DOI: 10.1016/S0140-6736(20)32519-8

Background

• 急性冠脉综合征(ACS)患者发生缺血和出血事件的风险很高，两者都是不良预后的驱动因素。

• 风险评估在每个患者的临床管理中起着至关重要的作用，对于选择二级预防的最佳药物治疗具有重要意义。

• 目前对急性冠脉综合征(ACS)后缺血和出血事件的预测工具的准确性对于个体化的患者管理策略来说仍然不够。

• 机器学习方法可能能够克服当前分析方法在风险预测中的一些限制,且有效性已在几个心血管应用中得到证明。

Methods and Result

Datasets

• 为了建立机器学习模型，我们使用了19826名成年急性冠脉综合征患者(≥18岁)的派生队列，并进行了1年的随访。

• 为了评估模型的性能，我们使用了外部验证队列，包括3444名住院的成年ACS患者，随访1年和2年。

研究人群的临床和治疗特征

Study outcomes：

特征量选择

结构化数据集包括25个变量:

• 16 clinical variables

• 5 thera­ peutic variables

• 2 angiographic variables

• 2 procedural variables

机器学习算法

• K-Nearest Neighbours (KNN)

• Naive Bayes (NB)

• Random Forest (RF)

• Adaptive  Boosting (ADB)

机器学习(ML)算法的评价

学习指标

• ROC曲线

• AUC值

• 校正图(观测与预测风险的十分位数)

• 其他评价指标如下:

  

ROC curves and AUC values

Death, ReAMI and BARC-major bleeding prediction

Performance metrics and algorithm choice for the PRAISE score

Observed vs. Predicted Risk

• Calibration plots

• Observed vs. predicted decile risk comparative bar plots

PRAISE model

AUCs for death, myocardial infarction, and major bleeding for the training, internal validation, and external validation datasets at 1-year follow-up

Risk of observed death, myocardial infarction, and major bleeding according to deciles of event  probability based on PRAISE scores

特征相对重要性

• 在训练过程中是否选择变量来分割节点中的数据

• 平方误差改善了多少

总之，如果在一个变量中找到最多的加权树和产生高纯度分裂，它将有较高的相对重要值。

• scaled importance:每个变量的相对重要性与最高的变量相对重要性之间的比值.

• feature importance weight on the PRAISE risk prediction:每个变量的相对重要性与所有变量的相对重要性之和的比值。

Radar plot for the eight most important predictors of death, myocardial  infarction, and major bleeding

Classes of risk

stratified by deciles of event prob­ ability according to the relating PRAISE  score

• low risk: first to sixth deciles;

• intermediate risk: seventh to ninth deciles;

• and high risk: tenth decile

Compared with low risk, being categorised as being at intermediate risk and high risk was associated with increased (p<0·0001) event occurrence for all the PRAISE scores.

Cross-classification of myocardial infarction and major bleeding risk classes and illustration of the hypothetical trade-off between the two typesof risk

PRAISE score with a lower number of patient features

Discussion

• Our score offers very high accuracy in detecting the risk of all­cause death after an ACS in a population treated with current standard therapies.

• According to such stratification, a tenth of patients (the highest decile) would be classified at discharge as being at high risk of either death, recurrent myocardial infarction, or major bleeding, thus being candidates for a tighter follow­up.

Limitations

• The first is the observational retrospective design of the two registries composing the derivation cohort.

• A further possible limitation of our approach can be identified in the slight under estimation of the adaptive boosting classifier among high­risk patients

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