Giang Kok Wai, Helgadottir Saga, Dellborg Mikael, Volpe Giovanni, Mandalenakis Zacharias
Institute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Diagnosvägen 11, 416 50 Gothenburg, Sweden.
Department of Physics, University of Gothenburg, Gothenburg, Sweden.
Eur Heart J Digit Health. 2021 Aug 17;2(4):568-575. doi: 10.1093/ehjdh/ztab065. eCollection 2021 Dec.
To improve short-and long-term predictions of mortality and atrial fibrillation (AF) among patients with congenital heart disease (CHD) from a nationwide population using neural networks (NN).
The Swedish National Patient Register and the Cause of Death Register were used to identify all patients with CHD born from 1970 to 2017. A total of 71 941 CHD patients were identified and followed-up from birth until the event or end of study in 2017. Based on data from a nationwide population, a NN model was obtained to predict mortality and AF. Logistic regression (LR) based on the same data was used as a baseline comparison. Of 71 941 CHD patients, a total of 5768 died (8.02%) and 995 (1.38%) developed AF over time with a mean follow-up time of 16.47 years (standard deviation 12.73 years). The performance of NN models in predicting the mortality and AF was higher than the performance of LR regardless of the complexity of the disease, with an average area under the receiver operating characteristic of >0.80 and >0.70, respectively. The largest differences were observed in mortality and complexity of CHD over time.
We found that NN can be used to predict mortality and AF on a nationwide scale using data that are easily obtainable by clinicians. In addition, NN showed a high performance overall and, in most cases, with better performance for prediction as compared with more traditional regression methods.
利用神经网络（NN）改进对全国范围内先天性心脏病（CHD）患者死亡率和心房颤动（AF）的短期和长期预测。
使用瑞典国家患者登记册和死亡原因登记册来识别1970年至2017年出生的所有CHD患者。共识别出71941例CHD患者，并从出生开始随访至2017年事件发生或研究结束。基于全国人口数据，获得了一个NN模型来预测死亡率和AF。将基于相同数据的逻辑回归（LR）用作基线比较。在71941例CHD患者中，共有5768例死亡（8.02%），995例（1.38%）随时间推移发生AF，平均随访时间为16.47年（标准差12.73年）。无论疾病的复杂性如何，NN模型在预测死亡率和AF方面的表现均高于LR，受试者操作特征曲线下面积平均分别>0.80和>0.70。随着时间的推移，在死亡率和CHD复杂性方面观察到最大差异。
我们发现，NN可用于利用临床医生易于获取的数据在全国范围内预测死亡率和AF。此外，NN总体表现良好，在大多数情况下，与更传统的回归方法相比，预测性能更好。
