Marques Tânia, Henriques Jorge, de Carvalho Paulo, Paredes Simão, Rocha Teresa, Morais João
Department of Informatics Engineering, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030-290, Coimbra, Portugal.
Department of Informatics and Systems Engineering, Polytechnic Institute of Coimbra (IPC/ISEC), Rua Pedro Nunes, Quinta da Nora, 3030-199, Coimbra, Portugal.
Cardiovasc Eng Technol. 2015 Sep;6(3):392-9. doi: 10.1007/s13239-015-0228-8. Epub 2015 May 27.
Cardiovascular diseases are the main cause of death in Europe, representing 47% of all deaths. This could be avoided, if each patient underwent the most adequate treatment. For this to happen, it is important to determine the patient's risk of having a cardiovascular event. This is known as risk assessment, and can be done using risk scores. However, there are several risk scores with similar performances, which makes it difficult to choose the most adequate one. We propose to overcome this by combining risk scores using personalization based on groups, where new patients are assigned to the most similar group and consequently to the most adequate risk score. This eliminates the need to choose a specific tool, and improves the overall performance (when compared with the performance of individual tools). This strategy was validated using the Santa Cruz Dataset. The results obtained were able to maintain the highest sensitivity while improving the specificity in 13% when compared with the highest values achieved by the selected individual risk scores (GRACE, TIMI, PURSUIT).
心血管疾病是欧洲的主要死因，占所有死亡人数的47%。如果每位患者都接受最适当的治疗，这种情况是可以避免的。要实现这一点，确定患者发生心血管事件的风险很重要。这被称为风险评估，可以使用风险评分来完成。然而，有几种风险评分的表现相似，这使得选择最适当的评分变得困难。我们建议通过基于群体的个性化组合风险评分来克服这一问题，即将新患者分配到最相似的群体，从而分配到最适当的风险评分。这消除了选择特定工具的必要性，并提高了整体性能（与单个工具的性能相比）。该策略使用圣克鲁斯数据集进行了验证。与所选个体风险评分（GRACE、TIMI、PURSUIT）所达到的最高值相比，获得的结果能够保持最高的敏感性，同时将特异性提高13%。
