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Lu D, Liu Y, Ma P, Hou R, Wang J. Severity of COVID-19 infection in patients with COVID-19 combined with diabetes. JOURNAL OF HEALTH, POPULATION, AND NUTRITION 2024; 43:55. [PMID: 38654371 DOI: 10.1186/s41043-024-00548-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE This study aimed to analyse the correlation between blood glucose control and the severity of COVID-19 infection in patients with diabetes. METHODS Clinical and imaging data of a total of 146 patients with diabetes combined with COVID-19 who visited our hospital between December 2022 and January 2023 were retrospectively collected. The patients were divided into the 'good blood glucose control' group and the 'poor blood glucose control' group based on an assessment of their blood glucose control. The clinical data, computed tomography (CT) appearance and score and the severity of COVID-19 infection of the two groups were compared, with the severity of COVID-19 infection being the dependent variable to analyse other influencing factors. RESULTS The group with poor blood glucose control showed a higher lobar involvement degree and total CT severity score (CTSS) than the group with good blood glucose control (13.30 ± 5.25 vs. 10.38 ± 4.84, p < 0.05). The two groups exhibited no statistically significant differences in blood lymphocyte, leukocyte, C-reaction protein, pleural effusion, consolidation, ground glass opacity or crazy-paving signs. Logistic regression analysis showed that the total CTSS significantly influences the clinical severity of patients (odds ratio 1.585, p < 0.05), whereas fasting plasma glucose and blood glucose control are not independent factors influencing clinical severity (both p > 0.05). The area under the curve (AUC) of CTSS prediction of critical COVID-19 was 0.895 with sensitivity of 79.3% and specificity of 88.1% when the threshold value is 12. CONCLUSION Blood glucose control is significantly correlated with the CTSS; the higher the blood glucose is, the more severe the lung manifestation. The CTSS can also be used to evaluate and predict the clinical severity of COVID-19.
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Affiliation(s)
- Dan Lu
- Department of Radiology, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 Renmin East Road, Panlong District, 650051, Kunming, Yunnan, China
| | - Yuhong Liu
- Department of Radiology, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 Renmin East Road, Panlong District, 650051, Kunming, Yunnan, China
| | - Pengcheng Ma
- Department of Radiology, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 Renmin East Road, Panlong District, 650051, Kunming, Yunnan, China
| | - Rui Hou
- Department of Radiology, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 Renmin East Road, Panlong District, 650051, Kunming, Yunnan, China
| | - Jin Wang
- Department of Radiology, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 Renmin East Road, Panlong District, 650051, Kunming, Yunnan, China.
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Casartelli C, Perrone F, Balbi M, Alfieri V, Milanese G, Buti S, Silva M, Sverzellati N, Bersanelli M. Review on radiological evolution of COVID-19 pneumonia using computed tomography. World J Radiol 2021; 13:294-306. [PMID: 34630915 PMCID: PMC8473435 DOI: 10.4329/wjr.v13.i9.294] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/28/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Pneumonia is the main manifestation of coronavirus disease 2019 (COVID-19) infection. Chest computed tomography is recommended for the initial evaluation of the disease; this technique can also be helpful to monitor the disease progression and evaluate the therapeutic efficacy.
AIM To review the currently available literature regarding the radiological follow-up of COVID-19-related lung alterations using the computed tomography scan, to describe the evidence about the dynamic evolution of COVID-19 pneumonia and verify the potential usefulness of the radiological follow-up.
METHODS We used pertinent keywords on PubMed to select relevant studies; the articles we considered were published until October 30, 2020. Through this selection, 69 studies were identified, and 16 were finally included in the review.
RESULTS Summarizing the included works’ findings, we identified well-defined stages in the short follow-up time frame. A radiographic deterioration reaching a peak roughly within the first 2 wk; after the peak, an absorption process and repairing signs are observed. At later radiological follow-up, with the limitation of little evidence available, the lesions usually did not recover completely.
CONCLUSION Following computed tomography scan evolution over time could help physicians better understand the clinical impact of COVID-19 pneumonia and manage the possible sequelae; a longer follow-up is advisable to verify the complete resolution or the presence of long-term damage.
