Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis
Authors: Siepel Sander , Dam Tariq A. , Fleuren Lucas M. , Gommers Diederik , Cremer Olaf L. , Bosman Rob J. , Rigter Sander , Wils Evert-Jan , Frenzel Tim , Dongelmans Dave A. , de Jong Remko , Peters Marco , Kamps Marlijn J.A , Ramnarain Dharmanand , Nowitzky Ralph , Nooteboom Fleur G.C.A. , de Ruijter Wouter , Urlings-Strop Louise C. , Smit Ellen G.M. , Mehagnoul-Schipper D. Jannet , Dormans Tom , de Jager Cornelis P.C. , Hendriks Stefaan H.A. , Achterberg Sefanja , Oostdijk Evelien , Reidinga Auke C. , Festen-Spanjer Barbara , Brunnekreef Gert B. , Cornet Alexander D. , Tempel Walter van den , Boelens Age D. , Koetsier Peter , Lens Judith , Faber Harald J. , Karakus A. , Entjes Robert , de Jong Paul , Rettig Thijs C.D. , Arbous Sesmu , Vonk Sebastiaan J.J. , Machado Tomas , Herter Willem E. , Girbes Armand R.J. , Hoogendoorn Mark , Thoral Patrick J. , Elbers Paul W.G. , Bennis Frank C. 
Published: February 2022

Abstract
Background: Identification of distinct clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment facilitating more personalized treatment. However, previous attempts did not take into account temporal dynamics of the disease. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.Methods: We used highly granular data from 3202 adult critically ill COVID patients in the multicenter 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 based on relevance and availability. Twenty-one consecutive datasets were created that each covered 24 hours of ICU data for each day of ICU treatment up until day 21. After aggregation and multiple imputation of the temporal data, clinical phenotypes in each dataset were identified by performing multiple cluster analyses. Clinical phenotypes were identified by aggregating values from all patients per cluster. 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 critically ill COVID-19 patients with a registered ICU mortality outcome. Forty-one parameters were chosen for the cluster analysis. On admission, both a mild and a more 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 strongly 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.