Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality
Background There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identifcation of specifc phenotypes underlying in the popula‑ tion might contribute towards a better understanding and characteri...
| Autores: | , , , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1845 |
| Acesso em linha: | http://hdl.handle.net/20.500.11824/1845 |
| Access Level: | acceso abierto |
| Palavra-chave: | COVID-19 SARS-CoV-2 pneumonia phenotypes clustering unsupervised machine learning |
| Resumo: | Background There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identifcation of specifc phenotypes underlying in the popula‑ tion might contribute towards a better understanding and characterization of the diferent courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumo‑ nia using machine learning clustering, and to study their association with subsequent clinical outcomes as severity and mortality. Methods Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. We included adult patients admitted for in-hospital stay due to SARS-CoV-2 pneumonia. We collected a broad spectrum of variables to describe exhaustively each case: patient demographics, comorbidities, symptoms, physiological status, baseline examinations (blood analytics, arterial gas test), etc. For the development and internal validation of the clustering/phenotype models, the dataset was split into train‑ ing and test sets (50% each). We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. The optimal cluster model parameters –including k, the number of phenotypes– were chosen automatically, by maximizing the average Silhouette score across the training set. Results We enrolled 1548 patients, each of them characterized by 92 clinical attributes (d=109 features after variable encoding). Our clustering algorithm identifed k=3 distinct phenotypes and 18 strongly informative variables: Pheno‑ type A (788 cases [50.9% prevalence] – age∼57, Charlson comorbidity∼1, pneumonia CURB-65 score∼0 to 1, respira‑ tory rate at admission∼18 min-1 , FiO2∼21%, C-reactive protein CRP∼49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] – age∼75, Charlson∼5, CURB-65∼1 to 2, respiration∼20 min-1 , FiO2∼21%, CRP∼101.5 mg/dL); and phenotype C (140 cases [9.0%] – age∼71, Charlson∼4, CURB-65∼0 to 2, respiration∼30 min-1 , FiO2∼38%, CRP∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clini‑ cal outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher |
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