Prats Lourdes, Izquierdo José Luis
Departamento de Medicina y Especialidades, Universidad de Alcalá, Alcalá de Henares, España.
Neumología, Hospital Universitario de Guadalajara, Guadalajara, España.
Open Respir Arch. 2020 Oct-Dec;2(4):284-288. doi: 10.1016/j.opresp.2020.07.003. Epub 2020 Sep 10.
One of the key elements of medicine in the second decade of the 21st century is the exponential growth of patient-produced information, due not only to the transition to the digitization of medical records, but also to the emergence of new sources of information and the capacity for analysis and interpretation of existing ones. The amount of medical information is expected to double every 2 years, which means that there will be 50 times more information available in 2020 than in 2011. In this setting, these large amounts of data or «big data» must be properly managed to implement new initiatives that improve the diagnosis, treatment, and prognosis of patients on the path to personalized medicine.The concept of personalization or precision medicine is of special interest in chronic respiratory disease. In recent years, research in entities such as asthma, COPD, cancer, or SAHS has focused on the identification of genomic, molecular, metabolic, and protein changes (biomarkers). Big data analysis tools can be used to move on from models based on the mean response to treatment, which are suboptimal for most patients, to focus on the individualized response. Part of this journey involves systems medicine, which also integrates clinical and population data to provide a multidimensional view of the disease and help identify causal associations that are usually only evident on big data analysis.
21世纪第二个十年医学的关键要素之一是患者产生的信息呈指数级增长,这不仅归因于医疗记录向数字化的转变,还归因于新信息源的出现以及对现有信息进行分析和解读的能力。预计医学信息的数量每两年就会翻一番,这意味着到2020年可用信息将比2011年多50倍。在这种情况下,必须对这些大量数据或“大数据”进行妥善管理,以实施新举措,改善患者在走向个性化医疗道路上的诊断、治疗和预后。个性化或精准医疗的概念在慢性呼吸道疾病中特别受关注。近年来,对哮喘、慢性阻塞性肺疾病、癌症或睡眠呼吸暂停低通气综合征等疾病的研究集中在识别基因组、分子、代谢和蛋白质变化(生物标志物)上。大数据分析工具可用于从基于治疗平均反应的模型(对大多数患者而言并非最优)转向关注个性化反应。这一过程的一部分涉及系统医学,它还整合临床和人群数据,以提供疾病的多维视图,并帮助识别通常只有在大数据分析中才明显的因果关联。