Department of Medical Sciences, Biostatistics Unit, University of Trieste, Trieste, Italy.
Aindo, Trieste, Italy.
PLoS One. 2023 Feb 21;18(2):e0281878. doi: 10.1371/journal.pone.0281878. eCollection 2023.
Patients with type 2 diabetes mellitus (T2DM) have more than twice the risk of developing heart failure (HF) compared to patients without diabetes. The present study is aimed to build an artificial intelligence (AI) prognostic model that takes in account a large and heterogeneous set of clinical factors and investigates the risk of developing HF in diabetic patients. We carried out an electronic health records- (EHR-) based retrospective cohort study that included patients with cardiological clinical evaluation and no previous diagnosis of HF. Information consists of features extracted from clinical and administrative data obtained as part of routine medical care. The primary endpoint was diagnosis of HF (during out-of-hospital clinical examination or hospitalization). We developed two prognostic models using (1) elastic net regularization for Cox proportional hazard model (COX) and (2) a deep neural network survival method (PHNN), in which a neural network was used to represent a non-linear hazard function and explainability strategies are applied to estimate the influence of predictors on the risk function. Over a median follow-up of 65 months, 17.3% of the 10,614 patients developed HF. The PHNN model outperformed COX both in terms of discrimination (c-index 0.768 vs 0.734) and calibration (2-year integrated calibration index 0.008 vs 0.018). The AI approach led to the identification of 20 predictors of different domains (age, body mass index, echocardiographic and electrocardiographic features, laboratory measurements, comorbidities, therapies) whose relationship with the predicted risk correspond to known trends in the clinical practice. Our results suggest that prognostic models for HF in diabetic patients may improve using EHRs in combination with AI techniques for survival analysis, which provide high flexibility and better performance with respect to standard approaches.
2 型糖尿病（T2DM）患者发生心力衰竭（HF）的风险是无糖尿病患者的两倍多。本研究旨在建立一个人工智能（AI）预后模型，该模型考虑了大量异质的临床因素，并调查糖尿病患者发生 HF 的风险。我们进行了一项基于电子健康记录（EHR）的回顾性队列研究，该研究纳入了有心脏临床评估但无 HF 既往诊断的患者。信息包括从临床和行政数据中提取的特征，这些数据是作为常规医疗护理的一部分获得的。主要终点是 HF 的诊断（在院外临床检查或住院期间）。我们使用（1）Cox 比例风险模型（COX）的弹性网正则化和（2）深度神经网络生存方法（PHNN）开发了两个预后模型，其中神经网络用于表示非线性风险函数，并应用可解释性策略来估计预测因子对风险函数的影响。在中位数为 65 个月的随访中，10614 名患者中有 17.3%发生了 HF。PHNN 模型在区分度（c 指数 0.768 与 0.734）和校准度（2 年综合校准指数 0.008 与 0.018）方面均优于 COX。AI 方法确定了 20 个不同领域（年龄、体重指数、超声心动图和心电图特征、实验室测量、合并症、治疗）的预测因子，这些预测因子与预测风险的关系与临床实践中的已知趋势相对应。我们的研究结果表明，使用 EHR 结合 AI 技术进行生存分析可能会改善糖尿病患者 HF 的预后模型，与标准方法相比，该方法具有更高的灵活性和更好的性能。
