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Study identifies risk factors and symptom clusters associated with long-term COVID

Study identifies risk factors and symptom clusters associated with long-term COVID

 


Recent research posted on medrex sib*Preprint server assessed long COVID-related risk factors.

Research suggests that some patients with coronavirus disease 2019 (COVID-19) develop post-COVID-19 syndrome (long-term COVID), a chronic fatigue state characterized by neuroimmune fatigue after exercise It has been. Fatigue, shortness of breath, brain fog, chest pain, cough, gastrointestinal symptoms, headache, and musculoskeletal pain are common symptoms.

Initially, long-lasting evidence of COVID emerged from patient-led studies, social media self-reports, and medical blogs. In the UK, the Office for National Statistics (ONS) estimates that her 13.7% of infected individuals have had her COVID for a long time. Risk factors associated with symptoms that persist beyond the acute phase include female gender, older age, asthma, pre-existing cardiopulmonary disease, and severity of COVID-19.

ZOE COVID-19 symptom tracking app is a promising tool for long-term research COVID-symptomsIt has been downloaded by over 4 million people and is recommended for daily symptom tracking. This dataset has helped identify predictors of hospitalization, symptom clusters, and vaccines in multiple COVID-19-related studies. Effectivenessand side effects.

Survey: Long Covid Risk Factors and Symptom Clusters: Analysis of UK Symptom Tracker App Data. Image credit: p.ill.i / Shutterstockstudy: Long Covid risk factors and symptom clusters: Analysis of UK symptom tracking app dataImage credit: p.ill.i / Shutterstock

About research

In the current study, researchers from Brighton and Sussex Medical School and the University of Sussex evaluated risk factors associated with long COVID and whether data from the ZOE symptom tracking app provide evidence for different long COVID subtypes. I decided what Users of the ZOE COVID-19 Symptom Tracker App receive daily messages asking them to record their symptoms. Data entered by users during app registration and daily inputs were used for analysis.

Participants were logged on for a minimum of 120 days overall, tested positive for SARS-CoV-2 between 1 July and 11 December 2020, and had a body mass index (BMI) between 15 and 55. between and had to log in within 7 days. of positive tests. Additionally, the sampled population was tested for selection bias against a reference sample that included people who logged on and tested positive for at least 120 days between 01 July 2020 and 1 January 2021.

The authors found a statistically significant proportion of negative health outcomes during the 12 to 15 weeks after the COVID-19 outbreak compared with the 2 to 12 weeks before the COVID-19 outbreak. defined as experiencing a state. A two-tailed proportion Z test was used for categorical data and a two-tailed Mann-Whitney U test was used for continuous data for univariate analysis of risk factors. In addition, we evaluated multiple predictors using logistic regression with a lasso penalty.

A multivariate model was run in two blocks. The first block included demographic variables and medical history (no symptom data), and the second block included his 0–8-week symptom score after a positive test. The authors utilized K-mode clustering, factor analysis, and hierarchical agglomerative clustering to make intercomparisons between methods and for his second aim of investigating evidence for long subtypes of COVID. We assessed how robust symptom clusters were.

findings

Researchers identified 4,040 app users after applying eligibility filters. Most app users were female (59.5%) and white (97.5%). Most of the participants lived in high-income areas. Her 13.6% of the sample met the criteria set for long COVID. Testing positive for SARS-CoV-2, 15.1% of the long COVID cohort were asymptomatic. A participant in a long-term cohort of COVID initially tested positive before he recovered from symptoms within 3-4 weeks.

Factor analysis heatmap showing load on symptom factorsFactor analysis heatmap showing load on symptom factors

Univariate analyzes revealed significant associations between COVID and female gender, hay fever, previous lung disease, asthma, vitamin D or other vitamin intake, and previous limited activity. rice field. Prolonged COVID was weakly associated with age and BMI. Symptoms 0–8 weeks post-infection strongly predicted her COVID in the long term. Olfactory problems and fatigue were strong predictors of her COVID long-term beyond weeks 4-6.

The authors observed a positive association between long-term COVID and pre-existing medical conditions for all subjects up to 70 years of age, but observed a negative association for subjects over 70 years of age. Did. Multivariate models run on demographics and medical history maintained for gender, limited activity, vitamin D intake, other intakes, and baseline health status. Except for the addition of her maximum symptom score during the first 2 weeks post-infection, the variables retained in the model with symptom data were the same as in the model without symptom data.

Conclusion

The authors identified female sex, pre-existing medical conditions, limited physical activity prior to COVID-19, and increased risk during COVID-19 as factors associated with an increased risk of progressing to long-term COVID-19 12 weeks after COVID-19. identified more symptoms of Her long-term COVID prediction accuracy was 69% at the onset of COVID-19 and 77% after her 8 weeks of symptoms, with the highest error rate for asymptomatic carriers.

Overall, this study found that gender, baseline health status, symptoms, and previous limited activity can predict long-term COVID in symptomatic COVID-19 patients with reasonable accuracy. In particular, symptom severity during the first 8 weeks of illness was the strongest predictor of prolonged COVID: there was no evidence to suggest multiple types of prolonged her COVID among patients.

*Important Notices

medrex sib We publish a non-peer-reviewed, preliminary scientific report and should not be taken as conclusive, to guide clinical practice/health-related actions, or to be treated as established information.

Sources

1/ https://Google.com/

2/ https://www.news-medical.net/news/20221117/Study-identifies-risk-factors-and-symptom-clusters-associated-with-long-COVID.aspx

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