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Affiliation(s)
- Chiara Casartelli
- Medical Oncology Unit, University Hospital of Parma, Parma 43126, Italy
- Department of Medicine and Surgery, University of Parma, Parma 43126, Italy
| | - Fabiana Perrone
- Medical Oncology Unit, University Hospital of Parma, Parma 43126, Italy
- Department of Medicine and Surgery, University of Parma, Parma 43126, Italy
| | - Maurizio Balbi
- Division of Radiology, University of Parma, Parma 43126, Italy
| | - Veronica Alfieri
- Department of Medicine and Surgery, Respiratory Disease and Lung Function Unit, University of Parma, Parma 43126, Italy
| | | | - Sebastiano Buti
- Medical Oncology Unit, University Hospital of Parma, Parma 43126, Italy
| | - Mario Silva
- Division of Radiology, University of Parma, Parma 43126, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery, University of Parma, Parma 43126, Italy
- Division of Radiology, University of Parma, Parma 43126, Italy
| | - Melissa Bersanelli
- Medical Oncology Unit, University Hospital of Parma, Parma 43126, Italy
- Department of Medicine and Surgery, University of Parma, Parma 43126, Italy
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Chen Y, Ding C, Yu L, Guo W, Feng X, Yu L, Su J, Xu T, Ren C, Shi D, Wu W, Yi P, Liu J, Tao J, Lang G, Li Y, Xu M, Sheng J, Li L, Xu K. One-year follow-up of chest CT findings in patients after SARS-CoV-2 infection. BMC Med 2021; 19:191. [PMID: 34365975 PMCID: PMC8349604 DOI: 10.1186/s12916-021-02056-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/07/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Knowledge about the 1-year outcome of COVID-19 is limited. The aim of this study was to follow-up and evaluate lung abnormalities on serial computed tomography (CT) scans in patients with COVID-19 after hospital discharge. METHODS A prospective cohort study of patients with COVID-19 from the First Affiliated Hospital, Zhejiang University School of Medicine was conducted, with assessments of chest CT during hospitalization and at 2 weeks, 1 month, 3 months, 6 months, and 1 year after hospital discharge. Risk factors of residual CT opacities and the influence of residual CT abnormalities on pulmonary functions at 1 year were also evaluated. RESULTS A total of 41 patients were followed in this study. Gradual recovery after hospital discharge was confirmed by the serial CT scores. Around 47% of the patients showed residual aberration on pulmonary CT with a median CT score of 0 (interquartile range (IQR) of 0-2) at 1 year after discharge, with ground-glass opacity (GGO) with reticular pattern as the major radiologic pattern. Patients with residual radiological abnormalities were older (p = 0.01), with higher rate in current smokers (p = 0.04), higher rate in hypertensives (p = 0.05), lower SaO2 (p = 0.004), and higher prevalence of secondary bacterial infections during acute phase (p = 0.02). Multiple logistic regression analyses indicated that age was a risk factor associated with residual radiological abnormalities (OR 1.08, 95% CI 1.01-1.15, p = 0.02). Pulmonary functions of total lung capacity (p = 0.008) and residual volume (p < 0.001) were reduced in patients with residual CT abnormalities and were negatively correlated with CT scores. CONCLUSION During 1-year follow-up after discharge, COVID-19 survivors showed continuous improvement on chest CT. However, residual lesions could still be observed and correlated with lung volume parameters. The risk of developing residual CT opacities increases with age.
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Affiliation(s)
- Yanfei Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Cheng Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Ling Yu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Wanru Guo
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Xuewen Feng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Liang Yu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Junwei Su
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Ting Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Cheng Ren
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Ding Shi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Wenrui Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Ping Yi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Jun Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Jingjing Tao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Guanjing Lang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Yongtao Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Min Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Jifang Sheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China.
| | - Kaijin Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City, 310003, China.
