Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 615
Filter
Add filters

Year range
2.
PLoS One ; 15(7): e0236621, 2020.
Article in English | MEDLINE | ID: covidwho-691350

ABSTRACT

This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.9yo; 29F 60.1±14.3yo; 4 missing information). Three expert chest radiologists scored the left and right lung separately based on the degree of opacity (0-3) and geographic extent (0-4). Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. Data were split into 80% training and 20% testing datasets. Correlation analysis between AI-predicted versus radiologist scores were analyzed. Comparison was made with traditional and transfer learning. The average opacity score was 2.52 (range: 0-6) with a standard deviation of 0.25 (9.9%) across three readers. The average geographic extent score was 3.42 (range: 0-8) with a standard deviation of 0.57 (16.7%) across three readers. The inter-rater agreement yielded a Fleiss' Kappa of 0.45 for opacity score and 0.71 for extent score. AI-predicted scores strongly correlated with radiologist scores, with the top model yielding a correlation coefficient (R2) of 0.90 (range: 0.73-0.90 for traditional learning and 0.83-0.90 for transfer learning) and a mean absolute error of 8.5% (ranges: 17.2-21.0% and 8.5%-15.5, respectively). Transfer learning generally performed better. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This approach may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.


Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/physiopathology , Deep Learning , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/physiopathology , Tomography, X-Ray Computed/instrumentation , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Radiologists , Severity of Illness Index
3.
PLoS One ; 15(7): e0236858, 2020.
Article in English | MEDLINE | ID: covidwho-690151

ABSTRACT

The purpose of this study was to describe the temporal evolution of quantitative lung lesion features on chest computed tomography (CT) in patients with common and severe types of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Records of patients diagnosed with SARS-CoV-2 pneumonia were reviewed retrospectively from 24 January 2020 to 15 March 2020. Patients were classified into common and severe groups according to the diagnostic criteria of severe pneumonia. The quantitative CT features of lung lesions were automatically calculated using artificial intelligence algorithms, and the percentages of ground-glass opacity volume (PGV), consolidation volume (PCV) and total lesion volume (PTV) were determined in both lungs. PGV, PCV and PTV were analyzed based on the time from the onset of initial symptoms in the common and severe groups. In the common group, PTV increased slowly and peaked at approximately 12 days from the onset of the initial symptoms. In the severe group, PTV peaked at approximately 17 days. The severe pneumonia group exhibited increased PGV, PCV and PTV compared with the common group. These features started to appear in Stage 2 (4-7 days from onset of initial symptoms) and were observed in all subsequent stages (p<0.05). In severe SARS-CoV-2 pneumonia patients, PGV, PCV and PTV began to significantly increase in Stage 2 and decrease in Stage 5 (22-30 days). Compared with common SARS-CoV-2 pneumonia patients, the patients in the severe group exhibited increased PGV, PCV and PTV as well as a later peak time of lesion and recovery time.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/pathology , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adolescent , Adult , Aged , Aged, 80 and over , Algorithms , Artificial Intelligence , Betacoronavirus , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Radiography, Thoracic , Retrospective Studies , Young Adult
5.
Indian J Med Microbiol ; 38(1): 87-93, 2020.
Article in English | MEDLINE | ID: covidwho-688925

ABSTRACT

Objective: This study aims to provide scientific basis for rapid screening and early diagnosis of the coronavirus disease 2019 (COVID-19) through analysing the clinical characteristics and early imaging/laboratory findings of the inpatients. Methods: Three hundred and three patients with laboratory-confirmed COVID-19 from the East Hospital of People's Hospital of Wuhan University (Wuhan, China) were selected and divided into four groups: youth (20-40 years, n = 64), middle-aged (41-60 years, n = 89), older (61-80 years, n = 118) and elderly (81-100 years, n = 32). The clinical characteristics and imaging/laboratory findings including chest computed tomography (CT), initial blood count, C-reactive protein [CRP]), procalcitonin (PCT) and serum total IgE were captured and analysed. Results: (1) The first symptoms of all age groups were primarily fever (76%), followed by cough (12%) and dyspnoea (5%). Beside fever, the most common initial symptom of elderly patients was fatigue (13%). (2) Fever was the most common clinical manifestation (80%), with moderate fever being the most common (40%), followed by low fever in patients above 40 years old and high fever in those under 40 years (35%). Cough was the second most common clinical manifestation and was most common (80%) in the middle-aged. Diarrhoea was more common in the middle-aged (21%) and the older (19%). Muscle ache was more common in the middle-aged (15%). Chest pain was more common in the youth (13%), and 13% of the youth had no symptoms. (3) The proportion of patients with comorbidities increased with age. (4) Seventy-one per cent of the patients had positive reverse transcription-polymerase chain reaction results and 29% had positive chest CT scans before admission to the hospital. (5) Lesions in all lobes of the lung were observed as the main chest CT findings (76%). (6) Decrease in lymphocytes and increase in monocytes were common in the patients over 40 years old but rare in the youth. Eosinophils (50%), red blood cells (39%) and haemoglobin (40%) decreased in all age groups. (7) The proportion of patients with CRP and PCT elevation increased with age. (8) Thirty-nine per cent of the patients had elevated IgE, with the highest proportion in the old (49%). Conclusion: The clinical characteristics and imaging/laboratory findings of COVID-19 patients vary in different age groups. Personalised criteria should be formulated according to different age groups in the early screening and diagnosis stage.


