Systematic Review of Machine Learning Models for Personalised Dosing of Heparin.

AIM: To identify and critically appraise studies of prediction models, developed using machine learning (ML) methods, for determining the optimal dosing of unfractionated heparin (UFH).

METHODS: Embase, PubMed, CINAHL, Web of Science, International Pharmaceutical Abstracts and IEEE Xplore databases were searched from inception to 31 January 2020 to identify relevant studies using key search terms synonymous with artificial intelligence or ML, 'prediction', 'dose', 'activated partial thromboplastin time (aPTT)' and 'UFH.' Studies had to have used ML methods for developing models that predicted optimal dose of UFH or target therapeutic aPTT levels in the hospital setting. The CHARMS Checklist was used to assess quality and risk of bias of included studies.

RESULTS: Of 8,393 retrieved abstracts, 61 underwent full text review, and eight studies met inclusion criteria. Four studies described models for predicting aPTT, three studies of models predicting optimal dose of heparin during dialysis, and one study a model that used surrogate outcomes of clotting and bleeding to predict a therapeutic aPTT. Studies varied widely in reporting of study participants, feature characterisation and selection, handling of missing data, sample size calculations and the intended clinical application of the model. Only one study conducted an external validation, and no studies evaluated model impacts in clinical practice.

CONCLUSION: Studies of ML models for UFH dosing are few, and none report a model ready for routine clinical use. Existing studies are limited by low methodological quality, inadequate reporting of study factors, and absence of external validation and impact analysis.