Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality
Abstract 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 identification of specific phenotypes underlying in the population might contribute towards a better understanding and c...
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| Format: | Article |
| Language: | English |
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BMC
2024-06-01
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| Series: | Pneumonia |
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| Online Access: | https://doi.org/10.1186/s41479-024-00132-0 |
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| author | Fernando García-García Dae-Jin Lee Mónica Nieves-Ermecheo Olaia Bronte Pedro Pablo España José María Quintana Rosario Menéndez Antoni Torres Luis Alberto Ruiz Iturriaga Isabel Urrutia COVID-19 & Air Pollution Working Group |
| author_facet | Fernando García-García Dae-Jin Lee Mónica Nieves-Ermecheo Olaia Bronte Pedro Pablo España José María Quintana Rosario Menéndez Antoni Torres Luis Alberto Ruiz Iturriaga Isabel Urrutia COVID-19 & Air Pollution Working Group |
| author_sort | Fernando García-García |
| collection | DOAJ |
| description | Abstract 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 identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia 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 training 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 identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] – age $$\sim$$ ∼ 57, Charlson comorbidity $$\sim$$ ∼ 1, pneumonia CURB-65 score $$\sim$$ ∼ 0 to 1, respiratory rate at admission $$\sim$$ ∼ 18 min-1, FiO2 $$\sim$$ ∼ 21%, C-reactive protein CRP $$\sim$$ ∼ 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] – age $$\sim$$ ∼ 75, Charlson $$\sim$$ ∼ 5, CURB-65 $$\sim$$ ∼ 1 to 2, respiration $$\sim$$ ∼ 20 min-1, FiO2 $$\sim$$ ∼ 21%, CRP $$\sim$$ ∼ 101.5 mg/dL); and phenotype C (140 cases [9.0%] – age $$\sim$$ ∼ 71, Charlson $$\sim$$ ∼ 4, CURB-65 $$\sim$$ ∼ 0 to 2, respiration $$\sim$$ ∼ 30 min-1, FiO2 $$\sim$$ ∼ 38%, CRP $$\sim$$ ∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A. Conclusion A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients – phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality. |
| format | Article |
| id | doaj-art-225bc5553bbc48a581bbb35098060c0b |
| institution | Kabale University |
| issn | 2200-6133 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Pneumonia |
| spelling | doaj-art-225bc5553bbc48a581bbb35098060c0b2025-01-12T12:26:35ZengBMCPneumonia2200-61332024-06-0116111610.1186/s41479-024-00132-0Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortalityFernando García-García0Dae-Jin Lee1Mónica Nieves-Ermecheo2Olaia Bronte3Pedro Pablo España4José María Quintana5Rosario Menéndez6Antoni Torres7Luis Alberto Ruiz Iturriaga8Isabel Urrutia9COVID-19 & Air Pollution Working GroupBasque Center for Applied Mathematics (BCAM)School of Science & Technology, IE UniversityBiocruces Bizkaia Health Research InstituteRespiratory Service, Galdakao-Usansolo University HospitalRespiratory Service, Galdakao-Usansolo University HospitalResearch Unit, Galdakao-Usansolo University HospitalPneumology Department, La Fe University and Polytechnic HospitalPneumology Department, Hospital Clínic of BarcelonaPneumology Service, Cruces University HospitalRespiratory Service, Galdakao-Usansolo University HospitalAbstract 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 identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia 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 training 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 identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] – age $$\sim$$ ∼ 57, Charlson comorbidity $$\sim$$ ∼ 1, pneumonia CURB-65 score $$\sim$$ ∼ 0 to 1, respiratory rate at admission $$\sim$$ ∼ 18 min-1, FiO2 $$\sim$$ ∼ 21%, C-reactive protein CRP $$\sim$$ ∼ 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] – age $$\sim$$ ∼ 75, Charlson $$\sim$$ ∼ 5, CURB-65 $$\sim$$ ∼ 1 to 2, respiration $$\sim$$ ∼ 20 min-1, FiO2 $$\sim$$ ∼ 21%, CRP $$\sim$$ ∼ 101.5 mg/dL); and phenotype C (140 cases [9.0%] – age $$\sim$$ ∼ 71, Charlson $$\sim$$ ∼ 4, CURB-65 $$\sim$$ ∼ 0 to 2, respiration $$\sim$$ ∼ 30 min-1, FiO2 $$\sim$$ ∼ 38%, CRP $$\sim$$ ∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A. Conclusion A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients – phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality.https://doi.org/10.1186/s41479-024-00132-0COVID-19SARS-CoV-2 pneumoniaPhenotypesClusteringUnsupervised machine learning |
| spellingShingle | Fernando García-García Dae-Jin Lee Mónica Nieves-Ermecheo Olaia Bronte Pedro Pablo España José María Quintana Rosario Menéndez Antoni Torres Luis Alberto Ruiz Iturriaga Isabel Urrutia COVID-19 & Air Pollution Working Group Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality Pneumonia COVID-19 SARS-CoV-2 pneumonia Phenotypes Clustering Unsupervised machine learning |
| title | Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality |
| title_full | Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality |
| title_fullStr | Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality |
| title_full_unstemmed | Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality |
| title_short | Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality |
| title_sort | obtaining patient phenotypes in sars cov 2 pneumonia and their association with clinical severity and mortality |
| topic | COVID-19 SARS-CoV-2 pneumonia Phenotypes Clustering Unsupervised machine learning |
| url | https://doi.org/10.1186/s41479-024-00132-0 |
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