scholarly journals A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

2020 ◽  
Author(s):  
Quan Cai ◽  
Siyao Du ◽  
Si Gao ◽  
Guoliang Huang ◽  
Zheng Zhang ◽  
...  

Abstract Background Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR assays lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on CT radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. Methods From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung lobes and lesions on lung CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. Results The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. Conclusions The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

2020 ◽  
Author(s):  
Quan Cai ◽  
Siyao Du ◽  
Si Gao ◽  
Guoliang Huang ◽  
Zheng Zhang ◽  
...  

Abstract Background: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. Methods: From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n=141; testing: n=62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. Results: The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p<0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. Conclusion: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.


2020 ◽  
Author(s):  
Quan Cai ◽  
Siyao Du ◽  
Si Gao ◽  
Guoliang Huang ◽  
Zheng Zhang ◽  
...  

Abstract Background: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment.Methods: From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n=141; testing: n=62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts.Results: The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p<0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively.Conclusion: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Quan Cai ◽  
Si-Yao Du ◽  
Si Gao ◽  
Guo-Liang Huang ◽  
Zheng Zhang ◽  
...  

Abstract Background Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. Methods From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. Results The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. Conclusion The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.


2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 507-507
Author(s):  
Kareem Rayn ◽  
Michael Daniel Weintraub ◽  
Gustavo Pena-LaGrave ◽  
Samuel Gold ◽  
Graham R. Hale ◽  
...  

507 Background: Urinary bladder paragangliomas (UBPGLs) are extremely rare, accounting for less than 6% of paragangliomas (PGLs) and 0.06% of bladder tumors. The goal of this study is to examine the presentation, clinical characteristics and outcomes of patients with UBPGLs. Methods: We determined the presenting symptoms, clinical characteristics, and outcomes of patients who presented to a single institution with UBPGLs from 2000-2017. Results: 28 patients with an average age of 27 ± 15.6 at symptom onset presented to the NIH from 2000-2017. The majority had standard paraganglioma symptoms (n = 24, 85.7%) defined as headaches, palpitations, pallor and anxiety, and hypertension (n = 20, 71.4%) on presentation. 8 patients (29%) presented with hematuria; hematuria was the only presenting symptom in 1 of these patients. 3 (10.7%) of the patients were completely asymptomatic and were discovered to have bladder paragangliomas incidentally on imaging. Overall, 9 patients (32%) were under 18 (average age = 10.9 ± 3.9) at symptom onset. 14 (50%) patients developed metastasis, with bone (n = 9) and lung (n = 8) being the most common metastatic sites. All but 1 patient received surgical treatment, with 6 patients receiving transurethral resection of bladder tumor (TURBT), 3 receiving robotic-assisted partial cystectomy (RAPC) and the remaining patients undergoing open cystectomy. In total, 2 patients experienced bladder cancer recurrence, both of whom had undergone TURBT. Comparing patients with and without hematuria, metastasis and standard paraganglioma symptoms, we found no statistically significant difference in mean diameter of the largest lesion or plasma catecholamine values. Conclusions: Our experience reveals that most patients with UBPGLs present at an early age with characteristic paraganglioma symptoms. Despite the variety of surgical methods used to manage these patients, the only 2 recurrences were in patients who underwent TURBT. Further work is necessary to establish preoperative indicators of disease severity in patients with UBPGLs. This research was supported by the Intramural Research Program of the National Cancer Institute, NIH and NIH Medical Research Scholars Program


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suochen Tian ◽  
Min Wu ◽  
Zhenqin Chang ◽  
Yunxia Wang ◽  
Guijie Zhou ◽  
...  

