Información del artículo
Resumen
Los registros de vida real han resultado fundamentales para el avance en el conocimiento de numerosas enfermedades raras (ER), entre las que se encuentra la hipertensión arterial pulmonar (HAP). Así, en las últimas cuatro décadas, un buen número de registros en este trastorno han permitido, entre otros, mejorar las definiciones del padecimiento y sus subgrupos, conocer mejor su fisiopatología, elaborar escalas pronósticas y comprobar la transferibilidad de los resultados de los ensayos clínicos a la práctica diaria. Sin embargo, en un momento histórico en el que abundan cantidades significativas de datos provenientes de diversas fuentes, existe la posibilidad de que estos no sean adecuadamente considerados en estos registros. Por ello, las técnicas de machine learning (ML) ofrecen una oportunidad única para obtener herramientas que mejoren el diagnóstico precoz de estos pacientes, ayuden a deducir su pronóstico, y permitan en definitiva avanzar en la medicina personalizada. De este modo, presentamos una revisión narrativa cuyos objetivos son, por un lado, resumir los aspectos para los que es importante la adquisición de datos en las ER, al tener el foco en el conocimiento extraído de los registros de vida real de HAP, y, por otro, describir los logros ya alcanzados y el potencial uso de la explotación de la información mediante métodos de inteligencia artificial (IA) en esta enfermedad.
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Palabras clave:
Hipertensión arterial pulmonar
Hipertensión pulmonar
Enfermedades raras
Registros
Inteligencia artificial
Machine learning
Big data
Abstract
Real-world registries have been critical to building the scientific knowledge of rare diseases, including Pulmonary Arterial Hypertension (PAH). In the past 4 decades, a considerable number of registries on this condition have allowed to improve the pathology and its subgroupś definition, to advance in the understanding of its pathophysiology, to elaborate prognostic scales and to check the transferability of the results from clinical trials to clinical practice. However, in a moment where a huge amount of data from multiple sources is available, they are not always taken into account by the registries. For that reason, Machine Learning (ML) offer a unique opportunity to manage all these data and, finally, to obtain tools that may help to get an earlier diagnose, to help to deduce the prognosis and, in the end, to advance in Personalized Medicine. Thus, we present a narrative revision with the aims of, in one hand, summing up the aspects in which data extraction is important in rare diseases -focusing on the knowledge gained from PAH real-world registries- and, on the other hand, describing some of the achievements and the potential use of the ML techniques on PAH.
Keywords:
Pulmonary Arterial Hypertension
Pulmonary hypertension
Rare diseases
Registries
Artificial intelligence
Machine learning
Big data
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