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: Different clinical presentations in COVID-19 are described to date, from mild to severe cases. 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 1 March to 31 July 2020, from the nationwide Spanish Society of Internal Medicine (SEMI)-COVID-19 Registry. (3) Results: Of the total of 12,066 patients included in the study, most were males (7052, 58.5%) and Caucasian (10,635, 89.5%), with a mean age at diagnosis of 67 years (standard deviation (SD) 16). The main pre-admission comorbidities were arterial hypertension (6030, 50%), hyperlipidemia (4741, 39.4%) and diabetes mellitus (2309, 19.2%). The average number of days from COVID-19 symptom onset to hospital admission was 6.7 (SD 7). The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes identified by clustering. Cluster C1 (8737 patients, 72.4%) was the largest, and comprised patients with the triad alone. Cluster C2 (1196 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 (1253 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 age, gender (male), body mass index (BMI), arterial hypertension, chronic obstructive pulmonary disease (COPD), ischemic cardiopathy, chronic heart failure, chronic hepatopathy, Charlson’s index, heart rate and respiratory rate upon admission >20 bpm, lower PaO2/FiO2 at admission, higher levels of C-reactive protein (CRP) and lactate dehydrogenase (LDH), and the phenotypic cluster as independent factors for in-hospital death. (4) Conclusions: The present study identified 4 phenotypic clusters in patients with COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes.

A Predictive Model and Risk Factors for Case Fatality of COVID-19

“…Recently, different studies have attempted to classify patients by phenotypes [ 18 ]. Our study allowed us to obtain risk levels regardless of specific phenotypes of patients, only taking into account rapid clinical parameters that allow decision-making in an effective way to save patients´ lives.…”

“…Rubio-Rivas et al [ 15 ] have shown the existence of different phenotypes of patients on the basis of their comorbidities and their clinical characteristics, relating how it can be effective at the healthcare level in clinical management from the point of view of survival. Along these lines, other studies have unified symptoms and signs of manifestation of this disease with the aim of improving and implementing an action model [ 16 , 17 , 18 ].…”

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

“…To date, only a few reports have used cluster analysis to describe subgroups heterogeneity in COVID-19 patient-level epidemiological or EHR data 12,13,14 but none included gender and age factors to implement age-gender clustering and meta-clustering analyses on such large dataset (778 692 patients), aiming to find potential patient strata throughout these factors. Thus, it is crucial to comprehend the inter-patient variability patterns to anticipate their risk, susceptibility for viral infection, and morbimortality, based on their clinical phenotypes and demographic characteristics, including age-gender groups analyses.…”

“…Other studies proposed unsupervised ML methods to subgroup aggregated population data 7 , CT image analyses 8,9 , molecular-level clustering 10 , or scientific texts to discover associations among coronavirus and other diseases 11 . However, to our knowledge, few studies provided to date results from unsupervised ML on patient-level epidemiological data 12,13,14 . The resultant subphenotypes can potentially establish target groups for automated stratification or triage systems to provide personalized therapies or treatments for the specific group severity patterns.…”

Genetic screening for TLR7 variants in young and previously healthy men with severe COVID-19: a case series

“…A proportion of patients with COVID-19 evolve to fatal lung injury and multi-organ failure due to systemic host-immune inflammatory processes triggered by the viral infection [1]. Advanced age, male sex and chronic disease such as diabetes and obesity are common in patients with more severe forms of COVID-19 [2][3][4]. However, these risk factors cannot explain why critical disease also occurs in younger (below 50 years of age) and apparently healthy individuals.…”