Skip to main content
Download PDF

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

BMC Infectious Diseases volume 22, Article number: 735 (2022) Cite this article

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.

Peer Review reports

Background

In December 2019, a disease outbreak was noticed after a massive admission of patients with common clinical symptoms of pneumonia in the local hospitals of Wuhan City, China. Upon further investigations, the World Health Organization confirmed that the novel coronavirus, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was responsible for these clinical symptoms and further denominated this disease as coronavirus disease (COVID-19) [1]. Its clinical course is diverse, ranging from mild self-limited illness to life-threatening organ dysfunctions [2,3,4].

Identifying disease sub-phenotypes could improve the understanding of the pathophysiology of critical care syndromes and lead to the discovery of new treatment targets by allowing future therapeutic trials to focus on predicted responders [5]. COVID-19 cluster analysis was previously used to identify distinct sub-phenotypes based on clinical and biochemical characteristics [6,7,8,9,10], for other heterogeneous syndromes, such as acute respiratory distress syndrome, sepsis, and acute kidney injury [11]. However, the main factors in the cluster analysis and methodology differed among these studies, as did the characteristics of the sub-phenotypes. Moreover, most studies used not only baseline characteristics but also laboratory test results and radiographic patterns [7,8,9,10].

Previous reports, including ours, revealed that baseline characteristics, such as age, sex, and comorbidities, can predict meaningful outcomes of COVID-19 [12, 13]. The clinical characteristics of COVID-19 may differ depending on the population. For instance, COVID-19 is milder in Japan than in other countries [14, 15]. Population differences may be influenced by complex factors, including the number of patients, medical infrastructure, resources of medical personnel, and patient background [15]. To the best of our knowledge, no clinical studies to date have examined the phenotypes of COVID-19 patients in Japan.

Based on the above, we hypothesized that cluster analysis using baseline characteristics reportedly related to COVID-19 outcomes may allow for simple meaningful phenotyping of Japanese COVID-19 patients, and that sub-phenotypes may differ according to population differences and cluster analysis methods. The present study aimed to demonstrate the usefulness of phenotyping in predicting meaningful outcomes of Japanese COVID-19 patients and to capture the patients’ post-hospitalization course.

Methods

Study design and settings

All COVID-19 cases in this retrospective cohort study were recruited through the Japan COVID-19 Task Force [16, 17]. From April 2020 to May 2021, data from consecutive inpatients aged ≥ 18 years diagnosed with COVID-19, using SARS-CoV2 polymerase chain reaction (PCR) test results at one among the > 100 affiliated hospitals, and who agreed to cooperate in the study were registered in an electronic case record form by the study subspecialist at the affiliated research institute. Patients meeting any of the following exclusion criteria were excluded: (i) non-Japanese patients, (ii) patients with incomplete medical records, such as missing outcome information, and (iii) patients lacking any of the selected 12 variables for cluster analysis (Fig. 1). All patients provided written informed consent. This study was approved by the ethics committees of Keio University School of Medicine (20200061) and related research institutions. All aspects of the study conformed to the principles of the Declaration of Helsinki adopted by WMA General Assembly, Fortaleza, Brazil, October 2013.

Fig. 1
figure 1

Process of patient selection in this study. Data from patients with known clinical outcomes and not missing any of the 12 variables used in the cluster analysis were analyzed

Full size image

Data collection

The following information was extracted from the electronic case record form: age, sex, height, weight, clinical symptoms and signs, laboratory findings on admission, comorbidities, disease severity (supplementary oxygen, intensive care unit (ICU) entry, need for invasive mechanical ventilation, and survival status), and treatment details. We defined disease severity as follows: most severe, need for support by high-flow oxygen devices, invasive mechanical ventilation, extracorporeal membrane oxygenation, or death; severe, need for support of low-flow oxygen devices; mild, symptomatic patients not requiring oxygen support; asymptomatic, asymptomatic patients without oxygen support [18]. All laboratory tests were performed according to the patients’ clinical care needs. Symptoms and signs were included not only at the time of referral and admission, but also during hospitalization. Blood tests such as biochemistry, peripheral blood analysis, and coagulation were performed within 48 h of the initial visit or admission. The collected data were reviewed by a team of respiratory clinicians. If core data were missing, the clinician who first diagnosed the disease was contacted to collect it. Missing or absent data in the patient background were noted as unknown.

Identification of COVID-19 phenotypes using cluster analysis

We selected 12 clinically relevant patient baseline characteristics reportedly associated with the severity or prognosis of COVID-19 [12, 19,20,21,22,23,24,25], namely, age, sex, obesity, smoking history, hypertension, diabetes mellitus, malignancy, chronic obstructive pulmonary disease, hyperuricemia, cardiovascular disease, chronic liver disease, and chronic kidney disease. We defined obesity as body mass index (BMI) > 25 and treated it as a nominal variable.

Statistical analysis

Data are presented as means ± standard deviation (SD). Data were compared among groups using analysis of variance (ANOVA) and χ2 tests. Hierarchical cluster analysis using the 12 variables mentioned above was performed using the Ward’s minimum-variance method [26, 27]. The results are graphically depicted by a dendrogram. Statistical significance was set at p < 0.05. All data were analyzed using the JMP 16 software (SAS Institute, Cary, NC, USA).

Results

Characteristics of the study population

Table 1 shows the baseline clinical characteristics of the participants. A total of 1322 inpatients (men, 65.1%; mean age, 58 ± 18.1 years) were enrolled in this study. The mean BMI was 24.4 ± 4.7 kg/m2, and 597 (45.2%) had a history of smoking. Based on their clinical presentation, participants were classified into the most severe (n = 63, 4.8%), severe (n = 426, 32.2%), mild (n = 777, 58.8%), and asymptomatic (n = 56, 4.2%) disease groups. The most common comorbidities were hypertension (n = 449, 34%), diabetes mellitus (n = 263, 19.9%), and hyperuricemia (n = 134, 10.1%).

Table 1 Baseline clinical characteristics of the study patients
Full size table

Comparison of baseline characteristics among clusters

We performed Ward’s cluster analysis based on 12 factors reportedly associated with the severity or prognosis of COVID-19 [12, 19,20,21,22,23,24,25]. Based on visual assessment of the resulting dendrogram (Fig. 2), data could be optimally grouped into four clusters, with each cluster corresponding to a potential phenotype. Table 2 presents the baseline characteristics of each cluster. Cluster 1 (young healthy cluster: n = 266) included the youngest population and tended to have fewer comorbidities than the other clusters. Cluster 3 (middle-aged obese cluster: n = 435) included mostly middle-aged patients, had the highest percentage of men with higher BMI and numerous comorbidities, such as hypertension, diabetes mellitus, and hyperuricemia. Although patients in Cluster 2 (middle-aged cluster: n = 245) were in the same age group as those in Cluster 3, they tended to have a lower BMI and fewer comorbidities compared to those in Cluster 3. Compared to other clusters, Cluster 4 (elderly: n = 376) included the oldest patients who tended to have numerous comorbidities, such as malignancy, cardiovascular diseases, and chronic kidney disease.

