| Literature DB >> 35794549 |
Ekaterina Pazukhina1,2, Margarita Andreeva3, Ekaterina Spiridonova3, Polina Bobkova3, Anastasia Shikhaleva3, Yasmin El-Taravi3, Mikhail Rumyantsev3, Aysylu Gamirova3, Ismail M Osmanov4,5, Daniel Munblit6,7,8, Anastasiia Bairashevskaia3, Polina Petrova3, Dina Baimukhambetova3, Maria Pikuza3, Elina Abdeeva3, Yulia Filippova3, Salima Deunezhewa3, Nikita Nekliudov3, Polina Bugaeva3, Nikolay Bulanov9, Sergey Avdeev10, Valentina Kapustina11, Alla Guekht12,4, Audrey DunnGalvin3,13, Pasquale Comberiati14, Diego G Peroni14, Christian Apfelbacher15, Jon Genuneit16, Luis Felipe Reyes17,18, Caroline L H Brackel19,20, Victor Fomin21, Andrey A Svistunov21, Peter Timashev22, Lyudmila Mazankova23, Alexandra Miroshina5, Elmira Samitova23,5, Svetlana Borzakova4,24, Elena Bondarenko3, Anatoliy A Korsunskiy3, Gail Carson25, Louise Sigfrid25, Janet T Scott26, Matthew Greenhawt27, Danilo Buonsenso28,29,30, Malcolm G Semple31,32, John O Warner33, Piero Olliaro25, Dale M Needham34,35,36, Petr Glybochko21, Denis Butnaru21.
Abstract
BACKGROUND: Previous studies assessing the prevalence of COVID-19 sequelae in adults and children were performed in the absence of an agreed definition. We investigated prevalence of post-COVID-19 condition (PCC) (WHO definition), at 6- and 12-months follow-up, amongst previously hospitalised adults and children and assessed risk factors.Entities:
Keywords: Adults; COVID-19; COVID-19 sequelae; Children; Long COVID; PASC; Post-COVID-19 condition; Post-acute sequelae of SARS-CoV-2 infection; Prevalence; Risk factor
Mesh:
Year: 2022 PMID: 35794549 PMCID: PMC9257572 DOI: 10.1186/s12916-022-02448-4
Source DB: PubMed Journal: BMC Med ISSN: 1741-7015 Impact factor: 11.150
Fig. 1Flow diagram of patients admitted with PCR-confirmed COVID-19 to Sechenov University Hospital Network (adults) and Z.A. Bashlyaeva Children’s Municipal Clinical Hospital (children)
Demographic characteristics of adults admitted to the Sechenov University Hospital Network and children admitted to the Z.A. Bashlyaeva Children’s Municipal Clinical Hospital. Data are n (%) or median (IQR) excluding missing values. ICU, intensive care unit
| Variable | Adults | Children |
|---|---|---|
| Number of participants | 1013 | 360 |
| Median age (IQR) at hospital admission, years | 56.8 (47.0–65.8) | 9.5 (2.4–14.8) |
| Median time from the hospital discharge to the 1st follow-up point (IQR), days | 215 (196–235) | 255 (223–270) |
| Median time from the hospital discharge to the 2nd follow-up point (IQR), days | 383 (376–390) | 367 (351–379) |
| Gender (female) | 513/1013 (51%) | 186/360 (52%) |
| Severe COVID-19 (requiring non-invasive ventilation or invasive ventilation or ICU) | 27/1013 (3%) | 12/360 (3%) |
| Heart diseases | 502/1013 (49%) | 12/360 (3%) |
| 198/1013 (20%) | N/A | |
| 458/1013 (45%) | N/A | |
| 51/1013 (5%) | N/A | |
| Respiratory diseases (not including asthma) | 79/1013 (8%) | 5/360 (1%) |
| Allergic respiratory diseases | N/A | 29/360 (8%) |
| 48/1013 (5%) | 5/360 (1%) | |
| N/A | 26/360 (7%) | |
| Kidney disease | 51/1013 (5%) | 6/360 (2%) |
| Overweight and obesity (as defined by clinical staff) | 198/1013 (20%) | 10/360 (3%) |
| Neurological disorder | 53/1013 (5%) | 11/360 (3%) |
| Malignancy | 42/1013 (4%) | 0/360 (0%) |
| Haematological conditions | 12/1013 (1%) | 9/360 (3%) |
| Diabetes Mellitus | 148/1013 (15%) | 0/360 (0%) |
| Rheumatologic disorder | 27/1013 (3%) | 2/360 (1%) |
| Tuberculosis | 1/1013 (0%) | 3/360 (1%) |
| Malnutrition | 1/1013 (0%) | 10/360 (3%) |
| Intestinal (gut) problems | N/A | 25/360 (7%) |
Fig. 2Forest plots demonstrating the prevalence of post-COVID-19 condition manifestations in adults and children 6 and 12 months after hospital discharge. Sixth-month prevalence is coloured in red, and 12-month prevalence is coloured in blue. Estimates of the prevalence 95% confidence intervals were calculated using the bootstrapping method
Fig. 3Interrelations between the post-COVID-19 condition manifestations in adults and children 6 and 12 months since hospital discharge. Bubble diameter is proportional to the proportion of individuals with the symptom category reported. Line thickness is proportional to the number of individuals with the coexisting manifestations. Cardiovascular, CRD; dermatological, DRM; fatigue, FTG; gastrointestinal, GST; musculoskeletal, MSC; neurocognitive, NRL; respiratory, RSP; sensory, SNS; sleep, SLP
Fig. 4A Radial plots representing post-COVID-19 condition phenotypes in adults at 6 months after discharge and 12 months after discharge. Manifestations are shown for each patient; each segment represents a single patient. Thick black lines are used to distinct phenotypes. Cardiovascular, CRD; dermatological, DRM; fatigue, FTG; gastrointestinal, GST; musculoskeletal, MSC; neurocognitive, NRL; respiratory, RSP; sensory, SNS; sleep, SLP. B Radial plots representing post-COVID-19 condition phenotypes in children at 6 months after discharge and 12 months after discharge. Manifestations are shown for each patient; each segment represents a single patient. Thick black lines are used to distinct phenotypes. Cardiovascular, CRD; dermatological, DRM; fatigue, FTG; gastrointestinal, GST; musculoskeletal, MSC; neurocognitive, NRL; respiratory, RSP; sensory, SNS; sleep, SLP
Fig. 5A Multivariable logistic regression model demonstrating risk factors associated with post-COVID-19 condition in adults at 6-month follow-up. Odds ratios and 95% CIs are presented. B Multivariable logistic regression model demonstrating risk factors associated with post-COVID-19 condition in adults at 12-month follow-up. Odds ratios and 95% CIs are presented. C Multivariable logistic regression model demonstrating risk factors associated with post-COVID-19 condition in children at 6-month follow-up. Odds ratios and 95% CIs are presented. D Multivariable logistic regression model demonstrating risk factors associated with post-COVID-19 condition in children at 12-month follow-up. Odds ratios and 95% CIs are presented