tetano
Editor, Senior Moderator
J Intensive Care Med
. 2023 Feb 6;8850666231153393.
doi: 10.1177/08850666231153393. Online ahead of print.
Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis
Sander Siepel[SUP] 1 2 [/SUP], Tariq A Dam[SUP] 1 2 [/SUP], Lucas M Fleuren[SUP] 1 2 [/SUP], Armand R J Girbes[SUP] 2 [/SUP], Mark Hoogendoorn[SUP] 1 [/SUP], Patrick J Thoral[SUP] 2 [/SUP], Paul W G Elbers[SUP] 2 [/SUP], Frank C Bennis[SUP] 1 3 [/SUP]; Dutch ICU Data Sharing Collaborators
Affiliations
Abstract
Background: Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.
Methods: We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked.
Results: The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype.
Conclusions: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.
Keywords: clinical phenotype half-life; clinical phenotypes; clustering; coronavirus disease 2019; endotypes; intensive care; subphenotypes.
. 2023 Feb 6;8850666231153393.
doi: 10.1177/08850666231153393. Online ahead of print.
Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis
Sander Siepel[SUP] 1 2 [/SUP], Tariq A Dam[SUP] 1 2 [/SUP], Lucas M Fleuren[SUP] 1 2 [/SUP], Armand R J Girbes[SUP] 2 [/SUP], Mark Hoogendoorn[SUP] 1 [/SUP], Patrick J Thoral[SUP] 2 [/SUP], Paul W G Elbers[SUP] 2 [/SUP], Frank C Bennis[SUP] 1 3 [/SUP]; Dutch ICU Data Sharing Collaborators
Affiliations
- PMID: 36744415
- DOI: 10.1177/08850666231153393
Abstract
Background: Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.
Methods: We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked.
Results: The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype.
Conclusions: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.
Keywords: clinical phenotype half-life; clinical phenotypes; clustering; coronavirus disease 2019; endotypes; intensive care; subphenotypes.