Tag: long covid predictors

Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

Abstract:

Background: Long COVID or long-term symptoms after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe acute COVID-19 cases. We did this study to estimate the incidence and identify the characteristics and predictors of Long COVID among our patients.

Methodology: We recruited adult (≥18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks and six months of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristics of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID.

Results: We have analyzed 487 and 371 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47) and 223 days (IQR:195,251), respectively. Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) and 9.4% (95% CI: 6.7%,12.9%) of participants at four weeks and six months of follow-up, respectively. Incidence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n = 72) at four weeks of follow-up. At six months, the incidence among mild/moderate (n = 319) was 7.2% (95% CI:4.6%,10.6%) as compared to 23.1% (95% CI:12.5%,36.8%) in severe/critical (n = 52). The most common Long COVID symptom was fatigue. Statistically significant predictors of Long COVID at four weeks of follow-up were-Pre-existing medical conditions (Adjusted Odds ratio (aOR) = 2.00, 95% CI: 1.16,3.44), having a higher number of symptoms during acute phase of COVID-19 disease (aOR = 11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR = 2.32, 95% CI: 1.17,4.58), the severity of illness (aOR = 5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR) = 3.89, 95% CI: 2.49,6.08).

Conclusion: A considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers.

Source: Arjun MC, Singh AK, Pal D, Das K, G A, Venkateshan M, Mishra B, Patro BK, Mohapatra PR, Subba SH. Characteristics and predictors of Long COVID among diagnosed cases of COVID-19. PLoS One. 2022 Dec 20;17(12):e0278825. doi: 10.1371/journal.pone.0278825. PMID: 36538532; PMCID: PMC9767341. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9767341/ (Full text)

Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome

Abstract:

Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months.

Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19.

Source: Liu Q, Su Q, Zhang F, Tun HM, Mak JWY, Lui GC, Ng SSS, Ching JYL, Li A, Lu W, Liu C, Cheung CP, Hui DSC, Chan PKS, Chan FKL, Ng SC. Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome. Nat Commun. 2022 Nov 10;13(1):6806. doi: 10.1038/s41467-022-34535-8. PMID: 36357381; PMCID: PMC9648868. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648868/ (Full text)

COVID-19 disease severity to predict persistent symptoms: a systematic review and meta-analysis

Abstract:

Background: It is unclear, whether the initial disease severity may help to predict which COVID-19 patients at risk of developing persistent symptoms.

Aim: The aim of this study was to examine whether the initial disease severity affects the risk of persistent symptoms in post-acute COVID-19 syndrome and long COVID.

Methods: A systematic search was conducted using PUBMED, Google Scholar, EMBASE, and ProQuest databases to identify eligible articles published after January 2020 up to and including 30 August 2021. Pooled odds ratio (OR) and confidence intervals (CIs) were calculated using random effects meta-analysis.

Findings: After searching a total of 7733 articles, 20 relevant observational studies with a total of 7840 patients were selected for meta-analysis. The pooled OR for persistent dyspnea in COVID-19 survivors with a severe versus nonsevere initial disease was 2.17 [95%CI 1.62 to 2.90], and it was 1.33 [95%CI 0.75 to 2.33] for persistent cough, 1.30 [95%CI 1.06 to 1.58] for persistent fatigue, 1.02 [95%CI 0.73 to 1.40] for persistent anosmia, 1.22 [95%CI 0.69 to 2.16] for persistent chest pain, and 1.30 [95%CI 0.93 to 1.81] for persistent palpitation.

Conclusions: Contrary to expectations, we did not observe an association between the initial COVID-19 disease severity and common persistent symptoms except for dyspnea and fatigue. In addition, it was found that being in the acute or prolonged post-COVID phase did not affect the risk of symptoms. Primary care providers should be alert to potential most prevalent persistent symptoms in all COVID-19 survivors, which are not limited to patients with critical-severe initial disease.

Source: Dirican E, Bal T. COVID-19 disease severity to predict persistent symptoms: a systematic review and meta-analysis. Prim Health Care Res Dev. 2022 Nov 10;23:e69. doi: 10.1017/S1463423622000585. PMID: 36352492.  https://www.cambridge.org/core/journals/primary-health-care-research-and-development/article/covid19-disease-severity-to-predict-persistent-symptoms-a-systematic-review-and-metaanalysis/479FC1E900E22673895FDAC1CF5C12B2 (Full text)

Clinical and laboratory predictors of long-COVID in children: a single center retrospective study

Abstract:

Objective: The majority of children experience a mild course of acute Coronavirus Disease 2019 (COVID-19). Only few studies have looked at long-term recovery from COVID-19 infection in children. The purpose of this study was to identify the predictors of long-COVID by performing a thorough analysis of the clinical, laboratory, and demographic characteristics of children with COVID-19.

