51
|
Carneiro ICR, Feronato SG, Silveira GF, Chiavegatto Filho ADP, dos Santos HG. Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13522. [PMID: 36294103 PMCID: PMC9603349 DOI: 10.3390/ijerph192013522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/04/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 has been widely explored in relation to its symptoms, outcomes, and risk profiles for the severe form of the disease. Our aim was to identify clusters of pregnant and postpartum women with severe acute respiratory syndrome (SARS) due to COVID-19 by analyzing data available in the Influenza Epidemiological Surveillance Information System of Brazil (SIVEP-Gripe) between March 2020 and August 2021. The study's population comprised 16,409 women aged between 10 and 49 years old. Multiple correspondence analyses were performed to summarize information from 28 variables related to symptoms, comorbidities, and hospital characteristics into a set of continuous principal components (PCs). The population was segmented into three clusters based on an agglomerative hierarchical cluster analysis applied to the first 10 PCs. Cluster 1 had a higher frequency of younger women without comorbidities and with flu-like symptoms; cluster 2 was represented by women who reported mainly ageusia and anosmia; cluster 3 grouped older women with the highest frequencies of comorbidities and poor outcomes. The defined clusters revealed different levels of disease severity, which can contribute to the initial risk assessment of the patient, assisting the referral of these women to health services with an appropriate level of complexity.
Collapse
|
52
|
Silverberg JI, Zyskind I, Naiditch H, Zimmerman J, Glatt AE, Pinter A, Theel ES, Joyner MJ, Hill DA, Lieberman MR, Bigajer E, Stok D, Frank E, Rosenberg AZ. Predictors of chronic COVID-19 symptoms in a community-based cohort of adults. PLoS One 2022; 17:e0271310. [PMID: 35925904 PMCID: PMC9352033 DOI: 10.1371/journal.pone.0271310] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/27/2022] [Indexed: 11/18/2022] Open
Abstract
Background COVID-19 can cause some individuals to experience chronic symptoms. Rates and predictors of chronic COVID-19 symptoms are not fully elucidated. Objective To examine occurrence and patterns of post-acute sequelae of SARS-CoV2 infection (PASC) symptomatology and their relationship with demographics, acute COVID-19 symptoms and anti-SARS-CoV-2 IgG antibody responses. Methods A multi-stage observational study was performed of adults (≥18 years) from 5 US states. Participants completed two rounds of electronic surveys (May-July 2020; April-May 2021) and underwent testing to anti-SARS-CoV-2 nucleocapsid protein IgG antibody testing. Latent Class Analysis was used to identify clusters of chronic COVID-19 symptoms. Results Overall, 390 adults (median [25%ile, 75%ile] age: 42 [31, 54] years) with positive SARS-CoV-2 antibodies completed the follow-up survey; 92 (24.7%) had ≥1 chronic COVID-19 symptom, with 11-month median duration of persistent symptoms (range: 1–12 months). The most common chronic COVID-19 symptoms were fatigue (11.3%), change in smell (9.5%) or taste (5.6%), muscle or joint aches (5.4%) and weakness (4.6%). There were significantly higher proportions of ≥1 persistent COVID-19 symptom (31.5% vs. 18.6%; Chi-square, P = 0.004), and particularly fatigue (15.8% vs. 7.3%, P = 0.008) and headaches (5.4% vs. 1.0%, P = 0.011) in females compared to males. Chronic COVID-19 symptoms were also increased in individuals with ≥6 acute COVID-19 symptoms, Latent class analysis revealed 4 classes of symptoms. Latent class-1 (change of smell and taste) was associated with lower anti-SARS-CoV-2 antibody levels; class-2 and 3 (multiple chronic symptoms) were associated with higher anti-SARS-CoV-2 antibody levels and more severe acute COVID-19 infection. Limitations Ambulatory cohort with less severe acute disease. Conclusion Individuals with SARS-CoV-2 infection commonly experience chronic symptoms, most commonly fatigue, changes in smell or taste and muscle/joint aches. Female sex, severity of acute COVID-19 infection, and higher anti-SARS-CoV-2 IgG levels were associated with the highest risk of having chronic COVID-19 symptoms.
