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Med Sci Monit ; 27: e931467, 2021 Oct 06.
Article in English | MEDLINE | ID: covidwho-1344552


BACKGROUND Effective identification of patients with suspected COVID-19 is vital for the management. This study aimed to establish a simple clinical prediction model for COVID-19 in primary care. MATERIAL AND METHODS We consecutively enrolled 60 confirmed cases and 152 suspected cases with COVID-19 into the study. The training cohort consisted of 30 confirmed and 78 suspected cases, whereas the validation cohort consisted of 30 confirmed and 74 suspected cases. Four clinical variables - epidemiological history (E), body temperature (T), leukocytes count (L), and chest computed tomography (C) - were collected to construct a preliminary prediction model (model A). By integerizing coefficients of model A, a clinical prediction model (model B) was constructed. Finally, the scores of each variable in model B were summed up to build the ETLC score. RESULTS The preliminary prediction model A was Logit (YA)=2.657X1+1.153X2+2.125X3+2.828X4-10.771, while the model B was Logit (YB)=2.5X1+1X2+2X3+3X4-10. No significant difference was found between the area under the curve (AUC) of model A (0.920, 95% CI: 0.875-0.953) and model B (0.919, 95% CI: 0.874-0.952) (Z=0.035, P=0.972). When ETLC score was more than or equal to 9.5, the sensitivity and specificity for COVID-19 was 76.7% (46/60) and 90.1% (137/152), respectively, and the positive and negative predictive values were 75.4% (46/61) and 90.7% (137/151), respectively. CONCLUSIONS The ETLC score is helpful for efficiently identifying patients with suspected COVID-19.

COVID-19/diagnosis , Diagnosis, Computer-Assisted/methods , Primary Health Care/methods , Body Temperature , COVID-19/epidemiology , Humans , Leukocyte Count , Logistic Models , SARS-CoV-2 , Tomography, X-Ray Computed
Int J Gen Med ; 14: 2047-2052, 2021.
Article in English | MEDLINE | ID: covidwho-1256165


INTRODUCTION: Novel coronavirus pneumonia (COVID-19) is an acute respiratory infectious disease, which has the characteristic of human-to-human transmission and is extremely contagious. Correctly standardizing the process of early screening of infection or suspected cases in the fever clinic has become a key part of the fight against the pandemic. METHODS: A retrospective analysis of patients in the fever clinic of Shenyang Medical College Affiliated Central Hospital from January 23 to March 1, 2020, was conducted in the present study. RESULTS: It was found that 16 suspected cases of COVID-19 in the fever clinic were diagnosed with respiratory infections, accounting for 0.59%. CONCLUSION: In case of a negative result in the second nucleic acid test, strategic triage and typing might be more conducive for the following nucleic acid tests for suspected cases in order to prevent the spread of the epidemic caused by missed diagnosis.

Clin Lab ; 67(1)2021 Jan 01.
Article in English | MEDLINE | ID: covidwho-1045292


BACKGROUND: The COVID-19 outbreak, which began in late 2019, continues to ravage the globe and has become the greatest threat to human health. As nucleic acid test is the primary means of screening for COVID-19, this makes the laboratory the most important node in the epidemic prevention and control system. METHODS: As a small laboratory in the hospital, we can meet a large number of demands for nucleic acid test by optimizing staff process, strictly disinfecting experimental batches and changing experimental methods. RESULTS: Through the improvement of the above aspects, our daily maximum detection quantity has been increased from 256/day to 1,012/day. Besides, none of the medical staff has been infected. And there have been no nosocomial infections. CONCLUSIONS: Nucleic acid laboratories, especially small laboratories, should promptly adjust their strategies in the face of unexpected outbreaks and conduct risk assessment in accordance with laboratory activities.

COVID-19 Nucleic Acid Testing , COVID-19/diagnosis , COVID-19/virology , Health Services Needs and Demand/organization & administration , Mass Screening/organization & administration , Specimen Handling , Workflow , Workload , Humans , Infection Control/organization & administration , Occupational Health , Predictive Value of Tests