Fig. 2
figure 2

Dendrogram illustrating the results of cluster analysis of 1322 COVID-19 patients using Ward’s hierarchical clustering method

Full size image
Table 2 Baseline characteristics for each cluster
Full size table

Comparison of clinical characteristics and laboratory findings among clusters

Table 3 shows a comparison of the subjective symptoms and physical findings among the four clusters. Sore throat, dysosmia, and dysgeusia, all reported as good prognostic factors [12, 28, 29], tended to be more frequent in Cluster 1 than in other clusters. In contrast, shortness of breath, reported as a poor prognostic factor [30], tended to be less frequent in Cluster 1 than in other clusters. Cluster 4 exhibited the lowest prevalence of sore throat, dysosmia, and dysgeusia among the four clusters, but more frequent consciousness disturbance, reportedly a poor prognostic factor [31], than other clusters. Table 4 shows a comparison of the laboratory findings among the clusters. Platelet count, reported as a poor prognostic factor [32], tended to be lower in Clusters 3 and 4, while lactate dehydrogenase (LDH), ferritin, Krebs von den Lungen-6 (KL-6), d-dimer, and C-reactive protein (CRP), also considered poor prognostic factors [33,34,35], tended to be lower in Cluster 1 and higher in Clusters 3 and 4. These results imply that Cluster 1 had COVID-19 related symptoms and laboratory findings associated with good prognosis, while Clusters 3 and 4 had poor prognosis.

Table 3 Comparison of subjective symptoms and physical findings among the four clusters
Full size table
Table 4 Comparison of laboratory findings among the four clusters
Full size table

Comparison of clinical outcomes between the four clusters

A comparison of the rate of supplemental oxygen needs, ICU admission, mechanical ventilation, and mortality is shown in Fig. 3. Cluster 3 exhibited a higher rate of patient receiving supplementary oxygen and/or mechanical ventilation, admitted to the ICU, and mortality compared to Clusters 1 and 2. Cluster 2 had intermediate rates of the above factors, between Clusters 1 and 3, and Cluster 1 exhibited the most favorable outcomes among all the clusters. Similar to Cluster 3, Cluster 4 also tended to have poor outcomes, coupled with a higher mortality rate. These results suggest that middle-aged obese men tend to have a similarly serious course as the elderly but with a lower risk of death. Consistent with the high rate of severe disease in Clusters 3 and 4, patients in these clusters received intensive drug treatment, including remdesivir and glucocorticoids, of current frequent use and considered to be effective in the treatment of COVID-19 [36] (Table 5).

Fig. 3
figure 3

Comparison of clinical outcomes among the four clusters. a Comparison of the rate of receiving supplementary oxygen. b Comparison of the rate of ICU admission. c Comparison of the rate of requiring mechanical ventilation. d Comparison of the mortality rate. *p < 0.05 and **p < 0.005

Full size image
Table 5 Comparison of drug treatment among the four clusters
Full size table

Discussion

This study was the first in Japan to perform a cluster analysis of COVID-19 patients. We identified four clinical sub-phenotypes, namely the “young healthy cluster” (Cluster 1), “middle-aged cluster” (Cluster 2), “middle-aged obese cluster” (Cluster 3), and “elderly cluster” (Cluster 4), which were associated with different outcomes in Japanese patients with COVID-19. Previous reports, including ours, have shown that comorbidities and mortality rates in Japan differed from inpatient studies in other countries [15, 17]. Thus, the identification of the meaningful sub-phenotypes of Japanese COVID-19 patients is important. Notably, our study used simple baseline characteristics as variables for cluster analysis. Several previous studies have shown that cluster analysis is useful for phenotyping and predicting COVID-19 outcomes [6,7,8,9,10]. However, most of these studies used complicated variables, combining a wide range of blood test results for clustering. Promptly indefinable is an important feature for defining COVID-19 sub-phenotypes [37]. We believe that the present simple clustering may be of great help to clinicians in predicting prognosis and performing individualized therapy.

Cluster 3 included mainly middle-aged patients with a high BMI, and a high rate of complications from lifestyle-related diseases, such as hypertension, diabetes, and hyperuricemia. Even though hyperuricemia has been previously reported to be associated with prognosis [38, 39], its rate was higher in Cluster 3 than in Cluster 4, which showed the highest mortality rate. This finding may be due to a possible association between obesity and hyperuricemia [40, 41]. Cluster 2 patients were similarly middle-aged but had lower BMI and lifestyle-related diseases. Cluster 3 revealed poorer outcomes, including need for oxygen, ICU admission, and intubation, than Cluster 2. This result is consistent with the fact that obesity has already been reported as a poor prognostic factor for COVID-19 [20], as have lifestyle-related diseases [12, 21, 22]. However, the mortality rate of Cluster 3 was lower than that of Cluster 4. Despite the high risk of severe disease, there is still lifesaving potential, suggesting that this cluster is likely to benefit from aggressive intensive care.

Cluster 1 consisted mainly of younger patients with fewer comorbidities. They showed the highest frequency of sore throat, dysosmia, and dysgeusia of all the clusters, and the outcomes were generally the most favorable. These results were consistent with previous reports showing that upper respiratory tract symptoms are related to a good prognosis. [12, 28, 29]. In addition, several biomarkers (LDH, ferritin, KL-6, d-dimer, and CRP) [33,34,35] reported as poor prognosis predictors were lower in Cluster 1 than in other clusters. A majority of young people with COVID-19 are reported to be asymptomatic or have few symptoms [42], and this cluster also tended to have fewer symptoms than other clusters, except for upper respiratory tract symptoms. It is possible that this group may have contributed to the spread of the disease.

Cluster 4 included predominantly older patients with comorbidities such as hypertension, diabetes, malignant disease, cardiovascular disease, and chronic kidney disease. They had the poorest outcomes in terms of oxygen demand, ICU admission, ventilator use, and death. These results were consistent with previous reports showing that old age and comorbidities are related with poor prognosis [12, 19, 21,22,23,24]. In addition, several poor prognostic biomarkers (LDH, ferritin, KL-6, d-dimer, and CRP) [33,34,35] were higher than those in Clusters 1 and 2. Lymphocyte count, which has been linked to severe disease and mortality, was also lowest in Cluster 4 [43]. The mechanism of this lymphocytopenia has been previously reported to be hypercytokinemia, leading to inhibition of hematopoiesis by TNF-α [44]. In fact, Cluster 4 patients with low lymphocyte count also showed a trend toward low hemoglobin level and platelet count, consistent with previous reports. Among patients in Cluster 4, 4% were admitted to the ICU and 17.6% of intubated patients died, indicating their potential as a target for future development of COVID-19 therapy.

