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[Prediction of severe outcomes of patients with COVID-19].
Peng, Z H; Chen, X F; Hu, Q Y; Hu, J C; Zhao, Z P; Zhang, M Z; Deng, S T; Xu, Q Q; Xia, Y K; Li, Y.
  • Peng ZH; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Chen XF; The First Affiliated Hospital of Nanjing Medical University, Nanjing 211166, China.
  • Hu QY; People's Hospital of Wuhan University, Wuhan 430060, China.
  • Hu JC; People's Hospital of Wuhan University, Wuhan 430060, China.
  • Zhao ZP; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Zhang MZ; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Deng ST; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Xu QQ; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Xia YK; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Li Y; The First Affiliated Hospital of Nanjing Medical University, Nanjing 211166, China.
Zhonghua Liu Xing Bing Xue Za Zhi ; 41(10): 1595-1600, 2020 Oct 10.
Article in Chinese | MEDLINE | ID: covidwho-968686
ABSTRACT

Objective:

To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients.

Methods:

Based on the patients' admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model.

Results:

In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as blood oxygen saturation <94%, peripheral white blood cells count >8.0×10(9), change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7%, and the missed diagnosis rate was 38.3%. The prediction sensitivity of the model based on the test set was 75.0%, and the missed diagnosis rate was 25.0%.

Conclusions:

Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model's prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Prognostic study Limits: Humans Language: Chinese Journal: Zhonghua Liu Xing Bing Xue Za Zhi Year: 2020 Document Type: Article Affiliation country: Cma.j.cn112338-20200331-00479

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Prognostic study Limits: Humans Language: Chinese Journal: Zhonghua Liu Xing Bing Xue Za Zhi Year: 2020 Document Type: Article Affiliation country: Cma.j.cn112338-20200331-00479