Risk prediction of inappropriate implantable cardioverter-defibrillator therapy using machine learning.

We aimed to develop machine learning-based predictive models for identifying inappropriate implantable cardioverter-defibrillator (ICD) therapy. Our study included 182 consecutive cases (average age 62.2 ± 4.5 years, 169 men) and employed 14 non-deep learning models for prediction (hold-out method). These models utilized selected electrocardiogram parameters and clinical features collected after ICD implantation. From the feature importance analysis of the best ML model, we established easily calculable scores. Among the patients, 25 (13.7%) experienced inappropriate therapy, and we identified 16 significant predictors. Using recursive feature elimination with cross-validation, we reduced the features to six with high feature importance: history of atrial arrhythmia (Atr-arrhythm), ischemic cardiomyopathy (ICM), absence of diabetes mellitus (DM), lack of cardiac resynchronization therapy (CRT), V3 ST level at J point (V3 STJ), and V5 R-wave amplitudes (V5R amp). The extra-trees classifier yielded the highest area under receiver operating characteristics curve (AUROC; 0.869 on test data). Thus, the Cardi35 score was defined as [+ 5.5*Atr-arrhythm - 1.5*CRT + 1.0*V3STJ + 1.0*V5R - 1.0*ICM - 0.5*DM], which demonstrated a hazard ratio of 1.62 (P < 0.001). A cut-off value of the score + 5.5 showed high AUROC (0.826). The ML approach can yield a robust prediction model, and the Cardi35 score was a convenient predictor for inappropriate therapy.

Optical coherence tomography and coronary angioscopy assessment of healed coronary plaque components.

PURPOSE: Histopathological or intracoronary image assessment of healed plaques (HPs) has been reported. However, the lesion characteristics of HPs remains undetermined yet. We assessed the healed plaque components in patients with coronary artery lesions using multiple imaging modalities. METHODS: We enrolled 33 stable angina pectoris (SAP) patients with 36 native coronary culprit lesions with angiography severe stenosis and without severe calcification undergoing pre-intervention optical coherence tomography (OCT) and coronary angioscopy (CAS). HPs were defined as layered phenotype on OCT. Lesion morphologies and plaque characteristics of HPs were assessed using OCT and CAS. RESULTS: HPs were observed in 19 lesions (52.8%). HP lesions had higher frequent B2/C lesions (89.4% vs. 52.9%, p = 0.02), worse pre-PCI coronary flow (corrected thrombolysis in myocardial infarction count 21.6 ± 13.5 vs. 13.8 ± 6.2, p = 0.047) and greater lumen-area stenosis (79.6 ± 10.6% vs. 68.0 ± 21.6%, p = 0.047) than non-HP lesions. HP lesions had higher prevalence of OCT-thin-cap fibroatheroma (TCFA) (31.6% vs. 0.0%, p = 0.02), OCT-macrophage (89.5% vs. 41.2%, p = 0.004), and CAS-red thrombus (89.5% vs. 41.2%, p = 0.004) than non-HP lesions. The combination of 3 features including OCT-TCFA, macrophages, and CAS-red thrombus showed higher predictive valuer for HPs on OCT than each single variable. Post-PCI irregular tissue protrusion was more frequently observed in lesions with HPs than in those without (52.6% vs. 13.3%, p = 0.03). CONCLUSIONS: SAP lesions with HPs might have more frequent vulnerable plaques with intraplaque inflammation and thrombus than those without, suggesting that layered phenotype on OCT might reflect not only healing process but also potential risks for future coronary events.

Outcomes of deep sedation for catheter ablation of paroxysmal supraventricular tachycardia, with adaptive servo ventilation.

BACKGROUND: Catheter ablation for paroxysmal supraventricular tachycardia (PSVT) is an established treatment, but the effect of deep sedation on PSVT inducibility remains unclear. AIM: We sought to examine PSVT inducibility and outcomes of catheter ablation under deep sedation using adaptive servo ventilation (ASV). METHODS: We retrospectively evaluated consecutive patients who underwent catheter ablation for PSVT under deep sedation (Propofol + Dexmedetomidine) with use of ASV. Anesthetic depth was controlled with BIS™ monitoring, and phenylephrine was administered to prevent anesthesia-induced hypotension. PSVT induction was attempted in all patients using extrastimuli at baseline, and after isoproterenol (ISP) infusion when necessary. RESULTS: PSVT was successfully induced in 145 of 147 patients, although ISP infusion was required in the majority (89%). The PSVT was atrioventricular nodal reentrant tachycardia (AVNRT) in 77 (53%), atrioventricular reciprocating tachycardia (AVRT) in 51 (35%), and atrial tachycardia (AT) in 17 (12%). A higher ISP dose was required for AT compared to other PSVT (AVNRT: 0.06 (IQR 0.03-0.06) vs AVRT: 0.03 (0.02-0.06) vs AT: 0.06 (0.03-0.12) mg/h, P = .013). More than half (51%) of the patients developed hypotension requiring phenylephrine; these patients were older. Acute success was obtained in 99% (patients with AVNRT had endpoints with single echo on ISP in 46%). Long-term success rate was 136 of 144 (94%) (AVNRT 96%, AVRT 92%, and AT 93%). There were no complications related to deep sedation. CONCLUSIONS: Deep sedation with use of ASV is a feasible anesthesia strategy for catheter ablation of PSVT with good long-term outcome. PSVT remains inducible if ISP is used.