One of the characteristics of the present study is the inclusion of a single racial group only. Many of the previous studies on cluster analysis of COVID-19 patients included multiple racial groups in their analyses [6, 7], and each cluster had different proportions of racial groups, suggesting that the clinical characteristics also reflect the racial differences. In contrast, since only Japanese patients were analyzed in this study, we focused more on basic clinical information, such as age, weight, and comorbidities, and the characteristics of the clusters can be easily grasped.

Some potential limitations of our study need to be discussed. First, the phenotyping of infectious diseases requires consideration of both the host and pathogen. SARS-CoV-2 is prone to genetic evolution, resulting in multiple variants with different characteristics compared to ancestral strains. Specifically, the transmissibility and virulence of these variants can greatly differ [45]. However, our study had no detailed data on viral load and/or strain. Second, we had no validation cohort data, necessitating additional studies. Third, we could not compare the differences in treatment response among the clusters. Five essential criteria could help define COVID-19 subtypes: (1) biologically plausible, (2) promptly identifiable, (3) nonsynonymous, (4) reproducible, and most importantly, (5) treatment responsive. To establish precision medicine against COVID-19 disease, further studies with more detailed and representative data are warranted.

Conclusions

We developed a simplified tool for clustering COVID-19 patients with diverse characteristics into sub-phenotypes. We identified four clusters that predicted in-hospital outcomes in a large nationwide series of Japanese COVID-19 patients. This simple clustering will be of great help to clinicians in predicting prognosis and performing individualized therapy. Further studies are needed to develop precision medicine for COVID-19.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

PCR:

Polymerase chain reaction

ICU:

Intensive care unit

BMI:

Body mass index

SD:

Standard deviation

LDH:

Lactate dehydrogenase

KL-6:

Krebs von den Lungen-6

CRP:

C-reactive protein

COVID-19:

Coronavirus disease

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

References

  1. Samudrala PK, Kumar P, Choudhary K, Thakur N, Wadekar GS, Dayaramani R, et al. Virology, pathogenesis, diagnosis and in-line treatment of COVID-19. Eur J Pharmacol. 2020;883:173375.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. 2020;324:782–93.

    CAS  PubMed  Article  Google Scholar 

  3. Tsai PH, Lai WY, Lin YY, Luo YH, Lin YT, Chen HK, et al. Clinical manifestation and disease progression in COVID-19 infection. J Chin Med Assoc. 2021;84(1):3–8.

    CAS  PubMed  Article  Google Scholar 

  4. Pollard CA, Morran MP, Nestor-Kalinoski AL. The COVID-19 pandemic: a global health crisis. Physiol Genomics. 2020;52(11):549–57.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Shankar-Hari M, Rubenfeld GD. Population enrichment for critical care trials: phenotypes and differential outcomes. Curr Opin Crit Care. 2019;25:489–97.

    PubMed  Article  Google Scholar 

  6. Rubio-Rivas M, Corbella X, Mora-Luján JM, Loureiro-Amigo J, López Sampalo A, Yera Bergua C, et al. Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-COVID-19. J Clin Med. 2020;9:3488.

    CAS  PubMed Central  Article  Google Scholar 

  7. Ye W, Lu W, Tang Y, Chen G, Li X, Ji C, et al. Identification of COVID-19 clinical phenotypes by principal component analysis-based cluster analysis. Front Med (Lausanne). 2020;7:570614.

    Article  Google Scholar 

  8. Lusczek ER, Ingraham NE, Karam BS, Proper J, Siegel L, Helgeson ES, et al. Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles. PLoS ONE. 2021;16:e0248956.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Dupont T, Caillat-Zucman S, Fremeaux-Bacchi V, Morin F, Lengliné E, Darmon M, et al. Identification of distinct immunophenotypes in critically ill coronavirus disease 2019 patients. Chest. 2021;159:1884–93.

    CAS  PubMed  Article  Google Scholar 

  10. Vasquez CR, Gupta S, Miano TA, Roche M, Hsu J, Yang W, et al. Identification of distinct clinical subphenotypes in critically ill patients with COVID-19. Chest. 2021;160:929–43.

    CAS  PubMed  Article  Google Scholar 

  11. Reddy K, Sinha P, O’Kane CM, Gordon AC, Calfee CS, McAuley DF. Subphenotypes in critical care: translation into clinical practice. Lancet Respir Med. 2020;8:631–43.

    PubMed  Article  Google Scholar 

  12. Ishii M, Terai H, Kabata H, Masaki K, Chubachi S, Tateno H, et al. Clinical characteristics of 345 patients with coronavirus disease 2019 in Japan: a multicenter retrospective study. J Infect. 2020;81:e3-5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Ministry of Health LaW, Situation report. https://www.mhlw.go.jp/stf/covid-19/kokunainohasseijoukyou.html.

  15. Matsunaga N, Hayakawa K, Terada M, Ohtsu H, Asai Y, Tsuzuki S, et al. Clinical epidemiology of hospitalized patients with COVID-19 in Japan: report of the COVID-19 REGISTRY JAPAN. Clin Infect Dis. 2020;73:e3677–89.

    PubMed Central  Article  CAS  Google Scholar 

  16. Namkoong H. Japan COVID-19 Task Force: a nation-wide consortium to elucidate host genetics of COVID-19 pandemic in Japan. medRxiv. 2021.

  17. Tanaka H, Namkoong H, Lee H, Morita A, Chubachi S, Kabata H, et al. Clinical characteristics of patients with coronavirus disease (COVID-19): preliminary baseline report of Japan COVID-19 task force, a nation-wide consortium to investigate host genetics of COVID-19. Int J Infect Dis. 2021;113:74–81.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. World Health Organization. Novel coronavirus: COVID-19 therapeutic trial synopsis. February 18, novel coronavirus: COVID-19 therapeutic trial synopsis. 2020. https://www.who.int/blueprint/priority-diseases/key-action/COVID-19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.pdf.

  19. Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, et al. Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis. J Infect. 2020;81:e16-25.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Zhou Y, Chi J, Lv W, Wang Y. Obesity and diabetes as high-risk factors for severe coronavirus disease 2019 (Covid-19). Diabetes Metab Res Rev. 2021;37:e3377.

