ABSTRACT
There have been concerning reports about people experiencing new onset persistent complications (greater than 30 days) following approved SARS-CoV-2 vaccines (BNT162b2 (Pfizer), mRNA-1273 (Moderna), Janssen (Johnson and Johnson), and ChAdOx1 nCoV-19 (AstraZeneca)). We sought to determine the immunologic abnormalities in these patients and to investigate whether the potential etiology was similar to Post-Acute Sequalae of COVID (PASC), or long COVID. We studied 50 individuals who received one of the approved COVID-19 vaccines and who experienced new onset PASC-like symptoms along with 45 individuals post-vaccination without symptoms as controls. We performed multiplex cytokine/chemokine profiling with machine learning as well as SARS-CoV-2 S1 protein detection on CD16+ monocyte subsets using flow cytometry and mass spectrometry. We determined that post-vaccination individuals with PASC-like symptoms had similar symptoms to PASC patients. When analyzing their immune profile, Post-vaccination individuals had statistically significant elevations of sCD40L (p<0.001), CCL5 (p=0.017), IL-6 (p=0.043), and IL-8 (p=0.022). Machine learning characterized these individuals as PASC using previously developed algorithms. Of the S1 positive post-vaccination patients, we demonstrated by liquid chromatography/ mass spectrometry that these CD16+ cells from post-vaccination patients from all 4 vaccine manufacturers contained S1, S1 mutant and S2 peptide sequences. Post-COVID vaccination individuals with PASC-like symptoms exhibit markers of platelet activation and pro-inflammatory cytokine production, which may be driven by the persistence of SARS-CoV-2 S1 proteins in intermediate and non-classical monocytes. The data from this study also cannot make any inferences on epidemiology and prevalence for persistent post-COVID vaccine symptoms. Thus, further studies and research need to be done to understand the risk factors, likelihood and prevalence of these symptoms.
Subject(s)
Immunologic Deficiency Syndromes , COVID-19ABSTRACT
Background: The COVID-19 pandemic has made neurology clinic waiting times longer. To prevent a build-up of patients waiting, we introduced a neurology advanced referral management system (NARMS) to deal with new referrals from GPs, using advice, investigations, or the telephone, as alternatives to face-to-face (FF) assessment. Methods: For six months, electronic referrals from GPs were triaged to the above categories. We recorded the numbers in each category, patient satisfaction, inter-consultant triage variation, re-referrals, and calculated CO2 emissions. Results: There were 573 referrals. Triage destinations were advice 33%, investigations 27%, telephone 17%, and FF 33%. Of patients referred for MRI, 95% were happy not to be seen if their investigation was normal. Less-experienced consultants triaged 20% and 30% respectively, to advice or investigations, compared with 40% by a triage-experienced neurologist. Four percent were re-referred. Numbers on the waiting list did not increase. CO2 emissions were reduced by 50%. Discussion: Two thirds of neurological referrals from GPs did not need to be seen FF and 50% were dealt with without the neurologist meeting the patient. Carbon emission was halved. This system should be employed more, with FF examination reserved for those patients who need a neurological examination for diagnosis and management.
ABSTRACT
Background: There is no single way to improve epilepsy care in low- and middle-income countries (LMICs). An epilepsy management aid application (app) has been described, which enables a non-physician health worker (NPHW) to communicate with an epilepsy specialist using a smartphone. In this study, we aimed to assess the validity and quality of this care system in building connections between NPHWs and specialists in Pakistan and the UK. Methods: A NPHW in Pakistan used the app on a series of referrals and sent the app-generated summary by email to a neurologist in the UK, who replied and suggested possible management. Patients were later seen in a face-to-face (FF) manner by the UK neurologist and a local neurologist, and diagnostic accuracy and quality parameters were assessed. Results: Over 10 months, 59 patients were recruited and 33 of them were available for FF assessment. The misdiagnosis rate of the app was 6% (2 cases). Treatment advice provided by the app was judged appropriate in 32 patients (97%). In addition, 46% of the referrals were completed within 2 h and 85% within 24 h. Conclusions: Consistent with an earlier study, this system is a safe method to provide care for patients who cannot access neurological services in person. In addition, it has advantage of timeliness compared to FF assessment and requires less specialist time, both of which are especially important during the coronavirus disease 2019 pandemic. This system can be generalised easily, depending on the willingness of referrers and specialists to use it. © 2022, The Author(s).