Optimising the Nadroparin Dose for Thromboprophylaxis During Hemodialysis by Developing a Population Pharmacodynamic Model Using Anti-Xa Levels

Patient Population and Study Design

This post hoc analysis was conducted with data obtained from two hospitals in the Netherlands: the University Medical Center Groningen (UMCG) in Groningen and the Haga hospital in The Hague. A waiver (METc 2021/429) was obtained from the medical ethics review committee.

University Medical Center Groningen (UMCG) Dataset

The UMCG dataset consisted of data collected previously in a prospective clinical trial, which was approved by the local medical ethics review committee. All patients provided written informed consent. The patient population, study design, and main results have been published previously [11].

Haga Hospital Dataset

The Haga dataset consisted of data collected during dialysis sessions in routine clinical practice. Data of patients, aged 18 years or older, who were on hemodialysis and received nadroparin as thromboprophylaxis were included. Patients who objected to participating in scientific research were excluded.

Dialysis Setting

In both datasets, patients receiving hemodialysis (with a session duration of 4 h) used low flux F8HPS or F6HPS dialyzers (Fresenius Medical Care, Bad Homburg, Germany), with an ultrafiltration coefficient (KUF) of 18 and 13 mL/h × mmHg and an effective surface area of 1.8 and 1.3 m2, respectively. Dialysate flow rate for patients receiving hemodialysis (HD) was 500 mL/min. For hemodiafiltration (HDF), the FX800 (Fresenius Medical care) dialyzer with a KUF of 63 mL/h × mmHg and an effective surface area of 1.8 m2 was used. HDF patients were treated with post-dilution hemodiafiltration with a dialysate flow of 500 mL/min. All HDF machines were equipped with AutoSub plus signal analysis software that automatically adapted the substitution fluid flow according to the blood flow, blood viscosity, and dialyzer characteristics. The target convection volume was ≥ 20 L/session.

Nadroparin Dosing

In both hospitals, nadroparin was administered by an intravenous bolus dose at the start of the dialysis session. Patients included in this study received nadroparin dosages as recommended by the Guideline of the Dutch Federation of Nephrology [5]. According to this Guideline, 2850 IU nadroparin was administered in patients weighing < 50 kg and 3800 IU in patients weighing ≥ 50 kg. Higher dosages are advised in patients with previously observed thrombus formation and lower dosages in patients with an anticipated increased risk of bleeding events [5]. The actual administered dose was recorded at the time of the dialysis session.

Blood Sampling and Analysis

In patients from the Haga hospital, anti-Xa levels were determined in samples collected at t = 5, t = 30, and t = 240 min after nadroparin administration. In patients from the UMCG, anti-Xa levels were determined in samples collected at t = 0 (immediately before nadroparin administration) and at 60, 180, and 240 min after nadroparin administration. Deviations from these sampling times were recorded.

Samples were collected in 3.2% buffered sodium citrate-containing tubes (0.109 M, BD Vacutainer, Becton Dickinson, UK) and analyzed in the Clinical Chemical Laboratories of the hospitals. Both laboratories are ISO15189 certified. Anti-Xa levels were measured using a two-stage anti-Xa chromogenic assay (Hyphen BioMed, Neuville-sur-Oise, France) and an automated anti-Xa chromogenic assay (Siemens, Marburg, Germany) in the Haga hospital and UMCG, respectively. All assays were subject to internal and external quality assessments.

Data Collection

All patient and dialysis characteristics were collected from Diamant® (Diasoft, Leusden, the Netherlands), an electronic patient record system specifically developed for dialysis patients. Data on drug use that could potentially influence the required nadroparin dose, such as platelet inhibitors, oral anticoagulants and calcium antagonists, were also extracted from Diamant® [9, 10, 12].

Development of the Population Pharmacodynamic Model

Pre- and post-processing of data were conducted using R (R version 4.0.3, R Foundation, Vienna, Austria). The population pharmacodynamic model was developed using non-linear mixed-effects modelling (NONMEM) software (version 7.5, ICON Development Solutions, Ellicott City, MD, USA). Model parameters were obtained using the first-order conditional estimation with interaction method.

Before the analysis, the anti-Xa levels were log-transformed to improve model stability. Anti-Xa levels below the limit of quantification (Haga hospital: < 0.01 IU/mL; UMCG < 0.08 IU/mL) were excluded. For the structural model, one- and two-compartmental models with first-order elimination were evaluated. Subsequently, for the stochastic model, between-subject variability was explored on all model parameters by including random effects that assumed a log-normal distribution. Covariance between these random effects was also evaluated. Residual or within-subject variability was modelled using a log additive error. Finally, the influence of covariates on model parameters was explored using a forward selection approach.

The following covariate effects on clearance and volume of distribution parameters were evaluated: age, sex, total body weight (TBW), body mass index (BMI), lean body mass (LBM), residual kidney function (RKF), use of co-medication (i.e., platelet inhibitors, oral anticoagulation, and calcium antagonists), type of dialyzer, mode of dialysis (HD or HDF), total ultrafiltration volume and dialysis center. RKF was defined as diuresis > 200 mL/24 h. LBM was calculated as follows [13]:

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LBM and TBW were normalized to 65 kg and 70 kg, respectively, and evaluated using allometric theory with fixed scaling exponents, i.e., 0.75 for clearance parameters and 1.0 for volume of distribution parameters [14]. Categorical covariates were modelled proportionally and continuous covariates were modelled median-normalized using a power model.

Model selection and evaluation were conducted numerically by comparison of the objective function value (OFV) [15]. A significance level of p < 0.05, corresponding to a decrease of 3.84 in OFV, was considered a statistically significant improvement in overall model fit. Furthermore, the relative standard error of the model parameters, shrinkage, and the condition number were evaluated [15, 16]. Visually, model performance was evaluated, using standard goodness-of-fit plots, distribution of the random effects, and individual observed and predicted anti-Xa level versus time curves. Prediction-corrected visual predictive checks were constructed for the final model using 1000 simulations [17].

Simulations

Model simulations were performed using the RxODE package (version 1.0.9) using R. Per timepoint 10,000 subjects were simulated.

The therapeutic window used in this study was defined as anti-Xa levels of 0.4-2.0 IU/mL during the entire dialysis session. An anti-Xa level ≥ 0.4 IU/mL has been suggested to provide adequate anticoagulation and was therefore selected as the efficacy reference value [9, 10]. The percentage of patients above the efficacy reference value at the end of hemodialysis (i.e., 240 min after nadroparin bolus) was simulated for different doses and patient and dialysis characteristics that significantly influenced anti-Xa levels. Simulations for efficacy were also conducted using anti-Xa levels of 0.2, 0.3, 0.5, 0.7, and 1.0 IU/mL to provide a comprehensive overview.

In patients with subcutaneously administered therapeutically dosed LMWHs, peak anti-Xa levels ≥ 2.0 IU/mL and ≥ 1.0 IU/mL with a once- and twice-daily dosing regimen, respectively, have been associated with a significantly increased bleeding risk [18,19,20]. As nadroparin is administered once before hemodialysis, we considered a peak anti-Xa level (i.e., 5 min after nadroparin bolus) of  2.0 IU/mL as the safety reference value. Simulations for safety were also conducted using peak anti-Xa levels of 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0 IU/mL.

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