Pitfalls in Valganciclovir Prophylaxis Dose Adjustment Based on Renal Function in Kidney Transplant Recipients.

Valganciclovir (VGC) is administered as prophylaxis to kidney transplant recipients (KTR) CMV donor (D)+/recipient (R)- and CMV R+ after thymoglobulin-induction (R+/TG). Although VGC dose adjustments based on renal function are recommended, there is paucity of real-life data on VGC dosing and associations with clinical outcomes. This is a retrospective Swiss Transplant Cohort Study-embedded observational study, including all adult D+/R- and R+/TG KTR between 2010 and 2020, who received prophylaxis with VGC. The primary objective was to describe the proportion of inappropriately (under- or over-) dosed VGC week-entries. Secondary objectives included breakthrough clinically significant CMV infection (csCMVi) and potential associations between breakthrough-csCMVi and cytopenias with VGC dosing. Among 178 KTR, 131 (73.6%) patients had ≥2 week-entries for the longitudinal data of interest and were included in the outcome analysis, with 1,032 VGC dose week-entries. Overall, 460/1,032 (44.6%) were appropriately dosed, while 234/1,032 (22.7%) and 338/1,032 (32.8%) were under- and over-dosed, respectively. Nineteen (14.5%) patients had a breakthrough-csCMVi, without any associations identified with VCG dosing (p = 0.44). Unlike other cytopenias, a significant association between VGC overdosing and lymphopenia (OR 5.27, 95% CI 1.71-16.22, p = 0.004) was shown. VGC prophylaxis in KTR is frequently inappropriately dosed, albeit without meaningful clinical associations, neither in terms of efficacy nor safety.

Surgical site infections after kidney transplantation are independently associated with graft loss.

Surgical site infections (SSIs) are common health care-associated infections. SSIs after kidney transplantation (K-Tx) can endanger patient and allograft survival. Multicenter studies on this early posttransplant complication are scarce. We analyzed consecutive adult K-Tx recipients enrolled in the Swiss Transplant Cohort Study who received a K-Tx between May 2008 and September 2020. All data were prospectively collected with the exception of the categorization of SSI which was performed retrospectively according to the Centers for Disease Control and Prevention criteria. A total of 58 out of 3059 (1.9%) K-Tx recipients were affected by SSIs. Deep incisional (15, 25.9%) and organ/space infections (34, 58.6%) predominated. In the majority of SSIs (52, 89.6%), bacteria were detected, most frequently Escherichia coli (15, 28.9%), Enterococcus spp. (14, 26.9%), and coagulase-negative staphylococci (13, 25.0%). A BMI ≥25 kg/m2 (multivariable OR 2.16, 95% CI 1.07-4.34, P = .023) and delayed graft function (multivariable OR 2.88, 95% CI 1.56-5.34, P = .001) were independent risk factors for SSI. In Cox proportional hazard models, SSI was independently associated with graft loss (multivariable HR 3.75, 95% CI 1.35-10.38, P = .011). In conclusion, SSI was a rare complication after K-Tx. BMI ≥25 kg/m2 and delayed graft function were independent risk factors. SSIs were independently associated with graft loss.

Impossibility Results on Stability of Phylogenetic Consensus Methods.

We answer two questions raised by Bryant, Francis, and Steel in their work on consensus methods in phylogenetics. Consensus methods apply to every practical instance where it is desired to aggregate a set of given phylogenetic trees (say, gene evolution trees) into a resulting, "consensus" tree (say, a species tree). Various stability criteria have been explored in this context, seeking to model desirable consistency properties of consensus methods as the experimental data are updated (e.g., more taxa, or more trees, are mapped). However, such stability conditions can be incompatible with some basic regularity properties that are widely accepted to be essential in any meaningful consensus method. Here, we prove that such an incompatibility does arise in the case of extension stability on binary trees and in the case of associative stability. Our methods combine general theoretical considerations with the use of computer programs tailored to the given stability requirements. [Associative stability; consensus; extension stability; phylogeny.].