Subject(s)
Betacoronavirus/growth & development , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Diagnostic Tests, Routine/methods , Pneumonia, Viral/diagnosis , Pneumonia, Viral/pathology , Tomography, X-Ray Computed/methods , Adult , Age Factors , Aged , Aged, 80 and over , China , Coronavirus Infections/diagnostic imaging , Early Diagnosis , Female , Hospitals, University , Humans , Male , Mass Screening/methods , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Young Adult
8.
Invest Radiol ; 55(5): 257-261, 2020 05.
Article in English | MEDLINE | ID: covidwho-684015

ABSTRACT

OBJECTIVES: The aim of this study was to investigate the chest computed tomography (CT) findings in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate its relationship with clinical features. MATERIALS AND METHODS: Study sample consisted of 80 patients diagnosed as COVID-19 from January to February 2020. The chest CT images and clinical data were reviewed, and the relationship between them was analyzed. RESULTS: Totally, 80 patients diagnosed with COVID-19 were included. With regards to the clinical manifestations, 58 (73%) of the 80 patients had cough, and 61 (76%) of the 80 patients had high temperature levels. The most frequent CT abnormalities observed were ground glass opacity (73/80 cases, 91%), consolidation (50/80 cases, 63%), and interlobular septal thickening (47/80, 59%). Most of the lesions were multiple, with an average of 12 ± 6 lung segments involved. The most common involved lung segments were the dorsal segment of the right lower lobe (69/80, 86%), the posterior basal segment of the right lower lobe (68/80, 85%), the lateral basal segment of the right lower lobe (64/80, 80%), the dorsal segment of the left lower lobe (61/80, 76%), and the posterior basal segment of the left lower lobe (65/80, 81%). The average pulmonary inflammation index value was (34% ± 20%) for all the patients. Correlation analysis showed that the pulmonary inflammation index value was significantly correlated with the values of lymphocyte count, monocyte count, C-reactive protein, procalcitonin, days from illness onset, and body temperature (P < 0.05). CONCLUSIONS: The common chest CT findings of COVID-19 are multiple ground glass opacity, consolidation, and interlobular septal thickening in both lungs, which are mostly distributed under the pleura. There are significant correlations between the degree of pulmonary inflammation and the main clinical symptoms and laboratory results. Computed tomography plays an important role in the diagnosis and evaluation of this emerging global health emergency.


Subject(s)
Coronavirus Infections/diagnostic imaging , Coronavirus Infections/pathology , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/pathology , Adult , Aged , Betacoronavirus/isolation & purification , Coronavirus Infections/virology , Cough/virology , Female , Fever/virology , Humans , Lung/pathology , Lung/virology , Male , Middle Aged , Pandemics , Pneumonia, Viral/virology , Retrospective Studies , Thorax/diagnostic imaging , Thorax/virology , Tomography, X-Ray Computed/methods , Young Adult
9.
Radiology ; 296(2): E97-E104, 2020 08.
Article in English | MEDLINE | ID: covidwho-683271

