A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study

Li, Dongkai; Gao, Jianwei; Hong, Na; Wang, Hao; Su, Longxiang; Li, Chun; He, Jie; Jiang, Huizhen; Wang, Qiang; Long, Yun; Zhu, Weiguo

doi:10.2196/27118

Cited by 9 publications

(17 citation statements)

References 20 publications

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“…Five other studies have reported using supervised learning in developing models to assist with UFH dosing [ 29 , 30 , 32 , 34 , 36 ]. To date, although 3 report accuracy [ 32 , 33 , 37 ] superior to that of our ensemble approach, these models were restricted to ICU data sets from the United States and China and are, therefore, not generalizable to the general medical and surgical wards of hospitals where UFH is most frequently administered. Furthermore, compared with all existing studies of ML in UFH dosing, ours was the only one, apart from one small external validation in a hemodialysis setting [ 31 , 66 ], to evaluate model performance when applied to new unseen data.…”

Section: Discussionmentioning

confidence: 99%

“…All studies provided limited reporting of reproducibility, and, except for one study by Smith et al [ 31 ], none were validated in a new cohort. Most recently, Li et al [ 37 ] reported the development and validation of a multiclass aPTT model and subsequent dose prediction application for use in the ICU setting using a shallow neural network approach. The top 5 features for both data sets included a patient’s baseline aPTT, patient weight, total UFH administered, serum creatinine, and age.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

Abdel-Hafez¹,

Scott²,

Falconer³

et al. 2022

Interact J Med Res

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Background Unfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range. Thus, a more efficient approach is required. Objective In this study, we aimed to develop and validate a machine learning (ML) algorithm to predict aPTT within 12 hours after a specified bolus and maintenance dose of UFH. Methods This was a retrospective cohort study of 3019 patient episodes of care from January 2017 to August 2020 using data collected from electronic health records of 5 hospitals in Queensland, Australia. Data from 4 hospitals were used to build and test ensemble models using cross-validation, whereas data from the fifth hospital were used for external validation. We built 2 ML models: a regression model to predict the aPTT value after a UFH bolus dose and a multiclass model to predict the aPTT, classified as subtherapeutic (aPTT <70 seconds), therapeutic (aPTT 70-100 seconds), or supratherapeutic (aPTT >100 seconds). Modeling was performed using Driverless AI (H2O), an automated ML tool, and 17 different experiments were iteratively conducted to optimize model accuracy. Results In predicting aPTT, the best performing model was an ensemble with 4x LightGBM models with a root mean square error of 31.35 (SD 1.37). In predicting the aPTT class using a repurposed data set, the best performing ensemble model achieved an accuracy of 0.599 (SD 0.0289) and an area under the receiver operating characteristic curve of 0.735. External validation yielded similar results: root mean square error of 30.52 (SD 1.29) for the aPTT prediction model, and accuracy of 0.568 (SD 0.0315) and area under the receiver operating characteristic curve of 0.724 for the aPTT multiclassification model. Conclusions To the best of our knowledge, this is the first ML model applied to intravenous UFH dosing that has been developed and externally validated in a multisite adult general medical and surgical inpatient setting. We present the processes of data collection, preparation, and feature engineering for replication.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

Abdel-Hafez¹,

Scott²,

Falconer³

et al. 2022

Interact J Med Res

View full text Add to dashboard Cite

show abstract

“…We prefer the prediction of the numerical value since it makes no assumption of the aPTT target range. However, most recent literature on similar-sized data sets consider aPTT after heparin treatment as a multiclass prediction with 3 distinct ranges (subtherapeutic, therapeutic, or supratherapeutic) [14,15,18].…”

Section: Introductionmentioning

confidence: 99%

“…In previous model comparison studies [15,16,18], it has been demonstrated that artificial neural networks show the highest performance on aPTT prediction tasks.…”

Section: Introductionmentioning

confidence: 99%

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A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study

Boie¹,

Engelhardt²,

Coenen³

et al. 2022

JMIR Med Inform

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Background Anticoagulation therapy with heparin is a frequent treatment in intensive care units and is monitored by activated partial thromboplastin clotting time (aPTT). It has been demonstrated that reaching an established anticoagulation target within 24 hours is associated with favorable outcomes. However, patients respond to heparin differently and reaching the anticoagulation target can be challenging. Machine learning algorithms may potentially support clinicians with improved dosing recommendations. Objective This study evaluates a range of machine learning algorithms on their capability of predicting the patients’ response to heparin treatment. In this analysis, we apply, for the first time, a model that considers time series. Methods We extracted patient demographics, laboratory values, dialysis and extracorporeal membrane oxygenation treatments, and scores from the hospital information system. We predicted the numerical values of aPTT laboratory values 24 hours after continuous heparin infusion and evaluated 7 different machine learning models. The best-performing model was compared to recently published models on a classification task. We considered all data before and within the first 12 hours of continuous heparin infusion as features and predicted the aPTT value after 24 hours. Results The distribution of aPTT in our cohort of 5926 hospital admissions was highly skewed. Most patients showed aPTT values below 75 s, while some outliers showed much higher aPTT values. A recurrent neural network that consumes a time series of features showed the highest performance on the test set. Conclusions A recurrent neural network that uses time series of features instead of only static and aggregated features showed the highest performance in predicting aPTT after heparin treatment.

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Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

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The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N=23, 35.9%) and anti‐infectives (N=21, 32.8%), and there is currently only very limited attention for special populations such as children (N=22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

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A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study

Cited by 9 publications

References 20 publications

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study

Machine Learning: A New Approach for Dose Individualization

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