstrated that chronic diseases have been associated with COVID-19 severity and death, such as chronic obstructive pulmonary disease, diabetes mellitus, hypertension and cardiovascular disease experience [ 2 ]. COPD is one of the most common chronic diseases in the world, which characterized by chronic respiratory symptoms due to abnormalities of the airways and/or alveoli [ 3 ]. Sanchez-Ramirez et al. found that people with COPD were associated with a four-time higher risk of severe disease including intensive care unit admission, death, or other critical outcomes [ 4 ]. Increasing evidence suggests that the up-regulation of Angiotensin Converting Enzyme 2 (ACE2) is a significant factor contributing to the susceptibility of COPD patients to SARS-CoV-2 infection [ 5 – 6 ]. Moreover, COPD patients often have impaired innate and adaptive immune responses, which can delay the clearance of respiratory viruses [ 7 ]. Consequently, SARS-CoV-2 may have a higher propensity to spread within the lungs of COPD patients and COPD patients have worse outcomes from COVID-19 [ 8 ]. A 2023 bibliometric analysis indicated that, as a key focus of COVID-19 epidemic prevention, COPD combined with COVID-19 still was a hot topic and trend [ 9 ]. The diagnostic value of computed tomography (CT) in COVID-19 has been well-established [ 10 ]. CT imaging can detect characteristic features of COVID-19 pneumonia, such as ground-glass opacity, crazy paving pattern, consolidation, and fibrosis [ 11 ]. Serial CT scans can track the evolution of lung lesions over time, providing insights into the effectiveness of therapeutic interventions [ 12 ]. Additionally, the chest CT severity score (CTSS) is a scoring method in the assessment of COVID-19 pulmonary parenchymal involvement, which has demonstrated strong discriminating power for the prediction of disease severity and outcome in COVID-19 patients [ 13 – 14 ]. These findings could help differentiate COVID-19 pneumonia from other respiratory infections and contribute to early detection and diagnosis of COVID-19 pneumonia. Previous studies revealed that the pre-existing COPD increased the severity and the risk of death of COVID-19 [ 15 – 16 ]. The classic COPD phenotypes of chronic bronchitis and emphysema have been recognized in Global Initiative for Chronic Obstructive Lung Disease and Global Initiative [ 17 ]. However, the differences of pulmonary parenchymal involvement between COVID-19 patients with two COPD phenotypes has not been explored. Due to the outbreak of COVID-19, global healthcare resources have been facing a tremendous burden. Thus, accumulating studies focus on identification of the patients whose nucleic acid can turn negative in a short time to make rational use of resources. Liu et al. found biochemical indicators such as lactate dehydrogenase, c-reactive protein and Albumin were useful prognostic marker for predicting nucleic acid turn negative time [ 18 ]. Zhu et al demonstrated that the value of nutrophil-to-lmphocyte ratio (NLR) and vaccination could predict the negative conversion time of nucleic acid [ 19 ]. The time of nucleic acid conversion to negative was closely related to the clinical manifestations and disease progression of COVID-19 patients [ 20 ]. With the wide spread of models based on artificial intelligence, automatic extraction of quantitative metrics based on deep learning method from lung computed tomography (CT) could provide the tool to the screening and evaluation of COVID-19 [ 21 – 22 ]. In addition, radiomics has been widely used in quantitative analysis of CT images for screening COVID-19 patients [ 23 – 24 ], discriminating the severity of pneumonia [ 25 ], and prognosis assessment in patients with COVID-19 pneumonia [ 26 ]. As of yet, the research on utilizing deep learning and radiomics for predicting the nucleic acid conversion time of COVID-19 patients is still in its early stages and remains relatively limited. Thus, our study is to explore the severity of pulmonary between COVID-19 patients with COPD and further analyze the difference with COPD phenotypes of chronic bronchitis and emphysema. Therefore, our study aims to evaluate the value of deep learning and radiomics features extracted from CT images in predicting the nucleic acid conversion time. Additionally, we evaluated the severity of pulmonary between COVID-19 patients with emphysema and those with chronic bronchitis, so as to provide reference for subsequent treatment. Patients and methods Participants This retrospective cohort study enrolled 292 patients with confirmed COVID-19 from Hohhot First Hospital between October 2022 and January 2023, who underwent chest CT scans. Inclusion criteria for this study were: (1) confirmed positive by real-time reverse-transcriptase polymerase-chain-reaction (RT-PCR) assay from nasal and pharyngeal swab specimens; (2) CT images demonstrated pneumonia; (3) Patients with pre-existing chronic obstructive pulmonary disease. The COPD patients were divided into two groups: chronic bronchitis predominant and emphysema predominant, who was made by experienced physicians based on Global Initiative for Chronic Obstructive Lung Disease and Global Initiative [ 17 ]. The definition of chronic bronchitis predominant was chronic sputum for most days, 3 months a year, or no radiological diagnosis of emphysema for at least 2 years. Emphysema predominant was defined as no chronic cough and sputum but having typical clinical and radiological manifestations of emphysema [ 27 ]. Exclusion criteria for this study were: (1) patients with inflammatory diseases such as pancreatitis, prostatitis, immunological diseases or with severe diseases such as hypertension, diabetes, cerebro-cardiovascular diseases, and chronic kidney disease; and (2) patients with missing data. Clinical data were collected and recorded from each patient, which included gender, age, vaccination status, nucleic acid turning-negative-time, and laboratory test results (procalcitonin level, C-reactive protein level, white leukocyte count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio (NLR). The criteria for confirming the patient’s nucleic acid turned negative were twice consecutive negatives, and the interval of tests > 24 hours [ 18 ]. CT acquisition Patients underwent chest CT imaging on two 64 detector CT scanners (Discovery CT 750 and u-CT 760). The protocol was as follows: tube voltage: 120 kV; automatic tube current (260 mA for Discovery CT 750, GE and 150 mA for u-CT, United Imaging); reconstruction slice thickness: 1.25 mm; pitch of 1.0; matrix, 512 × 512 and breath hold at full inspiration in a craniocaudal direction from the lung apices to lateral costophrenic sulci. The following windows were used for image display: a mediastinal window with window width of 400 HU and window level of 40 HU and a lung window with a width of 1600 HU and window level of -700 HU. The acquired images were subsequently reconstructed using an iterative reconstruction method. Segmentation and preprocessing of CT images The U-net [ 28 ] convolutional neural network was trained to segment the infection regions on CT scans based on 500 datasets in our study and the frame is illustrated in Fig. 1 . As seen in Fig. 1 , the dice coefficient on the test set reaches 0.85, which is confirmed by an experienced radiologist and meets clinical diagnostic standards. We refer readers to some predecessors' research, the pulmonary involvement was quantitatively estimated on the basis of these abnormalities area involved [ 12 – 14 ]. Extraction and analysis of radiomic features Radiomic features were extracted from the infection regions of CT images using the PyRadiomics Python tool (v3.11) [ 29 ]. A total of 107 features were extracted for each chest CT image, including 14 shape features, 18 first-order statistics from the gray-scale histogram, and 75 second-order statistics derived from the grey-level co-occurrence matrix. A total of 30 features were selected to model construction based on the LASSO method. Construction of the classification model We constructed two models using a universal Pytorch framework ( http://pytorch.org ) to predict the nucleic acid turning negative time in our study, including a deep learning model based on original CT image and a deep learning fusion model. The details of the deep learning fusion model were as follows. As seen in Fig. 2 , one input was the segmented image of the pneumonia area, and the other input was the original CT image in the classification model. The first branch consisted of 3 groups of 7×7conv2d, 5×5conv2d and 3×3 conv2d plus batch-norm. The rectified linear unit (ReLU) activation function was used. F1 features(2*128) from neural network and F2 feature map(2×30) from radiomics were concatenated in the feature dimension to obtain the total feature map(2×158). The total feature map was compressed through the global average pooling function and the fully connected layer