2021
Karwath, Andreas; Bunting, Karina V; Gill, Simrat K; Tica, Otilia; Pendleton, Samantha; Aziz, Furqan; Barsky, Andrey D; Chernbumroong, Saisakul; Duan, Jinming; Mobley, Alastair R; Cardoso, Victor Roth; Slater, Luke; Williams, John A; Bruce, Emma-Jane; Wang, Xiaoxia; Flather, Marcus D; Coats, Andrew J S; Gkoutos, Georgios V; Kotecha, Dipak
Redefining beta-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis Journal Article
In: The Lancet, 2021.
Abstract | Links | BibTeX | Tags: artificial intelligence, clustering, crossvalidation, deep learning, EHR, health data science, phenotypes, validation
@article{Karwath_2021,
title = {Redefining beta-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis},
author = {Andreas Karwath and Karina V Bunting and Simrat K Gill and Otilia Tica and Samantha Pendleton and Furqan Aziz and Andrey D Barsky and Saisakul Chernbumroong and Jinming Duan and Alastair R Mobley and Victor Roth Cardoso and Luke Slater and John A Williams and Emma-Jane Bruce and Xiaoxia Wang and Marcus D Flather and Andrew J S Coats and Georgios V Gkoutos and Dipak Kotecha},
url = {https://doi.org/10.1016%2Fs0140-6736%2821%2901638-x},
doi = {10.1016/s0140-6736(21)01638-x},
year = {2021},
date = {2021-08-01},
urldate = {2021-08-01},
journal = {The Lancet},
publisher = {Elsevier BV},
abstract = {Background
Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation.
Methods
Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012).
Findings
15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials.
Interpretation
An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality.
Funding
Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.},
keywords = {artificial intelligence, clustering, crossvalidation, deep learning, EHR, health data science, phenotypes, validation},
pubstate = {published},
tppubtype = {article}
}
Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation.
Methods
Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012).
Findings
15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials.
Interpretation
An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality.
Funding
Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.
2017
Karwath, Andreas; Hubrich, Markus; Kramer, Stefan
Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings, Springer Springer International Publishing, Cham, 2017, ISBN: 978-3-319-59758-4.
Abstract | Links | BibTeX | Tags: alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization
@conference{karwath2017a,
title = {Convolutional Neural Networks for the Identification of Regions of Interests in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer's Disease},
author = {Andreas Karwath and Markus Hubrich and Stefan Kramer},
editor = {en Teije, Annette and Popow, Christian and Holmes, John H. and Sacchi, Lucia},
url = {http://dx.doi.org/10.1007/978-3-319-59758-4_36},
doi = {10.1007/978-3-319-59758-4_36},
isbn = {978-3-319-59758-4},
year = {2017},
date = {2017-06-21},
urldate = {2017-06-21},
booktitle = {Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings},
pages = {316-321},
publisher = {Springer International Publishing},
address = {Cham},
organization = {Springer},
abstract = {When diagnosing patients suffering from dementia based on imaging data like PET scans, the identification of suitable predictive regions of interest (ROIs) is of great importance. We present a case study of 3-D Convolutional Neural Networks (CNNs) for the detection of ROIs in this context, just using voxel data, without any knowledge given a priori. Our results on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the predictive performance of the method is on par with that of state-of-the-art methods, with the additional benefit of potential insights into affected brain regions.},
keywords = {alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization},
pubstate = {published},
tppubtype = {conference}
}