tetano
Editor, Senior Moderator
Comput Biol Med
. 2021 Dec 27;141:105176.
doi: 10.1016/j.compbiomed.2021.105176. Online ahead of print.
ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints
Vasileios C Pezoulas[SUP] 1 [/SUP], Konstantina D Kourou[SUP] 1 [/SUP], Eugenia Mylona[SUP] 1 [/SUP], Costas Papaloukas[SUP] 2 [/SUP], Angelos Liontos[SUP] 3 [/SUP], Dimitrios Biros[SUP] 3 [/SUP], Orestis I Milionis[SUP] 3 [/SUP], Chris Kyriakopoulos[SUP] 4 [/SUP], Kostantinos Kostikas[SUP] 4 [/SUP], Haralampos Milionis[SUP] 3 [/SUP], Dimitrios I Fotiadis[SUP] 5 [/SUP]
Affiliations
Abstract
The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.
Keywords: COVID-19; Dynamic Bayesian Networks (DBNs); ICU admission; Mortality; Self-Organizing Maps (SOMs).
. 2021 Dec 27;141:105176.
doi: 10.1016/j.compbiomed.2021.105176. Online ahead of print.
ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints
Vasileios C Pezoulas[SUP] 1 [/SUP], Konstantina D Kourou[SUP] 1 [/SUP], Eugenia Mylona[SUP] 1 [/SUP], Costas Papaloukas[SUP] 2 [/SUP], Angelos Liontos[SUP] 3 [/SUP], Dimitrios Biros[SUP] 3 [/SUP], Orestis I Milionis[SUP] 3 [/SUP], Chris Kyriakopoulos[SUP] 4 [/SUP], Kostantinos Kostikas[SUP] 4 [/SUP], Haralampos Milionis[SUP] 3 [/SUP], Dimitrios I Fotiadis[SUP] 5 [/SUP]
Affiliations
- PMID: 35007991
- DOI: 10.1016/j.compbiomed.2021.105176
Abstract
The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.
Keywords: COVID-19; Dynamic Bayesian Networks (DBNs); ICU admission; Mortality; Self-Organizing Maps (SOMs).