    CAS  PubMed  Google Scholar 

  21. Li X, Xu S, Yu M, Wang K, Tao Y, Zhou Y, et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol. 2020;146:110–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Singh AK, Gupta R, Ghosh A, Misra A. Diabetes in COVID-19: prevalence, pathophysiology, prognosis and practical considerations. Diabetes Metab Syndr. 2020;14:303–10.

    PubMed  PubMed Central  Article  Google Scholar 

  23. Liang W, Guan W, Chen R, Wang W, Li J, Xu K, et al. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol. 2020;21:335–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Zhao Q, Meng M, Kumar R, Wu Y, Huang J, Lian N, et al. The impact of COPD and smoking history on the severity of COVID-19: a systemic review and meta-analysis. J Med Virol. 2020;92:1915–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Kovalic AJ, Satapathy SK, Thuluvath PJ. Prevalence of chronic liver disease in patients with COVID-19 and their clinical outcomes: a systematic review and meta-analysis. Hepatol Int. 2020;14:612–20.

    PubMed  Article  Google Scholar 

  26. McLachlan GJ. Cluster analysis and related techniques in medical research. Stat Methods Med Res. 1992;1:27–48.

    CAS  PubMed  Article  Google Scholar 

  27. Ye W, Robbins RT. Cluster analysis of longidorus species (nematoda: longidoridae), a new approach in species identification. J Nematol. 2004;36:207–19.

    PubMed  PubMed Central  Google Scholar 

  28. Foster KJ, Jauregui E, Tajudeen B, Bishehsari F, Mahdavinia M. Smell loss is a prognostic factor for lower severity of coronavirus disease 2019. Ann Allergy Asthma Immunol. 2020;125:481–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Porta-Etessam J, Núñez-Gil IJ, González García N, Fernandez-Perez C, Viana-Llamas MC, Eid CM, et al. COVID-19 anosmia and gustatory symptoms as a prognosis factor: a subanalysis of the HOPE COVID-19 (Health Outcome Predictive Evaluation for COVID-19) registry. Infection. 2021;49:677–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Liang W, Liang H, Ou L, Chen B, Chen A, Li C, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020;180:1081–9.

    CAS  PubMed  Article  Google Scholar 

  31. Sobhani S, Aryan R, Kalantari E, Soltani S, Malek N, Pirzadeh P, et al. Association between clinical characteristics and laboratory findings with outcome of hospitalized COVID-19 patients: a report from Northeast Iran. Interdiscip Perspect Infect Dis. 2021;2021:5552138.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  32. Liao D, Zhou F, Luo L, Xu M, Wang H, Xia J, et al. Haematological characteristics and risk factors in the classification and prognosis evaluation of COVID-19: a retrospective cohort study. Lancet Haematol. 2020;7:e671–8.

    PubMed  PubMed Central  Article  Google Scholar 

  33. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–62.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180:934–43.

    CAS  PubMed  Article  Google Scholar 

  35. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China. JAMA Cardiol. 2020;5:802–10.

    PubMed  PubMed Central  Article  Google Scholar 

  36. Gavriatopoulou M, Ntanasis-Stathopoulos I, Korompoki E, Fotiou D, Migkou M, Tzanninis IG, et al. Emerging treatment strategies for COVID-19 infection. Clin Exp Med. 2021;21:167–79.

    CAS  PubMed  Article  Google Scholar 

  37. DeMerle K, Angus DC, Seymour CW. Precision medicine for COVID-19: phenotype anarchy or promise realized? JAMA. 2021;325:2041–2.

    CAS  PubMed  Article  Google Scholar 

  38. Chauhan K, Pattharanitima P, Piani F, Johnson RJ, Uribarri J, Chan L, et al. Prevalence and outcomes associated with hyperuricemia in hospitalized patients with COVID-19. Am J Nephrol. 2022;53:78–86.

    CAS  PubMed  Article  Google Scholar 

  39. Deniz C, Selçuk Y, Kubilay I, Ahmed CG, Didar S, Ahmed BG, et al. Uric acid and mortality relationship in covid-19. Acta Medica Mediterranea. 2022;38(1):725–31.

    Google Scholar 

  40. Li C, Hsieh MC, Chang SJ. Metabolic syndrome, diabetes, and hyperuricemia. Curr Opin Rheumatol. 2013;25:210–6.

    CAS  PubMed  Article  Google Scholar 

  41. Song P, Wang H, Xia W, Chang X, Wang M, An L. Prevalence and correlates of hyperuricemia in the middle-aged and older adults in China. Sci Rep. 2018;8:4314.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. Ripabelli G, Sammarco ML, Cannizzaro F, Montanaro C, Ponzio GV, Tamburro M. A coronavirus outbreak linked to a funeral among a Romani community in Central Italy. Front Med (Lausanne). 2021;8:617264.

    Article  Google Scholar 

  43. Figliozzi S, Masci PG, Ahmadi N, Tondi L, Koutli E, Aimo A, et al. Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis. Eur J Clin Invest. 2020;50:e13362.

    CAS  PubMed  Article  Google Scholar 

  44. Fathi N, Rezaei N. Lymphopenia in COVID-19: Therapeutic opportunities. Cell Biol Int. 2020;44:1792–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Raman R, Patel KJ, Ranjan K. COVID-19: unmasking emerging SARS-CoV-2 variants, vaccines and therapeutic strategies. Biomolecules. 2021;11:993.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We would like to thank all the participants involved in this study, and all members of the Japan COVID-19 Task Force engaged in daily clinical and research work on COVID-19. All members contributed cases to this study.

Japan COVID-19 Task Force are composed of more than 70 institutions nationwide in Japan. The members who contributed to the collection and analysis of cases at each institution are shown as coauthors in the following list.

