TY - JOUR
T1 - Identification of Distinct Clinical Subphenotypes in Critically Ill Patients With COVID-19
AU - STOP-COVID Investigators
AU - Vasquez, Charles R.
AU - Gupta, Shruti
AU - Miano, Todd A.
AU - Roche, Meaghan
AU - Hsu, Jesse
AU - Yang, Wei
AU - Holena, Daniel N.
AU - Reilly, John P.
AU - Schrauben, Sarah J.
AU - Leaf, David E.
AU - Shashaty, Michael G.S.
AU - Walther, Carl P.
AU - Anumudu, Samaya J.
AU - Arunthamakun, Justin
AU - Kopecky, Kathleen F.
AU - Milligan, Gregory P.
AU - McCullough, Peter A.
AU - Nguyen, Thuy Duyen
AU - Shaefi, Shahzad
AU - Krajewski, Megan L.
AU - Shankar, Sidharth
AU - Pannu, Ameeka
AU - Valencia, Juan D.
AU - Waikar, Sushrut S.
AU - Kibbelaar, Zoe A.
AU - Athavale, Ambarish M.
AU - Hart, Peter
AU - Upadhyay, Shristi
AU - Vohra, Ishaan
AU - Oyintayo, Ajiboye
AU - Green, Adam
AU - Rachoin, Jean Sebastien
AU - Schorr, Christa A.
AU - Shea, Lisa
AU - Edmonston, Daniel L.
AU - Mosher, Christopher L.
AU - Shehata, Alexandre M.
AU - Cohen, Zaza
AU - Rose, Keith M.
AU - Chan, Lili
AU - Mathews, Kusum S.
AU - Coca, Steven G.
AU - Altman, Deena R.
AU - Saha, Aparna
AU - Soh, Howard
AU - Bose, Sonali
AU - Nadkarni, Girish N.
AU - Pattharanitima, Pattharawin
AU - Gallagher, Emily J.
AU - Cangialosi, Peter
N1 - Publisher Copyright:
© 2021 American College of Chest Physicians
PY - 2021/9
Y1 - 2021/9
N2 - Background: Subphenotypes have been identified in patients with sepsis and ARDS and are associated with different outcomes and responses to therapies. Research Question: Can unique subphenotypes be identified among critically ill patients with COVID-19? Study Design and Methods: Using data from a multicenter cohort study that enrolled critically ill patients with COVID-19 from 67 hospitals across the United States, we randomly divided centers into discovery and replication cohorts. We used latent class analysis independently in each cohort to identify subphenotypes based on clinical and laboratory variables. We then analyzed the associations of subphenotypes with 28-day mortality. Results: Latent class analysis identified four subphenotypes (SP) with consistent characteristics across the discovery (45 centers; n = 2,188) and replication (22 centers; n = 1,112) cohorts. SP1 was characterized by shock, acidemia, and multiorgan dysfunction, including acute kidney injury treated with renal replacement therapy. SP2 was characterized by high C-reactive protein, early need for mechanical ventilation, and the highest rate of ARDS. SP3 showed the highest burden of chronic diseases, whereas SP4 demonstrated limited chronic disease burden and mild physiologic abnormalities. Twenty-eight-day mortality in the discovery cohort ranged from 20.6% (SP4) to 52.9% (SP1). Mortality across subphenotypes remained different after adjustment for demographics, comorbidities, organ dysfunction and illness severity, regional and hospital factors. Compared with SP4, the relative risks were as follows: SP1, 1.67 (95% CI, 1.36-2.03); SP2, 1.39 (95% CI, 1.17-1.65); and SP3, 1.39 (95% CI, 1.15-1.67). Findings were similar in the replication cohort. Interpretation: We identified four subphenotypes of COVID-19 critical illness with distinct patterns of clinical and laboratory characteristics, comorbidity burden, and mortality.
AB - Background: Subphenotypes have been identified in patients with sepsis and ARDS and are associated with different outcomes and responses to therapies. Research Question: Can unique subphenotypes be identified among critically ill patients with COVID-19? Study Design and Methods: Using data from a multicenter cohort study that enrolled critically ill patients with COVID-19 from 67 hospitals across the United States, we randomly divided centers into discovery and replication cohorts. We used latent class analysis independently in each cohort to identify subphenotypes based on clinical and laboratory variables. We then analyzed the associations of subphenotypes with 28-day mortality. Results: Latent class analysis identified four subphenotypes (SP) with consistent characteristics across the discovery (45 centers; n = 2,188) and replication (22 centers; n = 1,112) cohorts. SP1 was characterized by shock, acidemia, and multiorgan dysfunction, including acute kidney injury treated with renal replacement therapy. SP2 was characterized by high C-reactive protein, early need for mechanical ventilation, and the highest rate of ARDS. SP3 showed the highest burden of chronic diseases, whereas SP4 demonstrated limited chronic disease burden and mild physiologic abnormalities. Twenty-eight-day mortality in the discovery cohort ranged from 20.6% (SP4) to 52.9% (SP1). Mortality across subphenotypes remained different after adjustment for demographics, comorbidities, organ dysfunction and illness severity, regional and hospital factors. Compared with SP4, the relative risks were as follows: SP1, 1.67 (95% CI, 1.36-2.03); SP2, 1.39 (95% CI, 1.17-1.65); and SP3, 1.39 (95% CI, 1.15-1.67). Findings were similar in the replication cohort. Interpretation: We identified four subphenotypes of COVID-19 critical illness with distinct patterns of clinical and laboratory characteristics, comorbidity burden, and mortality.
KW - COVID-19
KW - coronavirus
KW - latent class analysis
KW - phenotypes
KW - subphenotypes
UR - http://www.scopus.com/inward/record.url?scp=85111778652&partnerID=8YFLogxK
U2 - 10.1016/j.chest.2021.04.062
DO - 10.1016/j.chest.2021.04.062
M3 - Article
C2 - 33964301
AN - SCOPUS:85111778652
SN - 0012-3692
VL - 160
SP - 929
EP - 943
JO - Chest
JF - Chest
IS - 3
ER -