Epidemiology, Symptomatology, and Risk Factors for Long COVID Symptoms: Population-Based, Multicenter Study

Prevalence, risk factors and characterisation of individuals with long COVID using Electronic Health Records in over 1.5 million COVID cases in England

OBJECTIVES

This study examines clinically confirmed long-COVID symptoms and diagnosis among individuals with COVID in England, aiming to understand prevalence and associated risk factors using electronic health records. To further understand long COVID, the study also explored differences in risks and symptom profiles in three subgroups: hospitalised, non-hospitalised, and untreated COVID cases.

METHODS

A population-based longitudinal cohort study was conducted using data from 1,554,040 individuals with confirmed SARS-CoV-2 infection via Clinical Practice Research Datalink. Descriptive statistics explored the prevalence of long COVID symptoms 12 weeks post-infection, and Cox regression models analysed the associated risk factors. Sensitivity analysis was conducted to test the impact of right-censoring data.

RESULTS

During an average 400-day follow-up, 7.4% of individuals with COVID had at least one long-COVID symptom after acute phase, yet only 0.5% had long-COVID diagnostic codes. The most common long-COVID symptoms included cough (17.7%), back pain (15.2%), stomach-ache (11.2%), headache (11.1%), and sore throat (10.0%). The same trend was observed in all three subgroups. Risk factors associated with long-COVID symptoms were female sex, non-white ethnicity, obesity, and pre-existing medical conditions like anxiety, depression, type II diabetes, and somatic symptom disorders.

CONCLUSIONS

This study is the first to investigate the prevalence and risk factors of clinically confirmed long-COVID in the general population. The findings could help clinicians identify higher risk individuals for timely intervention and allow decision-makers to more efficiently allocate resources for managing long-COVID.

Long-COVID symptom monitoring: Insights from a two-year telemedicine study

BACKGROUND

The diverse manifestations of Long-COVID have become increasingly important due to their significant impact on patients' lives. Telemedicine has emerged as an important tool for post COVID-19 follow-up. This study is part of a large cohort study involving COVID-positive patients monitored by the COD19 telemedicine platform operations center. We recontacted patients who were initially monitored from February 2020 to May 2020 to assess the presence of Long-COVID symptoms at a 2-year follow-up.

METHODS

We conducted interviews to evaluate Long-COVID symptoms at the 2-year mark and investigated whether patients had contracted a second COVID-19 infection between the 1-year and 2-year follow-ups, and recorded their vaccination status.

RESULTS

Out of 165 patients, 139 (84%) reported symptoms at the 1-year follow-up, while only 101 (61%) reported symptoms at the 2-year follow-up. Among patients with Long-COVID symptoms at the 2-year follow-up, the majority (80, 49%) had experienced Long-COVID at the 1-year follow-up, received the SARS-CoV-2 vaccine, and had not experienced a second infection between the two follow-ups. Both having Long-COVID at the 1-year follow-up and contracting a second infection were significant risk factors for presenting with Long-COVID at the 2-year follow-up.

CONCLUSIONS

To the best of our knowledge, this study stands out as one of the few that includes a 2-year follow-up on Long-COVID symptoms using telemedicine. Telemedicine has proven to be an effective and innovative tool for long-term patient monitoring, early diagnosis, and treatment. Telemedicine represents a significant future challenge for healthcare.

Post-COVID syndrome prevalence: a systematic review and meta-analysis

BACKGROUND

Since the Coronavirus disease 2019 (COVID-19) pandemic began, the number of individuals recovering from COVID-19 infection have increased. Post-COVID Syndrome, or PCS, which is defined as signs and symptoms that develop during or after infection in line with COVID-19, continue beyond 12 weeks, and are not explained by an alternative diagnosis, has also gained attention. We systematically reviewed and determined the pooled prevalence estimate of PCS worldwide based on published literature.