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Li XL, Li T, Du QC, Yang L, He KL. Effects of angiotensin receptor blockers and angiotensin-converting enzyme inhibitors on COVID-19. World J Clin Cases 2021; 9:5462-5469. [PMID: 34307600 PMCID: PMC8281418 DOI: 10.12998/wjcc.v9.i20.5462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 04/30/2021] [Accepted: 05/24/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The World Health Organization reported that 28637952 people worldwide had been infected with severe acute respiratory syndrome coronavirus 2, the causative agent of coronavirus disease 2019 (COVID-19), by September 13.
AIM The aim was to investigate whether long-term use of renin-angiotensin-aldosterone system (RAAS) inhibitors for the treatment of hypertension aggravates the performance of COVID-19 patients with hypertension.
METHODS This was a retrospective analysis of lung computed tomography (CT) data and laboratory values of COVID-19 patients with hypertension who were admitted to Huoshenshan Hospital, Wuhan, Hubei Province, between February 18 and March 31, 2020. Patients were divided into two groups. Group A included 19 people who were long-term users of RAAS inhibitors for hypertension; and group B included 28 people who were randomly selected from the database and matched with group A by age, sex, basic diseases, and long-term use of other antihypertensive drugs. All patients underwent a series of CT and laboratory tests. We compared the most severe CT images of the two groups and the laboratory examination results within 2 d of the corresponding CT images.
RESULTS The time until the most severe CT images from the onset of COVID-19 was 30.37 ± 14.25 d group A and 26.50 ± 11.97 d in group B. The difference between the two groups was not significant (t = 1.01, P = 0.32). There were no significant differences in blood laboratory values, C-reactive protein, markers of cardiac injury, liver function, or kidney function between the two groups. There was no significant difference in the appearance of the CT images between the two groups. The semiquantitative scores of each involved lobe were 11.84 ± 5.88 in group A and 10.36 ± 6.04 group B. The difference was not significantly different (t = 0.84, P = 0.41).
CONCLUSION Chest CT is an important imaging tool to monitor the characteristics of COVID-19 and the degree of lung injury. Chronic use of RAAS inhibitors is not related to the severity of COVID-19, and it does not worsen the clinical process.
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Affiliation(s)
- Xiao-Long Li
- Department of Diagnostic Radiology, PLA General Hospital, Beijing 100853, China
| | - Tao Li
- Department of Radiology, The First Medical Center of PLA General Hospital, Beijing 100853, China
| | - Qi-Cong Du
- Department of Diagnostic Radiology, The First Medical Center of PLA General Hospital, Beijing 100853, China
| | - Li Yang
- Department of Radiology, The First Medical Center of PLA General Hospital, Beijing 100853, China
| | - Kun-Lun He
- Translational Medical Research Center, Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing 100853, China
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Yurdaisik I, Nurili F, Aksoy SH, Agirman AG, Aktan A. IONIZING RADIATION EXPOSURE IN PATIENTS WITH COVID-19: MORE THAN NEEDED. RADIATION PROTECTION DOSIMETRY 2021; 194:135-143. [PMID: 34151376 PMCID: PMC8344538 DOI: 10.1093/rpd/ncab092] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/13/2021] [Accepted: 05/25/2021] [Indexed: 05/06/2023]
Abstract
OBJECTIVE The aim of the study was to evaluate the ionizing radiation exposure in patients with Coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS This was a retrospective study in which all patients presented with suggestive symptoms of COVID-19 were included. The study was carried out in a university-affiliated private hospital in Istanbul, Turkey. Biological radiation dose exposure (cumulative effective dose: CED) was evaluated in millisievert (mSv) units. RESULTS A total of 1410 patients were included in the study. Of all study subjects, 804 patients (57%) underwent only one chest computed tomography (CT) procedure. Six hundred and six patients (43%) had two or more chest CT procedures. Median CED was 6.02 (min-max:1.67-16.27) mSv. The number of patients who were exposed to ≤ 5 mSv were 149 (24.6%), whereas 457 patients (75.4%) were exposed to >5 mSv. CONCLUSION The radiation exposure in COVID-19 patients seems unjustifiably high. Awareness should be increased as to the proper use of chest CT in COVID-19 as per to the society recommendations.