Takahiro Fukushima1, Shotaro Chubachi1, Ho Namkoong1, Shiro Otake1, Kensuke Nakagawara1, Hiromu Tanaka1, Ho Lee1, Atsuho Morita1, Mayuko Watase1, Takuya Kusumoto1, Katsunori Masaki1, Hiroki Kabata1, Hirofumi Kamata1, Makoto Ishii1, Naoki Hasegawa2, Kazuhisa Takahashi3, Norihiro Harada3, Toshio Naito4, Makoto Hiki5,6, Yasushi Matsushita7, Haruhi Takagi3, Ryousuke Aoki8, Ai Nakamura3, Sonoko Harada3,9, Hitoshi Sasano3, Shinnosuke Ikemura1, Satoshi Okamori1, Hideki Terai1, Takanori Asakura1, Junichi Sasaki10, Hiroshi Morisaki11, Yoshifumi Uwamino12, Kosaku Nanki13, Yohei Mikami13, Sho Uchida2, Shunsuke Uno2, Rino Ishihara13, Yuta Matsubara13, Tomoyasu Nishimura2,14, Takunori Ogawa1, Toshiro Sato15, Tetsuya Ueda16, Masanori Azuma16, Ryuichi Saito16, Toshikatsu Sado16, Yoshimune Miyazaki16, Ryuichi Sato16, Yuki Haruta16, Tadao Nagasaki16, Yoshinori Yasui17, Yoshinori Hasegawa16, Soichiro Ueda18, Ai Tada18, Masayoshi Miyawaki18, Masaomi Yamamoto18, Eriko Yoshida18, Reina Hayashi18, Tomoki Nagasaka18, Sawako Arai18, Yutaro Kaneko18, Kana Sasaki18, Takashi Ishiguro19, Taisuke Isono19, Shun Shibata19, Yuma Matsui19, Chiaki Hosoda19, Kenji Takano19, Takashi Nishida19, Yoichi Kobayashi19, Yotaro Takaku19, Noboru Takayanagi19, Etsuko Tagaya20, Masatoshi Kawana21, Ken Arimura20, Yasushi Nakamori22, Kazuhisa Yoshiya22, Fukuki Saito22, Tomoyuki Yoshihara22, Daiki Wada22, Hiromu Iwamura22, Syuji Kanayama22, Shuhei Maruyama22, Takanori Hasegawa23, Kunihiko Takahashi23, Tatsuhiko Anzai23, Satoshi Ito23, Akifumi Endo24, Yuji Uchimura25, Yasunari Miyazaki26, Takayuki Honda26, Tomoya Tateishi26, Shuji Tohda27, Naoya Ichimura27, Kazunari Sonobe27, Chihiro Tani Sassa27, Jun Nakajima27, Masumi Ai28, Takashi Yoshiyama29, Ken Ohta29, Hiroyuki Kokuto29, Hideo Ogata29, Yoshiaki Tanaka29, Kenichi Arakawa29, Masafumi Shimoda29, Takeshi Osawa29, Yasushi Nakano30, Yukiko Nakajima30, Ryusuke Anan30, Ryosuke Arai30, Yuko Kurihara30, Yuko Harada30, Kazumi Nishio30, Yoshikazu Mutoh31, Tomonori Sato32, Reoto Takei32, Satoshi Hagimoto32, Yoichiro Noguchi32, Yasuhiko Yamano32, Hajime Sasano32, Sho Ota32, Yusuke Suzuki33, Sohei Nakayama33, Keita Masuzawa33, Tomomi Takano34, Kazuhiko Katayama35, Koji Murakami36, Mitsuhiro Yamada36, Hisatoshi Sugiura36, Hirohito Sano36, Shuichiro Matsumoto36, Nozomu Kimura36, Yoshinao Ono36, Hiroaki Baba37, Rie Baba38, Daisuke Arai38, Takayuki Ogura38, Hidenori Takahashi38, Shigehiro Hagiwara38, Genta Nagao38, Shunichiro Konishi38, Ichiro Nakachi38, Hiroki Tateno39, Isano Hase39, Shuichi Yoshida39, Shoji Suzuki39, Miki Kawada40, Hirohisa Horinouchi41, Fumitake Saito42, Keiko Mitamura43, Masao Hagihara44, Junichi Ochi42, Tomoyuki Uchida44, Ryuya Edahiro45,46, Yuya Shirai45,46, Kyuto Sonehara46,47, Tatsuhiko Naito46, Kenichi Yamamoto46, Shinichi Namba46, Ken Suzuki46, Takayuki Shiroyama45, Yuichi Maeda45, Takuro Nii45, Yoshimi Noda45, Takayuki Niitsu45, Yuichi Adachi45, Takatoshi Enomoto45, Saori Amiya45, Reina Hara45, Toshihiro Kishikawa46,48,50, Shuhei Yamada49, Shuhei Kawabata49, Noriyuki Kijima49, Masatoshi Takagaki49,54, Noa Sasa46,48, Yuya Ueno48, Motoyuki Suzuki48, Norihiko Takemoto48, Hirotaka Eguchi48, Takahito Fukusumi48, Takao Imai48, Munehisa Fukushima48,53, Haruhiko Kishima49, Hidenori Inohara48, Kazunori Tomono51, Kazuto Kato52, Haruhiko Hirata45, Yoshito Takeda45, Atsushi Kumanogoh45,47,54,55, Naoki Miyazawa56, Yasuhiro Kimura56, Reiko Sado56, Hideyasu Sugimoto56, Akane Kamiya57, Naota Kuwahara58, Akiko Fujiwara58, Tomohiro Matsunaga58, Yoko Sato58, Takenori Okada58, Takashi Inoue59, Toshiyuki Hirano59, Keigo Kobayashi59, Hatsuyo Takaoka59, Koichi Nishi60, Masaru Nishitsuji60, Mayuko Tani60, Junya Suzuki60, Hiroki Nakatsumi60, Hidefumi Koh61, Tadashi Manabe61, Yohei Funatsu61, Fumimaro Ito61, Takahiro Fukui61, Keisuke Shinozuka61, Sumiko Kohashi61, Masatoshi Miyazaki61, Tomohisa Shoko62, Mitsuaki Kojima62, Tomohiro Adachi62, Motonao Ishikawa63, Kenichiro Takahashi64, Kazuyoshi Watanabe65, Yoshihiro Hirai66, Hidetoshi Kawashima66, Atsuya Narita66, Kazuki Niwa67, Yoshiyuki Sekikawa67, Hisako Sageshima68, Yoshihiko Nakamura69, Kota Hoshino69, Junichi Maruyama69, Hiroyasu Ishikura69, Tohru