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Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques.
Fu, Yacheng; Zhong, Weijun; Liu, Tao; Li, Jianmin; Xiao, Kui; Ma, Xinhua; Xie, Lihua; Jiang, Junyi; Zhou, Honghao; Liu, Rong; Zhang, Wei.
  • Fu Y; Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.
  • Zhong W; National Clinical Research Center for Geriatric Disorders, Changsha, China.
  • Liu T; Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.
  • Li J; National Clinical Research Center for Geriatric Disorders, Changsha, China.
  • Xiao K; Shenzhen Center for Chronic Disease Control, Shenzhen, China.
  • Ma X; Department of Pulmonary and Critical Care Medicine, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
  • Xie L; Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Jiang J; Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhou H; B7 Department, Zhongfa District of Tongji Hospital, Tongji Medical, Huazhong University of Science and Technology, Wuhan, China.
  • Liu R; Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.
  • Zhang W; National Clinical Research Center for Geriatric Disorders, Changsha, China.
Front Public Health ; 10: 880999, 2022.
Article in English | MEDLINE | ID: covidwho-1952828
ABSTRACT
Motivation Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission.

Methods:

In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness.

Results:

The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 (p < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI 0.78-0.86], also in the external validation cohort (n = 566, AUC = 0.84).

Conclusion:

A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Front Public Health Year: 2022 Document Type: Article Affiliation country: Fpubh.2022.880999

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Front Public Health Year: 2022 Document Type: Article Affiliation country: Fpubh.2022.880999