Development and Validation of a Novel Prognostic Tool to Predict Recurrence of Paroxysmal Atrial Fibrillation after the First-Time Catheter Ablation: A Retrospective Cohort Study.
Junjie HuangHao ChenQuan ZhangRukai YangShuai PengZhijian WuNa LiuLiang TangZhenjiang LiuShenghua ZhouPublished in: Diagnostics (Basel, Switzerland) (2023)
There is no gold standard to tell frustrating outcomes after the catheter ablation of paroxysmal atrial fibrillation (PAF). The study aims to construct a prognostic tool. We retrospectively analyzed 315 patients with PAF who underwent first-time ablation at the Second Xiangya Hospital of Central South University. The endpoint was identified as any documented relapse of atrial tachyarrhythmia lasting longer than 30 s after the three-month blanking period. Univariate Cox regression analyzed eleven preablation parameters, followed by two supervised machine learning algorithms and stepwise regression to construct a nomogram internally validated. Five factors related to ablation failure were as follows: female sex, left atrial appendage emptying flow velocity ≤31 cm/s, estimated glomerular filtration rate <65.8 mL/(min·1.73 m 2 ), P wave duration in lead aVF ≥ 120 ms, and that in lead V1 ≥ 100 ms, which constructed a nomogram. It was correlated with the CHA 2 DS 2 -VASc score but outperformed the latter evidently in discrimination and clinical utility, not to mention its robust performances in goodness-of-fit and calibration. In addition, the nomogram-based risk stratification could effectively separate ablation outcomes. Patients at risk of relapse after PAF ablation can be recognized at baseline using the proposed five-factor nomogram.
Keyphrases
- catheter ablation
- atrial fibrillation
- left atrial appendage
- machine learning
- left atrial
- lymph node metastasis
- oral anticoagulants
- direct oral anticoagulants
- mass spectrometry
- ms ms
- heart failure
- multiple sclerosis
- free survival
- healthcare
- percutaneous coronary intervention
- deep learning
- squamous cell carcinoma
- wastewater treatment
- artificial intelligence
- big data
- metabolic syndrome
- weight loss
- blood flow
- skeletal muscle
- type diabetes