to obtain a 2×4 final feature map, and the Softmax function was used to achieve the probability output of each target category. In formula (1), Z_i represented the output values of the i-th sample and n represented the number of classes in the classification task. With respect to the model construction, the backpropagation (BP) algorithm was employed, which updated the network parameters iteratively until an optimal or suboptimal solution was reached. The model iterated for a total of 60 epochs, with batch size setting to 2, and the learning rate setting to 0.0001. The weight cross entropy loss function was utilized to alleviate the sample imbalance problem according to the weights of different sample numbers. In formula (2), i represented the label or category of a sample, P(i) indicated the probability of the true label, and Q (i) being the probability predicted by the model. Specifically, according to the different time required for nucleic acid to turn negative, the subjects were divided into the following four categories: 0–5 days, 6–10 days, 11–15 days, and ≥ 16 days. The following metrics were employed to evaluate the performances of each model: accuracy, precision, sensitivity, specificity, F1-score. Statistical analysis Categorical variables were presented as frequencies and percentages, which were compared using the chi-square test. Continuous variables were described as means ± standard deviation (SD) or median (interquartile range, IQR) and compared between groups using either a two-sample t-test or Wilcoxon test, depending on the distribution. Spearman correlation was used to assess the correlations between variables. All statistical analyses were performed using IBM SPSS Statistics Software (version 27.0, SPSS Inc., Chicago, IL, USA). Patients and methods Demographics and baseline clinical characteristics A total of 281 patients satisfied the inclusion criteria in our study, including 95 COVID-19 patients, 94 COVID-19 patients with the emphysema‑predominant COPD, and 92 patients with the chronic bronchitis-predominant COPD. As shown in Table 1 , 62 patients (65.96%) were males in COVID-19 with emphysema group, and more than the other two groups. The laboratory tests including white leukocyte count, neutrophil count, NLR value, and C-reactive protein level increased in COVID-19 patients with COPD than COVID-19 patients. However, the Lymphocyte count was shown to be lower in COVID-19 patients with emphysema compared than other groups and the median was 1.07 x10^9cells/L. Among the patients with different time required for nucleic acid to turn negative, 51.58% patients during 6–10 days in COVID-19 group, 40.43% patients during 11–15 days of COVID-19 patients with emphysema and 40.22% patients during 11–15 days of COVID-19 patients with chronic bronchitis. A total of 85 patients (89.47%) received at least one dose of inactivated SARS-CoV-2 vaccine in COVID-19 group, in which 56 patients (58.95%) received three doses of inactivated vaccine. Only 6 patients (6.32%) received one dose of the inactivated vaccine, and 23 patients (24.21%) got two doses of the inactivated vaccine. However, 36 patients (38.30%) in COVID-19 with emphysema and 19 patients (20.65%)in COVID-19 with chronic bronchitis have not been vaccinated. There were significant statistical differences in sex, lymphocyte count, neutrophil count, NLR value, C-reactive protein level, nucleic acid turning-negative-time, and Vaccination dose between the three groups (p < 0.05). Table 1 Comparison of baseline characteristics among the patients. COVID-19(95) COVID-19 with emphysema(94) COVID-19 with chronic bronchitis(92) P value Sex (male),N(%) 36(37.89%) 62(65.96%) 35(38.04%) < 0.001 White leukocyte, per l 4.58(1.73) 5.08(2.06) 5.25(2.04) 0.076 Lymphocyte, per l 1.36(0.64) 1.07(0.57) 1.37(0.68) < 0.001 Neutrophil, per l 2.77(1.31) 3.27(1.99) 3.09(1.71) 0.032 NLR 2.00(1.43) 2.90(2.53) 2.34(1.52) < 0.001 C-reactive protein, per l 7.40(8.65) 13.16(28.87) 8.70(12.52) < 0.001 PCT, per ml 0.05(0.05) 0.06(0.05) 0.06(0.08) 0.191 Nucleic acid turning-negative-time, days 0.003 0–5 7(7.37%) 13(13.83%) 5(5.43%) 6–10 49(51.58%) 26(27.66%) 30(32.61%) 11–15 33(34.74%) 38(40.43%) 37(40.22%) > 15 6(6.32%) 17(18.09%) 20(21.74%) Vaccination dose, N (%) < 0.001 0 10(10.53%) 36(38.30%) 19(20.65%) 1 6(6.32%) 2(2.13%) 12(13.04%) 2 23(24.21%) 19(20.21%) 18(19.57%) 3 56(58.95%) 37(39.36%) 43(46.74%) The severity of pneumonia analysis As seen in Table 2 , COVID-19 combined with COPD show the higher proportion of pulmonary involvement than those patients with COVID-19 only. The average proportion of pulmonary involvement in COVID-19 with emphysema groups was 3.90% and has the most extensive range of pulmonary inflammation. Table 2 The pulmonary involvement degree in the three COVID-19 groups. COVID-19 COVID-19 with emphysema COVID-19 with chronic bronchitis pulmonary involvement 0.61% 3.90% 1.80% Correlations between different clinical variables As seen in Fig. 3 , in terms of pulmonary involvement indicators, six indicators show significant correlations: vaccination dose (correlation coefficient value (r) = -0.228, P < 0.001), lymphocyte count (r= -0.145, P < 0.05), neutrophil count (r = 0.122, P < 0.05), C-reactive protein level (r = 0.352, P < 0.001), PCT level (r = 0.217, P < 0.001), and NLR (r = 0.178, P < 0.01). Regarding the time to nucleic acid turning negative, two indicators exhibit significant correlations: vaccination dose (r= -0.201, P < 0.001) and lymphocyte count (r = 0.145, P < 0.05). Besides, significant positive correlations were observed between NLR value and white leukocyte count, neutrophil level, C-reactive protein, and PCT (P < 0.001) and significant negative correlations were identified between NLR value and lymphocyte count (P < 0.001). Prediction results of nucleic acid turning-negative-time The performances of the Model 1 on the test set with an overall accuracy of 78.7%, precision of 77.6%, sensitivity of 75.0%, specificity of 77.1% and F1 score of 77.1% (Table 3 ). The Model 2 improved the classification result, achieving an accuracy of 80.9%, precision of 77.9%, sensitivity of 77.8%, specificity of 75.8%, and F1 score of 77.8%. For either the Model 1 or the Model 2, the sensitivities for prediction of nucleic acid turning-negative-time during 6–10 and 11–15 days were better than the other two. Table 3 The classification results by using the two models. Model 1 Accuracy Precision Sensitivity Specificity F1-score Class 1(0–5 days) / 75.0% 66.7% 75.6% 70.6% Class 2 (6–10 days) / 76.5% 86.7% 85.7% 81.3% Class 3 (11–15 days) / 92.3% 80.0% 71.4% 85.7% Class 4( \(\ge\) 16 days) / 66.7% 66.7% 75.6% 70.6% Overall 78.7% 77.6% 75% 77.1% 77.1% Model 2 Class 1 (0–5 days) / 77.8% 77.8% 77.5% 77.8% Class 2 (6–10 days) / 85.7% 80% 74.3% 82.8% Class 3 (11–15 days) / 81.3% 86.7% 73.5% 83.9% Class 4( \(\ge\) 16 days) / 66.7% 66.7% 78% 66.7% Overall 80.9% 77.9% 77.8% 75.8% 77.8% Model 1: The model based on deep learning method; Model 2༚The model combined deep learning with radiomics features. Discussion As far as we know, this is the first study targeting two common subtypes of COPD combined with COVID-19 patients. In the current study, we have explored the laboratory variables difference and the severity of pneumonia between three groups, including COVID-19 only group, COVID-19 patients with emphysema groups, and COVID-19 patients with chronic bronchitis. Two predictive models, i.e. a deep learning model and a fusion model combining deep learning with radiomics, were constructed to predict the nucleic acid turning-negative-time and the fusion model achieved 80.9% of accuracy (Table 3 ). Rapid and effective prediction of the delayed negative conversion of SARS-CoV-2 in COVID-19 with COPD patients would contribute to the rational allocation of medical resources. Interestingly, the current study demonstrated that those COVID-19 patients with COPD have higher level of NLR values compared with the COVID-19 patients. The NLR values in COVID-19 with emphysema were highest with a median of 2.90 (Table 1 ) and the NLR values were positively correlated with the pulmonary involvement (r = 0.178, p < 0.01) (Fig. 3 ). Such findings were consistent with previous research that the NLR values of severe patients were higher than those that in mild patients [ 30 ] and the elevated NLR in COVID-19 patients was associated with poor outcomes [ 31 ]. However, the COVID-19 patients with emphysema had the lowest lymphocyte count compared with COVID-19 patients and COVID-19 patients with chronic bronchitis. As a prominent clinical manifestation, the lymphocytes level was shown to be decreased in COVID-19 patients, which related to the body's immune function and inflammatory status [ 32 ]. Especially in severe COVID-19 patients, serum secretion of Interleukin-2 Receptor (IL-2R) were remarkably increased, which was also considered as a sign of lymphopenia [ 33 ]. Similarly, COPD