An ensemble model for predicting therapeutic response to unfractionated heparin therapy (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Unfractionated heparin (UFH), is an anticoagulant drug considered a high-risk medication in that an excessive dose can cause bleeding, while an insufficient dose can lead to a recurrent embolic event. Following initiation of intravenous (IV) UFH, the therapeutic response is monitored using a measure of blood clotting time known as the activated partial thromboplastin time (aPTT). Clinicians iteratively adjust the dose of UFH to a target aPTT range, with the usual therapeutic target range between 60 to 100 seconds. </sec> <sec> <title>OBJECTIVE</title> The aim of this study was to develop and validate a ML algorithm to predict, aPTT within 12 hours after a specified bolus and maintenance dose of UFH. </sec> <sec> <title>METHODS</title> This was a retrospective cohort study of 3273 episodes of care from January 2017 to August 2020 using data collected from electronic health records (EHR) of five hospitals in Queensland, Australia. Data from four hospitals were used to build and test ensemble models using cross validation, while the data from the fifth hospital was used for external validation. Modelling was performed using H2O Driverless AI® an automated ML tool, and 17 different experiments were conducted in an iterative process to optimise model accuracy. </sec> <sec> <title>RESULTS</title> In predicting aPTT, the best performing experiment produced an ensemble with 4x LightGBM models with a root mean square error (RMSE) of 31.35. This dataset was re-purposed as a multi-classification task (sub-therapeutic, therapeutic, and supra-therapeutic aPTT result) and achieved a 59.9% accuracy and area under the receiver operating characteristic curve (AUC) of 0.735. External validation yielded similar results: RMSE of 30.52 +/- 1.29 for the prediction model, and accuracy of 56.8% +/- 3.15 and AUC of 0.724 for the multi-classification model. </sec> <sec> <title>CONCLUSIONS</title> According to our knowledge, this is the first study of ML applied to IV UFH dosing that has been developed and externally validated in a multisite adult general medical inpatient setting. We present the processes of data collection, preparation, and feature engineering for purposes of replication. </sec>

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