Machine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis.

BACKGROUND: Disease progression in patients with mild-to-moderate aortic stenosis is heterogenous and requires periodic echocardiographic examinations to evaluate severity. OBJECTIVES: This study sought to explore the use of machine learning to optimize aortic stenosis echocardiographic surveillance automatically. METHODS: The study investigators trained, validated, and externally applied a machine learning model to predict whether a patient with mild-to-moderate aortic stenosis will develop severe valvular disease at 1, 2, or 3 years. Demographic and echocardiographic patient data to develop the model were obtained from a tertiary hospital consisting of 4,633 echocardiograms from 1,638 consecutive patients. The external cohort was obtained from an independent tertiary hospital, consisting of 4,531 echocardiograms from 1,533 patients. Echocardiographic surveillance timing results were compared with the European and American guidelines echocardiographic follow-up recommendations. RESULTS: In internal validation, the model discriminated severe from nonsevere aortic stenosis development with an area under the receiver-operating characteristic curve (AUC-ROC) of 0.90, 0.92, and 0.92 for the 1-, 2-, or 3-year interval, respectively. In external application, the model showed an AUC-ROC of 0.85, 0.85, and 0.85, for the 1-, 2-, or 3-year interval. A simulated application of the model in the external validation cohort resulted in savings of 49% and 13% of unnecessary echocardiographic examinations per year compared with European and American guideline recommendations, respectively. CONCLUSIONS: Machine learning provides real-time, automated, personalized timing of next echocardiographic follow-up examination for patients with mild-to-moderate aortic stenosis. Compared with European and American guidelines, the model reduces the number of patient examinations.

Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.

Background: The integrated approach to electrical cardioversion (EC) in atrial fibrillation (AF) is complex; candidates can resolve spontaneously while waiting for EC, and post-cardioversion recurrence is high. Thus, it is especially interesting to avoid the programming of EC in patients who would restore sinus rhythm (SR) spontaneously or present early recurrence. We have analyzed the whole elective EC of the AF process using machine-learning (ML) in order to enable a more realistic and detailed simulation of the patient flow for decision making purposes. Methods: The dataset consisted of electronic health records (EHRs) from 429 consecutive AF patients referred for EC. For analysis of the patient outcome, we considered five pathways according to restoring and maintaining SR: (i) spontaneous SR restoration, (ii) pharmacologic-cardioversion, (iii) direct-current cardioversion, (iv) 6-month AF recurrence, and (v) 6-month rhythm control. We applied ML classifiers for predicting outcomes at each pathway and compared them with the CHA2DS2-VASc and HATCH scores. Results: With the exception of pathway (iii), all ML models achieved improvements in comparison with CHA2DS2-VASc or HATCH scores (p < 0.01). Compared to the most competitive score, the area under the ROC curve (AUC-ROC) was: 0.80 vs. 0.66 for predicting (i); 0.71 vs. 0.55 for (ii); 0.64 vs. 0.52 for (iv); and 0.66 vs. 0.51 for (v). For a threshold considered optimal, the empirical net reclassification index was: +7.8%, +47.2%, +28.2%, and +34.3% in favor of our ML models for predicting outcomes for pathways (i), (ii), (iv), and (v), respectively. As an example tool of generalizability of ML models, we deployed our algorithms in an open-source calculator, where the model would personalize predictions. Conclusions: An ML model improves the accuracy of restoring and maintaining SR predictions over current discriminators. The proposed approach enables a detailed simulation of the patient flow through personalized predictions.

Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics.

BACKGROUND: Machine learning (ML) has arrived in medicine to deliver individually adapted medical care. This study sought to use ML to discriminate stent restenosis (SR) compared with existing predictive scores of SR. To develop an easily applicable model, we performed our predictions without any additional variables other than those obtained in daily practice. METHODS: The dataset, obtained from the Grupo de Análisis de la Cardiopatía Isquémica Aguda (GRACIA)-3 trial, consisted of 263 patients with demographic, clinical, and angiographic characteristics; 23 (9%) of them presented with SR at 12 months after stent implantation. A methodology to work with small imbalanced datasets, based in cross-validation and the precision/recall (PR) plots, was used, and state-of-the-art ML classifiers were trained. RESULTS: Our best performing model (0.46, area under the PR curve [AUC-PR]) was developed with an extremely randomized trees classifier, which showed better performance than chance alone (0.09 AUC-PR, corresponding to the 9% of patients presenting SR in our dataset) and 3 existing scores; Prevention of Restenosis With Tranilast and its Outcomes (PRESTO)-1 (0.31 AUC-PR), PRESTO-2 (0.27 AUC-PR), and Evaluation of Drug-Eluting Stents and Ischemic Events (EVENT) (0.18 AUC-PR). The most important variables ranked according to their contribution to the predictions were diabetes, ≥2 vessel-coronary disease, post-percutaneous coronary intervention thrombolysis in myocardial infarction (PCI TIMI)-flow, abnormal platelets, post-PCI thrombus, and abnormal cholesterol. To counteract the lack of external validation for our study, we deployed our ML algorithm in an open source calculator, in which the model would stratify patients of high and low risk as an example tool to determine generalizability of prediction models from small imbalanced sample size. CONCLUSIONS: Applied immediately after stent implantation, a ML model better differentiates those patients who will present with SR over current discriminators.

