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Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.
Tang, Ming; Yu, Xia-Xia; Huang, Jia; Gao, Jun-Ling; Cen, Fu-Lan; Xiao, Qi; Fu, Shou-Zhi; Yang, Yang; Xiong, Bo; Pan, Yong-Jun; Liu, Ying-Xia; Feng, Yong-Wen; Li, Jin-Xiu; Liu, Yong.
  • Tang M; Department of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
  • Yu XX; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China.
  • Huang J; Department of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
  • Gao JL; Buddhism and Science Research Laboratory, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong 999077, China.
  • Cen FL; Department of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
  • Xiao Q; Department of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
  • Fu SZ; Department of Critical Care Medicine, Wuhan Third Hospital, Wuhan 433304, Hubei Province, China.
  • Yang Y; Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Provin
  • Xiong B; Department of Pediatrics, Wuhan Asia General Hospital, Wuhan 430022, Hubei Province, China.
  • Pan YJ; Department of Critical Care Medicine, Southern University of Science and Technology Hospital, Shenzhen 518055, Guangdong Province, China.
  • Liu YX; Department of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
  • Feng YW; Department of Critical Care Medicine, The Second People's Hospital of Shenzhen, Shenzhen 518035, Guangdong Province, China.
  • Li JX; Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China.
  • Liu Y; Shenzhen Hospital, Southern Medical University, Shenzhen 518000, Guangdong Province, China. liuyongjoy@outlook.com.
World J Clin Cases ; 9(13): 2994-3007, 2021 May 06.
Article in English | MEDLINE | ID: covidwho-1222306
ABSTRACT

BACKGROUND:

The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial.

AIM:

To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care.

METHODS:

This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model.

RESULTS:

Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO2)/(FiO2 × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI) 1.409-11.015; P = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI 1.449-34.849; P = 0.016), fever (OR 6.794; 95%CI 1.401-32.951; P = 0.017), Charlson index (OR 2.917; 95%CI 1.279-6.654; P = 0.011), PaO2/FiO2 ratio (OR 17.570; 95%CI 1.117-276.383; P = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI 1.048-12.191; P = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI 0.901-0.981) and 0.936 (95%CI 0.886-0.987) in both datasets. The calibration properties were good.

CONCLUSION:

The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Language: English Journal: World J Clin Cases Year: 2021 Document Type: Article Affiliation country: Wjcc.v9.i13.2994

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Language: English Journal: World J Clin Cases Year: 2021 Document Type: Article Affiliation country: Wjcc.v9.i13.2994