scholarly journals Diagnostic value of chest CT in outpatient for COVID-19 compare with RT-PCR

2020 ◽  
Author(s):  
Weiwei Zhang ◽  
Meifen Zhu ◽  
Min Zhang
Keyword(s):  
Laboratory Test ◽  
Ground Glass Opacity ◽  
Diagnostic Value ◽  
Chest Ct ◽  
Test Results ◽  
Imaging Data ◽  
Rt Pcr ◽  
Spider Web ◽  
Lesion Location ◽  
Laboratory Test Results

Abstract ObjectivesThe pneumonia caused by the 2019 novel coronavirus recently break out in Wuhan, China, and was named as COVID-19. With the spread of the disease, it bring numbers of casualties,so now we need a way could fast and accuracy diagnose the disease.This paper aims to compare two way for diagnose COVID-19 in outpatient :Chest CT and RT-PCR.Materials and methodsThe study picked 248 patients who treated in fever clinical of GanZhou people's hospital,their complete clinical and imaging data were analysed retrospectively.Epidemiological data,symoptoms,laboratory test results include RT-PCR and the CT results include CT features,lesion location,lesion distribution of suspected COVID-19 infected patients were gathered.ResultsAll of 248 patients,at last 20 patients confirmed COVID-19,15 patients were confirmed in outpatient.More than 200 cases has laboratory test results disnormal.Only 15/248 patients had initial positive RT-PCR for COVID-19,5 patients had COVID-19 confirmed by two or more RT-PCR.50 cases(20.2%) had Ground glass opacity,42 cases(16.9%) had Consolidation,39 cases(15.7%) had Spider web pattern,38 cases(15.3%) had Interlobular septal thickening.For lesion location,22 cases(8.9%) involved Single lobe of one lung,13 cases(5.2%) involved Multiple lobes of one lung,174 cases(70.2%) involved Multiple lobes of both lungs,9 cases(3.6%) involved Bilateral lower lungs,25 cases(10.1%) involved Bilateral middle and lower lungs.Regarding the distribution of the lesions in the lung lobes,119 cases(47.98%) involved Subpleural distribution,19 cases(7.7%) involved Diffuse distribution,7 cases(2.8%) involved Peribronchial distribution,81 cases(32.7%) involved Mixed distribution.ConclusionChest CT can be applied in outpatient to make early diagnosis with sensitivity and accuracy better than that of nucleic acid detection.Trial registrationChiCTR2000032574. Registered 3 May 2020. retrospectively registered

scholarly journals Early chest computed tomography to diagnose COVID-19 from suspected patients: A multicenter retrospective study

2020 ◽  
Author(s):  
Congliang Miao ◽  
Mengdi Jin ◽  
Li Miao ◽  
Xinying Yang ◽  
Peng Huang ◽  
...  
Keyword(s):  
Ground Glass Opacity ◽  
High Specificity ◽  
Diagnostic Value ◽  
Chest Ct ◽  
Imaging Features ◽  
Rt Pcr ◽  
Polymerase Chain ◽  
Low Sensitivity ◽  
Chest Ct Scan ◽  
Ct Features

AbstractObjectiveThe purpose of this study is to distinguish the imaging features of COVID-19 with other chest infectious diseases and evaluate diagnostic value of chest CT for suspected patients.MethodsAdult suspected patients aged>18 years within 14 days who underwent chest CT scan and reverse-transcription polymerase-chain-reaction (RT-PCR) tests were enrolled. The enrolled patients were confirmed and grouped according to results of RT-PCR tests. The data of basic demographics, single chest CT features, and combined chest CT features were analyzed for confirmed and non-confirmed groups.ResultsA total of 130 patients were enrolled with 54 cases positive and 76 cases negative. The typical CT imaging features of positive group were ground glass opacity (GGO), crazy-paving pattern and air bronchogram. The lesions were mostly distributed bilaterally, close to the lower lungs or the pleura. When features combined, GGO with bilateral pulmonary distribution and GGO with pleural distribution were more common, of which were 31 cases (57.4%) and 30 cases (55.6%) respectively. The combinations were almost presented statistically significant (P<0.05) except for the combination of GGO with consolidation. Most combinations presented relatively low sensitivity but extremely high specificity. The average specificity of these combinations is around 90%.ConclusionsThe combinations of GGO could be useful in the identification and differential diagnosis of COVID-19, which alerts clinicians to isolate patients for treatment promptly and repeat RT-PCR tests until incubation ends.

