The purpose of this study was to investigate the population pharmacokinetics of vancomycin in patients undergoing open heart surgery. In this observational pharmacokinetic study, multiple blood samples were drawn over a 48-h period of intravenous vancomycin in patients who were undergoing open heart surgery. Blood samples were analyzed using an Architect i4000SR immunoassay analyzer. Population pharmacokinetic models were developed using Monolix 4.4 software. Pharmacokinetic-pharmacodynamic (PK-PD) simulations were performed to explore the ability of different dosage regimens to achieve the pharmacodynamic targets. A total of 168 blood samples were analyzed from 28 patients. The pharmacokinetics of vancomycin are best described by a two-compartment model with between-subject variability in clearance (CL), the volume of distribution of the central compartment (), and volume of distribution of the peripheral compartment (). The CL and the of vancomycin were related to creatinine CL (CL), body weight, and albumin concentration. Dosing simulations showed that standard dosing regimens of 1 and 1.5 g failed to achieve the PK-PD target of AUC/MIC > 400 for an MIC of 1 mg/liter, while high weight-based dosing regimens were able to achieve the PK-PD target. In summary, the administration of standard doses of 1 and 1.5 g of vancomycin two times daily provided inadequate antibiotic prophylaxis in patients undergoing open heart surgery. The same findings were obtained when 15- and 20-mg/kg doses of vancomycin were administered. Achieving the PK-PD target required higher doses (25 and 30 mg/kg) of vancomycin.

The purpose of this study was to investigate the population pharmacokinetics (PK) of cefuroxime in patients undergoing coronary artery bypass graft (CABG) surgery. In this observational pharmacokinetic study, multiple blood samples were collected over a 48-h interval of intravenous cefuroxime administration. The samples were analyzed by using a validated high-performance liquid chromatography (HPLC) method. Population pharmacokinetic models were developed using Monolix (version 4.4) software. Pharmacokinetic-pharmacodynamic (PD) simulations were performed to explore the ability of different dosage regimens to achieve the pharmacodynamic targets. A total of 468 blood samples from 78 patients were analyzed. The PK for cefuroxime were best described by a two-compartment model with between-subject variability on clearance, the volume of distribution of the central compartment, and the volume of distribution of the peripheral compartment. The clearance of cefuroxime was related to creatinine clearance (CL). Dosing simulations showed that standard dosing regimens of 1.5 g could achieve the PK-PD target of the percentage of the time that the free concentration is maintained above the MIC during a dosing interval () of 65% for an MIC of 8 mg/liter in patients with a CL of 30, 60, or 90 ml/min, whereas this dosing regimen failed to achieve the PK-PD target in patients with a CL of ≥125 ml/min. In conclusion, administration of standard doses of 1.5 g three times daily provided adequate antibiotic prophylaxis in patients undergoing CABG surgery. Lower doses failed to achieve the PK-PD target. Patients with high CL values required either higher doses or shorter intervals of cefuroxime dosing. On the other hand, lower doses (1 g three times daily) produced adequate target attainment for patients with low CL values (≤30 ml/min).

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Background The debate about the optimal mitral valve prosthesis continues. We aimed to compare the early and late outcomes, including stroke, bleeding, survival, and reoperation after isolated mitral valve replacement (MVR) using tissue versus mechanical valves. Methods This retrospective cohort study included 291 patients who had isolated MVR from 2005 to 2015. Patients were grouped into the tissue valve group (n = 140) and the mechanical valve group (n = 151). Results There were no differences in duration of mechanical ventilation, hospital stay, and hospital mortality between groups. Fifteen patients required cardiac rehospitalization, nine in the tissue valve group, and six in the mechanical valve group (p = .44). Stroke occurred in nine patients, five with tissue valves, and four with mechanical valves (p = .66). Bleeding occurred in 22 patients, seven patients with tissue valves, and 15 patients with mechanical valves (p = .09). Freedom from reoperation was 95%, 93%, 84%, 67% at 3, 5, 7, and 10 years for tissue valve and 97%, 96%, 96%, and 93% for mechanical valves, respectively (p˂ .001). The median follow‐up was 84 months (Q1: Q3: 38–139). Survival at 3, 5, 7, and 10 years was 94%, 91%, 89%, 86% in tissue valves and 96%, 93%, 91%, 91% in mechanical valves, respectively (p = .49). Conclusions Tissue valve degeneration is still an issue even in the new generations of mitral tissue valves. The significant risk of reoperation in patients with mitral tissue valves should be considered when using those valves in younger patients. Mechanical valves remain a valid option for all age groups.

<p> Many long-term landscape evolution models are currently combining equations describing the evolution of the surface under fluvial incision (using the so-called stream-power incision model) and hillslope transport (often modeled as linear diffusion). Some models combine these two terms (e.g., Fastscape) and implicitly contain a transition from hillslope to fluvial processes dependent on the ratio of the diffusive and fluvial erosional parameters, D and K respectively (Perron et al., 2009). Other models require as input a hillslope-fluvial transition length (e.g., DAC) and apply hillslope erosion from the ridge-top to this lengthscale and fluvial incision only downstream of it. Still, in both cases the influence of non-linear processes such as landslide and debris-flow on this transition are not accounted.</p><p>We have analyzed the scaling between slope gradient and drainage areas in LIDAR-derived high-resolution DEM for >30 catchments, with apparent steady-state morphology, and where long-term denudation estimates, E, were estimated from cosmogenic nuclides . The catchments span different lithology, climate and denudation rates from ~0.05 to ~3 mm/yr but show a consistent pattern where substantial portion of upstream channels exhibit slope gradient roughly constant with drainage area, and transition towards a negative scaling between slope and area (characteristic of fluvial processes) after a critical drainage area, A<sub>c.</sub> Previous work (Stock and Dietrich, 2003) suggested the portion with constant slope may be dominated by erosion due to debris-flow processes, maintaining the channel at a critical slope, S<sub>df</sub>.</p><p>Here we show that both S<sub>df</sub>, and A<sub>c</sub>, are strongly correlated to the long-term denudation, E. Further, we find that S<sub>df</sub> seems to saturate at a critical slope angle, S<sub>c</sub> , near 40° when denudation rates reach about 1mm/yr consistent with predictions for the slope of a non-linear diffusive hillsllopes (Roering et al., 2007). Combining this expression with the empirical model for the steady-state slope of Stock and Dietrich, 2003, and enforcing the consistency with a stream-power-law downstream we find that the steady state values for S<sub>df</sub> and A<sub>c</sub> can be fully expressed as analytical functions of E, K, D and S<sub>c</sub>. We assess the validity of these expressions with independent estimate of K and D extracted from local channel steepness and hilltop curvature. </p><p>As the impact of debris flow on landscape morphology seems ubiquitous on landscape with more than 0.1 mm/yr of erosion, the classical landscape evolution formulation may need to be upgraded to correctly represent steady-state morphology of the upstream part of catchment (<span>i.e.</span>, <1km<sup>2</sup>). Even if it still lack physical basis, we propose a formulation that adequately represent the steady state morphology from ridge to large drainage area. We show that it yield a new definition of Chi that may be better match the morphology of channel approaching ridges and we also discuss how to implement this new-steady state formulation in landscape evolution models.</p>