Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 12, 2021
Date Accepted: Mar 24, 2021
A clinical prediction model to predict heparin treatment outcomes and provide dosage recommendations: Retrospective Study
ABSTRACT
Background:
Unfractionated heparin (UFH) is widely used in the ICU as an anticoagulant. However, weight-based heparin dosing has been shown to be suboptimal and may place patients at unnecessary risk during their ICU stay.
Objective:
In this study, we therefore intended to develop and validate machine learning based model to predict heparin treatment outcomes and to provide dosage recommendations to clinicians.
Methods:
A shallow neural network model was adopted on a retrospective cohort of patients from the Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC III) database and patients admitted to the Peking Union Medical College Hospital (PUMCH). We modeled the subtherapeutic, normal, and supratherapeutic activated partial thromboplastin time (aPTT) as the outcomes of heparin treatment and used a group of clinical features for modeling. Our model classifies patients into three different therapeutic states. We tested the prediction ability of our model and evaluated its performance by using accuracy, the kappa coefficient, precision, recall, and the F1 score. Furthermore, a dosage recommendation module was designed and evaluated for clinical decision support purpose.
Results:
A total of 3,607 patients selected from MIMIC III and 1,549 patients admitted to the PUMCH that met our criteria were included in this study. The shallow neural network model showed results of F1 scores 0.887 (MIMIC III) and 0.925 (PUMCH). When compared with actual dosage prescribed, our model recommended increasing dosage for 72.2% (MIMIC III) and 64.7% (PUMCH) subtherapeutic patients and decreasing dosage for 80.9% (MIMIC III) and 76.7% (PUMCH) supratherapeutic patients, suggesting that the recommendations can contribute to clinical improvements and that they may effectively reduce the time to optimal dosage in the clinical setting.
Conclusions:
The evaluation of our model for predicting heparin treatment outcomes demonstrated that the developed model is potentially applicable to reducing the mis-dosage of heparin and providing appropriate decision recommendations to clinicians.
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