Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule

Tang，Ming1; Yu，Xia Xia2; Huang，Jia1; Gao，Jun Ling3; Cen，Fu Lan1; Xiao，Qi1; Fu，Shou Zhi4; Yang，Yang5; Xiong，Bo6; Pan，Yong Jun7; Liu，Ying Xia1; Feng，Yong Wen8; Li，Jin Xiu9; Liu，Yong10

2021-05-06

10.12998/wjcc.v9.i13.2994

World Journal of Clinical Cases   影响因子和分区

2307-8960

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.

Clinical decision rules Communicable diseases COVID-19 Nomograms Prognosis

SCI

英语

第一

WOS:000645608800004

2-s2.0-85107085502

Scopus

1.Department of Critical Care Medicine,Shenzhen Third People’s Hospital,The Second Hospital Affiliated to Southern University of Science and Technology,Shenzhen,518114,China
2.School of Biomedical Engineering,Health Science Center,Shenzhen University,Shenzhen,518060,China
3.Buddhism and Science Research Laboratory,Centre of Buddhist Studies,he University of Hong Kong,999077,Hong Kong
4.Department of Critical Care Medicine,Wuhan Third Hospital,Wuhan,433304,China
5.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,China
6.Department of Pediatrics,Wuhan Asia General Hospital,Wuhan,430022,China
7.Department of Critical Care Medicine,Southern University of Science and Technology Hospital,Shenzhen,518055,China
8.Department of Critical Care Medicine,The Second People's Hospital of Shenzhen,Shenzhen,518055,China
9.Department of Critical Care Medicine,The Second Xiangya Hospital,Central South University,Changsha,410011,China
10.Shenzhen Hospital,Southern Medical University,Shenzhen,518000,China

Tang，Ming,Yu，Xia Xia,Huang，Jia,et al. Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule[J]. World Journal of Clinical Cases,2021,9(13):2994-3007.

Tang，Ming.,Yu，Xia Xia.,Huang，Jia.,Gao，Jun Ling.,Cen，Fu Lan.,...&Liu，Yong.(2021).Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.World Journal of Clinical Cases,9(13),2994-3007.

Tang，Ming,et al."Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule".World Journal of Clinical Cases 9.13(2021):2994-3007.