Predictors of the post-COVID condition following mild SARS-CoV-2 infection

Who develops long COVID? Longitudinal pre-pandemic predictors of long COVID and symptom clusters in a representative Dutch population

OBJECTIVES

Prior studies show that long COVID has a heterogeneous presentation. Whether specific risk factors are related to subclusters of long COVID remains unknown. This study aimed to determine pre-pandemic predictors of long COVID and symptom clustering.

METHODS

A total of 3,022 participants of a panel representative of the Dutch population completed an online survey about long COVID symptoms. Data was merged into 2018/2019 panel data covering sociodemographic, medical, and psychosocial predictors. A total of 415 participants were classified as having long COVID. K-means clustering was used to identify patient clusters. Multivariate and lasso regression was used to identify relevant predictors compared to a COVID-19 positive control group.

RESULTS

Predictors of long-term COVID included older age, Western ethnicity, BMI, chronic disease, COVID-19 reinfections, severity, and symptoms, lower self-esteem, and higher positive affect (AUC = 0.79, 95%CI 0.73-0.86). Four clusters were identified: a low and a high symptom severity cluster, a smell-taste and respiratory symptoms cluster, and a neuro-cognitive, psychosocial, and inflammatory symptom cluster. Predictors for the different clusters included regular health complaints, healthcare use, fear of COVID-19, anxiety, depressive symptoms, and neuroticism.

CONCLUSIONS

A combination of sociodemographic, medical, and psychosocial factors predicted long COVID. Heterogenous symptom clusters suggest that there are different phenotypes of long COVID-19 presentation.

Likelihood of Post-COVID Condition in people with hybrid immunity; data from the German National Cohort (NAKO)

OBJECTIVES

The risk of Post-COVID-19 condition (PCC) under hybrid immunity remains unclear.

METHODS

Using data from the German National Cohort (NAKO Gesundheitsstudie), we investigated risk factors for self-reported post-infection symptoms (any PCC is defined as having at least one symptom, and high symptom burden PCC as having nine or more symptoms).

RESULTS

Sixty percent of 109,707 participants reported at least one previous SARS-CoV-2 infection; 35% reported having had any symptoms 4-12 months after infection; among them 23% reported nine or more symptoms. Individuals, who did not develop PCC after their first infection, had a strongly reduced risk for PCC after their second infection (50%) and a temporary risk reduction, which waned over nine months after the preceding infection. The risk of developing PCC strongly depended on the virus variant. Within variants, there was no effect of the number of preceding vaccinations, apart from a strong protection by the fourth vaccination compared to three vaccinations for the Omicron variant (odds ratio=0.52; 95% confidence interval 0.45-0.61).

CONCLUSIONS

Previous infections without PCC and a fourth vaccination were associated with a lower risk of PCC after a new infection, indicating diminished risk under hybrid immunity. The two components of risk reduction after a preceding infection suggest different immunological mechanisms.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Higher level of physical activity reduces mental and neurological symptoms during and two years after COVID-19 infection in young women

Previous studies found that regular physical activity (PA) can lower the risk of SARS-CoV-2 (COVID-19) infection and post-COVID-19 condition (PCC), yet its specific effects in young women have not yet been investigated. Thus, we aimed to examine whether regular physical activity reduces the number of symptoms during and after COVID-19 infection among young women aged between 18 and 34 (N = 802), in which the confounding effect of other morbidities could be excluded. The average time since infection was 23.5 months. Participants were classified into low, moderate, and high PA categories based on the reported minutes per week of moderate and vigorous PA. Using the Post-COVID-19 Case Report Form, 50 different symptoms were assessed. Although regular PA did not decrease the prevalence of COVID-19 infection and PCC but significantly reduced the number of mental and neurological symptoms both in acute COVID-19 and PCC. Importantly, the high level of PA had a greater impact on health improvements. In addition, the rate of reinfection decreased with an increased level of PA. In conclusion, a higher level of regular PA can reduce the risk of reinfection and the number of mental and neurological symptoms in PCC underlying the importance of regular PA, even in this and likely other viral disease conditions.