Abstract Background In view of the ongoing coronavirus disease (COVID-19) pandemic, it remains unclear whether the severity of illness and time interval from symptom onset to release from quarantine differ between cases that originated from clusters and cases reported in other areas. This study aimed to assess epidemiological and intergenerational clinical characteristics of COVID-19 patients associated with cluster outbreaks to provide valuable data for the prevention and control of COVID-19. Methods We identified the first employee with COVID-19 at a supermarket and screened the close contacts of this index patient. Confirmed cases were divided into two groups according to the generation (first generation comprising supermarket employees [group A] and second or third generations comprising family members or friends of the supermarket employees [group B]). The epidemiological and clinical characteristics of the two groups were retrospectively compared. Results A total of 8437 people were screened, and 24 COVID-19 patients were identified. Seven patients (29.2%) were asymptomatic; three patients were responsible for six symptomatic cases. The interval from the confirmation of the first case to symptom onset in symptomatic patients was 5–11 days. The clinical manifestations of symptomatic patients upon admission were non-specific. All patients (including the seven asymptomatic patients) were admitted based on chest computed tomography features indicative of pneumonia. There were 11 cases in group A (first generation) and 13 cases in group B (second generation, 11 cases; third generation, 2 cases), with no significant differences in clinical and epidemiological characteristics between the two groups, except for sex, duration from symptom onset to hospitalization, and underlying disease (P > 0.05). Conclusions For cluster outbreaks, it is important to comprehensively screen close the contacts of the index patient. Special attention should be paid to asymptomatic cases. The clinical management of cluster patients is similar to that of other COVID-19 patients.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 3-4
Author(s):  
Maria do Rosario Ferraz Roberti ◽  
Tiago Paiva Prudente ◽  
Renato Gomes Castro ◽  
Marcos Antonio Candido ◽  
Roberta Luiza Rodrigues ◽  
...  

In March 2020, COVID-19 was declared a pandemic by the WHO. Since then, efforts have been made to increase our knowledge of the disease. The convalescent plasma (CP) donation involves a series of criteria for donor eligibility, such as pre-donation and serological tests. Currently, the antibody response against SARS-CoV-2 remains poorly understood and the usefulness of serological tests is unclear (Long, et al. Nature Medicine, 2020). Based on donor eligibility, one can better assess the antibody response to SARS-CoV-2 from post-infection candidates. This is an observational, prospective study, without intervention. From 06/26/2020 to 07/31/2020, serological data of candidates for CP donation were collected. Recovered COVID-19 patients who had been previously tested were interviewed. RT-PCR and serological test (chemiluminescence immunoassays) for SARS-CoV-2 were carried out to verify their eligibility for CP collection. The data were related to the time of the onset of symptoms and the collection of the material. Subjects with non-detectable RT-PCR and reagent IgG were considered eligible. Reference values were IgM &gt; 1.2 AU/mL and IgG &gt; 1.4 AU/mL. The characteristics of the candidates are summarized in Table 1. Of 234 interviewed subjects, 70 were screened for pre-collection tests, 49 were male. The average age was 36 (20 - 57). After serological screening, 44/70 (62.8%) were considered eligible for CP donation. The reasons for ineligibility were: 17/70 (24.3%) non-reagent IgG, 4/70 (5.7%) with detectable RT-PCR and 5/70 (7.1%) due to reasons in clinical screening. The median between the onset of symptoms and the serology sample collection was 32.5 (21 - 77) days, (IQR 28.75 to 37.25). Those who were more likely to be eligible to donate were the subjects who had a longer time interval between the symptoms onset and the sample collection (p &lt;0.012). Although viral clearance in the upper airways is expected from the 10th day of symptom onset, only 50% of patients will have an undetectable test (Özçürümez, et al. J Allergy Clin Immunol. 2020). In our sample, 5.7% (4/70) of the subjects had detectable RT-PCR, which can represent residual viral genome and not active infection. We observed that 20% of the subjects samples were non-reagent. Those who were tested up to the 21st of the onset of symptoms might not have had seroconversion yet. For those tested after the 28th day, we can infer that the antibodies had already been cleared. Some authors state that patients who had mild infections may react with less antibodies (Özçürümez, et al. J Allergy Clin Immunol. 2020), which could explain this fact. Likewise, it was not possible to relate serological titers to the severity of the disease, as this was not one of the selection criteria.In 40/70 donors (57.2%) IgM remained above 1.2 AU / mL after the 21st day of symptom onset. Interestingly, 2 of these had only reagent IgM after the 36th day of symptom onset. Most subjects who had reagent IgM after the 21st of symptoms also had reagent IgG. We inferred that they were in a vigorous convalescence phase. In addition, 75.7% of the subjects presented reagent IgG regardless of the date of onset of symptoms. Most of them had both reagent IgM and IgG. Only one donor's (1.4%) IgM and IgG were non-reagent 21 days after the onset of symptoms. As we did not collect serial samples, we could not verify the average amount of days for seroconversion to take place. Some authors recommend that the single collection should occur at least 21 days after the onset of symptoms, so seroconversion is observed (Deeks, et al., Cochrane Database Syst Rev. 2020). In our sample, 4 donors (5%) collected the samples on the 21st day after the symptom onset. Of these, 3 had seroconversion, 2 with IgM and IgG, 1 with IgG and 1 with reagent IgM. The values suggest that the subjects who could donate CP were those that presented a longer time interval between the onset of symptoms and the blood sample collection, in comparison to those who could not (p=0,012 and 0,409, respectively). The median of days between symptom onset and serology testing was also higher in the non-eligible group. Besides, the eligible group had a higher average concentration of IgM and IgG compared to the non-eligible one. In conclusion, regarding the serological criteria, about 25% of the studied population could not donate CP. Although a single serology sample collection after the 21st day of symptom onset is recommended, only 1 candidate did not show seroconversion. Disclosures No relevant conflicts of interest to declare.