patients are always accompanied by immune aging and immune dysfunction, which may lead to a decrease in lymphocyte count. The aging immune system has a weaker ability to respond to viral infections, which may lead to the loss of lymphocytes. The subtypes of emphysema cause structural destruction of lung tissue, including decreased elasticity of lung tissue and increased lung volume. When emphysema patients are infected with COVID-19, the lung tissue may release more inflammatory cells and cytokines, such as the highly prionflammatory macrophages abundant in lungs, further leading to the adverse effects on the survival of lymphocytes [ 34 ]. Furthermore, the current study showed that those COVID-19 patients with COPD had higher pulmonary parenchymal involvement compared with the COVID-19 patients, which may be related to the differential expression of e angiotensin-converting enzyme 2 (ACE-2) [ 35 ]. A previous study demonstrated that increased level of ACE-2 expression was strongly associated with viral load and lung injury [ 36 ]. In clinical practice, the drugs of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker can be used to treat COVID-19 patients and ameliorated lung injury [ 37 ]. Interferons (IFNs) was a group of cytokines with antiviral, antiproliferative, antiangiogenic, and immunomodulatory activities. Some studies have confirmed that type I interferon (IFN) is associated with CT airway wall thickness, lung function, and COPD exacerbation, which may increase susceptibility of COPD patients to SARS-CoV-2 infection [ 38 – 39 ]. In addition, inflammation-associated alveolar damage and vascular injury in COVID-19 patients causes emphysema alterations and the presence of pre-existing emphysematous lung area patients may cause the outcome of critically-ill COVID-19 patients [ 40 – 41 ]. In summary, the COVID-19 patients with COPD may have more pneumonia infection areas. Critically, it is worth noting that nucleic acid conversion time was negatively correlated with vaccination dose (r=-0.201, P < 0.001) in the current study. A previous study demonstrated that the nucleic acid turning-negative-time was related to clinical symptoms of constipation, fever, and expectoration. Specifically, if patients exhibit these symptoms, the nucleic acid turning-negative time is likely to be prolonged [ 42 ]. The vaccine based on mRNA can stimulate the immune system immediately as soon as possible and produce a remarkable effect through immune cells such as CD8 + T cells [ 43 ]. The patients receiving the vaccine can significantly reduce the incidence of severe symptoms, alleviate the severity of the disease, and lower the risks of hospitalization and death [ 45 – 47 ]. These research findings corroborate our findings that vaccination can shorten the time to turn negative for nucleic acid, therefore providing evidence for the effectiveness of COVID-19 vaccines in alleviating symptoms. Lastly, we found that the performance of the fusion model that combined deep learning with radiomics features outperformed the sole deep learning model in predicting the nucleic acid turning-negative-time. Usually, deep learning method focuses on the high-order features of the entire CT image and ignores the potential value of pneumonia area. Therefore, more complementary information was provided by extracting the low-order features of the area of interest in pneumonia, including first-order features, second-order features, as well as shape features. At the same time, the sensitivity for predicting nucleic acid turning-negative-time during 6–10 and 11–15 days were better than other periods in our study; this may be related to the particular distribution of the number of participants in the current study, i.e. those 2 categories are larger than other categories (Table 1 ). Our study has several limitations that should be acknowledged. Firstly, it was conducted at a single center, which may introduce potential biases and limit the generalizability of our findings to other populations or locations. The limited number of patients included in our study also restricts the statistical power and reliability of our results. To address these limitations and enhance the robustness of our findings, further multi-center studies involving a larger sample size are warranted. Secondly, we opted for a convolutional network given the limited availability of data. However, the transformer models and other advanced networks have demonstrated significant potential in various domains, including natural language processing and computer vision. Future research endeavors to employ larger datasets and more computational resources could be explored to further enhance the accuracy and performance of our study. Conclusion In the current study, we revealed that pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement in COVID-19 patients. Also, our findings have demonstrated that the lymphocyte number may be the key indicator for predicting severity and nucleic acid turning-negative-time of COVID-19 with COPD. Critically, we have shown that prediction of turning-negative-time could be achieved through deep learning combined with radiomics methods, which provides a viable avenue for clinical diagnosis decision-making. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Hohhot First Hospital. Consent for publication Not applicable. Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Conflicts of Interest All authors declare that they have no financial or competing interests. Funding This work was supported by the Inner Mongolia Autonomous Region Science and Technology Plan Project (2023YFSH0015). Authors’ contributions WZ and MS contributed to the conception and design of this research, data analysis, and manuscript writing. YL, LW, JZ, YH and HL were participated in the data acquisition and inspection. XY and XL were participated in the revision and proofreading of the paper. The authors' order was determined by their contributions. All authors have read and approved the final manuscript. References Guo YR, et al. 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International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer, Cham, 2015. https://doi.org/10.1007/978-3-319-24574-4_28 . van Griethuysen JJM, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017;77(21):e104–7. https://doi.org/10.1158/0008-5472.CAN-17-0339 . Li X, et al. Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis. Crit Care (London England). 2020;24(1):647. https://doi.org/10.1186/s13054-020-03374-8 . Sarkar S, Khanna P, Singh AK. The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis. J Intensive Care Med. 2022;37(7):857–69. https://doi.org/10.1177/08850666211045626 . Tan L, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target therapy. 2020;5(1):33. https://doi.org/10.1038/s41392-020-0148-4 . Mahmoodpoor A, et al. Reduction and exhausted features of T lymphocytes under serological changes, and prognostic factors in COVID-19 progression. Mol Immunol. 2021;138:121–7. https://doi.org/10.1016/j.molimm.2021.06.001 . Zhao Q, et al. Metabolic modeling of single bronchoalveolar macrophages reveals regulators of hyperinflammation in COVID-19. iScience. 2022;25(11):105319. https://doi.org/10.1016/j.isci.2022.105319 . Leung JM et al. ACE-2 expression in the small airway epithelia of smokers and COPD patients: implications for COVID-19. The European respiratory journal. 2020; 55(5):2000688. https://doi.org/10.1183/13993003.00688-2020 . Liu Y, et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci. 2020;63(3):364–74. https://doi.org/10.1007/s11427-020-1643-8 . Zhang L, et al. An ACE2 decoy can be administered by inhalation and potently targets omicron variants of SARS-CoV-2. EMBO Mol Med. 2022;14(11):e16109. https://doi.org/10.15252/emmm.202216109 . Fricke-Galindo I, et al. IFNAR2 relevance in the clinical outcome of individuals with severe COVID-19. Front Immunol. 2022;13:949413. https://doi.org/10.3389/fimmu.2022.949413 . Yun JH, et al. An interferon-inducible signature of airway disease from blood gene expression profiling. Eur Respir J. 2022;59(5):2100569. https://doi.org/10.1183/13993003.00569-2021 . Celik E, et al. Quantitative determination of pulmonary emphysema in follow-up LD-CTs of patients with COVID-19 infection. PLoS ONE. 2022;17(2):e0263261. https://doi.org/10.1371/journal.pone.0263261 . Zhang N, et al. Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients. Eur Radiol. 2020;30(11):6151–60. https://doi.org/10.1007/s00330-020-06955-x . Li Q et al. Symptoms associated with nucleic acid turning-negative-time in COVID-19 patients? Acupuncture and herbal medicine. 2022; 2(3):207–9. https://doi.org/10.1097/HM9.0000000000000037 . Liu J, et al. Vaccines elicit highly conserved cellular immunity to SARS-CoV-2 Omicron. Nature. 2022;603(7901):493–6. https://doi.org/10.1038/s41586-022-04465-y . Polack FP, et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N Engl J Med. 2020;383(27):2603–15. https://doi.org/10.1056/NEJMoa2034577 . Baden LR, et al. Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine. N Engl J Med. 2021;384(5):403–16. https://doi.org/10.1056/NEJMoa2035389 . Voysey M, et al. Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK. Lancet (London England). 2021;397(10269):99–111. https://doi.org/10.1016/S0140-6736(20)32661-1 . Zang X, et al. The Value of Early Positive Nucleic Acid Test and Negative Conversion Time of SARS-CoV-2 RNA in the Clinical Outcome of COVID-19 Patients. Front Med. 2022;9:826900. https://doi.org/10.3389/fmed.2022.826900 . Additional Declarations No competing interests reported. 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Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai","correspondingAuthor":false,"prefix":"","firstName":"Wenxiu","middleName":"","lastName":"Zhang","suffix":""},{"id":290994681,"identity":"afdb5c7a-382c-4129-8d7a-c7fd61651dbc","order_by":2,"name":"Mengzhou Sun","email":"","orcid":"","institution":"Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Mengzhou","middleName":"","lastName":"Sun","suffix":""},{"id":290994682,"identity":"6aed51f3-e441-4bc4-8d05-a9d5c8e82bae","order_by":3,"name":"Xiaoyun Liang","email":"","orcid":"","institution":"Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Liang","suffix":""},{"id":290994683,"identity":"5bc76875-5500-4f06-8424-0a06b322780f","order_by":4,"name":"Lu Wang","email":"","orcid":"","institution":"Medical Imaging Department, Hohhot First Hospital, Inner Mongolia","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Wang","suffix":""},{"id":290994684,"identity":"cbf27e7f-3d21-44a3-81c6-db8bafc8943d","order_by":5,"name":"Jiaqi Zhao","email":"","orcid":"","institution":"Medical Imaging Department, Hohhot First Hospital, Inner Mongolia","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Zhao","suffix":""},{"id":290994685,"identity":"7ce99288-76df-4025-ba20-b92379d0e9ba","order_by":6,"name":"Yongquan Hou","email":"","orcid":"","institution":"Respiratory and Critical Care Medicine Department, Hohhot First Hospital, Inner Mongolia","correspondingAuthor":false,"prefix":"","firstName":"Yongquan","middleName":"","lastName":"Hou","suffix":""},{"id":290994686,"identity":"3882ed9e-b4e4-48b9-8a0c-d98f53e465dd","order_by":7,"name":"Haina Li","email":"","orcid":"","institution":"Medical Imaging Department, Hohhot First Hospital, Inner Mongolia","correspondingAuthor":false,"prefix":"","firstName":"Haina","middleName":"","lastName":"Li","suffix":""},{"id":290994687,"identity":"5ae770f3-8205-4918-841d-6601710a4c36","order_by":8,"name":"Xiaoguang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3RrwvCQBTA8ScnS8PVNxD9F54Mphb9V+4YmBSWZEFkgmxBxGrzX1gS40RYOrtR8R/QZjB4XfFmM9wnvy/vfgAYxh+ynDT3noS9/ua6P/Nook9qKHloh52AYBDQWRb6pAGcbvYtqmQw9N3LnJU4GOQiQ0LWBulHIrbASRf8e8JmByLCWneWjE9iVweUx0y3ZUBcbYED256EtIBwpEt4+5ETVuIC/FAkrFRCrVglmaz6UC5Rj+ypscBdWwFyWdjauzRX6ivhOe05yPb3RzRpOOnye/LG/m3cMAzD+OgFE35HWzyLTewAAAAASUVORK5CYII=","orcid":"","institution":"Medical Imaging Department, Hohhot First Hospital, Inner Mongolia","correspondingAuthor":true,"prefix":"","firstName":"Xiaoguang","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-04-02 10:50:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4206078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4206078/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-024-03333-x","type":"published","date":"2024-10-14T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54997768,"identity":"d39eceb3-5a7f-4e3d-9ab4-7af3dd6361ab","added_by":"auto","created_at":"2024-04-19 18:14:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77395,"visible":true,"origin":"","legend":"\u003cp\u003eThe architecture diagram of the U-net convolutional network.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4206078/v1/3e93079607a390f5172f92f1.png"},{"id":54997769,"identity":"f359ffdb-099c-4cdd-891e-958db89a8d57","added_by":"auto","created_at":"2024-04-19 18:14:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177768,"visible":true,"origin":"","legend":"\u003cp\u003eThe classification model to predict nucleic acid turning-negative-time.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4206078/v1/e15da1f53e40e27bc92f2862.png"},{"id":54997770,"identity":"eb05be77-ac7a-4c04-a72f-c0698e32c671","added_by":"auto","created_at":"2024-04-19 18:14:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":200292,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlations between different clinical variables. *present P\u0026lt;0.05, **present P \u0026lt;0.01, ***present P\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4206078/v1/9f0dd86f2ebb45b70e7d59dc.png"},{"id":67149004,"identity":"fcc2b70a-9082-496f-9bbe-6ea18d3b2eed","added_by":"auto","created_at":"2024-10-21 16:10:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1044403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4206078/v1/b72cccfe-ac17-475d-b5be-ae5ba1849335.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCOVID-19 is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Mudatsir et al. demonstrated that chronic diseases have been associated with COVID-19 severity and death, such as chronic obstructive pulmonary disease, diabetes mellitus, hypertension and cardiovascular disease experience [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. COPD is one of the most common chronic diseases in the world, which characterized by chronic respiratory symptoms due to abnormalities of the airways and/or alveoli [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Sanchez-Ramirez et al. found that people with COPD were associated with a four-time higher risk of severe disease including intensive care unit admission, death, or other critical outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increasing evidence suggests that the up-regulation of Angiotensin Converting Enzyme 2 (ACE2) is a significant factor contributing to the susceptibility of COPD patients to SARS-CoV-2 infection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, COPD patients often have impaired innate and adaptive immune responses, which can delay the clearance of respiratory viruses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, SARS-CoV-2 may have a higher propensity to spread within the lungs of COPD patients and COPD patients have worse outcomes from COVID-19 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A 2023 bibliometric analysis indicated that, as a key focus of COVID-19 epidemic prevention, COPD combined with COVID-19 still was a hot topic and trend [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagnostic value of computed tomography (CT) in COVID-19 has been well-established [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. CT imaging can detect characteristic features of COVID-19 pneumonia, such as ground-glass opacity, crazy paving pattern, consolidation, and fibrosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Serial CT scans can track the evolution of lung lesions over time, providing insights into the effectiveness of therapeutic interventions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the chest CT severity score (CTSS) is a scoring method in the assessment of COVID-19 pulmonary parenchymal involvement, which has demonstrated strong discriminating power for the prediction of disease severity and outcome in COVID-19 patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings could help differentiate COVID-19 pneumonia from other respiratory infections and contribute to early detection and diagnosis of COVID-19 pneumonia. Previous studies revealed that the pre-existing COPD increased the severity and the risk of death of COVID-19 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The classic COPD phenotypes of chronic bronchitis and emphysema have been recognized in Global Initiative for Chronic Obstructive Lung Disease and Global Initiative [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the differences of pulmonary parenchymal involvement between COVID-19 patients with two COPD phenotypes has not been explored.