Patients and methods: Between August and October 2021, data were obtained retrospectively from the medical records of 251 children diagnosed with COVID-19 at a tertiary single-center hospital. The prognostic effects of admission-related factors were compared between patients who experienced long-lasting symptoms and those who did not.

Results: Long-COVID symptoms were noted in 12.4% of patients. Joint pain (7.6%), lumbago (4.8%), and headache (3.2%) were the most common symptoms. The mean onset of long-COVID symptoms was 1.35±0.49 months. The onset of long-COVID symptoms was 4 weeks after initial diagnosis in 64.5% of patients and 4-8 weeks later in 35.5% of the patients. The mean duration of long-COVID symptoms was 5.32±2.51 months. Children with long-COVID had higher leukocytes, neutrophils, monocytes, basophils, platelets, and D-dimer when compared with patients without long-COVID (p < 0.001). Leukocytes, neutrophils, monocytes, platelets, and D-dimer had the highest AUC in the ROC analysis (0.694, 0.658, 0.681, 0.667, and 0.612, respectively) and were statistically significant.

Conclusions: Despite the majority of children with COVID-19 having mild or asymptomatic acute disease, the majority of long-COVID symptoms were associated with functional impairment between 1 and 9 months after the start of the infection. Increased leukocytes, monocytes, neutrophils, platelets, and D-dimer appear to be the most powerful laboratory predictors for long-COVID and monitoring these predictors may assist clinicians to identify and follow-up patients with higher risk for long-COVID.

Source: Güven D, Buluş AD. Clinical and laboratory predictors of long-COVID in children: a single center retrospective study. Eur Rev Med Pharmacol Sci. 2022 Oct;26(20):7695-7704. doi: 10.26355/eurrev_202210_30046. PMID: 36314341.  https://www.europeanreview.org/article/30046 (Full text)

Serological biomarkers of COVID-19 severity at hospital admission are not related to long-term post-COVID pain symptoms in hospitalized COVID-19 survivors

Abstract:

This study investigated the association between serological biomarkers at hospital admission with the development of long-term post-COVID pain symptoms in previously hospitalized coronavirus disease, 2019 (COVID-19) survivors. A cohort study including patients hospitalised because of COVID-19 in 1 urban hospital of Madrid (Spain) during the first wave of the outbreak was conducted. Hospitalisation data, clinical data, and 11 serological biomarkers were collected at hospital admission. Participants were scheduled for an individual telephone interview after hospital discharge for collecting data about post-COVID pain symptoms.

A total of 412 patients (mean age: 62, SD: 15 years; 46.1% women) were assessed twice, at a mean of 6.8 and 13.2 months after discharge. The prevalence of post-COVID pain symptoms was 42.7% (n = 176) and 36.2% (n = 149) at 6.8 and 13.2 months after hospital discharge. Patients reporting post-COVID pain exhibited a greater number of COVID-19-associated symptoms at hospital admission, more medical comorbidities, higher lymphocyte count, and lower glucose and creatine kinase levels (all, P < 0.01) than those not reporting post-COVID pain. The multivariate analysis revealed that lower creatine kinase and glucose levels were significantly associated, but just explaining 6.9% of the variance of experiencing post-COVID pain.

In conclusion, the association between serological biomarkers associated with COVID-19 severity at hospital admission and the development of post-COVID pain is small. Other factors, eg, higher number of COVID-19 onset symptoms (higher symptom load) could be more relevant for the development of post-COVID pain. Because inflammatory biomarkers were not directly analyzed, they may have stronger predictive strengths for the development of post-COVID pain symptoms.

Source: Fernández-de-Las-Peñas C, Ryan-Murua P, de-la-Llave-Rincón AI, Gómez-Mayordomo V, Arendt-Nielsen L, Torres-Macho J. Serological biomarkers of COVID-19 severity at hospital admission are not related to long-term post-COVID pain symptoms in hospitalized COVID-19 survivors. Pain. 2022 Nov 1;163(11):2112-2117. doi: 10.1097/j.pain.0000000000002608. Epub 2022 Feb 3. PMID: 35121694; PMCID: PMC9560903.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560903/ (Full text)

Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long-COVID symptoms

Abstract:

Background: Autoimmunity has been reported in patients with severe COVID-19. We investigated whether antinuclear/extractable-nuclear antibodies (ANAs) were present up to a year after infection, and if they were associated with the development of clinically relevant Post-Acute Sequalae of COVID-19 (PASC) symptoms.