Takata70, Takashi Ogura71, Hideya Kitamura71, Eri Hagiwara71, Kota Murohashi71, Hiroko Okabayashi71, Takao Mochimaru72,73, Shigenari Nukaga72, Ryosuke Satomi72, Yoshitaka Oyamada73, Nobuaki Mori74, Tomoya Baba75, Yasutaka Fukui75, Mitsuru Odate75, Shuko Mashimo75, Yasushi Makino75, Kazuma Yagi76, Mizuha Hashiguchi76, Junko Kagyo76, Tetsuya Shiomi76, Kodai Kawamura77, Kazuya Ichikado77, Kenta Nishiyama77, Hiroyuki Muranaka77, Kazunori Nakamura77, Satoshi Fuke78, Hiroshi Saito78, Tomoya Tsuchida79, Shigeki Fujitani80, Mumon Takita80, Daiki Morikawa80, Toru Yoshida80, Takehiro Izumo81, Minoru Inomata81, Naoyuki Kuse81, Nobuyasu Awano81, Mari Tone81, Akihiro Ito82, Toshio Odani83, Masaru Amishima84, Takeshi Hattori84, Yasuo Shichinohe85, Takashi Kagaya86, Toshiyuki Kita86, Kazuhide Ohta86, Satoru Sakagami86, Kiyoshi Koshida86, Morio Nakamura86, Koutaro Yokote87, Taka-Aki Nakada88, Ryuzo Abe88, Taku Oshima88, Tadanaga Shimada88, Kentaro Hayashi89, Tetsuo Shimizu89, Yutaka Kozu89, Hisato Hiranuma89, Yasuhiro Gon89, Namiki Izumi90, Kaoru Nagata90, Ken Ueda90, Reiko Taki90, Satoko Hanada90, Naozumi Hashimoto91, Keiko Wakahara91, Koji Sakamoto91, Norihito Omote91, Akira Ando91, Yu Kusaka92, Takehiko Ohba92, Susumu Isogai92, Aki Ogawa92, Takuya Inoue92, Nobuhiro Kodama93, Yasunari Kaneyama93, Shunsuke Maeda93, Takashige Kuraki94, Takemasa Matsumoto94, Masahiro Harada95, Takeshi Takahashi95, Hiroshi Ono95, Toshihiro Sakurai95, Takayuki Shibusawa95, Yusuke Kawamura96, Akiyoshi Nakayama96, Hirotaka Matsuo96, Yoshifumi Kimizuka97, Akihiko Kawana97, Tomoya Sano97, Chie Watanabe97, Ryohei Suematsu97, Makoto Masuda98, Aya Wakabayashi98, Hiroki Watanabe98, Suguru Ueda98, Masanori Nishikawa98, Ayumi Yoshifuji99, Kazuto Ito99, Saeko Takahashi100, Kota Ishioka100, Yusuke Chihara101, Mayumi Takeuchi101, Keisuke Onoi101, Jun Shinozuka101, Atsushi Sueyoshi101, Yoji Nagasaki102, Masaki Okamoto103,104, Sayoko Ishihara105, Masatoshi Shimo105, Yoshihisa Tokunaga103,104, Masafumi Watanabe106, Sumito Inoue106, Akira Igarashi106, Masamichi Sato106, Nobuyuki Hizawa107, Yoshiaki Inoue108, Shigeru Chiba109, Kunihiro Yamagata110, Yuji Hiramatsu111, Hirayasu Kai110, Satoru Fukuyama112, Yoshihiro Eriguchi113, Akiko Yonekawa113, Keiko Kan-o112, Koichiro Matsumoto112, Kensuke Kanaoka114, Shoichi Ihara114, Kiyoshi Komuta114, Koichiro Asano115, Tsuyoshi Oguma115, Yoko Ito115, Satoru Hashimoto116, Masaki Yamasaki116, Yu Kasamatsu117, Yuko Komase118, Naoya Hida118, Takahiro Tsuburai118, Baku Oyama118, Yuichiro Kitagawa119, Tetsuya Fukuta119, Takahito Miyake119, Shozo Yoshida119, Shinji Ogura119, Minoru Takada120, Hidenori Kanda120, Shinji Abe121, Yuta Kono121, Yuki Togashi121, Hiroyuki Takoi121, Ryota Kikuchi121, Shinichi Ogawa122, Tomouki Ogata122, Shoichiro Ishihara122, Arihiko Kanehiro123,124, Shinji Ozaki123, Yasuko Fuchimoto123, Sae Wada123, Nobukazu Fujimoto124, Kei Nishiyama125, Mariko Terashima126, Satoru Beppu126, Kosuke Yoshida126, Osamu Narumoto127, Hideaki Nagai127, Nobuharu Ooshima127, Mitsuru Motegi128, Akira Umeda129, Kazuya Miyagawa130, Hisato Shimada131, Mayu Endo132, Yoshiyuki Ohira133, Hironori Sagara133, Akihiko Tanaka133, Shin Ohta133, Tomoyuki Kimura133, Yoko Shibata134, Yoshinori Tanino134, Takefumi Nikaido134, Hiroyuki Minemura134, Yuki Sato134, Yuichiro Yamada135, Takuya Hashino135, Masato Shinoki135, Hajime Iwagoe136, Hiroshi Takahashi137, Kazuhiko Fujii137, Hiroto Kishi137, Tomoo Ishii138, Masayuki Kanai139, Tomonori Imamura139, Tatsuya Yamashita139, Masakiyo Yatomi140, Toshitaka Maeno140, Shinichi Hayashi141, Mai Takahashi141, Mizuki Kuramochi141, Isamu Kamimaki141, Yoshiteru Tominaga141, Mitsuyoshi Utsugi142, Akihiro Ono142, Toru Tanaka143, Takeru Kashiwada143, Kazue Fujita143, Yoshinobu Saito143, Masahiro Seike143, Masahiro Kanai144, Ryunosuke Saiki145, Takayoshi Hyugaji146, Eigo Shimizu146, Kotoe Katayama146, Satoru Miyawaki147, Meiko Takahashi148, Fumihiko Matsuda148, Yosuke Omae149, Yasuhito Nannya145, Takafumi Ueno150, Yukinori Okada46,47,55,151, Ryuji Koike152, Yuko Kitagawa153, Katsushi Tokunaga149, Akinori Kimura154, Seiya Imoto146, Satoru Miyano23, Seishi Ogawa145,155,156, Takanori Kanai13, Koichi Fukunaga1

1. Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan

2. Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan.

3. Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.

4. Department of General Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.

5. Department of Emergency and Disaster Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.

6. Department of Cardiovascular Biology and Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.

7. Department of Internal Medicine and Rheumatology, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.

8. Department of Nephrology, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan.

9. Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo, Japan.

10. Department of Emergency and Critical Care Medicine, Keio University School of Medicine, Tokyo, Japan

11. Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan.

12. Department of Laboratory Medicine, Keio University School of Medicine, Tokyo, Japan

13. Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan.

14. Keio University Health Center, Keio University School of Medicine, Tokyo, Japan.

15. Department of Organoid Medicine, Keio University School of Medicine, Tokyo, Japan.

16. Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan.

17. Department of Infection Control, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan.

18. JCHO (Japan Community Health Care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan.

19. Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan.

20. Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan.

21. Department of General Medicine, Tokyo Women's Medical University, Tokyo, Japan.

22. Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan.

23. M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan.

24. Clinical Research Center, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan.

25. Department of Medical Informatics, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan.

26. Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan.

27. Clinical Laboratory, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan.

28. Department of Insured Medical Care Management, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan

29. Fukujuji Hospital, Kiyose, Japan.

30. Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan.

31. Department of Infectious Diseases, Tosei General Hospital, Seto, Japan.

32. Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan.

33. Department of Respiratory Medicine, Kitasato University Kitasato Institute Hospital, Tokyo, Japan.

34. School of Veterinary Medicine, Kitasato University, Towada, Japan.

35. Laboratory of Viral Infection I, Department of Infection Control and Immunology, Ōmura Satoshi Memorial Institute & Graduate School of Infection Control Sciences, Kitasato University, Tokyo, Japan.

36. Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan

37. Department of Infectious Diseases, Tohoku University Graduate School of Medicine, Sendai, Japan

38. Saiseikai Utsunomiya Hospital, Utsunomiya, Japan.

39. Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan.

40. Department of Infectious Diseases, Saitama City Hospital, Saitama, Japan.

41. Department of General Thoracic Surgery, Saitama City Hospital, Saitama, Japan.

42. Department of Pulmonary Medicine, Eiju General Hospital, Tokyo, Japan.

43. Division of Infection Control, Eiju General Hospital, Tokyo, Japan.

44. Department of Hematology, Eiju General Hospital, Tokyo, Japan.

45. Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan.

46. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.

47. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.

48. Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita, Japan.

49. Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan

50. Department of Head and Neck Surgery, Aichi Cancer Center Hospital, Nagoya, Japan.

51. Division of Infection Control and Prevention, Osaka University Hospital, Suita, Japan.

52. Department of Biomedical Ethics and Public Policy, Osaka University Graduate School of Medicine, Suita, Japan.

53. Department of Otolaryngology and Head and Neck Surgery, Kansai Rosai Hospital, Hyogo, Japan

54. Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.

55. The Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.

56. Department of Respiratory Medicine, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan.

57. Department of Clinical Laboratory, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan.

58. Internal Medicine, Internal Medicine Center, Showa University Koto Toyosu Hospital, Tokyo, Japan.

59. Internal Medicine, Sano Kosei General Hospital, Sano, Japan.

60. Ishikawa Prefectural Central Hospital, Kanazawa, Japan.

61. Tachikawa Hospital, Tachikawa, Japan.

62. Department of Emergency and Critical Care Medicine, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.

63. Department of Medicine, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.

64. Department of Pediatrics, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.

65. Japan Community Health care Organization Kanazawa Hospital, Kanazawa, Japan.

66. Department of Respiratory Medicine, Japan Organization of Occupational Health and Safety, Kanto Rosai Hospital, Kawasaki, Japan.

67. Department of General Internal Medicine, Japan Organization of Occupational Health and Safety, Kanto Rosai Hospital, Kawasaki, Japan.

68. Sapporo City General Hospital, Sapporo, Japan.

69. Department of Emergency and Critical Care Medicine, Faculty of Medicine, Fukuoka University, Fukuoka, Japan.

70. Department of Infection Control, Fukuoka University Hospital, Fukuoka, Japan.

71. Kanagawa Cardiovascular and Respiratory Center, Yokohama, Japan.

72. Department of Respiratory Medicine, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.

73. Department of Allergy, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.

74. Department of General Internal Medicine and Infectious Diseases, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.

75. Department of Respiratory Medicine, Toyohashi Municipal Hospital, Toyohashi, Japan.

76. Keiyu Hospital, Yokohama, Japan.

77. Division of Respiratory Medicine, Social Welfare Organization Saiseikai Imperial Gift Foundation, Inc., Saiseikai Kumamoto Hospital, Kumamoto, Japan.

78. KKR Sapporo Medical Center, Department of respiratory medicine, Sapporo, Japan.

79. Division of General Internal Medicine, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki, Japan.

80. Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, Kawasaki, Japan.

81. Japanese Red Cross Medical Center, Tokyo, Japan.

82. Matsumoto City Hospital, Matsumoto, Japan.

83. Department of Rheumatology, National Hospital Organization Hokkaido Medical Center, Sapporo, Japan.

84. Department of Respiratory Medicine, National Hospital Organization Hokkaido Medical Center, Sapporo, Japan.

85. Department of Emergency and Critical Care Medicine, National Hospital Organization Hokkaido Medical Center, Sapporo, Japan.

86. NHO Kanazawa Medical Center, Kanazawa, Japan.

87. Department of Endocrinology, Hematology and Gerontology, Chiba University Graduate School of Medicine, Chiba, Japan.

88. Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan.

89. Nihon University School of Medicine, Department of Internal Medicine, Division of Respiratory Medicine, Tokyo, Japan.

90. Musashino Red Cross Hospital, Musashino, Japan.

91. Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan.

92. Ome Municipal General Hospital, Ome, Japan.

93. Fukuoka Tokushukai Hospital, Department of Internal Medicine, Kasuga, Japan.

94. Fukuoka Tokushukai Hospital, Respiratory Medicine, Kasuga, Japan.

95. National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan.

96. Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan.

97. Division of Infectious Diseases and Respiratory Medicine, Department of Internal Medicine, National Defense Medical College, Tokorozawa, Japan.

98. Department of Respiratory Medicine, Fujisawa City Hospital, Fujisawa, Japan.

99. Department of Internal Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan.

100. Department of Pulmonary Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan.

101. Uji-Tokushukai Medical Center, Uji, Japan.

102. Department of Infectious Disease and Clinical Research Institute, National Hospital Organization Kyushu Medical Center, Fukuoka Japan.

103. Department of Respirology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan.

104. Division of Respirology, Rheumatology, and Neurology, Department of Internal Medicine, Kurume University School of Medicine, Kurume, Japan.

105. Department of Infectious Disease, National Hospital Organization Kyushu Medical Center, Fukuoka Japan.

106. Department of Cardiology, Pulmonology, and Nephrology, Yamagata University Faculty of Medicine, Yamagata, Japan.

107. Department of Pulmonary Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

108. Department of Emergency and Critical Care Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

109. Department of Hematology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

110. Department of Nephrology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

111. Department of Cardiovascular Surgery, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

112. Research Institute for Diseases of the Chest, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.

113. Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan.

114. Daini Osaka Police Hospital, Osaka, Japan.

115. Division of Pulmonary Medicine, Department of Medicine, Tokai University School of Medicine, Isehara, Japan.

116. Department of Anesthesiology and Intensive Care Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan.

117. Department of Infection Control and Laboratory Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan.

118. Department of Respiratory Internal Medicine, St. Marianna University School of Medicine, Yokohama-City Seibu Hospital, Yokohama, Japan.