Symptom trajectories of post-COVID sequelae in patients with acute Delta or Omicron infection in Bergen, Norway

Introduction

A substantial proportion of the over 700 million COVID-19 cases world-wide experience long-term symptoms. The objectives of this study were to compare symptom trajectories and risk factors for post-COVID-19 condition after Delta and Omicron infection.

Methods

This study consecutively recruited patients with SARS-CoV-2 infection from November 2021 to March 2022. We recorded demographics, comorbidities, vaccination status, sick leave, and 18 symptoms during acute infection and after 4 months. The primary outcome measures were symptoms during acute infection and after 4 months. Secondary outcome measures were work and school absenteeism.

Results

We followed a cohort of 1,374 non-hospitalized COVID-19 patients in Bergen, Norway, at three time points. The median age was 39.8 years and 11% were children <16 years. Common acute upper respiratory symptoms waned during follow-up. Fatigue remained common from acute infection (40%) until after 4 months (37%). Four months post-infection, patients reported increased frequencies of dyspnea (from 15% during acute illness to 25% at 4 months, p < 0.001), cognitive symptoms (from 9 to 32%, p < 0.001) and depression (from 1 to 17%, p < 0.001). Patients infected with Omicron reported less dyspnea (22% versus 27%, p = 0.046) and smell/taste problems (5% versus 19%, p < 0.001) at 4 months follow-up than those with Delta infection. Comorbidities and female sex were risk factors for persistent dyspnea and cognitive symptoms. Ten percent reported sick leave after acute illness, and vaccination reduced the risk of absenteeism (adjusted risk ratio: 0.36, 95% confidence interval: 0.15, 0.72, p = 0.008).

Conclusion

At 4 months, home-isolated patients infected with Omicron reported overall comparable symptom burden, but less dyspnea and smell/taste problems than Delta infected patients. Several acute symptoms waned during follow-up. It is worrying that dyspnea, neurocognitive symptoms, and particularly depression, increased significantly during the first 4 months after acute infection. Previous vaccination was protective against prolonged sick leave.

Prevalence, predictors, and patient-reported outcomes of long COVID in hospitalized and non-hospitalized patients from the city of São Paulo, Brazil

Background

Robust data comparing long COVID in hospitalized and non-hospitalized patients in middle-income countries are limited.

Methods

A retrospective cohort study was conducted in Brazil, including hospitalized and non-hospitalized patients. Long COVID was diagnosed at 90-day follow-up using WHO criteria. Demographic and clinical information, including the depression screening scale (PHQ-2) at day 30, was compared between the groups. If the PHQ-2 score is 3 or greater, major depressive disorder is likely. Logistic regression analysis identified predictors and protective factors for long COVID.

Results

A total of 291 hospitalized and 1,118 non-hospitalized patients with COVID-19 were included. The prevalence of long COVID was 47.1% and 49.5%, respectively. Multivariable logistic regression showed female sex (odds ratio [OR] = 4.50, 95% confidence interval (CI) 2.51-8.37), hypertension (OR = 2.90, 95% CI 1.52-5.69), PHQ-2 > 3 (OR = 6.50, 95% CI 1.68-33.4) and corticosteroid use during hospital stay (OR = 2.43, 95% CI 1.20-5.04) as predictors of long COVID in hospitalized patients, while female sex (OR = 2.52, 95% CI 1.95-3.27) and PHQ-2 > 3 (OR = 3.88, 95% CI 2.52-6.16) were predictors in non-hospitalized patients.

Conclusion

Long COVID was prevalent in both groups. Positive depression screening at day 30 post-infection can predict long COVID. Early screening of depression helps health staff to identify patients at a higher risk of long COVID, allowing an early diagnosis of the condition.