Methods: A rapid assessment line immunoassay was used to measure circulating levels of ANA/ENAs in 106 convalescent COVID-19 patients with varying acute phase severities at 3, 6, and 12 months post-recovery. Patient-reported fatigue, cough, and dyspnea were recorded at each timepoint. Multivariable logistic regression model and receiver-operating curves (ROC) were used to test the association of autoantibodies with patient-reported outcomes and pro-inflammatory cytokines.

Results: Compared to age- and sex-matched healthy controls (n=22) and those who had other respiratory infections (n=34), patients with COVID-19 had higher detectable ANAs at 3 months post-recovery (p<0.001). The mean number of ANA autoreactivities per individual decreased from 3 to 12 months (3.99 to 1.55) with persistent positive titers associated with fatigue, dyspnea, and cough severity. Antibodies to U1-snRNP and anti-SS-B/La were both positively associated with persistent symptoms of fatigue (p<0.028, AUC=0.86) and dyspnea (p<0.003, AUC=0.81). Pro-inflammatory cytokines such as tumour necrosis factor alpha (TNFα) and C-reactive protein predicted the elevated ANAs at 12 months. TNFα, D-dimer, and IL-1β had the strongest association with symptoms at 12 months. Regression analysis showed TNFα predicted fatigue (β=4.65, p=0.004) and general symptomaticity (β=2.40, p=0.03) at 12 months.

Interpretation: Persistently positive ANAs at 12 months post-COVID are associated with persisting symptoms and inflammation (TNFα) in a subset of COVID-19 survivors. This finding indicates the need for further investigation into the role of autoimmunity in PASC.

Source: Son K, Jamil R, Chowdhury A, Mukherjee M, Venegas C, Miyasaki K, Zhang K, Patel Z, Salter B, Yuen ACY, Lau KS, Cowbrough B, Radford K, Huang C, Kjarsgaard M, Dvorkin-Gheva A, Smith J, Li QZ, Waserman S, Ryerson CJ, Nair P, Ho T, Balakrishnan N, Nazy I, Bowdish DM, Svenningsen S, Carlsten C, Mukherjee M. Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long-COVID symptoms. Eur Respir J. 2022 Sep 22:2200970. doi: 10.1183/13993003.00970-2022. Epub ahead of print. PMID: 36137590. https://pubmed.ncbi.nlm.nih.gov/36137590/

Lowered Quality of Life in Long COVID Is Predicted by Affective Symptoms, Chronic Fatigue Syndrome, Inflammation and Neuroimmunotoxic Pathways

Abstract:

The physio-affective phenome of Long COVID-19 is predicted by (a) immune-inflammatory biomarkers of the acute infectious phase, including peak body temperature (PBT) and oxygen saturation (SpO2), and (b) the subsequent activation of immune and oxidative stress pathways during Long COVID. The purpose of this study was to delineate the effects of PBT and SpO2 during acute infection, as well as the increased neurotoxicity on the physical, psychological, social and environmental domains of health-related quality of life (HR-QoL) in people with Long COVID.

We recruited 86 participants with Long COVID and 39 normal controls, assessed the WHO-QoL-BREF (World Health Organization Quality of Life Instrument-Abridged Version, Geneva, Switzerland) and the physio-affective phenome of Long COVID (comprising depression, anxiety and fibromyalgia-fatigue rating scales) and measured PBT and SpO2 during acute infection, and neurotoxicity (NT, comprising serum interleukin (IL)-1β, IL-18 and caspase-1, advanced oxidation protein products and myeloperoxidase, calcium and insulin resistance) in Long COVID.

We found that 70.3% of the variance in HR-QoL was explained by the regression on the physio-affective phenome, lowered calcium and increased NT, whilst 61.5% of the variance in the physio-affective phenome was explained by calcium, NT, increased PBT, lowered SpO2, female sex and vaccination with AstraZeneca and Pfizer. The effects of PBT and SpO2 on lowered HR-QoL were mediated by increased NT and lowered calcium yielding increased severity of the physio-affective phenome which largely affects HR-QoL.

In conclusion, lowered HR-Qol in Long COVID is largely predicted by the severity of neuro-immune and neuro-oxidative pathways during acute and Long COVID.

