tetano
Editor, Senior Moderator
BMC Infect Dis
. 2022 Sep 14;22(1):735.
doi: 10.1186/s12879-022-07701-y.
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
Shiro Otake[SUP] 1 [/SUP], Shotaro Chubachi[SUP] 2 [/SUP], Ho Namkoong[SUP] 1 [/SUP], Kensuke Nakagawara[SUP] 1 [/SUP], Hiromu Tanaka[SUP] 1 [/SUP], Ho Lee[SUP] 1 [/SUP], Atsuho Morita[SUP] 1 [/SUP], Takahiro Fukushima[SUP] 1 [/SUP], Mayuko Watase[SUP] 1 [/SUP], Tatsuya Kusumoto[SUP] 1 [/SUP], Katsunori Masaki[SUP] 1 [/SUP], Hirofumi Kamata[SUP] 1 [/SUP], Makoto Ishii[SUP] 1 [/SUP], Naoki Hasegawa[SUP] 3 [/SUP], Norihiro Harada[SUP] 4 [/SUP], Tetsuya Ueda[SUP] 5 [/SUP], Soichiro Ueda[SUP] 6 [/SUP], Takashi Ishiguro[SUP] 7 [/SUP], Ken Arimura[SUP] 8 [/SUP], Fukuki Saito[SUP] 9 [/SUP], Takashi Yoshiyama[SUP] 10 [/SUP], Yasushi Nakano[SUP] 11 [/SUP], Yoshikazu Mutoh[SUP] 12 [/SUP], Yusuke Suzuki[SUP] 13 [/SUP], Koji Murakami[SUP] 14 [/SUP], Yukinori Okada[SUP] 15 [/SUP], Ryuji Koike[SUP] 16 [/SUP], Yuko Kitagawa[SUP] 17 [/SUP], Akinori Kimura[SUP] 18 [/SUP], Seiya Imoto[SUP] 19 [/SUP], Satoru Miyano[SUP] 20 [/SUP], Seishi Ogawa[SUP] 21 [/SUP], Takanori Kanai[SUP] 22 [/SUP], Koichi Fukunaga[SUP] 1 [/SUP], Japan COVID-19 Task Force
Affiliations
Abstract
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.
Keywords: COVID-19; Cluster analysis; Japan; Phenotype; Pneumonia.
. 2022 Sep 14;22(1):735.
doi: 10.1186/s12879-022-07701-y.
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
Shiro Otake[SUP] 1 [/SUP], Shotaro Chubachi[SUP] 2 [/SUP], Ho Namkoong[SUP] 1 [/SUP], Kensuke Nakagawara[SUP] 1 [/SUP], Hiromu Tanaka[SUP] 1 [/SUP], Ho Lee[SUP] 1 [/SUP], Atsuho Morita[SUP] 1 [/SUP], Takahiro Fukushima[SUP] 1 [/SUP], Mayuko Watase[SUP] 1 [/SUP], Tatsuya Kusumoto[SUP] 1 [/SUP], Katsunori Masaki[SUP] 1 [/SUP], Hirofumi Kamata[SUP] 1 [/SUP], Makoto Ishii[SUP] 1 [/SUP], Naoki Hasegawa[SUP] 3 [/SUP], Norihiro Harada[SUP] 4 [/SUP], Tetsuya Ueda[SUP] 5 [/SUP], Soichiro Ueda[SUP] 6 [/SUP], Takashi Ishiguro[SUP] 7 [/SUP], Ken Arimura[SUP] 8 [/SUP], Fukuki Saito[SUP] 9 [/SUP], Takashi Yoshiyama[SUP] 10 [/SUP], Yasushi Nakano[SUP] 11 [/SUP], Yoshikazu Mutoh[SUP] 12 [/SUP], Yusuke Suzuki[SUP] 13 [/SUP], Koji Murakami[SUP] 14 [/SUP], Yukinori Okada[SUP] 15 [/SUP], Ryuji Koike[SUP] 16 [/SUP], Yuko Kitagawa[SUP] 17 [/SUP], Akinori Kimura[SUP] 18 [/SUP], Seiya Imoto[SUP] 19 [/SUP], Satoru Miyano[SUP] 20 [/SUP], Seishi Ogawa[SUP] 21 [/SUP], Takanori Kanai[SUP] 22 [/SUP], Koichi Fukunaga[SUP] 1 [/SUP], Japan COVID-19 Task Force
Affiliations
- PMID: 36104674
- DOI: 10.1186/s12879-022-07701-y
Abstract
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.
Keywords: COVID-19; Cluster analysis; Japan; Phenotype; Pneumonia.