ABSTRACT

Background A categorical CT assessment scheme for suspicion of pulmonary involvement of coronavirus disease 2019 (COVID-19 provides a basis for gathering scientific evidence and improved communication with referring physicians. Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for use in the standardized assessment of pulmonary involvement of COVID-19 on unenhanced chest CT images and to report its initial interobserver agreement and performance. Materials and Methods The Dutch Radiological Society developed CO-RADS based on other efforts for standardization, such as the Lung Imaging Reporting and Data System or Breast Imaging Reporting and Data System. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients with moderate to severe symptoms of COVID-19. The system was evaluated by using 105 chest CT scans of patients admitted to the hospital with clinical suspicion of COVID-19 and in whom reverse transcription-polymerase chain reaction (RT-PCR) was performed (mean, 62 years ± 16 [standard deviation]; 61 men, 53 with positive RT-PCR results). Eight observers used CO-RADS to assess the scans. Fleiss κ value was calculated, and scores of individual observers were compared with the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared with results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss κ value was 0.47 (95% confidence interval [CI]: 0.45, 0.47), with the highest κ value for CO-RADS categories 1 (0.58, 95% CI: 0.54, 0.62) and 5 (0.68, 95% CI: 0.65, 0.72). The average AUC was 0.91 (95% CI: 0.85, 0.97) for predicting RT-PCR outcome and 0.95 (95% CI: 0.91, 0.99) for clinical diagnosis. The false-negative rate for CO-RADS 1 was nine of 161 cases (5.6%; 95% CI: 1.0%, 10%), and the false-positive rate for CO-RADS category 5 was one of 286 (0.3%; 95% CI: 0%, 1.0%). Conclusion The coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) is a categorical assessment scheme for pulmonary involvement of COVID-19 at unenhanced chest CT that performs very well in predicting COVID-19 in patients with moderate to severe symptoms and has substantial interobserver agreement, especially for categories 1 and 5. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/standards , Adult , Aged , Communication , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Netherlands , Observer Variation , Pandemics , Radiology Information Systems , Reverse Transcriptase Polymerase Chain Reaction/methods , Tomography, X-Ray Computed/methods
11.
Korean J Radiol ; 21(5): 541-544, 2020 05.
Article in English | MEDLINE | ID: covidwho-678713

ABSTRACT

The coronavirus disease 2019 (COVID-19) pneumonia is a recent outbreak in mainland China and has rapidly spread to multiple countries worldwide. Pulmonary parenchymal opacities are often observed during chest radiography. Currently, few cases have reported the complications of severe COVID-19 pneumonia. We report a case where serial follow-up chest computed tomography revealed progression of pulmonary lesions into confluent bilateral consolidation with lower lung predominance, thereby confirming COVID-19 pneumonia. Furthermore, complications such as mediastinal emphysema, giant bulla, and pneumothorax were also observed during the course of the disease.


Subject(s)
Coronavirus Infections/complications , Mediastinal Emphysema/etiology , Pneumonia, Viral/complications , Pneumothorax/etiology , Adult , Betacoronavirus , Blister , China , Clinical Laboratory Techniques , Coronavirus , Coronavirus Infections/diagnosis , Coronavirus Infections/diagnostic imaging , Disease Progression , Humans , Lung/pathology , Male , Pandemics , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed
12.
Medicine (Baltimore) ; 99(29): e20781, 2020 Jul 17.
Article in English | MEDLINE | ID: covidwho-676380

ABSTRACT

BACKGROUND: No specific anti-virus drugs or vaccines have been available for the treatment of COVID-19. Integrative traditional Chinese and western medicine has been proposed as a therapeutic option with substantial applications in China. This protocol is proposed for a systematic review and meta-analysis that aims to evaluate the efficacy of integrative traditional Chinese and western medicine treatment on patients with COVID-19. METHODS: Ten databases including PubMed, EMBASE, Cochrane Library, CIHAHL, Web of Science, Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP), Wanfang database, China Biomedical Literature Database (CBM) and Chinese Biomedical Literature Service System (SinoMed) will be searched. All published randomized controlled trials, clinical controlled trials, case-control, and case series that meet the pre-specified eligibility criteria will be included. Primary outcome measures include mortality, clinical recovery rate, duration of fever, progression rate from mild or moderate to severe, improvement of symptoms, biomarkers of laboratory examination and changes in computed tomography. Secondary outcomes include dosage of hormonotherapy, incidence and severity of adverse events and quality of life. Study selection, data extraction and assessment of bias risk will be conducted by 2 reviewers independently. RevMan software (V.5.3.5) will be used to perform data synthesis. Subgroup and sensitivity analysis will be performed when necessary. The strength of evidence will be assessed by the GRADE system. RESULTS: This study will provide a well-reported and high-quality synthesis on the efficacy of integrative traditional Chinese and western medicine treatment on patients with COVID-19. CONCLUSION: This systematic review protocol will be helpful for providing evidence of whether integrative traditional Chinese and western medicine treatment is an effective therapeutic approach for patients with COVID-19. ETHICS AND DISSEMINATION: Ethical approval is unnecessary as no individual patient or privacy data is collected. The results of this study will be disseminated in a peer-reviewed scientific journal and/or conference presentation. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020167205.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/therapy , Medicine, Chinese Traditional/methods , Pneumonia, Viral/therapy , Biomarkers/analysis , Case-Control Studies , China/epidemiology , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Humans , Outcome Assessment, Health Care , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/epidemiology , Pneumonia, Viral/mortality , Quality of Life , Randomized Controlled Trials as Topic , Research Design , Tomography, X-Ray Computed/methods , Treatment Outcome
13.
AIDS Res Ther ; 17(1): 46, 2020 07 23.
Article in English | MEDLINE | ID: covidwho-671088

