A real‐world data‐driven approach to optimizing enoxaparin dosing in burn patients

A population PK model of enoxaparin was developed in burn patients using real-world data. Model-informed dosing improved early prophylactic target attainment compared with EQ dosing, providing a practical strategy for optimizing enoxaparin prophylaxis in this high-risk population.

Authors

Institutions

Publication Details

Journal
British Journal of Clinical Pharmacology
Published
2025-11-23
DOI
https://doi.org/10.1002/bcp.70347
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A real‐world data‐driven approach to optimizing enoxaparin dosing in burn patients

Venkata Yellepeddi, Mathangi Gopalakrishnan, Ann Marie Prazak, Babajide Shenkoya
British Journal of Clinical Pharmacology
Sepsis Diagnosis and Treatment
article

A real‐world data‐driven approach to optimizing enoxaparin dosing in burn patients

Venkata Yellepeddi, Mathangi Gopalakrishnan, Ann Marie Prazak, Babajide Shenkoya
article en

Abstract

A population PK model of enoxaparin was developed in burn patients using real-world data. Model-informed dosing improved early prophylactic target attainment compared with EQ dosing, providing a practical strategy for optimizing enoxaparin prophylaxis in this high-risk population.

British Journal of Clinical PharmacologyVol. 92(4)
University of Maryland, Baltimore (US), University of Utah (US), Spencer Foundation (US)
Openalex Percentile: Top 53%
Sepsis Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.