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Affiliation(s)
- Isil Yurdaisik
- Department of Radiology, Istinye University, Gaziosmanpasa Medical Park Hospital, 34250 Istanbul, Turkey
| | - Fuat Nurili
- Department of Radiology, Memorial Sloan Ketteting Cancer Center, Interventional Radiology, New York, NY 10065, USA
| | - Suleyman Hilmi Aksoy
- Department of Radiology, Galata University, Hisar Intercontinental Hospital, 34768 Istanbul, Turkey
| | - Ayse Gul Agirman
- Department of Radiology, Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, 34668 Istanbul, Turkey
| | - Ahmet Aktan
- Department of Radiology, Yalova Private Hospital, 77100 Yalova, Turkey
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Wu D, Rao Q, Zhang W. The natural course of COVID-19 patients without clinical intervention. J Med Virol 2021; 93:5527-5537. [PMID: 33990975 PMCID: PMC8242845 DOI: 10.1002/jmv.27087] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/05/2021] [Accepted: 05/11/2021] [Indexed: 01/08/2023]
Abstract
The natural course of coronavirus disease 2019 (COVID‐19) patients without clinical intervention has not yet been documented. One hundred and fifty‐eight patients from two hospitals were enrolled to identify the indicators of severe COVID‐19 and observe the natural course of COVID‐19 patients without clinical intervention. The total computed tomography (CT) score, a quantitative score based on assessment of the number, quadrant, and area of the lesions in CT, tended to perform better than assessment based only on the number or area of the lesions (p = 0.0004 and p = 0.0887, respectively). Multivariate logistic regression showed that the total CT score, chest tightness, lymphocyte, and lactate dehydrogenase (LDH) were independent factors for severe COVID‐19. For patients admitted in 2 weeks from onset to hospitalization, the frequency of severe COVID‐19 was gradually increased with the delayed hospitalization. The symptoms of fatigue, dry cough, sputum production, chest tightness, and polypnea were gradually more frequent. The levels of C‐reactive protein, alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, γ‐glutamyl transpeptidase, LDH, and d‐dimer were also gradually increased, as well as the scores based on CT. Conversely, the lymphocyte count and the albumin level were gradually decreased with the delayed hospitalization. Detail turning points of the above alterations were observed after 10–14 days from onset to hospitalization. Total CT score was a simple and feasible score for identifying severe COVID‐19. COVID‐19 patients without clinical intervention deteriorated gradually during the initial 10–14 days but gradually improved thereafter. Present study developed a simple and feasible score based on CT to identify severe COVID‐19. Present study was the first to investigate the natural course of COVID‐19 patients without clinical intervention. COVID‐19 patients without clinical intervention deteriorated gradually during the initial 10‐14 days but gradually improved thereafter.
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Affiliation(s)
- Daxian Wu
- Department of Infectious Diseases, The First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Qunfang Rao
- Department of Infectious Diseases, The First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Wenfeng Zhang
- Department of Infectious Diseases, The First Affiliated Hospital, Nanchang University, Nanchang, China
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Rezaeijo SM, Abedi-Firouzjah R, Ghorvei M, Sarnameh S. Screening of COVID-19 based on the extracted radiomics features from chest CT images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:229-243. [PMID: 33612539 DOI: 10.3233/xst-200831] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Radiomics has been widely used in quantitative analysis of medical images for disease diagnosis and prognosis assessment. The objective of this study is to test a machine-learning (ML) method based on radiomics features extracted from chest CT images for screening COVID-19 cases. METHODS The study is carried out on two groups of patients, including 138 patients with confirmed and 140 patients with suspected COVID-19. We focus on distinguishing pneumonia caused by COVID-19 from the suspected cases by segmentation of whole lung volume and extraction of 86 radiomics features. Followed by feature extraction, nine feature-selection procedures are used to identify valuable features. Then, ten ML classifiers are applied to classify and predict COVID-19 cases. Each ML models is trained and tested using a ten-fold cross-validation method. The predictive performance of each ML model is evaluated using the area under the curve (AUC) and accuracy. RESULTS The range of accuracy and AUC is from 0.32 (recursive feature elimination [RFE]+Multinomial Naive Bayes [MNB] classifier) to 0.984 (RFE+bagging [BAG], RFE+decision tree [DT] classifiers) and 0.27 (mutual information [MI]+MNB classifier) to 0.997 (RFE+k-nearest neighborhood [KNN] classifier), respectively. There is no direct correlation among the number of the selected features, accuracy, and AUC, however, with changes in the number of the selected features, the accuracy and AUC values will change. Feature selection procedure RFE+BAG classifier and RFE+DT classifier achieve the highest prediction accuracy (accuracy: 0.984), followed by MI+Gaussian Naive Bayes (GNB) and logistic regression (LGR)+DT classifiers (accuracy: 0.976). RFE+KNN classifier as a feature selection procedure achieve the highest AUC (AUC: 0.997), followed by RFE+BAG classifier (AUC: 0.991) and RFE+gradient boosting decision tree (GBDT) classifier (AUC: 0.99). CONCLUSION This study demonstrates that the ML model based on RFE+KNN classifier achieves the highest performance to differentiate patients with a confirmed infection caused by COVID-19 from the suspected cases.
