University of Zielona Góra, ul. Licealna 9, 65-417 Zielona Góra, Poland.
Clinical Department of Cardiology, Nowa Sól Multidisciplinary Hospital, ul. Chałubińskiego 7, 67-100, Poland.
Dis Markers. 2019 Jan 30;2019:9056402. doi: 10.1155/2019/9056402. eCollection 2019.
Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality after acute coronary syndrome based on such features has not been studied before.
We aim to create an alternative risk assessment tool, which is based on easily obtainable features, including hematological indices and inflammation markers.
We obtained the study data from the electronic medical records of 5053 patients hospitalized with acute coronary syndrome during a 5-year period. The time of follow-up ranged from 12 to 72 months. A machine learning classifier was trained to predict death during hospitalization and within 180 and 365 days from admission. Our method was compared with the Global Registry of Acute Coronary Events (GRACE) Score 2.0 on a test dataset.
For in-hospital mortality, our model achieved a -statistic of 0.89 while the GRACE score 2.0 achieved 0.90. For six-month mortality, the results of our model and the GRACE score on the test set were 0.77 and 0.73, respectively. Red cell distribution width (HR 1.23; 95% CL 1.16-1.30; < 0.001) and neutrophil to lymphocyte ratio (HR 1.08; 95% CL 1.05-1.10; < 0.001) showed independent association with all-cause mortality in multivariable Cox regression.
Hematological markers, such as neutrophil count and red cell distribution width have a strong association with all-cause mortality after acute coronary syndrome. A machine-learned model which uses the abovementioned parameters can provide long-term predictions of accuracy comparable or superior to well-validated risk scores.
红细胞分布宽度和中性粒细胞与淋巴细胞比值等血液学指标已被证明与急性冠状动脉综合征的预后相关。以前尚未研究过基于这些特征的机器学习技术在预测急性冠状动脉综合征后死亡率方面的应用。
我们旨在创建一种基于易于获得的特征（包括血液学指标和炎症标志物）的替代风险评估工具。
我们从 5 年内因急性冠状动脉综合征住院的 5053 名患者的电子病历中获取了研究数据。随访时间从住院开始到 12 至 72 个月不等。训练了一种机器学习分类器来预测住院期间以及入院后 180 天和 365 天内的死亡。我们的方法与全球急性冠状动脉事件登记处（GRACE）评分 2.0 在测试数据集上进行了比较。
对于院内死亡率，我们的模型的 C 统计量为 0.89，而 GRACE 评分 2.0 为 0.90。对于 6 个月的死亡率，我们的模型和 GRACE 评分在测试集上的结果分别为 0.77 和 0.73。红细胞分布宽度（HR 1.23；95%CI 1.16-1.30；<0.001）和中性粒细胞与淋巴细胞比值（HR 1.08；95%CI 1.05-1.10；<0.001）在多变量 Cox 回归中与全因死亡率有独立关联。
血液学标志物，如中性粒细胞计数和红细胞分布宽度与急性冠状动脉综合征后全因死亡率有很强的关联。使用上述参数的机器学习模型可以提供与经过充分验证的风险评分相当或更高的准确性的长期预测。
