Clinical clustering with prognostic implications in Japanese COVID-19 patients: report from Japan COVID-19 Task Force, a nation-wide consortium to investigate COVID-19 host genetics.
COVID-19
Cluster analysis
Japan
Phenotype
Pneumonia
Journal
BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551
Informations de publication
Date de publication:
14 Sep 2022
14 Sep 2022
Historique:
received:
07
03
2022
accepted:
23
08
2022
entrez:
14
9
2022
pubmed:
15
9
2022
medline:
17
9
2022
Statut:
epublish
Résumé
The clinical course of coronavirus disease (COVID-19) is diverse, and the usefulness of phenotyping in predicting the severity or prognosis of the disease has been demonstrated overseas. This study aimed to investigate clinically meaningful phenotypes in Japanese COVID-19 patients using cluster analysis. From April 2020 to May 2021, data from inpatients aged ≥ 18 years diagnosed with COVID-19 and who agreed to participate in the study were collected. A total of 1322 Japanese patients were included. Hierarchical cluster analysis was performed using variables reported to be associated with COVID-19 severity or prognosis, namely, age, sex, obesity, smoking history, hypertension, diabetes mellitus, malignancy, chronic obstructive pulmonary disease, hyperuricemia, cardiovascular disease, chronic liver disease, and chronic kidney disease. Participants were divided into four clusters: Cluster 1, young healthy (n = 266, 20.1%); Cluster 2, middle-aged (n = 245, 18.5%); Cluster 3, middle-aged obese (n = 435, 32.9%); and Cluster 4, elderly (n = 376, 28.4%). In Clusters 3 and 4, sore throat, dysosmia, and dysgeusia tended to be less frequent, while shortness of breath was more frequent. Serum lactate dehydrogenase, ferritin, KL-6, D-dimer, and C-reactive protein levels tended to be higher in Clusters 3 and 4. Although Cluster 3 had a similar age as Cluster 2, it tended to have poorer outcomes. Both Clusters 3 and 4 tended to exhibit higher rates of oxygen supplementation, intensive care unit admission, and mechanical ventilation, but the mortality rate tended to be lower in Cluster 3. We have successfully performed the first phenotyping of COVID-19 patients in Japan, which is clinically useful in predicting important outcomes, despite the simplicity of the cluster analysis method that does not use complex variables.
Sections du résumé
BACKGROUND
BACKGROUND
The clinical course of coronavirus disease (COVID-19) is diverse, and the usefulness of phenotyping in predicting the severity or prognosis of the disease has been demonstrated overseas. This study aimed to investigate clinically meaningful phenotypes in Japanese COVID-19 patients using cluster analysis.
METHODS
METHODS
From April 2020 to May 2021, data from inpatients aged ≥ 18 years diagnosed with COVID-19 and who agreed to participate in the study were collected. A total of 1322 Japanese patients were included. Hierarchical cluster analysis was performed using variables reported to be associated with COVID-19 severity or prognosis, namely, age, sex, obesity, smoking history, hypertension, diabetes mellitus, malignancy, chronic obstructive pulmonary disease, hyperuricemia, cardiovascular disease, chronic liver disease, and chronic kidney disease.
RESULTS
RESULTS
Participants were divided into four clusters: Cluster 1, young healthy (n = 266, 20.1%); Cluster 2, middle-aged (n = 245, 18.5%); Cluster 3, middle-aged obese (n = 435, 32.9%); and Cluster 4, elderly (n = 376, 28.4%). In Clusters 3 and 4, sore throat, dysosmia, and dysgeusia tended to be less frequent, while shortness of breath was more frequent. Serum lactate dehydrogenase, ferritin, KL-6, D-dimer, and C-reactive protein levels tended to be higher in Clusters 3 and 4. Although Cluster 3 had a similar age as Cluster 2, it tended to have poorer outcomes. Both Clusters 3 and 4 tended to exhibit higher rates of oxygen supplementation, intensive care unit admission, and mechanical ventilation, but the mortality rate tended to be lower in Cluster 3.
CONCLUSIONS
CONCLUSIONS
We have successfully performed the first phenotyping of COVID-19 patients in Japan, which is clinically useful in predicting important outcomes, despite the simplicity of the cluster analysis method that does not use complex variables.
