We have located links that may give you full text access.
JOURNAL ARTICLE
REVIEW
Systematic Review of Machine Learning Models for Personalised Dosing of Heparin.
British Journal of Clinical Pharmacology 2021 April 10
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.
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.
Full text links
Trending Papers
Acute and non-acute decompensation of liver cirrhosis (47/130).Liver International : Official Journal of the International Association for the Study of the Liver 2024 March 2
Guide to Utilization of the Microbiology Laboratory for Diagnosis of Infectious Diseases: 2024 Update by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM).Clinical Infectious Diseases 2024 March 6
Status epilepticus: what's new for the intensivist.Current Opinion in Critical Care 2024 Februrary 15
Administration of methylene blue in septic shock: pros and cons.Critical Care : the Official Journal of the Critical Care Forum 2024 Februrary 17
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app