Reassessing the Pediatric Dosing Recommendations for Unfractionated Heparin Using Real-World Data: a Pharmacokinetic-Pharmacodynamic Modeling Approach

Heparin Dosing Regimen Optimization in Veno-Arterial Extracorporeal Membrane Oxygenation: A Pharmacokinetic Analysis

BACKGROUND

Unfractionated heparin is administered in patients undergoing veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Anticoagulation monitoring is recommended, with an anti-activated factor X (anti-Xa) targeting 0.3 to 0.7 IU/mL. Owing to heparin's heterogeneous pharmacokinetic properties, anti-Xa is unpredictable, generating a challenge in anticoagulation practices. The aim of this study was to build a pharmacokinetic model of heparin accounting for potential confounders, and derive an optimized dosing regimen for a given anti-Xa target.

METHODS

Adult patients undergoing VA-ECMO were included between January 2020 and June 2021. Anticoagulation was managed with an initial 100 IU/kg heparin loading dose followed by a continuous infusion targeting 0.2 to 0.7 IU/mL anti-Xa. The data were split into model development and model validation cohorts. Statistical analysis was performed using a nonlinear mixed effects modeling population approach. Model-based simulations were performed to develop an optimized dosing regimen targeting the desired anti-Xa.

RESULTS

A total of 74 patients were included, with 1703 anti-Xa observations. A single-compartment model best fitted the data. Interpatient variability for distribution volume was best explained by body weight, C-reactive protein and ECMO indication (post-cardiotomy shock or medical cardiogenic shock), and interpatient variability for elimination clearance was best explained by serum creatinine and C-reactive protein. Simulations using the optimized regimen according to these covariates showed accurate anti-Xa target attainment.

CONCLUSION

In adult patients on VA-ECMO, heparin's effect increased with serum creatinine and medical indication, whereas it decreased with body weight and systemic inflammation. We propose an optimized dosing regimen accounting for key covariates, capable of accurately predicting a given anti-Xa target.

Factors Influencing Unfractionated Heparin Pharmacokinetics and Pharmacodynamics During a Cardiopulmonary Bypass

BACKGROUND

Unfractionated heparin (UFH) is commonly used during cardiac surgery with a cardiopulmonary bypass to prevent blood clotting. However, empirical administration of UFH leads to variable responses. Pharmacokinetic and pharmacodynamic modeling can be used to optimize UFH dosing and perform real-time individualization. In previous studies, many factors that could influence UFH pharmacokinetics/pharmacodynamics had not been taken into account such as hemodilution or the type of UFH. Few covariates were identified probably owing to a lack of statistical power. This study aims to address these limitations through a meta-analysis of individual data from two studies.

METHODS

An individual patient data meta-analysis was conducted using data from two single-center prospective observational studies, where different UFH types were used for anticoagulation. A pharmacodynamic/pharmacodynamic model of UFH was developed using a non-linear mixed-effects approach. Time-varying covariates such as hemodilution and fluid infusions during a cardiopulmonary bypass were considered.

RESULTS

Activities of UFH's anti-activated factor/anti-thrombin were best described by a two-compartment model. Unfractionated heparin clearance was influenced by body weight and the specific UFH type. Volume of distribution was influenced by body weight and pre-operative fibrinogen levels. Pharmacodynamic data followed a log-linear model, accounting for the effect of hemodilution and the pre-operative fibrinogen level. Equations were derived from the model to personalize UFH dosing based on the targeted activated clotting time level and patient covariates.

CONCLUSIONS

The population model effectively characterized UFH's pharmacokinetics/pharmacodynamics in cardiopulmonary bypass patients. This meta-analysis incorporated new covariates related to UFH's pharmacokinetics/pharmacodynamics, enabling personalized dosing regimens. The proposed model holds potential for individualization using a Bayesian estimation.

Pharmacokinetic Modeling Using Real-World Data to Optimize Unfractionated Heparin Dosing in Pediatric Patients on Extracorporeal Membrane Oxygenation and Evaluate Target Achievement-Clinical Outcomes Relationship

Unfractionated heparin (UFH) is a commonly used anticoagulant for pediatric patients undergoing extracorporeal membrane oxygenation (ECMO), but evidence is lacking on the ideal dosing. We aimed to (1) develop a population pharmacokinetic (PK) model for UFH, measured through anti-factor Xa assay; (2) optimize UFH starting infusions and dose titrations through simulations; and (3) explore UFH exposure-clinical outcomes relationship. Data from 218 patients admitted to Utah's Primary Children's Hospital were retrospectively collected. A 1-compartment PK model with time-varying clearance (CL) adequately described UFH PK. Weight on CL and volume of distribution and ECMO circuit change on CL were significant covariates. The typical estimates for initial CL and first-order rate constant to reach steady-state CL were 0.57 L/(h·10 kg) and 0.02/h. Comparable to non-ECMO patients, the typical steady-state CL was 0.81 L/(h·10 kg). Simulations showed that a 75 IU/kg UFH bolus dose followed by starting infusions of 25 and 20 IU/h/kg for patients aged younger than 6 years and 6 years or older, respectively, achieved the therapeutic target in 56.6% of all patients, whereas only 3.1% exceeded the target. The proposed UFH titration schemes achieved the target in more than 90% of patients while less than 0.63% were above the target after 24 and 48 hours of treatment. The median intensive care unit survival time in patients within and below the target at 24 hours was 136 and 66 hours, respectively. In conclusion, PK model of UFH was developed for pediatric patients on ECMO. The proposed UFH dosing scheme attained the anti-factor Xa target rapidly and safely.

Model-based approach to identify predictors of paclitaxel-induced myelosuppression in "real-world" administration

Taxanes are currently the most frequently used chemotherapeutic agents in cancer care, where real-world use has focused on minimizing adverse events and standardizing the delivery. Myelosuppression is a well-characterized, adverse pharmacodynamic effect of taxanes. Electronic health records (EHRs) comprise data collected during routine clinical care that include patients with heterogeneous demographic, clinical, and treatment characteristics. Application of pharmacokinetic/pharmacodynamic (PK/PD) modeling to EHR data promises new insights on the real-world use of taxanes and strategies to improve therapeutic outcomes especially for populations who are typically excluded from clinical trials, including the elderly. This investigation: (i) leveraged previously published PK/PD models developed with clinical trial data and addressed challenges to fit EHR data, and (ii) evaluated predictors of paclitaxel-induced myelosuppression. Relevant EHR data were collected from patients treated with paclitaxel-containing chemotherapy at Inova Schar Cancer Institute between 2015 and 2019 (n = 405). Published PK models were used to simulate mean individual exposures of paclitaxel and carboplatin, which were linearly linked to absolute neutrophil count (ANC) using a published semiphysiologic myelosuppression model. Elderly patients (≥70 years) constituted 21.2% of the dataset and 2274 ANC measurements were included in the analysis. The PD parameters were estimated and matched previously reported values. The baseline ANC and chemotherapy regimen were significant predictors of paclitaxel-induced myelosuppression. The nadir ANC and use of supportive treatments, such as growth factors and antimicrobials, were consistent across age quantiles suggesting age had no effect on paclitaxel-induced myelosuppression. In conclusion, EHR data could complement clinical trial data in answering key therapeutic questions.