TY - JOUR
T1 - Clinical clustering with prognostic implications in Japanese COVID-19 patients
T2 - report from Japan COVID-19 Task Force, a nation-wide consortium to investigate COVID-19 host genetics
AU - The Japan COVID-19 Task Force
AU - Otake, Shiro
AU - Chubachi, Shotaro
AU - Namkoong, Ho
AU - Nakagawara, Kensuke
AU - Tanaka, Hiromu
AU - Lee, Ho
AU - Morita, Atsuho
AU - Fukushima, Takahiro
AU - Watase, Mayuko
AU - Kusumoto, Tatsuya
AU - Masaki, Katsunori
AU - Kamata, Hirofumi
AU - Ishii, Makoto
AU - Hasegawa, Naoki
AU - Harada, Norihiro
AU - Ueda, Tetsuya
AU - Ueda, Soichiro
AU - Ishiguro, Takashi
AU - Arimura, Ken
AU - Saito, Fukuki
AU - Yoshiyama, Takashi
AU - Nakano, Yasushi
AU - Mutoh, Yoshikazu
AU - Suzuki, Yusuke
AU - Murakami, Koji
AU - Okada, Yukinori
AU - Koike, Ryuji
AU - Kitagawa, Yuko
AU - Kimura, Akinori
AU - Imoto, Seiya
AU - Miyano, Satoru
AU - Ogawa, Seishi
AU - Kanai, Takanori
AU - Fukunaga, Koichi
N1 - Funding Information:
This study was supported by AMED (JP20nk0101612, JP20fk0108415, JP21jk0210034, JP21km0405211, JP21km0405217), JST CREST (JPMJCR20H2), MHLW (20CA2054), Takeda Science Foundation, Mitsubishi Foundation, and Bioinformatics Initiative of Osaka University Graduate School of Medicine, Osaka University. Precursory Research for Embryonic Science and Technology (JPMJPR21R7).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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: 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: 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: 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.
AB - 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: 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: 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: 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.
KW - COVID-19
KW - Cluster analysis
KW - Japan
KW - Phenotype
KW - Pneumonia
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UR - http://www.scopus.com/inward/citedby.url?scp=85137878038&partnerID=8YFLogxK
U2 - 10.1186/s12879-022-07701-y
DO - 10.1186/s12879-022-07701-y
M3 - Article
C2 - 36104674
AN - SCOPUS:85137878038
SN - 1471-2334
VL - 22
JO - BMC infectious diseases
JF - BMC infectious diseases
IS - 1
M1 - 735
ER -