Aplicaciones de la inteligencia artificial en cardiología: el futuro ya está aquí / Applications of artificial intelligence in cardiology. The future is already here

Existen pocos temas de actualidad equiparables a la posibilidad de la tecnología actual para desarrollar las mismas capacidades que el ser humano, incluso en medicina. Esta capacidad de simular los procesos de inteligencia humana por parte de máquinas o sistemas informáticos es lo que conocemos hoy en día como inteligencia artificial (IA). Este artículo pretende aclarar diferentes términos que todavía nos resultan lejanos como IA, machine learning (aprendizaje automático, AA), deep learning (aprendizaje profundo, AP), data science o big data; describir en profundidad el concepto de IA y sus tipos, las técnicas de aprendizaje y la metodología que se utiliza en el AA, el análisis en imagen cardiaca con AP, la aportación de esta revolución tecnológica a la estadística clásica, sus limitaciones actuales, sus aspectos legales y, fundamentalmente, sus aplicaciones iniciales en cardiología. En este sentido se ha realizado una búsqueda detallada en PubMed de la evolución en el último lustro de las contribuciones de la IA a las diferentes áreas de aplicación en cardiología, y se ha identificado un total de 673 artículos originales. Se describen en detalle 19 ejemplos de diferentes áreas de la cardiología que utilizando IA han mostrado mejoras diagnósticas y terapéuticas, y que facilitarán la comprensión de la metodología AA y AP

There is currently no other hot topic like the ability of current technology to develop capabilities similar to those of human beings, even in medicine. This ability to simulate the processes of human intelligence with computer systems is known as artificial intelligence (AI). This article aims to clarify the various terms that still sound foreign to us, such as AI, machine learning (ML), deep learning (DL), and big data. It also provides an in-depth description of the concept of AI and its types; the learning techniques and technology used by ML; cardiac imaging analysis with DL; and the contribution of this technological revolution to classical statistics, as well as its current limitations, legal aspects, and initial applications in cardiology. To do this, we conducted a detailed PubMed search on the evolution of original contributions on AI to the various areas of application in cardiology in the last 5 years and identified 673 research articles. We provide 19 detailed examples from distinct areas of cardiology that, by using AI, have shown diagnostic and therapeutic improvements, and which will aid understanding of ML and DL methodology

Applications of Artificial Intelligence in Cardiology. The Future is Already Here.

There is currently no other hot topic like the ability of current technology to develop capabilities similar to those of human beings, even in medicine. This ability to simulate the processes of human intelligence with computer systems is known as artificial intelligence (AI). This article aims to clarify the various terms that still sound foreign to us, such as AI, machine learning (ML), deep learning (DL), and big data. It also provides an in-depth description of the concept of AI and its types; the learning techniques and technology used by ML; cardiac imaging analysis with DL; and the contribution of this technological revolution to classical statistics, as well as its current limitations, legal aspects, and initial applications in cardiology. To do this, we conducted a detailed PubMed search on the evolution of original contributions on AI to the various areas of application in cardiology in the last 5 years and identified 673 research articles. We provide 19 detailed examples from distinct areas of cardiology that, by using AI, have shown diagnostic and therapeutic improvements, and which will aid understanding of ML and DL methodology.

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis.

INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. METHODS AND ANALYSIS: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. ETHICS AND DISSEMINATION: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease. For the first time, a detailed cardiovascular map showing the spatial distribution and a predictive machine learning system of different structural heart diseases and associated risk factors will be created and will be used as a regional policy to establish effective public health programmes to fight heart disease. At least 10 publications in the first-quartile scientific journals are planned. TRIAL REGISTRATION NUMBER: NCT03429452.