scholarly journals Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
He S. Yang ◽  
Yu Hou ◽  
Hao Zhang ◽  
Amy Chadburn ◽  
Lars F. Westblade ◽  
...  
Keyword(s):  
Machine Learning ◽  
New York ◽  
High Risk ◽  
Laboratory Test ◽  
Laboratory Test Result ◽  
Test Results ◽  
Rt Pcr ◽  
Laboratory Test Results ◽  
Laboratory Profile ◽  
Test Result

Background. New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods. We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results. A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions. Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

scholarly journals Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning

2020 ◽  
Author(s):  
He Sarina Yang ◽  
Ljiljana V. Vasovic ◽  
Peter Steel ◽  
Amy Chadburn ◽  
Yu Hou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Laboratory Test ◽  
Patient Care ◽  
Characteristic Curve ◽  
Rapid Identification ◽  
Routine Laboratory ◽  
Test Results ◽  
Rt Pcr ◽  
Diagnostic Strategies ◽  
Laboratory Test Results

AbstractBackgroundAccurate diagnostic strategies to rapidly identify SARS-CoV-2 positive individuals for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results of this test are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.MethodWe developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory test results obtained within two days before the release of SARS-CoV-2-RT-PCR result were used to train a gradient boosted decision tree (GBDT) model from 3,346 SARS-CoV-2 RT-PCR tested patients (1,394 positive and 1,952 negative) evaluated at a large metropolitan hospital.ResultsThe model achieved an area under the receiver operating characteristic curve (AUC) of 0.853 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within two days.ConclusionThis model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-COV-2 infected patients before their RT-PCR results are available. This may facilitate patient care and quarantine, indicate who requires retesting, and direct personal protective equipment use while awaiting definitive RT-PCR results.

scholarly journals COVID-19 confirmed patients with negative antibodies results

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jian Wang ◽  
Chong Chen ◽  
Qilin Li ◽  
Pengcheng Cai ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Small Proportion ◽  
Chest Ct ◽  
Test Results ◽  
Igg Antibodies ◽  
Rt Pcr ◽  
Cell Counts ◽  
Blood Cell Counts ◽  
Laboratory Test Results ◽  
Initial Symptoms ◽  
Case Presentations

Abstract Background A new coronavirus disease 2019 (COVID-19) has escalated to a pandemic since its first outbreak in Wuhan, China. A small proportion of patients may have difficulty in generating IgM or IgG antibodies against SARS-CoV-2, and little attention has been paid to them. Case presentations We present two cases of confirmed COVID-19 patients and characterize their initial symptoms, chest CT results, medication, and laboratory test results in detail (including RT-PCR, IgM/ IgG, cytokine and blood cell counts). Conclusion Both of patients with confirmed COVID-19 pneumonia failed to produce either IgM or IgG even 40 to 50 days after their symptoms onset. This work provides evidence demonstrating that at least a small proportion of patients may have difficulty in rapidly gaining immunity against SARS-CoV-2.

scholarly journals Machine learning analysis highlights the down-trending of the proportion of COVID-19 patients with a distinct laboratory result profile

2020 ◽  
Author(s):  
He S. Yang ◽  
Yu Hou ◽  
Hao Zhang ◽  
Amy Chadburn ◽  
Lars F. Westblade ◽  
...  
Keyword(s):  
Machine Learning ◽  
New York ◽  
High Risk ◽  
Laboratory Test ◽  
Test Results ◽  
Rt Pcr ◽  
Viral Loads ◽  
Laboratory Test Results ◽  
Risk Patients ◽  
Learning Analysis

AbstractBackgroundNew York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2 confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome.MethodsWe performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis.ResultsA COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle-threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without COVID19-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase.ConclusionsOur study visualized the down-trending of the proportion of SARS-CoV-2 patients with the distinct COVID19-HRP. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

