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Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission.
Wang, Hai; Ai, Haibo; Fu, Yunong; Li, Qinglin; Cui, Ruixia; Ma, Xiaohua; Ma, Yan-Fen; Wang, Zi; Liu, Tong; Long, Yunxiang; Qu, Kai; Liu, Chang; Zhang, Jingyao.
  • Wang H; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Ai H; Rehabilitation Medicine Department, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
  • Fu Y; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Li Q; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Cui R; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Ma X; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Ma YF; Department of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Wang Z; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Liu T; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Long Y; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Qu K; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Liu C; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Zhang J; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Front Med (Lausanne) ; 8: 699243, 2021.
Article in English | MEDLINE | ID: covidwho-1399148
ABSTRACT

Introduction:

COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment. However, there is still a lack of a simple and effective tool to identify these patients early.

Methods:

A retrospective cohort study was conducted to develop an early warning model for predicting the death risk of COVID-19. Seventy-five percent of the cases were used to construct the prediction model, and the remaining 25% were used to verify the prediction model based on data immediately available on admission.

Results:

From March 1, 2020, to April 16, 2020, a total of 4,711 COVID-19 patients were included in our study. The average age was 63.37 ± 16.70 years, of which 1,148 (24.37%) died. Finally, age, SpO2, body temperature (T), and mean arterial pressure (MAP) were selected for constructing the model by univariate analysis, multivariate analysis, and a review of the literature. We used five common methods for constructing the model and finally found that the full model had the best specificity and higher accuracy. The area under the ROC curve (AUC), specificity, sensitivity, and accuracy of full model in train cohort were, respectively, 0.798 (0.779, 0.816), 0.804, 0.656, and 0.768, and in the validation cohort were, respectively, 0.783 (0.751, 0.815), 0.800, 0.616, and 0.755. Visualization tools of the prediction model included a nomogram and an online dynamic nomogram (https//wanghai.shinyapps.io/dynnomapp/).

Conclusion:

We developed a prediction model that might aid in the early identification of COVID-19 patients with a high probability of mortality on admission. However, further research is required to determine whether this tool can be applied for outpatient or home-based COVID-19 patients.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Front Med (Lausanne) Year: 2021 Document Type: Article Affiliation country: Fmed.2021.699243

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Front Med (Lausanne) Year: 2021 Document Type: Article Affiliation country: Fmed.2021.699243