Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program

Association between acquiring SARS-CoV-2 during pregnancy and post-acute sequelae of SARS-CoV-2 infection: RECOVER electronic health record cohort analysis

Background

Little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) after acquiring SARS-CoV-2 infection during pregnancy. We aimed to evaluate the association between acquiring SARS-CoV-2 during pregnancy compared with acquiring SARS-CoV-2 outside of pregnancy and the development of PASC.

Methods

This retrospective cohort study from the Researching COVID to Enhance Recovery (RECOVER) Initiative Patient-Centred Clinical Research Network (PCORnet) used electronic health record (EHR) data from 19 U.S. health systems. Females aged 18-49 years with lab-confirmed SARS-CoV-2 infection from March 2020 through June 2022 were included. Validated algorithms were used to identify pregnancies with a delivery at >20 weeks' gestation. The primary outcome was PASC, as previously defined by computable phenotype in the adult non-pregnant PCORnet EHR dataset, identified 30-180 days post-SARS-CoV-2 infection. Secondary outcomes were the 24 component diagnoses contributing to the PASC phenotype definition. Univariable comparisons were made for baseline characteristics between individuals with SARS-CoV-2 infection acquired during pregnancy compared with outside of pregnancy. Using inverse probability of treatment weighting to adjust for baseline differences, the association between SARS-CoV-2 infection acquired during pregnancy and the selected outcomes was modelled. The incident risk is reported as the adjusted hazard ratio (aHR) with 95% confidence intervals.

Findings

In total, 83,915 females with SARS-CoV-2 infection acquired outside of pregnancy and 5397 females with SARS-CoV-2 infection acquired during pregnancy were included in analysis. Non-pregnant females with SARS-CoV-2 infection were more likely to be older and have comorbid health conditions. SARS-CoV-2 infection acquired in pregnancy as compared with acquired outside of pregnancy was associated with a lower incidence of PASC (25.5% vs 33.9%; aHR 0.85, 95% CI 0.80-0.91). SARS-CoV-2 infection acquired in pregnant females was associated with increased risk for some PASC component diagnoses including abnormal heartbeat (aHR 1.67, 95% CI 1.43-1.94), abdominal pain (aHR 1.34, 95% CI 1.16-1.55), and thromboembolism (aHR 1.88, 95% CI 1.17-3.04), but decreased risk for other diagnoses including malaise (aHR 0.35, 95% CI 0.27-0.47), pharyngitis (aHR 0.36, 95% CI 0.26-0.48) and cognitive problems (aHR 0.39, 95% CI 0.27-0.56).

Interpretation

SARS-CoV-2 infection acquired during pregnancy was associated with lower risk of development of PASC at 30-180 days after incident SARS-CoV-2 infection in this nationally representative sample. These findings may be used to counsel pregnant and pregnant capable individuals, and direct future prospective study.

Funding

National Institutes of Health (NIH) Other Transaction Agreement (OTA) OT2HL16184.

A survey on the role of artificial intelligence in managing Long COVID

In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

Clinical Evolution and Risk Factors in Patients Infected during the First Wave of COVID-19: A Two-Year Longitudinal Study

A limited number of longitudinal studies have examined the symptoms associated with long-COVID-19. We conducted an assessment of symptom onset, severity and patient recovery, and determined the percentage of patients who experienced reinfection up to 2 years after the initial onset of the disease. Our cohort comprises 377 patients (≥18 years) with laboratory-confirmed COVID-19 in a secondary hospital (Madrid, Spain), throughout March 3-16, 2020. Disease outcomes and clinical data were followed-up until August 12, 2022. We reviewed the evolution of the 253 patients who had survived as of April 2020 (67.1%). Nine died between April 2020 and August 2022. A multivariate regression analysis performed to detect the risk factors associated with long-COVID-19 revealed that the increased likelihood was associated with chronic obstructive lung disease (OR 14.35, 95% CI 1.89-109.09; p = 0.010), dyspnea (5.02, 1.02-24.75; p = 0.048), higher LDH (3.23, 1.34-7.52; p = 0.006), and lower D-dimer levels (0.164, 0.04-0.678; p = 0.012). Reinfected patients (n = 45) (47.8 years; 39.7-67.2) were younger than non-reinfected patients (64.1 years; 48.6-74.4)) (p < 0.001). Patients who received a combination of vaccines exhibited fewer symptoms (44.4%) compared to those who received a single type of vaccine (77.8%) (p = 0.048). Long-COVID-19 was detected in 27.05% (66/244) of patients. The early detection of risk factors helps predict the clinical course of patients with COVID-19. Middle-aged adults could be susceptible to reinfection, highlighting the importance of prevention and control measures regardless of vaccination status.

