Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-COVID-19
Abstract (1) Background This study aims to identify different clinical phenotypes in COVID-19 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in such patients.(2) Methods Cluster analysis including 11 phenotypic variables was performed in a large cohort of 12,066 COVID-19 patients, collected and followed-up from March 1, to July 31, 2020, from the nationwide Spanish SEMI-COVID-19 Registry.(3) Results Of the total of 12,066 patients included in the study, most were males (7,052, 58.5%) and Caucasian (10,635, 89.5%), with a mean age at diagnosis of 67 years (SD 16). The main pre-admission comorbidities were arterial hypertension (6,030, 50%), hyperlipidemia (4,741, 39.4%) and diabetes mellitus (2,309, 19.2%). The average number of days from COVID-19 symptom onset to hospital admission was 6.7 days (SD 7). The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes identified by clustering. Cluster C1 (8,737 patients, 72.4%) was the largest, and comprised patients with the triad alone. Cluster C2 (1,196 patients, 9.9%) also presented with ageusia and anosmia; cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 (1,253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 18.6%; p<0.001). The multivariate study identified phenotypic clusters as an independent factor for in-hospital death.(4) Conclusion The present study identified 4 phenotypic clusters in patients with COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 18. Nov. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rubio-Rivas, Manuel [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2020.09.14.20193995 |
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| PPN (Katalog-ID): |
XBI01949033X |
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| 245 | 1 | 0 | |a Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-COVID-19 |
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| 520 | |a Abstract (1) Background This study aims to identify different clinical phenotypes in COVID-19 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in such patients.(2) Methods Cluster analysis including 11 phenotypic variables was performed in a large cohort of 12,066 COVID-19 patients, collected and followed-up from March 1, to July 31, 2020, from the nationwide Spanish SEMI-COVID-19 Registry.(3) Results Of the total of 12,066 patients included in the study, most were males (7,052, 58.5%) and Caucasian (10,635, 89.5%), with a mean age at diagnosis of 67 years (SD 16). The main pre-admission comorbidities were arterial hypertension (6,030, 50%), hyperlipidemia (4,741, 39.4%) and diabetes mellitus (2,309, 19.2%). The average number of days from COVID-19 symptom onset to hospital admission was 6.7 days (SD 7). The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes identified by clustering. Cluster C1 (8,737 patients, 72.4%) was the largest, and comprised patients with the triad alone. Cluster C2 (1,196 patients, 9.9%) also presented with ageusia and anosmia; cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 (1,253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 18.6%; p<0.001). The multivariate study identified phenotypic clusters as an independent factor for in-hospital death.(4) Conclusion The present study identified 4 phenotypic clusters in patients with COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes. | ||
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