\u003c/p\u003e \u003cp\u003eDue to the outbreak of COVID-19, global healthcare resources have been facing a tremendous burden. Thus, accumulating studies focus on identification of the patients whose nucleic acid can turn negative in a short time to make rational use of resources. Liu et al. found biochemical indicators such as lactate dehydrogenase, c-reactive protein and Albumin were useful prognostic marker for predicting nucleic acid turn negative time [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Zhu et al demonstrated that the value of nutrophil-to-lmphocyte ratio (NLR) and vaccination could predict the negative conversion time of nucleic acid [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The time of nucleic acid conversion to negative was closely related to the clinical manifestations and disease progression of COVID-19 patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. With the wide spread of models based on artificial intelligence, automatic extraction of quantitative metrics based on deep learning method from lung computed tomography (CT) could provide the tool to the screening and evaluation of COVID-19 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, radiomics has been widely used in quantitative analysis of CT images for screening COVID-19 patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], discriminating the severity of pneumonia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and prognosis assessment in patients with COVID-19 pneumonia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As of yet, the research on utilizing deep learning and radiomics for predicting the nucleic acid conversion time of COVID-19 patients is still in its early stages and remains relatively limited.\u003c/p\u003e \u003cp\u003eThus, our study is to explore the severity of pulmonary between COVID-19 patients with COPD and further analyze the difference with COPD phenotypes of chronic bronchitis and emphysema.\u003c/p\u003e \u003cp\u003eTherefore, our study aims to evaluate the value of deep learning and radiomics features extracted from CT images in predicting the nucleic acid conversion time. Additionally, we evaluated the severity of pulmonary between COVID-19 patients with emphysema and those with chronic bronchitis, so as to provide reference for subsequent treatment.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eThis retrospective cohort study enrolled 292 patients with confirmed COVID-19 from Hohhot First Hospital between October 2022 and January 2023, who underwent chest CT scans.\u003c/p\u003e\n \u003cp\u003eInclusion criteria for this study were: (1) confirmed positive by real-time reverse-transcriptase polymerase-chain-reaction (RT-PCR) assay from nasal and pharyngeal swab specimens; (2) CT images demonstrated pneumonia; (3) Patients with pre-existing chronic obstructive pulmonary disease. The COPD patients were divided into two groups: chronic bronchitis predominant and emphysema predominant, who was made by experienced physicians based on Global Initiative for Chronic Obstructive Lung Disease and Global Initiative [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. The definition of chronic bronchitis predominant was chronic sputum for most days, 3 months a year, or no radiological diagnosis of emphysema for at least 2 years. Emphysema predominant was defined as no chronic cough and sputum but having typical clinical and radiological manifestations of emphysema [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eExclusion criteria for this study were: (1) patients with inflammatory diseases such as pancreatitis, prostatitis, immunological diseases or with severe diseases such as hypertension, diabetes, cerebro-cardiovascular diseases, and chronic kidney disease; and (2) patients with missing data.\u003c/p\u003e\n \u003cp\u003eClinical data were collected and recorded from each patient, which included gender, age, vaccination status, nucleic acid turning-negative-time, and laboratory test results (procalcitonin level, C-reactive protein level, white leukocyte count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio (NLR). The criteria for confirming the patient’s nucleic acid turned negative were twice consecutive negatives, and the interval of tests \u0026gt; 24 hours [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eCT acquisition\u003c/h2\u003e\n \u003cp\u003ePatients underwent chest CT imaging on two 64 detector CT scanners (Discovery CT 750 and u-CT 760). The protocol was as follows: tube voltage: 120 kV; automatic tube current (260 mA for Discovery CT 750, GE and 150 mA for u-CT, United Imaging); reconstruction slice thickness: 1.25 mm; pitch of 1.0; matrix, 512 × 512 and breath hold at full inspiration in a craniocaudal direction from the lung apices to lateral costophrenic sulci. The following windows were used for image display: a mediastinal window with window width of 400 HU and window level of 40 HU and a lung window with a width of 1600 HU and window level of -700 HU. The acquired images were subsequently reconstructed using an iterative reconstruction method.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eSegmentation and preprocessing of CT images\u003c/h2\u003e\n \u003cp\u003eThe U-net [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] convolutional neural network was trained to segment the infection regions on CT scans based on 500 datasets in our study and the frame is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. As seen in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the dice coefficient on the test set reaches 0.85, which is confirmed by an experienced radiologist and meets clinical diagnostic standards. We refer readers to some predecessors' research, the pulmonary involvement was quantitatively estimated on the basis of these abnormalities area involved [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eExtraction and analysis of radiomic features\u003c/h2\u003e\n \u003cp\u003eRadiomic features were extracted from the infection regions of CT images using the PyRadiomics Python tool (v3.11) [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. A total of 107 features were extracted for each chest CT image, including 14 shape features, 18 first-order statistics from the gray-scale histogram, and 75 second-order statistics derived from the grey-level co-occurrence matrix. A total of 30 features were selected to model construction based on the LASSO method.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction of the classification model\u003c/h2\u003e\n \u003cp\u003eWe constructed two models using a universal Pytorch framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pytorch.org\u003c/span\u003e\u003c/span\u003e) to predict the nucleic acid turning negative time in our study, including a deep learning model based on original CT image and a deep learning fusion model. The details of the deep learning fusion model were as follows. As seen in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, one input was the segmented image of the pneumonia area, and the other input was the original CT image in the classification model. The first branch consisted of 3 groups of 7×7conv2d, 5×5conv2d and 3×3 conv2d plus batch-norm. The rectified linear unit (ReLU) activation function was used. F1 features(2*128) from neural network and F2 feature map(2×30) from radiomics were concatenated in the feature dimension to obtain the total feature map(2×158). The total feature map was compressed through the global average pooling function and the fully connected layer to obtain a 2×4 final feature map, and the Softmax function was used to achieve the probability output of each target category.\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn formula (1), Z_i represented the output values of the i-th sample and n represented the number of classes in the classification task.\u003c/p\u003e\n \u003cp\u003eWith respect to the model construction, the backpropagation (BP) algorithm was employed, which updated the network parameters iteratively until an optimal or suboptimal solution was reached. The model iterated for a total of 60 epochs, with batch size setting to 2, and the learning rate setting to 0.0001. The weight cross entropy loss function was utilized to alleviate the sample imbalance problem according to the weights of different sample numbers.\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWIAAAAyCAYAAABmpdE5AAAMiklEQVR4Ae2cvYoUTRSG9xIEA0NBwcTYzExBYwMFAzMDU/ECxEwQMddcjEVzNTczETTUC/AC5uMZvnc9ezxV0z3TMz07+xYM1V116vy856drqnf2aOFmBIyAETACsyJwNKt0CzcCRsAIGIGFC7GDwAgYASMwMwIuxDM7wOKNgBEwAi7EjgEjYASMwMwIuBDP7ACLNwJGwAi4EDsGjIARMAIzI+BCPLMDLN4IGAEj4ELsGDACRsAIzIyAC/HMDrB4I2AEjIALsWPACBgBIzAzAi7EMzvA4o2AETACLsSOASMQEHjx4sXi6Ohoo8/79+8DR18agdUIuBCvxsgU/yPw9u3bxevXr7eOBzKQNVeLxfjGjRuLP3/+DFIFnc+fP7+YohC3sO5hg54vX75c3L17d7DOgwzbkGgdWzYUeeqWr12Iv379urh9+/bxzuHatWuLDx8+LINwikDcJpIPHjw41ru1+9l3G9bBJ9t96dKlxa9fv5as8hwFBR+rUZweP368kwSnoFAAkTlXW7cYEzdZ74ytYg6Mnz59+g+mPax72CAb3mMeHuviq6KPDbKHevD9+/cTLNe15QSTM3AzuhDjgIcPHy6f/DzpuKfhAAIgJ/C+YkjQVrpiBw+VWIT21YZ19GJHReJUO1sVn1xIuKeY7LKp4Mz5QBQeY4obD7Z37979A5Vwj/ZoBx2xbWENXx6c0PawYX7bhVh6xwezdIo5takt/4B4wAOjCjHBQJEiIPKTD4woXvv2tajlO4Ik7ggjHYmknWIc712TaKeheGN3K1GrJJbP57ANmcTbWF/0/DR2jmKjHV8LtyE8W/EG5orDMVi3sKl8OES/oTTYAR4U49zQXw+LKWzJ/A/5flQhxsnxiZeBAXy+ap2Ghi181Ajsaiej+V5PUCqZenRzz2nXEu2WTq05Eq+i17pt98hGhznbFMUYO6pCzrhiZyzWFTYtOVPgp6OPlj8UQ9j57NmzUXFT2TKFzqeFx+BCLCcAWK/9/v17eZbIzvjz58/LHU0MQH2t4alKUc9fkZHDOB/W41AcTOvN9XTKc3pyw48Gf5It7vpEg54KPOlO4vCNABps046JHn3RnWv4i7eSTbrA68qVK0s66KULOmg9cnVUAj/tDvEB99BJZ+TcunVrcfHixcXPnz8l5kQvmyQrTkpunIMn9sn+SM+11qBLS9e8Jt7Dn5dL2IFtxELGCb4xfuL6XV4Lc2wdq49wzzgKP3i3sMYfyKzkVtjAK+unuIUHWOecQzbHjZqPMSCMZUPmrXl62XD9+vUFn2zvWFsi721f//jxY5k/yAEvYf7o0aNj0eQXdNtogwsxDo6JXykjR2AEicXLO4xScilwdKwhg+FNw9k3b948Li6ih29vrtKlNxYDQoBLx7hO9kBP8KIvyUMBpaeJJgYdumI/NB8/flyuUwBDT9FnXjiwNspX0FPUKVSsgRYaYQVv7iUXmvv37y8uXLiw+PbtWzTj+LqyW/bTZ/8igzHWtdoQXau1wk04wIeHt3DSGmRnvZgDh6h7dS1sxGvTPsrMevZ4y4aII7mBXdiP7T2sNZftEV/m1dAx6sYa7hVrOedYF9doHjwzH8ayDpJLLz2vXr26OHfuXBk3osl8Klsi721ef/nyZXH58uWlCAqtii/X2Pz8+fNj8dxDP3UbVIiVNLFY9BTBsZlWCRuDER7QKtFwEkWOIKUhVzvi3lxPl2qOIIj6wZvimBu6QkcxJEBpGsMemvSiV4OGIH7z5s0yyTROj2zZq3HWMiZs6HPQywfgRcv3jKETMlstJlymqebQI+ua1w3RNa/hPvtA9uQEzdhUvHY1ho7ghG/4KCZWyZfP7927d7wWXPWQZX0Pa62PMcaaCpvoxyE5Jxrh3vIDfFfFAjzA5c6dO03aMbaswnWqeXRWy0WWIsxOOLZIH8c3uf6rQYeLnBWfkB3yE09Y0Slh6WOL4woCDKUgE+iM0Xpzkd+qa/EhsNSwrzofJmjQIxZp1sW10GRcmCfpPn36JBHLXjiyJrachBXPvLayo/eSsaKXDq25rJfoYz9E10jPtQpIxEH25fgQbR7PPIfc4xdiq/eJOlU8wQo+2ecVLWMtbDN9D+uWvAqbSAtPbM3YxXHhLrsrfTUWNy9Z/0jDt8dW0Y76RR6VLXF+W9cU2rjjzXKoQbkQr1qTeQy5H1SII8g4blWrwI7Oj+vzOLJ0bkgQxYDvzUWevesceC3aymaNKWhZi6181EQTxzSHrVWAQqsg1/oog/XVWtYJHwI5n/1JLn0v0FtzlczIc4yucR22yV6NIyuP9fTG9l5BZS5jKFmb9MQPD9kheYCcofHWwrq3vvJbjAl4rirE6Ch/cHyhI7CInfysWKvwg16yprKlkjP1GEcSeRccZVB08zefeJQRaTe5HlSIESCgo4OiYByoXWUMBtEoaJiLLSYgQRf/6gIAKFzQ9OYiv1XXrSDROooZulYJwHg8HxYNPNVkZxzTHNjlYiN64Vrx1JhoIj+SgxekvSMJ6Ht2t+akW2ULPKVXnNdY1lU60+f4YA1npVWit3SL/HZ1TUHiHBtchrah+rew7q2v5iK24tnLOewAf36MQa5RTPkGiK2xwaPaREBDnrJOciQX/WKr9NV8b0402+jRu/cCTmfHUbbOjnvrIv2Q68GFWE9FFI+/BmIcR8QCGoMhKkFysl5PGIo3yaekVRJrHoeSoPS9uSijdy0bqoRHBm+ONUdg5KKJnszzwIFewcNfd1AI4V+tk06sIZhln17YxKMP8aSngREYKMjFix590PHJkydLOuEOVrFxj96yLc71MNGc/BPXcT1EV2zTw1Tr0RO9sY0P2HGuWMkR5ugyd8MW+WWILsIvx1G1VrQZA9nPw1bvS7RecxEbxYDGoOnlHLywi9jsNcUuMYTPaMhgLfyj3lPZ0tNnirlVBVUv7bKsVesy/ZD7wYUYZgDMsQG7QsDno7NczeMozVUBqPMjaGJRYj0Fg6QUTZzvzQ0xlASSXr1eidYKcnTS2a8KUUwQEoFPq1GE4YEOFFi9mBS9Ekc6RgxEo17ySRJ8oySMhbjip6TrzUkGNC17qvV6yLBeOmELhZZ7mh4u2Iav0ZdYEfaSTY9s5Mzd0I2iM7RV2Aj3Fo8Ka+wHp/hiT+szNlFm9JnyCT/keBL2ijf1xGaMI2SiP+OigRebFxVm6UW/qS2R1zavsaXa2VZHEtJj9kIsRdxvBwEKFQ8ygnhIozhUxWvI2qE0JCPJl4vIWF31jaGSqwRvJX4er3hscwz9dvGL0RbWlW0tzCraVWPa1arAqq/ikKIOvR6qLd5z2dLSpzXOi7h8RsxmIr7AYz7SzHpG3DLE49MhQPC2doZZComYd9OZZqp7in0uRGN11beISicSvjo2yTu+au22x7BzzMu5TfWpsK54ToUNBTV+o5Ms4kvvfBiDjgJMIW41sIprdm1LS6/eOAU3HkFwrQeR+nxOzJpYqHv8h86NOpoYytR06yFAkFfHOeJGYFOwOJPuJYTop+xzsVylq2TzsCCpW03HFHH3paQfcxTQ4r/JOHqMfTmHPPzUs3mVThnrSD81NpIVjxeQwTcYCiuN+3jkSIHi5V7cCLCeNbmJPzxym9qWzH/oPfZUxxOt9dBP3abnOLWGHX4EO+dUenJVPfObJEVH/GRTVaCTzLkR1Niz6yIsPfjK9urVq3+SstJVa3o9u7roM+yjYV88a+7x2OYc+oy1DV+uU7yzHdhf+XlqbCig+nmzfNE7eiCX8nui6l1HtGdXtkSZY645asD2IY3d8TZic5j0IRqaxggcEAIU4LE7cooUO8fet5oDguigTGFHnH+4kQ2szpMzzbr3LsTrIud1B4vAkG9a2j1WfXXefbBg2bBJEHAhngRGMzkUBPQSsiqwQ8fin44dCi62Y7sIuBBvF19zNwJGwAisRMCFeCVEJjACRsAIbBcBF+Lt4mvuRsAIGIGVCLgQr4TIBEbgLwL8eVr1A4i/FL4yAuMRcCEej5lXGAEjYAQmRcCFeFI4zeyQEdAPb/xXEYfs5XlscyGeB3dLPaUI8Mu2sb+2O6WmWu0dIuBCvEOwLep0I8COmJ8D638wnG5rrP0+IeBCvE/esC57jQC/uBv7s+e9NsjK7Q0CLsR74worsu8IcCTR+kfo+6679dtvBFyI99s/1s4IGIEzgIAL8Rlwsk00AkZgvxFwId5v/1g7I2AEzgACLsRnwMk20QgYgf1GwIV4v/1j7YyAETgDCLgQnwEn20QjYAT2GwEX4v32j7UzAkbgDCDwH0XSiK6Fq3hDAAAAAElFTkSuQmCC\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn formula (2), i represented the label or category of a sample, P(i) indicated the probability of the true label, and Q (i) being the probability predicted by the model.\u003c/p\u003e\n \u003cp\u003eSpecifically, according to the different time required for nucleic acid to turn negative, the subjects were divided into the following four categories: 0–5 days, 6–10 days, 11–15 days, and ≥ 16 days. The following metrics were employed to evaluate the performances of each model: accuracy, precision, sensitivity, specificity, F1-score.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eCategorical variables were presented as frequencies and percentages, which were compared using the chi-square test. Continuous variables were described as means ± standard deviation (SD) or median (interquartile range, IQR) and compared between groups using either a two-sample t-test or Wilcoxon test, depending on the distribution. Spearman correlation was used to assess the correlations between variables. All statistical analyses were performed using IBM SPSS Statistics Software (version 27.0, SPSS Inc., Chicago, IL, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003ePatients and methods\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003eDemographics and baseline clinical characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 281 patients satisfied the inclusion criteria in our study, including 95 COVID-19 patients, 94 COVID-19 patients with the emphysema‑predominant COPD, and 92 patients with the chronic bronchitis-predominant COPD. As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, 62 patients (65.96%) were males in COVID-19 with emphysema group, and more than the other two groups. The laboratory tests including white leukocyte count, neutrophil count, NLR value, and C-reactive protein level increased in COVID-19 patients with COPD than COVID-19 patients. However, the Lymphocyte count was shown to be lower in COVID-19 patients with emphysema compared than other groups and the median was 1.07 x10^9cells/L. Among the patients with different time required for nucleic acid to turn negative, 51.58% patients during 6–10 days in COVID-19 group, 40.43% patients during 11–15 days of COVID-19 patients with emphysema and 40.22% patients during 11–15 days of COVID-19 patients with chronic bronchitis. A total of 85 patients (89.47%) received at least one dose of inactivated SARS-CoV-2 vaccine in COVID-19 group, in which 56 patients (58.95%) received three doses of inactivated vaccine. Only 6 patients (6.32%) received one dose of the inactivated vaccine, and 23 patients (24.21%) got two doses of the inactivated vaccine. However, 36 patients (38.30%) in COVID-19 with emphysema and 19 patients (20.65%)in COVID-19 with chronic bronchitis have not been vaccinated. There were significant statistical differences in sex, lymphocyte count, neutrophil count, NLR value, C-reactive protein level, nucleic acid turning-negative-time, and Vaccination dose between the three groups (p \u0026lt; 0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of baseline characteristics among the patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19(95)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 with emphysema(94)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 with chronic bronchitis(92)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (male),N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36(37.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62(65.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35(38.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite leukocyte, per l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.58(1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.08(2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.25(2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte, per l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36(0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07(0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.37(0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil, per l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.77(1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.27(1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09(1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00(1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.90(2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.34(1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-reactive protein, per l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.40(8.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.16(28.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.70(12.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCT, per ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNucleic acid turning-negative-time, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0–5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7(7.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13(13.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5(5.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6–10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49(51.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26(27.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30(32.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11–15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33(34.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38(40.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37(40.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6(6.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17(18.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20(21.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaccination dose, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10(10.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36(38.