CRDM Research Division, Medtronic, Inc., Moundsview, MN 55112, USA.
IEEE Trans Biomed Eng. 2013 Jan;60(1):147-50. doi: 10.1109/TBME.2012.2209646. Epub 2012 Jul 20.
A method for combining heart failure (HF) diagnostic information in a Bayesian belief network (BBN) framework to improve the ability to identify when patients are at risk for HF hospitalization (HFH) is investigated in this paper. Implantable devices collect HF related diagnostics, such as intrathoracic impedance, atrial fibrillation (AF) burden, ventricular rate during AF, night heart rate, heart rate variability, and patient activity, on a daily basis. Features were extracted that encoded information regarding out of normal range values as well as temporal changes at weekly and monthly time scales. A BBN is used to combine the features to generate a risk score defined as the probability of a HFH given the diagnostic evidence. Patients with a very high risk score at follow-up are 15 times more likely to have a HFH in the next 30 days compared to patients with a low-risk score. The combined score has improved ability to identify patients at risk for HFH compared to the individual diagnostic parameters. A score of this nature allows clinicians to manage patients by exception; a patient with higher risk score needs more attention than a patient with lower risk score.
本研究探讨了一种在贝叶斯信念网络（BBN）框架中结合心力衰竭（HF）诊断信息的方法，以提高识别患者何时面临 HF 住院（HFH）风险的能力。植入式设备每天收集与 HF 相关的诊断信息，如胸腔内阻抗、心房颤动（AF）负担、AF 期间心室率、夜间心率、心率变异性和患者活动。提取的特征编码了关于超出正常范围值以及每周和每月时间尺度上的时间变化的信息。BBN 用于结合特征生成风险评分，该评分定义为给定诊断证据的 HFH 概率。与低风险评分的患者相比，在随访期间具有非常高风险评分的患者在接下来的 30 天内发生 HFH 的可能性高 15 倍。与单个诊断参数相比，组合评分具有更好的识别 HFH 风险患者的能力。这种性质的评分可让临床医生通过例外情况来管理患者；风险评分较高的患者比风险评分较低的患者需要更多的关注。