ABSTRACT

BACKGROUND: The COVID-19 has been a severe pandemic all around the world. Nowadays the patient with co-infection of HIV and SARS-CoV-2 was rarely reported. Here we reported a special case with HIV and SARS-CoV-2 co-infection, which showed a prolonged viral shedding duration. CASE PRESENTATION: The patient was infected with HIV 8 years ago through sexual transmission and had the normal CD4+T cell count. She was found SARS-CoV-2 positive using real-time Polymerase Chain Reaction (RT-PCR) during the epidemic. Most importantly, the patient had a prolonged viral shedding duration of SARS-CoV-2 about 28 days. CONCLUSION: The viral shedding duration may be prolonged in people living with HIV. The 14 days isolation strategy might not be long enough for them. The isolation or discharge of these patients needs further confirmation for preventing epidemics.


Subject(s)
Anti-Retroviral Agents/therapeutic use , Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , HIV Infections/complications , Pneumonia, Viral/diagnosis , Virus Shedding , Benzoxazines/administration & dosage , Betacoronavirus/genetics , Betacoronavirus/immunology , C-Reactive Protein/analysis , CD4 Lymphocyte Count , Chills , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/drug therapy , Fatigue , Female , Fever , HIV/growth & development , HIV Infections/drug therapy , HIV Infections/immunology , Humans , Immunocompromised Host , Immunoglobulin M/blood , Lamivudine/administration & dosage , Middle Aged , Pandemics , Pharyngitis , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/drug therapy , Real-Time Polymerase Chain Reaction , Sputum/virology , Time Factors , Tomography, X-Ray Computed , Virus Shedding/immunology , Zidovudine/administration & dosage
14.
Anaesthesiol Intensive Ther ; 52(2): 83-90, 2020.
Article in English | MEDLINE | ID: covidwho-663002

ABSTRACT

Respiratory failure is a dominating medical issue in the severe course of COVID-19. Both at the stage of diagnostics prior to admission to the intensive care unit and during the monitoring of lesion evolution, diagnostic imaging techniques may significantly influence clinical decisions. Although computed tomography remains the gold standard for diagnosing lung diseases, its usefulness for infected, critically ill patients has been largely limited during the pandemic. Reports from those countries in which the healthcare systems were most seriously overloaded with patients with COVID-19-induced pneumonia stress the key role of point-of-care lung ultrasound performed by clinicians first during preliminary diagnostics and then while monitoring disease dynamics. This consensus, worked out by an interdisciplinary team of specialists forming the Study Group for Point-of-Care Lung Ultrasound in the Intensive Care Management of COVID-19 Patients, presents a broad spectrum of aspects regarding the analysed issue. Its concise form is meant to serve clinicians who perform ultrasound as a straightforward and informative guide.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Critical Care , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Point-of-Care Systems , Ultrasonography/methods , Consensus , Coronavirus Infections/therapy , Humans , Pandemics , Pneumonia, Viral/therapy , Respiration, Artificial
16.
PLoS One ; 15(7): e0236312, 2020.
Article in English | MEDLINE | ID: covidwho-658804