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Affiliation(s)
- Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | | | - Mohammadreza Ghorvei
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Samad Sarnameh
- Department of Otorhinolaryngology, Isfahan University of Medical Sciences, Isfahan, Iran
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Huang D, Miao H, Zhang Z, Yang Y, Zhang L, Lure FYM, Wang Z, Jaeger S, Guo L, Xu T, Liu J. Longitudinal changes of laboratory measurements after discharged from hospital in 268 COVID-19 pneumonia patients. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:741-762. [PMID: 34397444 DOI: 10.3233/xst-210920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Monitoring recovery process of coronavirus disease 2019 (COVID-19) patients released from hospital is crucial for exploring residual effects of COVID-19 and beneficial for clinical care. In this study, a comprehensive analysis was carried out to clarify residual effects of COVID-19 on hospital discharged patients. METHODS Two hundred sixty-eight cases with laboratory measured data at hospital discharge record and five follow-up visits were retrospectively collected to carry out statistical data analysis comprehensively, which includes multiple statistical methods (e.g., chi-square, T-test and regression) used in this study. RESULTS Study found that 13 of 21 hematologic parameters in laboratory measured dataset and volume ratio of right lung lesions on CT images highly associated with COVID-19. Moderate patients had statistically significant lower neutrophils than mild and severe patients after hospital discharge, which is probably caused by more efforts on severe patients and slightly neglection of moderate patients. COVID-19 has residual effects on neutrophil-to-lymphocyte ratio (NLR) of patients who have hypertension or chronic obstructive pulmonary disease (COPD). After released from hospital, female showed better performance in T lymphocytes subset cells, especially T helper lymphocyte% (16% higher than male). According to this sex-based differentiation of COVID-19, male should be recommended to take clinical test more frequently to monitor recovery of immune system. Patients over 60 years old showed unstable recovery process of immune cells (e.g., CD45 + lymphocyte) within 75 days after discharge requiring longer clinical care. Additionally, right lung was vulnerable to COVID-19 and required more time to recover than left lung. CONCLUSIONS Criterion of hospital discharge and strategy of clinical care should be flexible in different cases due to residual effects of COVID-19, which depend on several impact factors. Revealing remaining effects of COVID-19 is an effective way to eliminate disorder of mental health caused by COVID-19 infection.
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Affiliation(s)
- Deyang Huang
- Guangzhou Eighth People's Hospital, Guangdong, China
| | - Hengyuan Miao
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China
| | - Ziqi Zhang
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China
| | - Yanhong Yang
- Guangzhou Eighth People's Hospital, Guangdong, China
| | | | | | - Zixian Wang
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China
| | - Stefan Jaeger
- National Library of Medicine, National Institutes of Health, Rockville Pike, Bethesda, MD, USA
| | - Lin Guo
- Shenzhen Zhiying Medical Co., Ltd, Shenzhen, China
| | - Tao Xu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijings, Department of Mechanical Engineering, Tsinghua University, Beijing, China
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Department of Mechanical Engineering, Tsinghua University, Beijing, China
| | - Jinxin Liu
- Guangzhou Eighth People's Hospital, Guangdong, China
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