Abstract BACKGROUND AND AIMS As in the general population the prevalence of monoclonal gammopathies (MG) in kidney transplant (KT) candidates increases with age. Little is known about the epidemiology and long-term consequences of MG on overall survival, graft outcomes and infectious and neoplastic complications in patients undergoing KT. METHOD We retrospectively identified patients with MG at the time of transplant (KTMG) or appearing de novo after the KT (DNMG) in 3059 patients who underwent a KT from January 2007 to June 2019 at Necker Hospital Paris (N = 1978) and from January 1998 to December 2017 at CHU Poitiers, France (N = 1081). Using a propensity score, we established a control cohort of KTMG from Necker Hospital, matched on the main covariates influencing post KT outcomes. RESULTS We identified 70 (2.3%) KTMG and 114 (3.7%) DNMG patients, presenting with identical demographic characteristics except for an older age in KTMG (62 versus 57 years; P = 0.03). MG isotypes among the 184 KT recipients with MGUS were IgG (n = 117, 64%), IgA (n = 16, 9%) or IgM (n = 16, 9%). Interestingly, DNMG patients presented more frequently with bi- or triclonal MG than KTMG patients (n = 28, 25% versus n = 7, 10%, respectively; P = 0.02). At the time of KT, among 62 patients with available sFLC measurement in KTMG, 11 (17%) had an abnormal kappa/lambda ratio. Because of systematic evaluation of SPEP during follow-up, we observed that MG disappeared during follow up more frequently in DNMG compared to KTMG (n = 51, 45% versus n = 17, 24%, respectively; P = 0.007). Figure 1 represents the results of SPEP during follow-up. FIGURE 1: Monoclonal gammopathy timescale. Serum protein electrophoresis follow-up from KT;each patient is represented by one line. Blueand green lines represent negative SPEP, performed before and after the detection of MG, and red lines represent positive SPEP with MG identification. Clinical last follow-up is represented by dots: red dot for death, blue dot for graft loss and green dot for last follow-up alive with functional graft. Overall survival was poorer in KTMG than in DNMG (87 versus 176 months; P < 0.001). Cox model including age, sex, rank of transplantation and KTMG or DNMG revealed that age, rank of transplantation and KTMG were independent risk factors for death. Graft survival, occurrence of rejection and haematological complications were comparable between the two groups. To better evaluate the effect of MGUS at the time of KT, KTMG patients from Necker Hospital were compared with matched KT recipients. There was no difference between KTMG and controls regarding the main baseline predictors of post-KT outcomes except for lower level of residual gammaglobulins [7.5 g/L (IQR 6–9.9)] in KTMG versus 10.7 g/l (IQR 8.9–13.1) in controls (P < 0.0001). KTMG patients developed more frequently solid cancers (15% versus 5%; P = 0.04) and bacterial infections (63% versus 48%; P = 0.08). There was no significant difference regarding survival, frequency of rejection or haematological complications. However, KTMG patients with an abnormal kappa/lambda ratio at the time of KT tended to have poorer overall survival than those with normal kappa/lambda ratio and controls (respective medians 72, 87 and 103 months; P = 0.08) (Figure 2). CONCLUSION We report here the largest cohort of KT recipients who underwent systematic SPEP screening at the time of KT and yearly during post-transplant follow-up. We observed that MGUS prevalence in KT candidates is close to that reported in the general population. We confirm that outcomes of patients harboring MGUS at the time of KT are similar to those of matched controls without MGUS, supporting that MGUS should not be a contraindication for KT. Nevertheless, we observed that KTMG patients developed more frequently and earlier solid cancers and infections, suggesting that MGUS is associated with a relative immunodeficiency in these patients. We also showed that patients with abnormal sFLC kappa/lambda ratio experienced poor outcomes, claiming for systematic measurement of sFLC in the pre-KT workup. Clinical last follow-up is represented by dots: red dot for death, blue dot for graft loss and green dot for last follow-up alive with functional graft.
Disposition of gentamicin and amikacin during extracorporeal membrane oxygenation has not been addressed in in vitro models. The HLS Advanced 7.0® circuit with the Cardio Help® monitor, Getinge, was used. The 5-L central compartment (CC) was loaded with gentamicin and amikacin at a targeted concentration of 40 and 80 mg/L in the same bag prior connection to the circuit. Samples were collected in the CC, the inlet and outlet ports from 15 min to 6 h post-connection. Pharmacokinetic analyses were performed using the NeckEpur® method. Analysis of results of gentamicin and amikacin showed in the filter-pump block (i) the extremely low value of the extraction coefficients, (ii) similar values of the areas under the curve (AUCs) at the inlet and outlet ports, (iii) using the Wilcoxon matched pairs signed rank test no significant differences of the inlet-outlet concentrations in the filter-pump. In the whole system (i) the amounts recovered in the CC at the end of the 6-h session were not significantly different from the initial values, (ii) the extremely low values of the total clearance of gentamicin and amikacin from the CC in comparison with the measured simulated blood flowrate, (iii) the lack of significant time-concentration interactions in the CC and the inlet and outlet ports. These findings allow concluding no detectable adsorption of gentamicin and amikacin occurred in the HLS Advanced 7.0 circuit.
Neurological biomarkers are of great use for clinicians, as they can be used for numerous purposes: guiding clinical diagnosis, estimating prognosis, assessing disease stage and monitoring progression or response to treatment. This field of neurology has evolved considerably in recent years due to analytical improvements in assay methods, now allowing the detection of biomarkers not only in cerebrospinal fluid (CSF) but also in blood. This progress greatly facilitates the repeated quantification of biomarkers, the collection of blood being much less invasive than that of CSF. Among the various informative biomarkers of neurological disorders, neurofilaments light chains (NfL) have proven to be particularly attractive in many contexts, in particular for the diagnosis and prognosis of neurodegenerative diseases (which this review will present), but also in other contexts of neurological disorders (which will be detailed in part 2). We further address the added value of NfL compared to other biomarkers commonly used to monitor the diseases described in this review.Les biomarqueurs neurologiques sont d'une grande utilité, car ils peuvent être utilisés à de nombreuses fins : orienter le diagnostic clinique, estimer le pronostic, évaluer le stade de la maladie et surveiller la progression ou la réponse au traitement. Ce domaine de la neurologie a considérablement évolué ces dernières années grâce à l'amélioration des méthodes de dosage, permettant désormais la détection de biomarqueurs non seulement dans le liquide cérébro-spinal (LCS) mais aussi dans le sang. Ce progrès facilite la quantification répétée des biomarqueurs, le prélèvement de sang étant beaucoup moins invasif que celui du LCS. Parmi les différents biomarqueurs informatifs des troubles neurologiques, la chaîne légère des neurofilaments (NfL) s'est révélée particulièrement intéressante dans de nombreux contextes, notamment pour le diagnostic et le pronostic des maladies neurodégénératives (que cette revue présentera), mais aussi dans d'autres contextes de troubles neurologiques (qui seront détaillés dans la partie 2). La valeur ajoutée du NfL par rapport aux autres biomarqueurs couramment utilisés est analysée.
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