METHODS

Relevant articles from the Web of Science, Scopus, PubMed, Cochrane Library, and Ovid MEDLINE databases were screened using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-guided systematic search process. The included studies were in English, published from January 2020 to April 2024, had overall PCS prevalence as one of the outcomes studied, involved a human population with confirmed COVID-19 diagnosis and undergone assessment at 12 weeks post-COVID infection or beyond. As the primary outcome measured, the pooled prevalence of PCS was estimated from a meta-analysis of the PCS prevalence data extracted from individual studies, which was conducted via the random-effects model. This study has been registered on PROSPERO (CRD42023435280).

RESULTS

Forty eight studies met the eligibility criteria and were included in this review. 16 were accepted for meta-analysis to estimate the pooled prevalence for PCS worldwide, which was 41.79% (95% confidence interval [CI] 39.70-43.88%, I2 = 51%, p = 0.03). Based on different assessment or follow-up timepoints after acute COVID-19 infection, PCS prevalence estimated at ≥ 3rd, ≥ 6th, and ≥ 12th months timepoints were each 45.06% (95% CI: 41.25-48.87%), 41.30% (95% CI: 34.37-48.24%), and 41.32% (95% CI: 39.27-43.37%), respectively. Sex-stratified PCS prevalence was estimated at 47.23% (95% CI: 44.03-50.42%) in male and 52.77% (95% CI: 49.58-55.97%) in female. Based on continental regions, pooled PCS prevalence was estimated at 46.28% (95% CI: 39.53%-53.03%) in Europe, 46.29% (95% CI: 35.82%-56.77%) in America, 49.79% (95% CI: 30.05%-69.54%) in Asia, and 42.41% (95% CI: 0.00%-90.06%) in Australia.

CONCLUSION

The prevalence estimates in this meta-analysis could be used in further comprehensive studies on PCS, which might enable the development of better PCS management plans to reduce the effect of PCS on population health and the related economic burden.

Long COVID in Children, Adults, and Vulnerable Populations: A Comprehensive Overview for an Integrated Approach

Long COVID affects both children and adults, including subjects who experienced severe, mild, or even asymptomatic SARS-CoV-2 infection. We have provided a comprehensive overview of the incidence, clinical characteristics, risk factors, and outcomes of persistent COVID-19 symptoms in both children and adults, encompassing vulnerable populations, such as pregnant women and oncological patients. Our objective is to emphasize the critical significance of adopting an integrated approach for the early detection and appropriate management of long COVID. The incidence and severity of long COVID symptoms can have a significant impact on the quality of life of patients and the course of disease in the case of pre-existing pathologies. Particularly, in fragile and vulnerable patients, the presence of PASC is related to significantly worse survival, independent from pre-existing vulnerabilities and treatment. It is important try to achieve an early recognition and management. Various mechanisms are implicated, resulting in a wide range of clinical presentations. Understanding the specific mechanisms and risk factors involved in long COVID is crucial for tailoring effective interventions and support strategies. Management approaches involve comprehensive biopsychosocial assessments and treatment of symptoms and comorbidities, such as autonomic dysfunction, as well as multidisciplinary rehabilitation. The overall course of long COVID is one of gradual improvement, with recovery observed in the majority, though not all, of patients. As the research on long-COVID continues to evolve, ongoing studies are likely to shed more light on the intricate relationship between chronic diseases, such as oncological status, cardiovascular diseases, psychiatric disorders, and the persistent effects of SARS-CoV-2 infection. This information could guide healthcare providers, researchers, and policymakers in developing targeted interventions.

Defining the Subtypes of Long COVID and Risk Factors for Prolonged Disease: Population-Based Case-Crossover Study

BACKGROUND

There have been over 772 million confirmed cases of COVID-19 worldwide. A significant portion of these infections will lead to long COVID (post-COVID-19 condition) and its attendant morbidities and costs. Numerous life-altering complications have already been associated with the development of long COVID, including chronic fatigue, brain fog, and dangerous heart rhythms.

OBJECTIVE

We aim to derive an actionable long COVID case definition consisting of significantly increased signs, symptoms, and diagnoses to support pandemic-related clinical, public health, research, and policy initiatives.