Author(s):  
Jan Schaible ◽  
Stefanie Meiler ◽  
Florian Poschenrieder ◽  
Gregor Scharf ◽  
Florian Zeman ◽  
...  

Background CT is important in the care of patients with COVID-19 pneumonia. However, CT morphology can change significantly over the course of the disease. To evaluate the CT morphology of RT-PCR-proven COVID-19 pneumonia in a German cohort with special emphasis on identification of potential differences of CT features depending on duration and severity of disease. Method All patients with RT-PCR-proven COVID-19 pneumonia and chest CT performed between March 1 and April 15, 2020 were retrospectively identified. The CT scans were evaluated regarding the presence of different CT features (e. g. ground glass opacity, consolidation, crazy paving, vessel enlargement, shape, and margin of opacifications), distribution of lesions in the lung and extent of parenchymal involvement. For subgroup analyses the patients were divided according to the percentage of parenchymal opacification (0–33 %, 34–66 %, 67–100 %) and according to time interval between symptom onset and CT date (0–5 d, 6–10 d, 11–15 d, > 15 d). Differences in CT features and distribution between subgroups were tested using the Mantel-Haenszel Chi Squared for trend. Results The frequency of CT features (ground glass opacity, consolidation, crazy paving, bronchial dilatation, vessel enlargement, lymphadenopathy, pleural effusion) as well as pattern of parenchymal involvement differed significantly depending on the duration of disease and extent of parenchymal involvement. The early phase of disease was characterized by GGO and to a lesser extent consolidation. The opacifications tended to be round and to some extent with sharp margins and a geographic configuration. The vessels within/around the opacifications were frequently dilated. Later on, the frequency of consolidation and especially crazy paving increased, and the round/geographic shape faded. After day 15, bronchial dilatation occurred, and lymphadenopathy and pleural effusion were seen more frequently than before. Conclusion The prevalence of CT features varied considerably during the course of disease and depending on the severity of parenchymal involvement. Radiologists should take into account the time interval between symptom onset and date of CT and the severity of disease when discussing the likelihood of COVID-19 pneumonia based on CT morphology. Key Points:  Citation Format


2020 ◽  
Vol 7 (8) ◽  
Author(s):  
Tingting Liao ◽  
Zhengrong Yin ◽  
Juanjuan Xu ◽  
Zhilei Lv ◽  
Sufei Wang ◽  
...  

Abstract Background Patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can shed virus, thereby causing human-to-human transmission, and the viral RNA shedding is commonly used as a proxy measure for infectivity. Methods We retrospectively reviewed confirmed cases of COVID-19 who attended the fever clinic of Wuhan Union Hospital from January 14 to February 24. In terms of the viral RNA shedding (median values) at first visit, patients were divided into a high–viral RNA shedding group and a low–viral RNA shedding group. Univariate and multivariate logistic regression analysis were performed to investigate the correlation between viral RNA shedding and clinical features. Results A total of 918 consecutive COVID-19 patients were enrolled, and severe patients made up 26.1%. After univariate and multivariate logistic regression, advanced age (odds ratio [OR], 1.02; 95% CI, 1.01–1.03; P = .001), having severe chronic diseases (OR, 1.44; 95% CI, 1.03–2.01; P = .04), and severe illness (OR, 1.60; 95% CI, 1.12–2.28; P = .01) were independent risk factors for high viral RNA shedding. Shorter time interval from symptom onset to viral detection was a protective factor for viral RNA shedding (OR, 0.97; 95% CI, 0.94–0.99; P = .01). Compared with mild patients, severe patients have higher virus shedding over a long period of time after symptom onset (P = .01). Conclusions Outpatients who were old, had severe illness, and had severe underlying diseases had high viral RNA shedding.