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19(20.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6(6.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(2.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12(13.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23(24.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19(20.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18(19.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56(58.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37(39.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43(46.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eThe severity of pneumonia analysis\u003c/h2\u003e\n \u003cp\u003eAs seen in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, COVID-19 combined with COPD show the higher proportion of pulmonary involvement than those patients with COVID-19 only. The average proportion of pulmonary involvement in COVID-19 with emphysema groups was 3.90% and has the most extensive range of pulmonary inflammation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe pulmonary involvement degree in the three COVID-19 groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 with emphysema\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 with chronic bronchitis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epulmonary involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelations between different clinical variables\u003c/h2\u003e\n \u003cp\u003eAs seen in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in terms of pulmonary involvement indicators, six indicators show significant correlations: vaccination dose (correlation coefficient value (r) = -0.228, P \u0026lt; 0.001), lymphocyte count (r= -0.145, P \u0026lt; 0.05), neutrophil count (r = 0.122, P \u0026lt; 0.05), C-reactive protein level (r = 0.352, P \u0026lt; 0.001), PCT level (r = 0.217, P \u0026lt; 0.001), and NLR (r = 0.178, P \u0026lt; 0.01). Regarding the time to nucleic acid turning negative, two indicators exhibit significant correlations: vaccination dose (r= -0.201, P \u0026lt; 0.001) and lymphocyte count (r = 0.145, P \u0026lt; 0.05). Besides, significant positive correlations were observed between NLR value and white leukocyte count, neutrophil level, C-reactive protein, and PCT (P \u0026lt; 0.001) and significant negative correlations were identified between NLR value and lymphocyte count (P \u0026lt; 0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003ePrediction results of nucleic acid turning-negative-time\u003c/h2\u003e\n \u003cp\u003eThe performances of the Model 1 on the test set with an overall accuracy of 78.7%, precision of 77.6%, sensitivity of 75.0%, specificity of 77.1% and F1 score of 77.1% (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The Model 2 improved the classification result, achieving an accuracy of 80.9%, precision of 77.9%, sensitivity of 77.8%, specificity of 75.8%, and F1 score of 77.8%. For either the Model 1 or the Model 2, the sensitivities for prediction of nucleic acid turning-negative-time during 6–10 and 11–15 days were better than the other two.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe classification results by using the two models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF1-score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 1(0–5 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 2 (6–10 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 3 (11–15 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 4(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e16 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 1 (0–5 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 2 (6–10 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 3 (11–15 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClass 4(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e16 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eModel 1: The model based on deep learning method; Model 2༚The model combined deep learning with radiomics features.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs far as we know, this is the first study targeting two common subtypes of COPD combined with COVID-19 patients. In the current study, we have explored the laboratory variables difference and the severity of pneumonia between three groups, including COVID-19 only group, COVID-19 patients with emphysema groups, and COVID-19 patients with chronic bronchitis. Two predictive models, i.e. a deep learning model and a fusion model combining deep learning with radiomics, were constructed to predict the nucleic acid turning-negative-time and the fusion model achieved 80.9% of accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Rapid and effective prediction of the delayed negative conversion of SARS-CoV-2 in COVID-19 with COPD patients would contribute to the rational allocation of medical resources.\u003c/p\u003e \u003cp\u003eInterestingly, the current study demonstrated that those COVID-19 patients with COPD have higher level of NLR values compared with the COVID-19 patients. The NLR values in COVID-19 with emphysema were highest with a median of 2.90 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the NLR values were positively correlated with the pulmonary involvement (r\u0026thinsp;=\u0026thinsp;0.178, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Such findings were consistent with previous research that the NLR values of severe patients were higher than those that in mild patients [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and the elevated NLR in COVID-19 patients was associated with poor outcomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the COVID-19 patients with emphysema had the lowest lymphocyte count compared with COVID-19 patients and COVID-19 patients with chronic bronchitis. As a prominent clinical manifestation, the lymphocytes level was shown to be decreased in COVID-19 patients, which related to the body's immune function and inflammatory status [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Especially in severe COVID-19 patients, serum secretion of Interleukin-2 Receptor (IL-2R) were remarkably increased, which was also considered as a sign of lymphopenia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, COPD patients are always accompanied by immune aging and immune dysfunction, which may lead to a decrease in lymphocyte count. The aging immune system has a weaker ability to respond to viral infections, which may lead to the loss of lymphocytes. The subtypes of emphysema cause structural destruction of lung tissue, including decreased elasticity of lung tissue and increased lung volume. When emphysema patients are infected with COVID-19, the lung tissue may release more inflammatory cells and cytokines, such as the highly prionflammatory macrophages abundant in lungs, further leading to the adverse effects on the survival of lymphocytes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the current study showed that those COVID-19 patients with COPD had higher pulmonary parenchymal involvement compared with the COVID-19 patients, which may be related to the differential expression of e angiotensin-converting enzyme 2 (ACE-2) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A previous study demonstrated that increased level of ACE-2 expression was strongly associated with viral load and lung injury [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In clinical practice, the drugs of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker can be used to treat COVID-19 patients and ameliorated lung injury [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Interferons (IFNs) was a group of cytokines with antiviral, antiproliferative, antiangiogenic, and immunomodulatory activities. Some studies have confirmed that type I interferon (IFN) is associated with CT airway wall thickness, lung function, and COPD exacerbation, which may increase susceptibility of COPD patients to SARS-CoV-2 infection [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In addition, inflammation-associated alveolar damage and vascular injury in COVID-19 patients causes emphysema alterations and the presence of pre-existing emphysematous lung area patients may cause the outcome of critically-ill COVID-19 patients [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In summary, the COVID-19 patients with COPD may have more pneumonia infection areas.