ABSTRACT

COVID-19 pneumonia typically begins with subpleural ground glass opacities with progressive extension on computerized tomography studies. Lung ultrasound is well suited to this interstitial, subpleural involvement, and it is now broadly used in intensive care units (ICUs). The extension and severity of lung infiltrates can be described numerically with a reproducible and validated lung ultrasound score (LUSS). We hypothesized that LUSS might be useful as a tool to non-invasively monitor the evolution of COVID-19 pneumonia at the bedside. LUSS monitoring was rapidly implemented in the management of our COVID-19 patients with RT-PCR-documented COVID-19. The LUSS was evaluated repeatedly at the bedside. We present a graphic description of the course of LUSS during COVID-19 in 10 consecutive patients admitted in our intensive care unit with moderate to severe ARDS between March 15 and 30th. LUSS appeared to be closely related to the disease progression. In successfully extubated patients, LUSS decreased and was lower than at the time of intubation. LUSS increased inexorably in a patient who died from refractory hypoxemia. LUSS helped with the diagnosis of ventilator-associated pneumonia (VAP), showing an increased score and the presence of new lung consolidations in all 5 patients with VAPs. There was also a good agreement between CT-scans and LUSS as for the presence of lung consolidations. In conclusion, our early experience suggests that LUSS monitoring accurately reflect disease progression and indicates potential usefulness for the management of COVID-19 patients with ARDS. It might help with early VAP diagnosis, mechanical ventilation weaning management, and potentially reduce the need for X-ray and CT exams. LUSS evaluation is easy to use and readily available in ICUs throughout the world, and might be a safe, cheap and simple tool to optimize critically ill COVID-19 patients care during the pandemic.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Respiratory Distress Syndrome, Adult/virology , Ultrasonography , Betacoronavirus , Coronavirus Infections/complications , Disease Progression , Female , France , Humans , Intensive Care Units , Male , Middle Aged , Pandemics , Pneumonia, Ventilator-Associated/diagnostic imaging , Pneumonia, Ventilator-Associated/virology , Pneumonia, Viral/complications , Tomography, X-Ray Computed
17.
Radiology ; 296(2): E65-E71, 2020 08.
Article in English | MEDLINE | ID: covidwho-657750

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Clinical Laboratory Techniques/methods , Community-Acquired Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Deep Learning , Diagnosis, Differential , Female , Humans , Imaging, Three-Dimensional/methods , Male , Middle Aged , Pandemics , ROC Curve , Radiographic Image Interpretation, Computer-Assisted/methods , Retrospective Studies , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
19.
BMC Infect Dis ; 20(1): 517, 2020 Jul 16.
Article in English | MEDLINE | ID: covidwho-651422

ABSTRACT

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health emergency of major international concern. Real-time RT-PCR assays are recommended for diagnosis of COVID-19. Here we report a rare case of COVID-19 with multiple negative results for PCR assays outside Wuhan, China. CASE PRESENTATION: A 32-year old male was admitted to our hospital because of 6 days of unexplained fever on January 29, 2020. He had come from Wuhan city 10 days before admission. Five days before admission, no abnormality was noted in laboratory test, chest radiography, and nasopharyngeal swab test for the SARS-CoV-2 nucleic acid. The patient was treated with ibuprofen for alleviating fever. On admission, chest computed tomography showed multiple ground-glass opacities in right lower lung field. COVID-19 was suspected. Three times of nasopharyngeal swab specimens were collected after admission. However, none of the specimens were positive. The patient was confirmed with COVID-19 after fifth SARS-CoV-2 nucleic acid test. He was treated with lopinavir/ritonavir, recombinant human interferon alfa-2b inhalation, methylprednisolone. After 18 days of treatment, he was discharged with improved symptoms, lung lesions and negative results of nasopharyngeal swab. CONCLUSION: This case reminds clinician that a patient with high clinical suspicion of COVID-19 but multiple negative RT-PCR result should not be taken out of isolation. A combination of patient's exposure history, clinical manifestations, laboratory tests, and typical imaging findings plays a vital role in making preliminary diagnosis and guide early isolation and treatment. Repeat swab tests are helpful in diagnosis for this kind of patients.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Negative Results , Pneumonia, Viral/diagnosis , Pneumonia, Viral/virology , Adult , Betacoronavirus/genetics , China/epidemiology , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/physiopathology , Fever/etiology , Fever/virology , Hospitalization , Humans , Male , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/physiopathology , Quarantine , Radiography , Real-Time Polymerase Chain Reaction , Sensitivity and Specificity , Tomography, X-Ray Computed , Uncertainty
SELECTION OF CITATIONS
SEARCH DETAIL