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

Pathogenesis Underlying Neurological Manifestations of Long COVID Syndrome and Potential Therapeutics

The development of long-term symptoms of coronavirus disease 2019 (COVID-19) more than four weeks after primary infection, termed "long COVID" or post-acute sequela of COVID-19 (PASC), can implicate persistent neurological complications in up to one third of patients and present as fatigue, "brain fog", headaches, cognitive impairment, dysautonomia, neuropsychiatric symptoms, anosmia, hypogeusia, and peripheral neuropathy. Pathogenic mechanisms of these symptoms of long COVID remain largely unclear; however, several hypotheses implicate both nervous system and systemic pathogenic mechanisms such as SARS-CoV2 viral persistence and neuroinvasion, abnormal immunological response, autoimmunity, coagulopathies, and endotheliopathy. Outside of the CNS, SARS-CoV-2 can invade the support and stem cells of the olfactory epithelium leading to persistent alterations to olfactory function. SARS-CoV-2 infection may induce abnormalities in innate and adaptive immunity including monocyte expansion, T-cell exhaustion, and prolonged cytokine release, which may cause neuroinflammatory responses and microglia activation, white matter abnormalities, and microvascular changes. Additionally, microvascular clot formation can occlude capillaries and endotheliopathy, due to SARS-CoV-2 protease activity and complement activation, can contribute to hypoxic neuronal injury and blood-brain barrier dysfunction, respectively. Current therapeutics target pathological mechanisms by employing antivirals, decreasing inflammation, and promoting olfactory epithelium regeneration. Thus, from laboratory evidence and clinical trials in the literature, we sought to synthesize the pathophysiological pathways underlying neurological symptoms of long COVID and potential therapeutics.

The Role of Nutrition in Mitigating the Effects of COVID-19 from Infection through PASC

The expansive and rapid spread of the SARS-CoV-2 virus has resulted in a global pandemic of COVID-19 infection and disease. Though initially perceived to be acute in nature, many patients report persistent and recurrent symptoms beyond the infectious period. Emerging as a new epidemic, "long-COVID", or post-acute sequelae of coronavirus disease (PASC), has substantially altered the lives of millions of people globally. Symptoms of both COVID-19 and PASC are individual, but share commonality to established respiratory viruses, which include but are not limited to chest pain, shortness of breath, fatigue, along with adverse metabolic and pulmonary health effects. Nutrition plays a critical role in immune function and metabolic health and thus is implicated in reducing risk or severity of symptoms for both COVID-19 and PASC. However, despite the impact of nutrition on these key physiological functions related to COVID-19 and PASC, the precise role of nutrition in COVID-19 infection and PASC onset or severity remains to be elucidated. This narrative review will discuss established and emerging nutrition approaches that may play a role in COVID-19 and PASC, with references to the established nutrition and clinical practice guidelines that should remain the primary resources for patients and practitioners.

Factors Associated With Long COVID Symptoms in an Online Cohort Study

Background

Few prospective studies of Long COVID risk factors have been conducted. The purpose of this study was to determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are associated with Long COVID.

Methods

In March 26, 2020, the COVID-19 Citizen Science study, an online cohort study, began enrolling participants with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection. Adult participants who reported a positive SARS-CoV-2 test result before April 4, 2022 were surveyed for Long COVID symptoms. The primary outcome was at least 1 prevalent Long COVID symptom greater than 1 month after acute infection. Exposures of interest included age, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, variant wave, number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, and exercise.

Results

Of 13 305 participants who reported a SARS-CoV-2 positive test, 1480 (11.1%) responded. Respondents' mean age was 53 and 1017 (69%) were female. Four hundred seventy-six (32.2%) participants reported Long COVID symptoms at a median 360 days after infection. In multivariable models, number of acute symptoms (odds ratio [OR], 1.30 per symptom; 95% confidence interval [CI], 1.20-1.40), lower socioeconomic status/financial insecurity (OR, 1.62; 95% CI, 1.02-2.63), preinfection depression (OR, 1.08; 95% CI, 1.01-1.16), and earlier variants (OR = 0.37 for Omicron compared with ancestral strain; 95% CI, 0.15-0.90) were associated with Long COVID symptoms.