\u003c/p\u003e \u003cp\u003eCritically, it is worth noting that nucleic acid conversion time was negatively correlated with vaccination dose (r=-0.201, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the current study. A previous study demonstrated that the nucleic acid turning-negative-time was related to clinical symptoms of constipation, fever, and expectoration. Specifically, if patients exhibit these symptoms, the nucleic acid turning-negative time is likely to be prolonged [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The vaccine based on mRNA can stimulate the immune system immediately as soon as possible and produce a remarkable effect through immune cells such as CD8\u0026thinsp;+\u0026thinsp;T cells [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The patients receiving the vaccine can significantly reduce the incidence of severe symptoms, alleviate the severity of the disease, and lower the risks of hospitalization and death [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These research findings corroborate our findings that vaccination can shorten the time to turn negative for nucleic acid, therefore providing evidence for the effectiveness of COVID-19 vaccines in alleviating symptoms.\u003c/p\u003e \u003cp\u003eLastly, we found that the performance of the fusion model that combined deep learning with radiomics features outperformed the sole deep learning model in predicting the nucleic acid turning-negative-time. Usually, deep learning method focuses on the high-order features of the entire CT image and ignores the potential value of pneumonia area. Therefore, more complementary information was provided by extracting the low-order features of the area of interest in pneumonia, including first-order features, second-order features, as well as shape features. At the same time, the sensitivity for predicting nucleic acid turning-negative-time during 6\u0026ndash;10 and 11\u0026ndash;15 days were better than other periods in our study; this may be related to the particular distribution of the number of participants in the current study, i.e. those 2 categories are larger than other categories (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study has several limitations that should be acknowledged. Firstly, it was conducted at a single center, which may introduce potential biases and limit the generalizability of our findings to other populations or locations. The limited number of patients included in our study also restricts the statistical power and reliability of our results. To address these limitations and enhance the robustness of our findings, further multi-center studies involving a larger sample size are warranted. Secondly, we opted for a convolutional network given the limited availability of data. However, the transformer models and other advanced networks have demonstrated significant potential in various domains, including natural language processing and computer vision. Future research endeavors to employ larger datasets and more computational resources could be explored to further enhance the accuracy and performance of our study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the current study, we revealed that pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement in COVID-19 patients. Also, our findings have demonstrated that the lymphocyte number may be the key indicator for predicting severity and nucleic acid turning-negative-time of COVID-19 with COPD. Critically, we have shown that prediction of turning-negative-time could be achieved through deep learning combined with radiomics methods, which provides a viable avenue for clinical diagnosis decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Hohhot First Hospital.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no financial or competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Inner Mongolia Autonomous Region Science and Technology Plan Project (2023YFSH0015).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eWZ and MS contributed to the conception and design of this research, data analysis, and manuscript writing. YL, LW, JZ, YH and HL were participated in the data acquisition and inspection. XY and XL were participated in the revision and proofreading of the paper. The authors\u0026apos; order was determined by their contributions. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuo YR, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status. Military Med Res. 2020;7(1):11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40779-020-00240-0\u003c/span\u003e\u003cspan address=\"10.1186/s40779-020-00240-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMudatsir M, et al. Predictors of COVID-19 severity: a systematic review and meta-analysis. 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The Value of Early Positive Nucleic Acid Test and Negative Conversion Time of SARS-CoV-2 RNA in the Clinical Outcome of COVID-19 Patients. Front Med. 2022;9:826900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmed.2022.826900\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2022.826900\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19 with COPD, Pulmonary involvement, Nucleic acid turning-negative time, Deep learning method, Radiomics features","lastPublishedDoi":"10.21203/rs.3.rs-4206078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4206078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that patients with pre-existing chronic pulmonary inflammations of chronic obstructive pulmonary diseases (COPD) were more likely to be infected with COVID-19 and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD.\u003c/p\u003e\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe aim of this study to investigate the value of the deep learning and radiomics features to evaluated the severity and predict the nucleic acid turning-negative time in COVID-19 patients with COPD including two phenotype of chronic bronchitis predominant patients and emphysema predominant patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema groups of 94 patients, COVID-19 with chronic bronchitis groups of 92 patients. All patients underwent chest scans and recorded clinical data. The U-net network was trained to segment the infection regions on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation between the data and visualize it through a heatmap. Then we respectively establish a deep learning model using original CT image and a fusion model combined deep learning with radiomics features to predict the time for nucleic acid turning-negative.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCOVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time to nucleic acid turning negative (r=-0.145, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Importantly, our results demonstrated that the fusion model achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.\u003c/p\u003e","manuscriptTitle":"The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:14:00","doi":"10.21203/rs.3.rs-4206078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-16T09:44:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T09:06:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T07:44:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328830884187481083182684973083325184277","date":"2024-06-27T03:02:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277546904201667362586567557147941628056","date":"2024-06-22T12:36:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T10:06:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215048890843542985625800343394566625133","date":"2024-06-18T13:04:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122000681181489036170022414188423910801","date":"2024-06-18T08:39:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-16T23:36:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327220961923164508332723956307304504760","date":"2024-06-16T12:53:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252477056589225637761647144791434188910","date":"2024-05-30T16:52:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14319690205022398043203308838465234043","date":"2024-05-30T16:47:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66d95f06-7207-4096-8acb-dbb2e3a4ffd1","date":"2024-04-19T07:46:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132be520-41e9-4008-9995-6ec5b3470776","date":"2024-04-18T17:41:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-12T09:28:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-11T16:03:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-11T16:01:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-11T15:49:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-04-02T10:49:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64a93a72-a273-40ac-9c42-a2557b35b4a4","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T16:02:26+00:00","versionOfRecord":{"articleIdentity":"rs-4206078","link":"https://doi.org/10.1186/s12890-024-03333-x","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2024-10-14 15:57:37","publishedOnDateReadable":"October 14th, 2024"},"versionCreatedAt":"2024-04-19 18:14:00","video":"","vorDoi":"10.1186/s12890-024-03333-x","vorDoiUrl":"https://doi.org/10.1186/s12890-024-03333-x","workflowStages":[]},"version":"v1","identity":"rs-4206078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4206078","identity":"rs-4206078","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}