Identifiants
pubmed: 36104674
doi: 10.1186/s12879-022-07701-y
pii: 10.1186/s12879-022-07701-y
pmc: PMC9472186
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
735Subventions
Organisme : Precursory Research for Embryonic Science and Technology
ID : JPMJPR21R7
Organisme : Japan Agency for Medical Research and Development
ID : JP20nk0101612
Organisme : Japan Agency for Medical Research and Development
ID : JP20fk0108415
Organisme : Japan Agency for Medical Research and Development
ID : JP21jk0210034
Organisme : Japan Agency for Medical Research and Development
ID : JP21km0405211
Organisme : Japan Agency for Medical Research and Development
ID : JP21km0405217
Organisme : Core Research for Evolutional Science and Technology
ID : JPMJCR20H2
Organisme : Ministry of Health, Labour and Welfare
ID : 20CA2054
Informations de copyright
© 2022. The Author(s).
Références
JAMA. 2020 Aug 25;324(8):782-793
pubmed: 32648899
Clin Exp Med. 2021 May;21(2):167-179
pubmed: 33128197
J Infect. 2020 Aug;81(2):e16-e25
pubmed: 32335169
JAMA Intern Med. 2020 Aug 1;180(8):1081-1089
pubmed: 32396163
PLoS One. 2021 Mar 31;16(3):e0248956
pubmed: 33788884
J Nematol. 2004 Sep;36(3):207-19
pubmed: 19262809
Interdiscip Perspect Infect Dis. 2021 Feb 5;2021:5552138
pubmed: 33628234
Biomolecules. 2021 Jul 06;11(7):
pubmed: 34356617
Lancet Oncol. 2020 Mar;21(3):335-337
pubmed: 32066541
Stat Methods Med Res. 1992;1(1):27-48
pubmed: 1341650
JAMA Intern Med. 2020 Jul 1;180(7):934-943
pubmed: 32167524
Am J Nephrol. 2022;53(1):78-86
pubmed: 34883482
JAMA. 2021 May 25;325(20):2041-2042
pubmed: 33961002
Eur J Pharmacol. 2020 Sep 15;883:173375
pubmed: 32682788
Sci Rep. 2018 Mar 12;8(1):4314
pubmed: 29531237
Lancet Respir Med. 2020 Jun;8(6):631-643
pubmed: 32526190
Diabetes Metab Res Rev. 2021 Feb;37(2):e3377
pubmed: 32588943
Lancet. 2020 Mar 28;395(10229):1054-1062
pubmed: 32171076
J Allergy Clin Immunol. 2020 Jul;146(1):110-118
pubmed: 32294485
Physiol Genomics. 2020 Nov 1;52(11):549-557
pubmed: 32991251
Hepatol Int. 2020 Sep;14(5):612-620
pubmed: 32725453
Infection. 2021 Aug;49(4):677-684
pubmed: 33646505
Lancet Haematol. 2020 Sep;7(9):e671-e678
pubmed: 32659214
Curr Opin Rheumatol. 2013 Mar;25(2):210-6
pubmed: 23370374
Chest. 2021 Sep;160(3):929-943
pubmed: 33964301
Front Med (Lausanne). 2021 Jun 04;8:617264
pubmed: 34150789
Eur J Clin Invest. 2020 Oct;50(10):e13362
pubmed: 32726868
Nature. 2020 Aug;584(7821):430-436
pubmed: 32640463
J Clin Med. 2020 Oct 29;9(11):
pubmed: 33137919
Front Med (Lausanne). 2020 Nov 12;7:570614
pubmed: 33282887
J Chin Med Assoc. 2021 Jan 1;84(1):3-8
pubmed: 33230062
JAMA Cardiol. 2020 Jul 1;5(7):802-810
pubmed: 32211816
Chest. 2021 May;159(5):1884-1893
pubmed: 33316234
Clin Infect Dis. 2021 Dec 6;73(11):e3677-e3689
pubmed: 32986793
J Infect. 2020 Nov;81(5):e3-e5
pubmed: 32920063
J Med Virol. 2020 Oct;92(10):1915-1921
pubmed: 32293753
Curr Opin Crit Care. 2019 Oct;25(5):489-497
pubmed: 31335383
Cell Biol Int. 2020 Sep;44(9):1792-1797
pubmed: 32458561
Int J Infect Dis. 2021 Dec;113:74-81
pubmed: 34601141
Ann Allergy Asthma Immunol. 2020 Oct;125(4):481-483
pubmed: 32717301
Diabetes Metab Syndr. 2020 Jul - Aug;14(4):303-310
pubmed: 32298981