Observational studyBMC infectious diseases2025
Impact of long COVID phenotypes on quality of life following symptomatic omicron infection in Brazil: a machine learning analysis.
Observational study in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
51 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to identify phenotypes of long COVID symptoms in adults following Omicron infection and assess their association with health-related quality of life (HRQoL).
methodsWe analyzed three prospective observational studies in Brazil, enrolling adult patients who sought care for symptomatic Omicron infection between December 2021 and March 2023. The infection was confirmed by either an antigen test or reverse transcriptase polymerase chain reaction. Long COVID symptoms were assessed three months after enrollment through structured interviews. Phenotypes of Long COVID-19 were identified using a machine learning-based clustering approach. Exploratory analyses were conducted to examine predisposing factors and health-related quality of life utilities, measured by EQ-5D-3 L, associated with each phenotype.
resultsA total of 2,989 patients were analyzed (39% women, median age 41 years, and 96% had completed the primary series of COVID-19 vaccination). Long COVID symptoms at three months were reported by 1,155 (38.6%) patients. Three phenotypes were identified: cluster 1 (n = 459 [39.7%]), characterized by a median of three symptoms (IQR, 2-5) with memory loss (80.4%), concentration problems (38.3%) and fatigue (35.7%) being most common; cluster 2 (n = 549 [47.5%]), characterized by a median of two symptoms (IQR, 1-4) with fatigue (43.7%), other symptoms (42.3%), and cough (20.6%) being most common; and cluster 3 (n = 147, 12.7%), characterized by a higher number of symptoms (median, 8; IQR, 7-10), with fatigue (89.9%), memory loss (88.4%), and anxiety (64.6%) as the most common. The mean EQ-5D-3 L utility at 3 months was 0.75 for cluster 1, 0.73 for cluster 2, and 0.59 for cluster 3 (p < 0.001). After adjusted regression analysis, cluster 3 was independently associated with the lowest EQ-5D-3 L utilities (mean difference, -0.21; 95%CI, -0.24 to -0.18; p < 0.001).
conclusionsDistinct phenotypic presentations of Long COVID following Omicron infection in Brazil were identified, with significant differences in quality of life. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.