Effect of Ibuprofen on Serum Laboratory Test Results

1983 ◽  
Vol 40 (6) ◽  
pp. 1025-1034
Author(s):  
Carol L. Colvin ◽  
Raymond J. Townsend ◽  
William R. Gillespie ◽  
Kenneth S. Albert
Keyword(s):  
Laboratory Test ◽  
Test Results ◽  
Laboratory Test Results

scholarly journals COVID-19 and pregnancy – where are we now? A review

2020 ◽  
Vol 48 (5) ◽  
pp. 428-434 ◽  
Author(s):  
Aleksandra Rajewska ◽  
Wioletta Mikołajek-Bedner ◽  
Joanna Lebdowicz-Knul ◽  
Małgorzata Sokołowska ◽  
Sebastian Kwiatkowski ◽  
...  
Keyword(s):  
Case Fatality ◽  
Fetal Distress ◽  
Delivery Mode ◽  
Test Results ◽  
Rt Pcr ◽  
Third Trimester ◽  
The Third ◽  
Laboratory Test Results ◽  
Protective Antibodies ◽  
Polymerase Chain

AbstractThe new acute respiratory disease severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is highly contagious. It has caused many deaths, despite a relatively low general case fatality rate (CFR). The most common early manifestations of infection are fever, cough, fatigue and myalgia. The diagnosis is based on the exposure history, clinical manifestation, laboratory test results, chest computed tomography (CT) findings and a positive reverse transcription-polymerase chain reaction (RT-PCR) result for coronavirus disease 2019 (COVID-19). The effect of SARS-CoV-2 on pregnancy is not already clear. There is no evidence that pregnant women are more susceptible than the general population. In the third trimester, COVID-19 can cause premature rupture of membranes, premature labour and fetal distress. There are no data on complications of SARS-CoV-2 infection before the third trimester. COVID-19 infection is an indication for delivery if necessary to improve maternal oxygenation. Decision on delivery mode should be individualised. Vertical transmission of coronavirus from the pregnant woman to the fetus has not been proven. As the virus is absent in breast milk, the experts encourage breastfeeding for neonatal acquisition of protective antibodies.

scholarly journals Diagnostic performance of chest CT in differentiating COVID-19 from other causes of ground-glass opacities

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Ali H. Elmokadem ◽  
Dalia Bayoumi ◽  
Sherif A. Abo-Hedibah ◽  
Ahmed El-Morsy
Keyword(s):  
Diagnostic Performance ◽  
Pulmonary Nodules ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Ground Glass ◽  
Rt Pcr ◽  
Laboratory Findings ◽  
Halo Sign ◽  
Traction Bronchiectasis ◽  
Chest Ct Scan

Abstract Background To evaluate the diagnostic performance of chest CT in differentiating coronavirus disease 2019 (COVID-19) and non-COVID-19 causes of ground-glass opacities (GGO). Results A total of 80 patients (49 males and 31 females, 46.48 ± 16.09 years) confirmed with COVID-19 by RT-PCR and who underwent chest CT scan within 2 weeks of symptoms, and 100 patients (55 males and 45 females, 48.94 ± 18.97 years) presented with GGO on chest CT were enrolled in the study. Three radiologists reviewed all CT chest exams after removal of all identifying data from the images. They expressed the result as positive or negative for COVID-19 and recorded the other pulmonary CT features with mention of laterality, lobar affection, and distribution pattern. The clinical data and laboratory findings were recorded. Chest CT offered diagnostic accuracy ranging from 59 to 77.2% in differentiating COVID-19- from non-COVID-19-associated GGO with sensitivity from 76.25 to 90% and specificity from 45 to 67%. The specificity was lower when differentiating COVID-19 from non-COVID-19 viral pneumonias (30.5–61.1%) and higher (53.1–70.3%) after exclusion of viral pneumonia from the non-COVID-19 group. Patients with COVID-19 were more likely to have lesions in lower lobes (p = 0.005), peripheral distribution (p < 0.001), isolated ground-glass opacity (p = 0.043), subpleural bands (p = 0.048), reverse halo sign (p = 0.005), and vascular thickening (p = 0.013) but less likely to have pulmonary nodules (p < 0.001), traction bronchiectasis (p = 0.005), pleural effusion (p < 0.001), and lymphadenopathy (p < 0.001). Conclusions Chest CT offered reasonable sensitivity when differentiating COVID-19- from non-COVID-19-associated GGO with low specificity when differentiating COVID-19 from other viral pneumonias and moderate specificity when differentiating COVID-19 from other causes of GGO.

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