119. Gifu University School of Medicine Graduate School of Medicine, Emergency and Disaster Medicine, Gifu, Japan.

120. KINSHUKAI Hanwa The Second Hospital, Osaka, Japan.

121. Department of Respiratory Medicine, Tokyo Medical University Hospital, Tokyo, Japan.

122. JA Toride medical hospital, Toride, Japan.

123. Okayama Rosai Hospital, Okayama, Japan.

124. Himeji St. Mary's Hospital, Himeji, Japan.

125. Emergency & Critical Care, Niigata University, Niigata, Japan.

126. Emergency & Critical Care Center, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.

127. National Hospital Organization Tokyo National Hospital, Kiyose, Japan.

128. Fujioka General Hospital, Fujioka, Japan.

129. Department of General Medicine, School of Medicine, International University of Health and Welfare Shioya Hospital, Ohtawara Japan.

130. Department of Pharmacology, School of Pharmacy, International University of Health and Welfare Shioya Hospital, Ohtawara Japan.

131. Department of Respiratory Medicine, International University of Health and Welfare Shioya Hospital, Ohtawara Japan.

132. Department of Clinical Laboratory, International University of Health and Welfare Shioya Hospital, Ohtawara Japan.

133. Department of General Medicine, School of Medicine, International University of Health and Welfare, Narita Japan.

134. Department of Pulmonary Medicine, Fukushima Medical University, Fukushima, Japan.

135. Kansai Electric Power Hospital, Osaka, Japan.

136. Department of Infectious Diseases, Kumamoto City Hospital, Kumamoto, Japan.

137. Department of Respiratory Medicine, Kumamoto City Hospital, Kumamoto, Japan.

138. Tokyo Medical University Ibaraki Medical Center, Inashiki, Japan.

139. Department of Emergency and Critical Care Medicine, Tokyo Metropolitan Police Hospital, Tokyo, Japan.

140. Department of Respiratory Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan.

141. National hospital organization Saitama Hospital, Wako, Japan.

142. Department of Internal Medicine, Kiryu Kosei General Hospital, Kiryu, Japan.

143. Department of Pulmonary Medicine and Oncology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan

144. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

145. Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan.

146. Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan.

147. Department of Neurosurgery, Faculty of Medicine, the University of Tokyo, Tokyo, Japan.

148. Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.

149. Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Tokyo, Japan.

150. Department of Biomolecular Engineering, Graduate School of Tokyo Institute of Technology, Tokyo, Japan.

151. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.

152. Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan.

153. Department of Surgery, Keio University School of Medicine, Tokyo, Japan.

154. Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan.

155. Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.

156. Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden.

Funding

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).

Author information

Authors and Affiliations

  1. Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Tokyo, 160-8582, Japan

    Shiro Otake, Shotaro Chubachi, Ho Namkoong, Kensuke Nakagawara, Hiromu Tanaka, Ho Lee, Atsuho Morita, Takahiro Fukushima, Mayuko Watase, Tatsuya Kusumoto, Katsunori Masaki, Hirofumi Kamata, Makoto Ishii & Koichi Fukunaga

  2. Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan

    Naoki Hasegawa

  3. Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan

    Norihiro Harada

  4. Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan

    Tetsuya Ueda

  5. JCHO (Japan Community Health Care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan

    Soichiro Ueda

  6. Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan

    Takashi Ishiguro

  7. Department of Respiratory Medicine, Tokyo Women’s Medical University, Tokyo, Japan

    Ken Arimura

  8. Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan

    Fukuki Saito

  9. Department of Respiratory Medicine, Fukujuji Hospital, Kiyose, Japan

    Takashi Yoshiyama

  10. Department of Internal Medicine, Kawasaki Municipal Ida Hospital, Kawasaki, Japan

    Yasushi Nakano

  11. Department of Infectious Diseases, Tosei General Hospital, Seto, Japan

    Yoshikazu Mutoh

  12. Department of Respiratory Medicine, Kitasato University Kitasato Institute Hospital, Tokyo, Japan

    Yusuke Suzuki

  13. Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan

    Koji Murakami

  14. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Yukinori Okada

  15. Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan

    Ryuji Koike

  16. Department of Surgery, Keio University School of Medicine, Tokyo, Japan

    Yuko Kitagawa

  17. Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan

    Akinori Kimura

  18. Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Seiya Imoto

  19. M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan

    Satoru Miyano

  20. Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan

    Seishi Ogawa

  21. Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan

    Takanori Kanai

Authors
  1. Shiro Otake
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shotaro Chubachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ho Namkoong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kensuke Nakagawara
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hiromu Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ho Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Atsuho Morita
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Takahiro Fukushima
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mayuko Watase
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tatsuya Kusumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Katsunori Masaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hirofumi Kamata
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Makoto Ishii
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Naoki Hasegawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Norihiro Harada
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Tetsuya Ueda
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Soichiro Ueda
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Takashi Ishiguro
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Ken Arimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Fukuki Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Takashi Yoshiyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Yasushi Nakano
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Yoshikazu Mutoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Yusuke Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Koji Murakami
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Yukinori Okada
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Ryuji Koike
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Yuko Kitagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Akinori Kimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Seiya Imoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Satoru Miyano
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Seishi Ogawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Takanori Kanai
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Koichi Fukunaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

The Japan COVID-19 Task Force

Contributions

Conceptualization: SO, SC, HN, KM, HK, MI, NH, KF. Data curation: SO, KN, HT, HL, AM, TF, MW, TK. Formal analysis: SO, SC. Methodology: SO, SC, HN. Supervision: SC, HN, KM, HK, MI, NoH, NaH, TU, SU, TI, KA, FS, TY, YN, YM, YS, KM, YO, RK, YK, AK, SI, SM, SO, TK, KF. Visualization: SC, HN. Writing—original draft: SO, SC. Writing—review and editing: SO, SC, HN, KM, HK, MI, NaH, NoH, TU, SU, TI, KA, FS, TY, YN, YM, YS, KM, YO, RK, YK, AK, SI, SM, SO, TK, KF. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shotaro Chubachi.

Ethics declarations

Ethics approval and consent to participate

This study was performed in accordance with the Declaration of Helsinki and was approved by the ethics committees of Keio University School of Medicine (20200061) and related research institutions. All adult participants provided written informed consent to participate in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Otake, S., Chubachi, S., Namkoong, H. et al. 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. BMC Infect Dis 22, 735 (2022). https://doi.org/10.1186/s12879-022-07701-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12879-022-07701-y

Keywords

  • COVID-19
  • Pneumonia
  • Phenotype
  • Cluster analysis
  • Japan

BMC Infectious Diseases

ISSN: 1471-2334

Contact us