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A Simple Clinical Prediction Tool for COVID-19 in Primary Care with Epidemiology: Temperature-Leukocytes-CT Results.
Hao, Wanming; Zhao, Long; Yu, Xinjuan; Wu, Song; Xie, Weifeng; Wang, Ning; Lv, Weihong; Sood, Akshay; Leng, Shuguang; Li, Yongchun; Sun, Qing; Guan, Jun; Han, Wei.
  • Hao W; Department of Pulmonary and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Zhao L; Department of Laboratory Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Yu X; Department of Pulmonary and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Wu S; School of Integrated Traditional and Western Medicine, Anhui University of Chinese Medicine, Hefei, Anhui, China (mainland).
  • Xie W; Department of Intensive Care Unit, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Wang N; Department of Pulmonary and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Lv W; Department of Hospital Infection, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Sood A; Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
  • Leng S; Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
  • Li Y; Department of Pulmonary and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Sun Q; Department of Special Medicine, No.971 Hospital Navy, Qingdao, Shandong, China (mainland).
  • Guan J; Department of Cardiology, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
  • Han W; Department of Pulmonary and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China (mainland).
Med Sci Monit ; 27: e931467, 2021 Oct 06.
Article in English | MEDLINE | ID: covidwho-1344552
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Primary Health Care / Diagnosis, Computer-Assisted / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Humans Language: English Journal: Med Sci Monit Journal subject: Medicine Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Primary Health Care / Diagnosis, Computer-Assisted / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Humans Language: English Journal: Med Sci Monit Journal subject: Medicine Year: 2021 Document Type: Article