CISUC, Center for Informatics and Systems of University of Coimbra, Coimbra 3030-290, Portugal.
Polytechnic Institute of Coimbra, Coimbra Institute of Engineering (IPC/ISEC), Rua Pedro Nunes, Coimbra 3030-199, Portugal; CISUC, Center for Informatics and Systems of University of Coimbra, Coimbra 3030-290, Portugal.
Comput Methods Programs Biomed. 2023 Mar;230:107347. doi: 10.1016/j.cmpb.2023.107347. Epub 2023 Jan 10.
Cardiovascular disease has a huge impact on health care services, originating unsustainable costs at clinical, social, and economic levels. In this context, patients' risk stratification tools are central to support clinical decisions contributing to the implementation of effective preventive health care. Although useful, these tools present some limitations, in particular, some lack of performance as well as the impossibility to consider new risk factors potentially important in the prognosis of severe cardiac events. Moreover, the actual use of these tools in the daily practice requires the physicians' trust. The main goal of this work addresses these two issues: (i) evaluate the importance of inflammation biomarkers when combined with a risk assessment tool; (ii) incorporation of personalization and interpretability as key elements of that assessment.
Firstly, machine learning based models were created to assess the potential of the inflammation biomarkers applied in secondary prevention, namely in the prediction of the six month risk of death/myocardial infarction. Then, an approach based on three main phases was created: (i) set of interpretable rules supported by clinical evidence; (ii) selection based on a machine learning classifier able to identify for a given patient the most suitable subset of rules; (iii) an ensemble scheme combining the previous subset of rules in the estimation of the patient cardiovascular risk. All the results were statistically validated (t-test, Wilcoxon-signed rank test) according to a previous verification of data normality (Shapiro-Wilk).
The proposed methodology was applied to a real acute coronary syndrome patients dataset (N = 1544) from the Cardiology Unit of Coimbra Hospital and Universitary centre. The first assessment was based on the GRACE tool and a Random Forest classifier, the incorporation of inflammation biomarkers achieved SE=0.83; SP=0.84 whereas the original GRACE risk factors reached SE=0.75; SP=0.85. In the second phase, the proposed approach with inflammation biomarkers achieved SE=0.763 and SP=0.778.
This approach confirms the potential of combining inflammation markers with the GRACE score, increasing SE and SP, when compared with the original GRACE. Additionally, it assures interpretability and personalization, which are critical issues to allow its application in the daily clinical practice.
心血管疾病对医疗服务有巨大影响，在临床、社会和经济层面造成了不可持续的成本。在这种情况下，患者的风险分层工具对于支持临床决策至关重要，有助于实施有效的预防性医疗保健。尽管这些工具很有用，但它们存在一些局限性，特别是缺乏性能，以及无法考虑到新的风险因素，这些因素可能对严重心脏事件的预后很重要。此外，这些工具在日常实践中的实际应用需要医生的信任。这项工作的主要目标是解决这两个问题：（i）评估炎症生物标志物与风险评估工具相结合的重要性；（ii）将个性化和可解释性作为评估的关键要素。
首先，创建了基于机器学习的模型，以评估炎症生物标志物在二级预防中的应用潜力，即预测六个月内死亡/心肌梗死的风险。然后，创建了一种基于三个主要阶段的方法：（i）基于临床证据的可解释规则集；（ii）基于机器学习分类器的选择，该分类器能够为给定患者识别最合适的规则子集；（iii）一个组合方案，将之前的规则子集用于估计患者的心血管风险。所有结果均根据数据正态性的先前验证（Shapiro-Wilk）进行了统计学验证（t 检验、Wilcoxon 符号秩检验）。
该方法应用于来自科英布拉医院和大学中心心脏病科的真实急性冠状动脉综合征患者数据集（N=1544）。第一个评估基于 GRACE 工具和随机森林分类器，炎症生物标志物的加入使 SE 达到 0.83；SP 为 0.84，而原始 GRACE 风险因素的 SE 为 0.75；SP 为 0.85。在第二阶段，加入炎症标志物的方法的 SE 为 0.763，SP 为 0.778。
该方法证实了将炎症标志物与 GRACE 评分相结合的潜力，与原始 GRACE 相比，SE 和 SP 均有所提高。此外，它还保证了可解释性和个性化，这是允许其在日常临床实践中应用的关键问题。