METHODS

This research employs a case-crossover population-based study using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) data generated at Veterans Affairs medical centers nationwide between January 1, 2020, and August 18, 2022. In total, 367,148 individuals with ICD-10-CM data both before and after a positive COVID-19 test were selected for analysis. We compared ICD-10-CM codes assigned 1 to 7 months following each patient's positive test with those assigned up to 6 months prior. Further, 350,315 patients had novel codes assigned during this window of time. We defined signs, symptoms, and diagnoses as being associated with long COVID if they had a novel case frequency of ≥1:1000, and they significantly increased in our entire cohort after a positive test. We present odds ratios with CIs for long COVID signs, symptoms, and diagnoses, organized by ICD-10-CM functional groups and medical specialty. We used our definition to assess long COVID risk based on a patient's demographics, Elixhauser score, vaccination status, and COVID-19 disease severity.

RESULTS

We developed a long COVID definition consisting of 323 ICD-10-CM diagnosis codes grouped into 143 ICD-10-CM functional groups that were significantly increased in our 367,148 patient post-COVID-19 population. We defined 17 medical-specialty long COVID subtypes such as cardiology long COVID. Patients who were COVID-19-positive developed signs, symptoms, or diagnoses included in our long COVID definition at a proportion of at least 59.7% (268,320/449,450, based on a denominator of all patients who were COVID-19-positive). The long COVID cohort was 8 years older with more comorbidities (2-year Elixhauser score 7.97 in the patients with long COVID vs 4.21 in the patients with non-long COVID). Patients who had a more severe bout of COVID-19, as judged by their minimum oxygen saturation level, were also more likely to develop long COVID.

CONCLUSIONS

An actionable, data-driven definition of long COVID can help clinicians screen for and diagnose long COVID, allowing identified patients to be admitted into appropriate monitoring and treatment programs. This long COVID definition can also support public health, research, and policy initiatives. Patients with COVID-19 who are older or have low oxygen saturation levels during their bout of COVID-19, or those who have multiple comorbidities should be preferentially watched for the development of long COVID.

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.

Serological markers and long COVID-A rapid systematic review

BACKGROUND

Long COVID is highly heterogeneous, often debilitating, and may last for years after infection. The aetiology of long COVID remains uncertain. Examination of potential serological markers of long COVID, accounting for clinical covariates, may yield emergent pathophysiological insights.

METHODS

In adherence to PRISMA guidelines, we carried out a rapid review of the literature. We searched Medline and Embase for primary observational studies that compared IgG response in individuals who experienced COVID-19 symptoms persisting ≥12 weeks post-infection with those who did not. We examined relationships between serological markers and long COVID status and investigated sources of inter-study variability, such as severity of acute illness, long COVID symptoms assessed and target antigen(s).

RESULTS

Of 8018 unique records, we identified 29 as being eligible for inclusion in synthesis. Definitions of long COVID varied. In studies that reported anti-nucleocapsid (N) IgG (n = 10 studies; n = 989 participants in aggregate), full or partial anti-Spike IgG (i.e. the whole trimer, S1 or S2 subgroups, or receptor binding domain, n = 19 studies; n = 2606 participants), or neutralizing response (n = 7 studies; n = 1123 participants), we did not find strong evidence to support any difference in serological markers between groups with and without persisting symptoms. However, most studies did not account for severity or level of care required during acute illness, and other potential confounders.

CONCLUSIONS

Pooling of studies would enable more robust exploration of clinical and serological predictors among diverse populations. However, substantial inter-study variations hamper comparability. Standardized reporting practices would improve the quality, consistency and comprehension of study findings.

Looking at the Data on Smoking and Post-COVID-19 Syndrome-A Literature Review

Long COVID is a recently described entity that is responsible for significant morbidity and that has consequences ranging from mild to life-threatening. The underlying mechanisms are not completely understood, and treatment options are currently limited, as existing data focus more on risk factors and predictors. Smoking has been reported as a risk factor for poor outcomes of acute SARS-CoV-2 infection and seems to also play a role in mediating post-COVID-19 symptoms. We aimed to review relevant work addressing the interaction between smoking and long COVID in order to characterize smoking's role as a risk factor and possibly identify new research directions. Methods: The PubMed/MEDLINE database was searched using the keywords 'smoking', 'long COVID', and 'post-acute COVID' to identify relevant English-language articles published up to October 2023. Results and conclusions: From the 374 initial hits, a total of 36 papers were deemed relevant to the aim of the review. There was significant variability concerning the ways in which tobacco usage was quantified and reported; still, there is compelling evidence linking smoking to an increased risk of developing manifestations of post-acute-COVID disease. Some clinical conditions, such as dyspnea, cardiovascular symptoms, and cognitive or mental-health impairment, seem to be relatively strongly associated with smoking, while the connection between smoking and upper-airway involvement seems less certain. The available data support recommending smoking cessation as a clinical tool for the prevention of long COVID.

Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population

BACKGROUND

Post COVID-19 condition (PCC) can lead to considerable morbidity, including prolonged sick-leave. Identifying risk groups is important for informing interventions. We investigated heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave and identified subgroups at higher risk.

METHODS

We conducted a hybrid survey and register-based retrospective cohort study of Danish residents who tested positive for SARS-CoV-2 between November 2020 and February 2021 and a control group who tested negative, with no known history of SARS-CoV-2. We estimated the causal risk difference (RD) of long-term sick-leave due to PCC and used the causal forest method to identify individual-level heterogeneity in the effect of infection on sick-leave. Sick-leave was defined as >4 weeks of full-time sick-leave from 4 weeks to 9 months after the test.

RESULTS

Here, in a cohort of 88,818 individuals, including 37,482 with a confirmed SARS-CoV-2 infection, the RD of long-term sick-leave is 3.3% (95% CI 3.1% to 3.6%). We observe a high degree of effect heterogeneity, with conditional RDs ranging from -3.4% to 13.7%. Age, high BMI, depression, and sex are the most important variables explaining heterogeneity. Among three-way interactions considered, females with high BMI and depression and persons aged 36-45 years with high BMI and depression have an absolute increase in risk of long-term sick-leave above 10%.

CONCLUSIONS

Our study supports significant individual-level heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave, with age, sex, high BMI, and depression identified as key factors. Efforts to curb the PCC burden should consider multimorbidity and individual-level risk.

Association of Sedentary Lifestyle with Risk of Acute and Post-Acute COVID-19 Sequelae: A Retrospective Cohort Study

BACKGROUND

Evidence suggests that coronavirus disease 2019 (COVID-19) survivors could experience COVID-19 sequelae. Although various risk factors for COVID-19 sequelae have been identified, little is known about whether a sedentary lifestyle is an independent risk factor.

METHODS

In this retrospective cohort study, 4850 participants self-reported their COVID-19 sequelae symptoms between June and August 2022. A sedentary lifestyle included physical inactivity (<150 min/week of moderate-to-vigorous intensity physical activity) and prolonged sedentary behavior (≥10 h/day) before the fifth COVID-19 wave was recorded. Logistic regression analysis was performed to determine the relationships between sedentary lifestyle and risk of acute and post-acute (lasting ≥2 months) COVID-19 sequelae.

RESULTS

A total of 1443 COVID-19 survivors and 2962 non-COVID-19 controls were included. Of the COVID-19 survivors, >80% and >40% self-reported acute and post-acute COVID-19 sequelae, respectively. In the post-acute phase, COVID-19 survivors who were physically inactive had a 37% lower risk of insomnia, whereas those with prolonged sedentary behavior had 25%, 67%, and 117% higher risks of at least one symptom, dizziness, and "pins and needles" sensation, respectively. For the acute phase, prolonged sedentary behavior was associated with a higher risk of fatigue, "brain fog," dyspnea, muscle pain, joint pain, dizziness, and "pins and needles" sensation. Notably, sedentary behavior, rather than physical inactivity, was correlated with a higher risk of severe post-COVID-19 sequelae in both acute and post-acute phases.

CONCLUSIONS

Prolonged sedentary behavior was independently associated with a higher risk of both acute and post-acute COVID-19 sequelae, whereas physical inactivity played contradictory roles in COVID-19 sequelae.

Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm

BACKGROUND

Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses.

METHODS

We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance.

RESULTS

Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC.

CONCLUSIONS

We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts.