Source: Maes M, Al-Rubaye HT, Almulla AF, Al-Hadrawi DS, Stoyanova K, Kubera M, Al-Hakeim HK. Lowered Quality of Life in Long COVID Is Predicted by Affective Symptoms, Chronic Fatigue Syndrome, Inflammation and Neuroimmunotoxic Pathways. Int J Environ Res Public Health. 2022 Aug 19;19(16):10362. doi: 10.3390/ijerph191610362. PMID: 36011997. https://www.mdpi.com/1660-4601/19/16/10362/htm (Full text)

Risk of long COVID associated with delta versus omicron variants of SARS-CoV-2

The omicron variant of SARS-CoV-2 (PANGO B.1.1.529) spread rapidly across the world, out-competing former variants soon after it was first detected in November, 2021. According to the Our World in Data COVID-19 database, In Europe, the number of confirmed cases reported between December, 2021, and March, 2022 (omicron period) has exceeded all previously reported cases. Omicron appears to cause less severe acute illness than previous variants, at least in vaccinated populations. However, the potential for large numbers of people to experience long-term symptoms is a major concern, and health and workforce planners need information urgently to appropriately scale resource allocation.
In this case-control observational study, we set out to identify the relative odds of long-COVID (defined following the National Institute for Health and Care Excellence guidelines as having new or ongoing symptoms 4 weeks or more after the start of acute COVID-19) in the UK during the omicron period compared with the delta period. We used self-reported data from the COVID Symptom Study app. (King’s College London Research Ethics Management Application System number 18210, reference LRS-19/20-18210). Data were extracted and pre-processed using ExeTera13 (version 0.5.5).
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Source: Antonelli M, Pujol JC, Spector TD, Ourselin S, Steves CJ. Risk of long COVID associated with delta versus omicron variants of SARS-CoV-2. Lancet. 2022 Jun 18;399(10343):2263-2264. doi: 10.1016/S0140-6736(22)00941-2. PMID: 35717982; PMCID: PMC9212672. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(22)00941-2/fulltext (Full text)

Covid-19: Antibody “signature” could predict risk of long covid

Researchers have identified an immunoglobulin “signature” that could be used to predict which patients are most at risk of developing post-acute covid syndrome (PACS), otherwise known as long covid.

In a multicentre prospective study, 175 patients with covid-19 and 40 healthy control group participants were followed for up to a year. More than half of the patients with covid reported long covid symptoms lasting longer than a month. Those who developed long covid were found to have lower levels of IgM and IgG3 antibodies than those who quickly recovered, found the research, published in Nature Communications.1 A history of asthma was also highly associated with PACS, the study found.

The researchers combined data on immunoglobulin concentrations with a patient’s age, history of asthma, and five symptoms during the primary infection to develop a PACS score that could predict the risk of developing long term illness. The PACS score was then validated in an independent group of 395 people with covid-19.

The researchers, from the University of Zurich, said that the score might be especially helpful in hospital settings for early identification of those patients at a very high risk of developing PACS. It could also allow the study of targeted preventive treatments such as inhaled corticosteroids or intravenous immunoglobulin treatments.

The researchers said more research was still needed but that a PACS score or long covid risk calculator would be available soon at pacs-score.com.

The study’s limitations included that participants were infected between April 2020 and August 2021, before the omicron variant took hold. And the study didn’t take into account participants’ vaccination status.

Claire Steves, a senior clinical lecturer at King’s College London, welcomed the research, saying, “With cases high still, more people are at risk of developing long term symptoms. We urgently need to scale up research on how to prevent this happening. Tools such as these predictive models could be used to identify people at higher risk for enrolment into research trials for therapeutics.”

But she added, “This is a small study that was undertaken in a selected population, and so in particular the immune findings do need to be replicated elsewhere.”

Amitava Banerjee, professor of clinical data science and honorary consultant cardiologist at University College London, commented, “There are three implications from this research. First, the immunoglobulin signature points more clearly towards the mechanism of disease, although replication of the results in different, larger cohorts is needed. Second, this raises the possibility of being able to predict the risk of long covid in individuals post-initial infection. Third, further research is required to understand whether similar risk factor profiles can be used to predict the prognosis or speed of recovery.”

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Source: Jacqui Wise. Covid-19: Antibody “signature” could predict risk of long covid. BMJ 2022; 376 doi: https://doi.org/10.1136/bmj.o245 (Published 28 January 2022)

Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome

Abstract:

Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS.

Source: Cervia C, Zurbuchen Y, Taeschler P, Ballouz T, Menges D, Hasler S, Adamo S, Raeber ME, Bächli E, Rudiger A, Stüssi-Helbling M, Huber LC, Nilsson J, Held U, Puhan MA, Boyman O. Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome. Nat Commun. 2022 Jan 25;13(1):446. doi: 10.1038/s41467-021-27797-1. PMID: 35078982. https://www.nature.com/articles/s41467-021-27797-1 (Full text)