PMID- 34129653
OWN - NLM
STAT- MEDLINE
DCOM- 20210629
LR  - 20231103
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 16
IP  - 6
DP  - 2021
TI  - The selection of indicators from initial blood routine test results to improve 
      the accuracy of early prediction of COVID-19 severity.
PG  - e0253329
LID - 10.1371/journal.pone.0253329 [doi]
LID - e0253329
AB  - The global pandemic of COVID-19 poses a huge threat to the health and lives of 
      people all over the world, and brings unprecedented pressure to the medical 
      system. We need to establish a practical method to improve the efficiency of 
      treatment and optimize the allocation of medical resources. Due to the influx of 
      a large number of patients into the hospital and the running of medical 
      resources, blood routine test became the only possible check while COVID-19 
      patients first go to a fever clinic in a community hospital. This study aims to 
      establish an efficient method to identify key indicators from initial blood 
      routine test results for COVID-19 severity prediction. We determined that age is 
      a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and 
      an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a 
      Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for 
      Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naive Bayes (NB) 
      classifier, to further select effective indicators from patients' initial blood 
      test results. The MCDM algorithm selected 3 dominant feature subsets: Age, WBC, 
      LYMC, NEUT with a selection rate of 44%, Age, NEUT, LYMC with a selection rate 
      of 38%, and Age, WBC, LYMC with a selection rate of 9%. Using these feature 
      subsets, the optimized prediction model could achieve an accuracy of 0.82 and an 
      AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key 
      factors for COVID-19 severity prediction. Using age and the indicators selected 
      by the MCDM algorithm from initial blood routine test results can effectively 
      predict the severity of COVID-19. Our research could not only help medical 
      workers identify patients with severe COVID-19 at an early stage, but also help 
      doctors understand the pathogenesis of COVID-19 through key indicators.
FAU - Luo, Jiaqing
AU  - Luo J
AD  - School of Computer Science and Engineering, University of Electronic Science and 
      Technology of China, Chengdu, China.
FAU - Zhou, Lingyun
AU  - Zhou L
AUID- ORCID: 0000-0002-6756-6725
AD  - Center of Infectious Diseases, West China Hospital of Sichuan University, 
      Chengdu, China.
FAU - Feng, Yunyu
AU  - Feng Y
AD  - State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, 
      Sichuan University and Collaborative Innovation Center, Chengdu, China.
FAU - Li, Bo
AU  - Li B
AD  - Department of Otorhinolaryngology, Head & Neck Surgery, West China Hospital, 
      Sichuan University, Chengdu, China.
FAU - Guo, Shujin
AU  - Guo S
AD  - The Geriatric Respiratory Department, Sichuan Provincial People's Hospital, 
      University of Electronic Science and Technology of China, Chengdu, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210615
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
RN  - 0 (Biomarkers)
SB  - IM
MH  - Adult
MH  - Age of Onset
MH  - Aged
MH  - Biomarkers/blood
MH  - COVID-19/blood/*diagnosis
MH  - Diagnostic Tests, Routine/*methods
MH  - Female
MH  - Hematologic Tests/*methods
MH  - Humans
MH  - Machine Learning
MH  - Male
MH  - Middle Aged
MH  - Prospective Studies
MH  - Risk Assessment/methods
MH  - SARS-CoV-2/isolation & purification
MH  - *Severity of Illness Index
MH  - Triage/*methods
PMC - PMC8208037
COIS- The authors have declared that no competing interests exist.
EDAT- 2021/06/16 06:00
MHDA- 2021/06/30 06:00
PMCR- 2021/06/15
CRDT- 2021/06/15 17:23
PHST- 2021/02/03 00:00 [received]
PHST- 2021/06/03 00:00 [accepted]
PHST- 2021/06/15 17:23 [entrez]
PHST- 2021/06/16 06:00 [pubmed]
PHST- 2021/06/30 06:00 [medline]
PHST- 2021/06/15 00:00 [pmc-release]
AID - PONE-D-21-03740 [pii]
AID - 10.1371/journal.pone.0253329 [doi]
PST - epublish
SO  - PLoS One. 2021 Jun 15;16(6):e0253329. doi: 10.1371/journal.pone.0253329. 
      eCollection 2021.