A Systematic Review of the Prevalence of Persistent Gastrointestinal Symptoms and Incidence of New Gastrointestinal Illness after Acute SARS-CoV-2 Infection

It is known that SARS-CoV-2 infection can result in gastrointestinal symptoms. For some, these symptoms may persist beyond acute infection, in what is known as 'post-COVID syndrome'. We conducted a systematic review to examine the prevalence of persistent gastrointestinal symptoms and the incidence of new gastrointestinal illnesses following acute SARS-CoV-2 infection. We searched the scientific literature using MedLine, SCOPUS, Europe PubMed Central and medRxiv from December 2019 to July 2023. Two reviewers independently identified 45 eligible articles, which followed participants for various gastrointestinal outcomes after acute SARS-CoV-2 infection. The study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools. The weighted pooled prevalence for persistent gastrointestinal symptoms of any nature and duration was 10.8% compared with 4.9% in healthy controls. For seven studies at low risk of methodological bias, the symptom prevalence ranged from 0.2% to 24.1%, with a median follow-up time of 18 weeks. We also identified a higher risk for future illnesses such as irritable bowel syndrome, dyspepsia, hepatic and biliary disease, liver disease and autoimmune-mediated illnesses such as inflammatory bowel disease and coeliac disease in historically SARS-CoV-2-exposed individuals. Our review has shown that, from a limited pool of mostly low-quality studies, previous SARS-CoV-2 exposure may be associated with ongoing gastrointestinal symptoms and the development of functional gastrointestinal illness. Furthermore, we show the need for high-quality research to better understand the SARS-CoV-2 association with gastrointestinal illness, particularly as population exposure to enteric infections returns to pre-COVID-19-restriction levels.

Recent Advancements on Long COVID in China: A Narrative Review

The COVID-19 pandemic is a huge public health crisis in the 21st century. In addition to the acute symptoms, a considerable proportion of patients worldwide have suffered from post-COVID-19 syndrome, commonly known as Long COVID. The impact of Long COVID on individual and public health burden cannot be ignored. According to recent researches, Long COVID has been affecting multiple organ systems throughout the body, with respiratory, mental, nervous and digestive symptoms often seen in Chinese population. Clinical studies have proved that symptoms were alleviated by a variety of treatments, such as physical therapy, rehabilitation training, psychological support, behavioral cognitive therapy, stem cell therapy, etc. Based on the current clinical evidence, it is recommended to strengthen the scientific research on Long COVID and actively carry out early monitoring and intervention in the future, so as to effectively prevent the long-term disease burden and economic pressure.

Prevalence and risk factors of long COVID 6-12 months after infection with the Omicron variant among nonhospitalized patients in Hong Kong

Long COVID has been reported among patients with COVID-19, but little is known about the prevalence and risk factors associated with long COVID 6-12 months after infection with the Omicron variant. This is a large-scale retrospective study. A total of 6242 out of 12 950 nonhospitalized subjects of all ages with SARS-CoV-2 infection (confirmed by polymerase chain reaction/rapid antigen test) during the Omicron dominant outbreak (December 31, 2021-May 6, 2022) in Hong Kong were included. Prevalence of long COVID, frequencies of symptoms, and risk factors were analyzed. Three thousand four hundred and thirty (55.0%) subjects reported at least one long COVID symptom. The most reported symptom was fatigue (1241, 36.2%). Female gender, middle age, obesity, comorbidities, vaccination after infection, having more symptoms, and presenting fatigue/chest tightness/headache/diarrhea in the acute stage of illness were identified as associated risk factors for long COVID. Patients who had received three or more doses of vaccine were not associated with a lower risk of long COVID (adjusted odds ratio 1.105, 95% confidence interval 0.985-1.239, p = 0.088). Among patients with at least three doses of vaccine, there was no significant difference in the risk of long COVID between the CoronaVac vaccine and BNT162b2 vaccine (p > 0.05). Omicron infection can lead to long COVID in a significant proportion of nonhospitalized patients 6-12 months after infection. Further investigation is needed to uncover the mechanisms underlying the development of long COVID and determine the impact of various risk factors such as vaccines.