Asirvatham, Sam

Background: Cuffless blood pressure (BP) monitoring devices with the ability to continuously and noninvasively collect data could transform the screening and management of hypertension. While some wearable devices can estimate BP, their accuracy and reliability remain limited. Objective: To evaluate the feasibility of using a wrist-worn photoplethysmography (PPG) device (Lifeleaf®) in combination with a supervised learning algorithm to infer clinically useful BP measurements. Methods: A total of 87 patients undergoing electrophysiology procedures were enrolled (Table 1). Time-synchronized BP data from an intra-arterial line served as the reference. These data were divided into (1) a training set to develop a convolutional neural network (CNN) and (2) a testing set to assess the algorithm’s predictive performance. Data from 60 patients were used to train the model, and the remaining 27 patients were used for validation. Results: Data from 81 of 87 patients were included in the final analysis. The mean age was 63.6 ± 10.5 years, and 69.1% were male. A total of 6,977 paired PPG and arterial BP readings were analyzed. The mean absolute deviation for systolic BP was 13.08 ± 17.22 mm Hg, and for diastolic BP, 8.42 ± 10.56 mm Hg. Subgroup analysis revealed reduced correlation in patients with BP >140/90 mm Hg, likely due to fewer training samples in this range. Conclusion: This study demonstrates the feasibility of extracting clinically meaningful BP measurements from a PPG device using a supervised learning algorithm. The use of arterial line BP as the reference standard adds methodological rigor. Future work will focus on enhancing model performance, particularly in the hypertensive cohort.

Abstract Background Accurate automated wide QRS complex tachycardia (WCT) discrimination between ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using novel calculations derived from computerized electrocardiogram (ECG) data from paired WCT and baseline ECGs. Purpose Our aim was to develop and trial novel WCT discrimination approaches for WCT patients with and without a corresponding baseline ECG. Central to this analysis was the creation and use of a novel parameter (i.e., percent monophasic time-voltage area [PMonoTVA] [%]) that may be derived from computerized ECG measurements present on the WCT ECG alone. Methods In a two-part study, we derived and tested WCT differentiation models comprised of novel and previously established parameters formulated from computerized data of paired WCT and baseline ECGs. In Part 1, novel and established parameters generated from WCT and baseline ECG data were used to derive, validate, and compare five different binary classification models: (i) logistic regression [LR], (ii) artificial neural network [ANN], (iii) Random Forests [RF], (iv) support vector machine [SVM], and (v) ensemble learning (EL). In Part 2, two unique LR models were derived, validated, and compared using parameters generated from computerized data of the (i) WCT ECG alone (i.e., Solo Model) and (ii) paired WCT and baseline ECGs (i.e., Paired Model). Results In Part 1, among 103 patients with VT or SWCT diagnoses established from corroborating electrophysiology studies or intra-cardiac device recordings, favorable diagnostic performance was achieved by all modeling technique subtypes: LR (area under the receiver operating characteristic curve [AUC] 0.95), ANN (AUC 0.91), RF (AUC 0.97), SVM (AUC 0.98), and EL (AUC 0.97). In Part 2, among 235 patients with a VT or SWCT diagnosis established with (Gold Standard cohort) or without (Non-Gold Standard cohort) a corroborating electrophysiology procedure or intra-cardiac device recording, favorable diagnostic performance was achieved by the Solo Model (AUC 0.86) and Paired Model (AUC 0.95) (Table). Conclusion Accurate WCT discrimination may be accomplished using novel parameters derived from computerized data of the WCT ECG alone and paired WCT and baseline ECGs. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health

Background— Although segmental or circumferential ablation is effective in eliminating pulmonary vein (PV)–mediated atrial fibrillation (AF), this procedure may be complicated by the occurrence of PV stenosis. Methods and Results— To establish the clinical presentation, diagnostic manifestations, and interventional management of PV stenosis, 23 patients with stenosis of 34 veins complicating ablation of AF were evaluated. Each patient became symptomatic 103±100 days after undergoing ablation. In 8 veins, the ablation producing the PV stenosis was a repeated procedure for continued AF. Nineteen patients presented with dyspnea on exertion, 7 with dyspnea at rest, 9 with cough, and 6 with chest pain. On multirow spiral computed tomography examination, the narrowest lumen of the affected PVs measured 3±2 mm compared with 13±3 mm at baseline ( P ≤0.001). The relative perfusion of affected lung segments on isotope scans was reduced to 4±3% of total perfusion compared with 22±10% in unaffected segments. At percutaneous intervention, these veins showed 80±13% stenosis, with a mean gradient of 12±5 mm Hg. This was significantly reduced to a residual stenosis of 9±8% ( P ≤0.001) and a residual gradient of 3±4 mm Hg ( P ≤0.001). Twenty veins were treated with balloon dilatation alone, whereas 14 veins were stented with standard 10-mm-diameter bare-metal stents. Although the symptomatic response was nearly immediate and impressive, 14 patients developed in-stent or in-segment restenosis, requiring repeated interventions in 13. Conclusions— Percutaneous intervention produces rapid and dramatic symptom relief in patients with highly symptomatic PV stenosis after radiofrequency ablation for AF. Nevertheless, alternative treatment methods will be required to decrease recurrent in-stent or in-segment restenosis.