Wonsuk Oh1,2,3,4, Pushkala Jayaraman2,3,4, Ashwin S Sawant5, Lili Chan4,5,6,7, Matthew A Levin2,3,8, Alexander W Charney2,9,10, Patricia Kovatch2,11, Benjamin S Glicksberg1,2,3, Girish N Nadkarni1,2,3,4,5,6,7. 1. Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 3. Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 4. Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 5. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 6. Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 7. Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 8. Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 9. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 10. Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 11. Department of Pharmacological Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Abstract
OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes. MATERIALS AND METHODS: We analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward's minimum variance linkage. RESULTS: We identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation. CONCLUSION: We utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches.
OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes. MATERIALS AND METHODS: We analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward's minimum variance linkage. RESULTS: We identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation. CONCLUSION: We utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches.
Authors: E Rivers; B Nguyen; S Havstad; J Ressler; A Muzzin; B Knoblich; E Peterson; M Tomlanovich Journal: N Engl J Med Date: 2001-11-08 Impact factor: 91.245
Authors: Lindsey R Baden; Hana M El Sahly; Brandon Essink; Karen Kotloff; Sharon Frey; Rick Novak; David Diemert; Stephen A Spector; Nadine Rouphael; C Buddy Creech; John McGettigan; Shishir Khetan; Nathan Segall; Joel Solis; Adam Brosz; Carlos Fierro; Howard Schwartz; Kathleen Neuzil; Larry Corey; Peter Gilbert; Holly Janes; Dean Follmann; Mary Marovich; John Mascola; Laura Polakowski; Julie Ledgerwood; Barney S Graham; Hamilton Bennett; Rolando Pajon; Conor Knightly; Brett Leav; Weiping Deng; Honghong Zhou; Shu Han; Melanie Ivarsson; Jacqueline Miller; Tal Zaks Journal: N Engl J Med Date: 2020-12-30 Impact factor: 91.245
Authors: Sulaiman Somani; Adam J Russak; Akhil Vaid; Jessica K De Freitas; Fayzan F Chaudhry; Ishan Paranjpe; Kipp W Johnson; Samuel J Lee; Riccardo Miotto; Felix Richter; Shan Zhao; Noam D Beckmann; Nidhi Naik; Arash Kia; Prem Timsina; Anuradha Lala; Manish Paranjpe; Eddye Golden; Matteo Danieletto; Manbir Singh; Dara Meyer; Paul F O'Reilly; Laura Huckins; Patricia Kovatch; Joseph Finkelstein; Robert M Freeman; Edgar Argulian; Andrew Kasarskis; Bethany Percha; Judith A Aberg; Emilia Bagiella; Carol R Horowitz; Barbara Murphy; Eric J Nestler; Eric E Schadt; Judy H Cho; Carlos Cordon-Cardo; Valentin Fuster; Dennis S Charney; David L Reich; Erwin P Bottinger; Matthew A Levin; Jagat Narula; Zahi A Fayad; Allan C Just; Alexander W Charney; Girish N Nadkarni; Benjamin S Glicksberg Journal: J Med Internet Res Date: 2020-11-06 Impact factor: 5.428