2020 ◽  
Author(s):  
Marine Thieux ◽  
Anne-Charlotte Kalenderian ◽  
Aurélie Chabrol ◽  
Laurent Gendt ◽  
Emma Giraudier ◽  
...  

AbstractObjectivesTo assess the relevance of a diagnostic strategy for COVID-19 based on chest computed tomography (CT) in patients with hospitalization criteria.SettingObservational study with retrospective analysis in a French emergency department (ED).Participants and interventionFrom March 3 to April 2, 2020, 385 adult patients presenting to the ED for suspected COVID-19 underwent an evaluation that included history, physical examination, blood tests, real-time reverse transcription-polymerase chain reaction (RT-PCR) and chest CT. When the time-interval between chest CT and RT-PCR assays was longer than 7 days, patients were excluded from the study. Only patients with hospitalization criteria were included. Diagnosis accuracy was assessed using the sensitivity and specificity of RT-PCR.OutcomesSensitivity and specificity of RT-PCR, chest CT (also accompanied by lymphopenia) were measured and were also analyzed by subgroups of age and sex.ResultsAmong 377 included subjects, RT-PCR was positive in 36%, while chest CT was compatible with a COVID-19 diagnosis in 59%. In the population with positive RT-PCR, there were more men (55% vs 37%, p=0.015), a higher frequency of reticular and, or, interlobular septal thickening (53% vs 31%, p=0.02) as well as a higher frequency of bilateral lesion distribution (98% vs 86%, p=0.01) compared to the population with negative RT-PCR. The proportion of lymphopenia was higher in men vs women (47% vs 39%, p=0.03) and varies between patients >80 versus 50-80 and p<0.001).Using CT as reference, RT-PCR obtained a sensitivity of 61%, specificity of 93%. There was a significant difference between CT and RT-PCR diagnosis performance (p<0.001). When CT was accompanied by lymphopenia, sensitivity and specificity of RT-PCR were respectively 71% and 94%. CT abnormalities and lymphopenia provided diagnosis in 29% of patients with negative PCR.ConclusionsChest CT had a superior yield than RT-PCR in COVID-19 hospitalized patients, especially when accompanied by lymphopenia.


2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qi Fan ◽  
Xiaoyan Han ◽  
Xiangjia Zhu ◽  
Lei Cai ◽  
Xiaodi Qiu ◽  
...  

Purpose. To investigate the clinical characteristics of patients with intraocular lens (IOL) dislocation after IOL implantation in Chinese Han populations. Methods. The medical records of patients with IOL dislocation were retrospectively reviewed from January 2007 to December 2017, and a total of 312 patients (male: 231, female: 97) (328 eyes) were included in this study. The axial length (AL), IOL power, and the time interval between cataract surgery and IOL dislocation as well as the ocular conditions associated with IOL dislocation were recorded. The IOL dislocation was classified and graded based on its relationship with the capsule and the position of the dislocated IOL. Results. The mean time between original cataract surgery and IOL dislocation was 5.63 ± 5.13 years; IOL dislocation occurred in up to 56.1% (184 eyes) of the eyes within 5 years. Trauma was found in 136 eyes (41.5%); pars plana vitrectomies were performed in 61 eyes (18.6%), and high myopia was detected in 108 eyes (32.9%). A total of 243 eyes (74.1%) had out-of-the-bag IOL dislocations, while 85 eyes (25.9%) had in-the-bag IOL dislocations. There was a statistically significant difference in the constituent ratio of trauma between in-the-bag dislocation and out-of-the-bag dislocation (Pearson’s chi2 = 33.3992, P<0.001); ocular blunt traumas were significantly higher in in-the-bag dislocations, while open-globe injuries were significantly higher in out-of-the-bag dislocations. A statistically significant difference was found for the ratio of patients with AL longer than 30 mm between in-the-bag dislocation and out-of-the-bag dislocation (Pearson’s chi2 = 9.7355, P<0.002). Conclusions. In Chinese Han populations, the most common IOL dislocation is out-of-the-bag dislocation; the most common risk factors were trauma, long axial length, and eyes undergoing pars plana vitrectomy; a minimum follow-up of 5 years is suggested for IOL dislocation-predisposed eyes undergoing cataract surgery.


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