Conclusions

Variant wave, severity of acute infection, lower socioeconomic status, and pre-existing depression are associated with Long COVID symptoms.

Neurological post-acute sequelae of SARS-CoV-2 infection

The novel coronavirus disease 19 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can have two phases: acute (generally 4 weeks after onset) and chronic (>4 weeks after onset). Both phases include a wide variety of signs and symptoms including neurological and psychiatric symptoms. The signs and symptoms that are considered sequelae of COVID-19 are termed post-COVID condition, long COVID-19, and post-acute sequelae of SARS-CoV-2 infection (PASC). PASC symptoms include fatigue, dyspnea, palpitation, dysosmia, subfever, hypertension, alopecia, sleep problems, loss of concentration, amnesia, numbness, pain, gastrointestinal symptoms, depression, and anxiety. Because the specific pathophysiology of PASC has not yet been clarified, there are no definite criteria of the condition, hence the World Health Organization's definition is quite broad. Consequently, it is difficult to correctly diagnose PASC. Approximately 50% of patients may show at least one PASC symptom up to 12 months after COVID-19 infection; however, the exact prevalence of PASC has not been determined. Despite extensive research in progress worldwide, there are currently no clear diagnostic methodologies or treatments for PASC. In this review, we discuss the currently available information on PASC and highlight the neurological sequelae of COVID-19 infection. Furthermore, we provide clinical suggestions for diagnosing and caring for patients with PASC based on our outpatient clinic experience.

Factors Associated with Long Covid Symptoms in an Online Cohort Study

Importance

Prolonged symptoms following SARS-CoV-2 infection, or Long COVID, is common, but few prospective studies of Long COVID risk factors have been conducted.

Objective

To determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute SARS-CoV-2 infection are associated with Long COVID.

Design

Cohort study with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection, and cross-sectional assessment of Long COVID symptoms using data from the COVID-19 Citizen Science (CCS) study.

Setting

CCS is an online cohort study that began enrolling March 26, 2020. We included data collected between March 26, 2020, and May 18, 2022.

Participants

Adult CCS participants who reported a positive SARS-CoV-2 test result (PCR, Antigen, or Antibody) more than 30 days prior to May 4, 2022, were surveyed.

Exposures

Age, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, time of infection (variant wave), number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, exercise.

Main Outcome

Presence of at least 1 Long COVID symptom greater than 1 month after acute infection. Sensitivity analyses were performed considering only symptoms beyond 3 months and only severe symptoms.

Results

13,305 participants reported a SARS-CoV-2 positive test more than 30 days prior, 1480 (11.1% of eligible) responded to a survey about Long COVID symptoms, and 476 (32.2% of respondents) reported Long COVID symptoms (median 360 days after infection).Respondents' mean age was 53 and 1017 (69%) were female. Common Long COVID symptoms included fatigue, reported by 230/476 (48.3%), shortness of breath (109, 22.9%), confusion/brain fog (108, 22.7%), headache (103, 21.6%), and altered taste or smell (98, 20.6%). In multivariable models, number of acute COVID-19 symptoms (OR 1.30 per symptom, 95%CI 1.20-1.40), lower socioeconomic status/financial insecurity (OR 1.62, 95%CI 1.02-2.63), pre-infection depression (OR 1.08, 95%CI 1.01-1.16), and earlier variants (OR 0.37 for Omicron compared to ancestral strain, 95%CI 0.15-0.90) were associated with Long COVID symptoms.

Conclusions and Relevance

Variant wave, severity of acute infection, lower socioeconomic status and pre-existing depression are associated with Long COVID symptoms.

Key Points

Question: What are the patterns of symptoms and risk factors for Long COVID among SARS-CoV-2 infected individuals?Findings: Persistent symptoms were highly prevalent, especially fatigue, shortness of breath, headache, brain fog/confusion, and altered taste/smell, which persisted beyond 1 year among 56% of participants with symptoms; a minority of participants reported severe Long COVID symptoms. Number of acute symptoms during acute SARS-CoV-2 infection, financial insecurity, pre-existing depression, and infection with earlier variants are associated with prevalent Long COVID symptoms independent of vaccination, medical history, and other factors.Meaning: Severity of acute infection, SARS-CoV-2 variant, and financial insecurity and depression are associated with Long COVID symptoms.