<b><i>Background and Objectives:</i></b> Patients undergoing maintenance hemodialysis have an attenuated immune response to vaccination. The aim of our study was to determine the predictive factors for humoral response to vaccination with the BNT162b2 mRNA vaccine (Pfizer-BioNTech) in patients on maintenance hemodialysis. <b><i>Design, Setting, Participants, and Measurements:</i></b> In this retrospective, single-center study, we included patients on maintenance hemodialysis already vaccinated with two doses of the BNT162b2 vaccine (Pfizer-BioNTech) and with a post-vaccination serological follow-up. <b><i>Results:</i></b> 252 patients were included for study with a mean age of 71.9 (±14.4) years. Twelve patients (4.7%) were under immunosuppressive therapy (calcineurin inhibitors: <i>n</i> = 4; chemotherapy for myeloma: <i>n</i> = 3; last infusion of rituximab over the previous 4 years: <i>n</i> = 2; abatacept: <i>n</i> = 2; adalimumab <i>n</i> = 1). Three of these patients were under immunosuppressive therapy for nonrenal solid organ transplantation. Multivariate analysis identified immunosuppressive therapy (OR 4.73 [1.38–16.17], <i>p</i> = 0.013) and lower baseline albumin levels (OR 1.23 [1.09–1.38], <i>p</i> &#x3c; 0.001) as independent predictive factors of nonresponse to vaccination. Older age (β = −0.59 ± 0.21, <i>p</i> = 0.006) and immunosuppressive therapy (β = 40.33 ± 13.33, <i>p</i> = 0.003) were significantly associated with lower humoral response to vaccination. <b><i>Conclusions:</i></b> Approximately 90% of patients under maintenance hemodialysis developed specific antibodies to the BNT162b2 mRNA vaccine. Immunosuppressive therapy, malnutrition, and older age were associated with a higher risk of nonseroconversion or lower humoral response to mRNA-based vaccination against SARS-CoV-2. We strongly recommend serological monitoring after vaccination to determine booster timing, especially for patients with malnutrition or on immunosuppressive therapy.

Osteoarticular infections are major disabling diseases that can occur after orthopedic implant surgery in patients. The management of these infections is very complex and painful, requiring surgical intervention in combination with long-term antibiotic treatment. Therefore, early and accurate diagnosis of the causal pathogens is essential before formulating chemotherapeutic regimens. Although culture-based microbiology remains the most common diagnosis of osteoarticular infections, its regular failure to identify the causative pathogen as well as its long-term modus operandi motivates the development of rapid, accurate, and sufficiently comprehensive bacterial species-specific diagnostics that must be easy to use by routine clinical laboratories. Based on these criteria, we reported on the feasibility of our DendrisCHIP® technology using DendrisCHIP®OA as an innovative molecular diagnostic method to diagnose pathogen bacteria implicated in osteoarticular infections. This technology is based on the principle of microarrays in which the hybridization signals between oligoprobes and complementary labeled DNA fragments from isolates queries a database of hybridization signatures corresponding to a list of pre-established bacteria implicated in osteoarticular infections by a decision algorithm based on machine learning methods. In this way, this technology combines the advantages of a PCR-based method and next-generation sequencing (NGS) while reducing the limitations and constraints of the two latter technologies. On the one hand, DendrisCHIP®OA is more comprehensive than multiplex PCR tests as it is able to detect many more germs on a single sample. On the other hand, this method is not affected by the large number of nonclinically relevant bacteria or false positives that characterize NGS, as our DendrisCHIP®OA has been designed to date to target only a subset of 20 bacteria potentially responsible for osteoarticular infections. DendrisCHIP®OA has been compared with microbial culture on more than 300 isolates and a 40% discrepancy between the two methods was found, which could be due in part but not solely to the absence or poor identification of germs detected by microbial culture. We also demonstrated the reliability of our technology in correctly identifying bacteria in isolates by showing a convergence (i.e., same bacteria identified) with NGS superior to 55% while this convergence was only 32% between NGS and microbial culture data. Finally, we showed that our technology can provide a diagnostic result in less than one day (technically, 5 h), which is comparatively faster and less labor intensive than microbial cultures and NGS.

Distribution électronique Cairn.info pour Société d'études soréliennes. © Société d'études soréliennes. Tous droits réservés pour tous pays. La reproduction ou représentation de cet article, notamment par photocopie, n'est autorisée que dans les limites des conditions générales d'utilisation du site ou, le cas échéant, des conditions générales de la licence souscrite par votre établissement. Toute autre reproduction ou représentation, en tout ou partie, sous quelque forme et de quelque manière que ce soit, est interdite sauf accord préalable et écrit de l'éditeur, en dehors des cas prévus par la législation en vigueur en France. Il est précisé que son stockage dans une base de données est également interdit.

No abstract

No abstract