Stationary distributions via decomposition of stochastic reaction networks.

We examine reaction networks (CRNs) through their associated continuous-time Markov processes. Studying the dynamics of such networks is in general hard, both analytically and by simulation. In particular, stationary distributions of stochastic reaction networks are only known in some cases. We analyze class properties of the underlying continuous-time Markov chain of CRNs under the operation of join and examine conditions such that the form of the stationary distributions of a CRN is derived from the parts of the decomposed CRNs. The conditions can be easily checked in examples and allow recursive application. The theory developed enables sequential decomposition of the Markov processes and calculations of stationary distributions. Since the class of processes expressible through such networks is big and only few assumptions are made, the principle also applies to other stochastic models. We give examples of interest from CRN theory to highlight the decomposition.

Bacteremia During the First Year After Solid Organ Transplantation: An Epidemiological Update.

Background: There are limited contemporary data on the epidemiology and outcomes of bacteremia in solid organ transplant recipients (SOTr). Methods: Using the Swiss Transplant Cohort Study registry from 2008 to 2019, we performed a retrospective nested multicenter cohort study to describe the epidemiology of bacteremia in SOTr during the first year post-transplant. Results: Of 4383 patients, 415 (9.5%) with 557 cases of bacteremia due to 627 pathogens were identified. One-year incidence was 9.5%, 12.8%, 11.4%, 9.8%, 8.3%, and 5.9% for all, heart, liver, lung, kidney, and kidney-pancreas SOTr, respectively (P = .003). Incidence decreased during the study period (hazard ratio, 0.66; P < .001). One-year incidence due to gram-negative bacilli (GNB), gram-positive cocci (GPC), and gram-positive bacilli (GPB) was 5.62%, 2.81%, and 0.23%, respectively. Seven (of 28, 25%) Staphylococcus aureus isolates were methicillin-resistant, 2/67 (3%) enterococci were vancomycin-resistant, and 32/250 (12.8%) GNB produced extended-spectrum beta-lactamases. Risk factors for bacteremia within 1 year post-transplant included age, diabetes, cardiopulmonary diseases, surgical/medical post-transplant complications, rejection, and fungal infections. Predictors for bacteremia during the first 30 days post-transplant included surgical post-transplant complications, rejection, deceased donor, and liver and lung transplantation. Transplantation in 2014-2019, CMV donor-negative/recipient-negative serology, and cotrimoxazole Pneumocystis prophylaxis were protective against bacteremia. Thirty-day mortality in SOTr with bacteremia was 3% and did not differ by SOT type. Conclusions: Almost 1/10 SOTr may develop bacteremia during the first year post-transplant associated with low mortality. Lower bacteremia rates have been observed since 2014 and in patients receiving cotrimoxazole prophylaxis. Variabilities in incidence, timing, and pathogen of bacteremia across different SOT types may be used to tailor prophylactic and clinical approaches.

Fast reactions with non-interacting species in stochastic reaction networks.

We consider stochastic reaction networks modeled by continuous-time Markov chains. Such reaction networks often contain many reactions, potentially occurring at different time scales, and have unknown parameters (kinetic rates, total amounts). This makes their analysis complex. We examine stochastic reaction networks with non-interacting species that often appear in examples of interest (e.g. in the two-substrate Michaelis Menten mechanism). Non-interacting species typically appear as intermediate (or transient) chemical complexes that are depleted at a fast rate. We embed the Markov process of the reaction network into a one-parameter family under a two time-scale approach, such that molecules of non-interacting species are degraded fast. We derive simplified reaction networks where the non-interacting species are eliminated and that approximate the scaled Markov process in the limit as the parameter becomes small. Then, we derive sufficient conditions for such reductions based on the reaction network structure for both homogeneous and time-varying stochastic settings, and study examples and properties of the reduction.