Collapse
Affiliation(s)
- Jonathan I Silverberg
- Department of Dermatology, George Washington University School of Medicine and Health Sciences, Washington, DC, United States of America
| | - Israel Zyskind
- Department of Pediatrics, NYU Langone Medical Center, New York, NY, United States of America
- Maimonides Medical Center, Brooklyn, NY, United States of America
| | - Hiam Naiditch
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Jason Zimmerman
- Maimonides Medical Center, Brooklyn, NY, United States of America
| | - Aaron E Glatt
- Department of Medicine, Mount Sinai South Nassau and the Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Abraham Pinter
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, United States of America
| | - Elitza S Theel
- Division of Clinical Microbiology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States of America
| | - Michael J Joyner
- Department of Anesthesiology & Perioperative Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - D Ashley Hill
- ResourcePath, Sterling, VA, United States of America
| | - Miriam R Lieberman
- Department of Dermatology, State University of New York Downstate Medical Center, New York, NY, United States of America
| | - Elliot Bigajer
- Division of Gastroenterology, Department of Medicine, Brookdale University Hospital and Medical Center, Brooklyn, NY, United States of America
| | - Daniel Stok
- Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Elliot Frank
- Division of Infectious Diseases, Department of Medicine, Jersey Shore University Medical Center, Neptune, NJ, United States of America
- The Hackensack Meridian School of Medicine, Clifton, New Jersey, United States of America
| | - Avi Z Rosenberg
- Department of Pathology, Johns Hopkins University, Baltimore, MD, United States of America
| |
Collapse
|
53
|
Ahamed J, Laurence J. Long COVID endotheliopathy: hypothesized mechanisms and potential therapeutic approaches. J Clin Invest 2022; 132:e161167. [PMID: 35912863 PMCID: PMC9337829 DOI: 10.1172/jci161167] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
SARS-CoV-2-infected individuals may suffer a multi-organ system disorder known as "long COVID" or post-acute sequelae of SARS-CoV-2 infection (PASC). There are no standard treatments, the pathophysiology is unknown, and incidence varies by clinical phenotype. Acute COVID-19 correlates with biomarkers of systemic inflammation, hypercoagulability, and comorbidities that are less prominent in PASC. Macrovessel thrombosis, a hallmark of acute COVID-19, is less frequent in PASC. Female sex at birth is associated with reduced risk for acute COVID-19 progression, but with increased risk of PASC. Persistent microvascular endotheliopathy associated with cryptic SARS-CoV-2 tissue reservoirs has been implicated in PASC pathology. Autoantibodies, localized inflammation, and reactivation of latent pathogens may also be involved, potentially leading to microvascular thrombosis, as documented in multiple PASC tissues. Diagnostic assays illuminating possible therapeutic targets are discussed.
Collapse
Affiliation(s)
- Jasimuddin Ahamed
- Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Jeffrey Laurence
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, New York, USA
| |
Collapse
|
54
|
Reese JT, Blau H, Bergquist T, Loomba JJ, Callahan T, Laraway B, Antonescu C, Casiraghi E, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.05.24.22275398. [PMID: 35665012 PMCID: PMC9164456 DOI: 10.1101/2022.05.24.22275398] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
Collapse
|
55
|
The Multifaceted Manifestations of Multisystem Inflammatory Syndrome during the SARS-CoV-2 Pandemic. Pathogens 2022; 11:pathogens11050556. [PMID: 35631077 PMCID: PMC9143280 DOI: 10.3390/pathogens11050556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/02/2022] [Accepted: 05/05/2022] [Indexed: 12/12/2022] Open
Abstract
The novel coronavirus SARS-CoV-2, which has similarities to the 2002–2003 severe acute respiratory syndrome coronavirus known as SARS-CoV-1, causes the infectious disease designated COVID-19 by the World Health Organization (Coronavirus Disease 2019). Although the first reports indicated that activity of the virus is centered in the lungs, it was soon acknowledged that SARS-CoV-2 causes a multisystem disease. Indeed, this new pathogen causes a variety of syndromes, including asymptomatic disease; mild disease; moderate disease; a severe form that requires hospitalization, intensive care, and mechanical ventilation; multisystem inflammatory disease; and a condition called long COVID or postacute sequelae of SARS-CoV-2 infection. Some of these syndromes resemble previously described disorders, including those with no confirmed etiology, such as Kawasaki disease. After recognition of a distinct multisystem inflammatory syndrome in children, followed by a similar syndrome in adults, various multisystem syndromes occurring during the pandemic associated or related to SARS-CoV-2 began to be identified. A typical pattern of cytokine and chemokine dysregulation occurs in these complex syndromes; however, the disorders have distinct immunological determinants that may help to differentiate them. This review discusses the origins of the different trajectories of the inflammatory syndromes related to SARS-CoV-2 infection.
Collapse
|