Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: 2 an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-3 COVID-19.

(1) Background: This study aims to identify different clinical phenotypes in COVID-19 88 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in 89 such patients. (2) Methods: Cluster analysis including 11 phenotypic variables was performed in a 90 large cohort of 12,066 COVID-19 patients, collected and followed-up from March 1, to July 31, 2020, 91 from the nationwide Spanish SEMI-COVID-19 Registry. (3) Results: Of the total of 12,066 patients 92 included in the study, most were males (7,052, 58.5%) and Caucasian (10,635, 89.5%), with a mean 93 age at diagnosis of 67 years (SD 16). The main pre-admission comorbidities were arterial 94 hypertension (6,030, 50%), hyperlipidemia (4,741, 39.4%) and diabetes mellitus (2,309, 19.2%). The 95 average number of days from COVID-19 symptom onset to hospital admission was 6.7 days (SD 7). 96 The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes 97 identified by clustering. Cluster C1 (8,737 patients, 72.4%) was the largest, and comprised patients 98 with the triad alone. Cluster C2 (1,196 patients, 9.9%) also presented with ageusia and anosmia; 99 cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 100 (1,253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to 101 each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 102 18.6%; p<0.001). The multivariate study identified phenotypic clusters as an independent factor for 103 in-hospital death. (4) Conclusion: The present study identified 4 phenotypic clusters in patients with 104 COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes.