Associate Professor, Director of the Institute of Information Technologies; Vladivostok State University, 41 Gogolya St., Vladivostok, 690014, Russia; Head of the Laboratory of Big Data Analysis in Medicine and Healthcare; Far East Federal University, 10 Ayaks Village, Russkiy Island, Vladivostok, 690922, Russia.
PhD Student, Institute of Mathematics and Computer Technologies; Far East Federal University, 10 Ayaks Village, Russkiy Island, Vladivostok, 690922, Russia.
Sovrem Tekhnologii Med. 2024;16(1):15-25. doi: 10.17691/stm2024.16.1.02. Epub 2024 Feb 28.
is to assess the performance of predictive models developed on the basis of predictors in the continuous and categorical forms to predict the probability of in-hospital mortality (IHM) in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI).
A single-center retrospective study has been conducted, within the framework of which data from 4674 medical records of patients with STEMI after PCI, treated at the Regional Vascular Center of Vladivostok (Russia), have been analyzed. Two groups of patients were identified: group 1 consisted of 318 (6.8%) individuals who died in the hospital, group 2 included 4356 (93.2%) patients with a favorable outcome of treatment. IHM prognostic models were developed using multivariate logistic regression (MLR), random forest (RF), and stochastic gradient boosting (SGB). 6-metric qualities were used to evaluate the accuracy of the models. Threshold values of IHM predictors were determined using a grid search to find the optimal cut-off points, calculating centroids, and Shapley additive explanations. The latter helped evaluate the degree to which the model predictors influence the endpoint.
Based on the results of the multi-stage analysis of indicators of clinical and functional status of the STEMI patients, new predictors of IHM have been identified and validated, complementing the factors of the GRACE scoring system, their categorization has been carried out and prognostic models with continuous and categorical variables based on MLR, RF, and SGB have been developed. These models had a high (AUC - 0.88 to 0.90) and comparable predictive accuracy, but their predictors differed in various degrees of influence on the endpoint. The comparative analysis has shown that the Shapley additive explanation method has advantages in categorizing predictors compared to other methods and allows for detailing the structure of their relationships with IHM.
The use of modern data mining methods, including machine learning algorithms, categorization of predictors, and assessment of the degree of their effect on the endpoint, makes it possible to develop predictive models possessing high accuracy and the properties of explanation of the generated conclusions.
评估基于连续和分类形式的预测因子开发的预测模型在经皮冠状动脉介入治疗（PCI）后预测 ST 段抬高型心肌梗死（STEMI）患者住院死亡率（IHM）的性能。
进行了一项单中心回顾性研究，在此框架内分析了在俄罗斯符拉迪沃斯托克区域血管中心接受 PCI 治疗的 4674 例 STEMI 患者的病历数据。确定了两组患者：第 1 组由 318 名（6.8%）院内死亡的个体组成，第 2 组包括 4356 名（93.2%）治疗结局良好的患者。使用多变量逻辑回归（MLR）、随机森林（RF）和随机梯度提升（SGB）开发 IHM 预后模型。使用 6 项指标质量评估模型的准确性。使用网格搜索确定 IHM 预测因子的阈值，以找到最佳截断点，计算质心和 Shapley 加性解释。后者有助于评估模型预测因子对终点的影响程度。
基于对 STEMI 患者临床和功能状态指标的多阶段分析结果，确定并验证了 IHM 的新预测因子，补充了 GRACE 评分系统的因素，对其进行了分类，并基于 MLR、RF 和 SGB 开发了具有连续和分类变量的预后模型。这些模型具有较高的（AUC-0.88 至 0.90）和可比预测准确性，但它们的预测因子在不同程度上对终点有影响。比较分析表明，Shapley 加性解释方法在分类预测因子方面优于其他方法，并允许详细说明它们与 IHM 之间的关系结构。
使用现代数据挖掘方法，包括机器学习算法、预测因子分类和评估其对终点的影响程度，可开发具有高精度和生成结论解释性的预测模型。
