Predictive utility of the impedance drop on AF recurrence using digital intraprocedural data linked to electronic health record data.

Background: Local impedance drop in cardiac tissue during catheter ablation may be a valuable measure to guide atrial fibrillation (AF) ablation procedures for greater effectiveness. Objective: The study sought to assess whether local impedance drop during catheter ablation to treat AF predicts 1-year AF recurrence and what threshold of impedance drop is most predictive. Methods: We identified patients with AF undergoing catheter ablation in the Mercy healthcare system. We downloaded AF ablation procedural data recorded by the CARTO system from a cloud-based analytical tool (CARTONET) and linked them to individual patient electronic health records. Average impedance drops in anatomical region of right and left pulmonary veins were calculated. Effectiveness was measured by a composite outcome of repeat ablation, AF rehospitalization, direct current cardioversion, or initialization of a new antiarrhythmic drug post-blanking period. The association between impedance drop and 1-year AF recurrence was assessed by logistic regression adjusting for demographics, clinical, and ablation characteristics. Bootstrapping was used to determine the most predictive threshold for impedance drop based on the Youden index. Results: Among 242 patients, 23.6% (n = 57) experienced 1-year AF recurrence. Patients in the lower third vs upper third of average impedance drop had a 5.9-fold (95% confidence interval [CI] 1.81-21.8) higher risk of recurrence (37.0% vs 12.5%). The threshold of 7.2 Ω (95% CI 5.75-7.7 Ω) impedance drop best predicted AF recurrence, with sensitivity of 0.73 and positive predictive value of 0.33. Patients with impedance drop ≤7.2 Ω had 3.5-fold (95% CI 1.39-9.50) higher risk of recurrence than patients with impedance drop >7.2 Ω, and there was no statistical difference in adverse events between the 2 groups of patients. Sensitivity analysis on right and left wide antral circumferential ablation impedance drop was consistent. Conclusion: Average impedance drop is a strong predictor of clinical success in reducing AF recurrence but as a single criterion for predicting recurrence only reached 73% sensitivity and 33% positive predictive value.

Does radiofrequency ablation procedural data improve the accuracy of identifying atrial fibrillation recurrence?

Radiofrequency ablation (RFA) using the CARTO 3D mapping system is a common approach for pulmonary vein isolation to treat atrial fibrillation (AF). Linkage between CARTO procedural data and patients' electronical health records (EHR) provides an opportunity to identify the ablation-related parameters that would predict AF recurrence. The objective of this study is to assess the incremental accuracy of RFA procedural data to predict post-ablation AF recurrence using machine learning model. Procedural data generated during RFA procedure were downloaded from CARTONET and linked to deidentified Mercy Health EHR data. Data were divided into train (70%) and test (30%) data for model development and validation. Automate machine learning (AutoML) was used to predict 1 year AF recurrence, defined as a composite of repeat ablation, electrical cardioversion, and AF hospitalization. At first, AutoML model only included Patients' demographic and clinical characteristics. Second, an AutoML model with procedural variables and demographical/clinical variables was developed. Area under receiver operating characteristic curve (AUROC) and net reclassification improvement (NRI) were used to compare model performances using test data. Among 306 patients, 67 (21.9%) patients experienced 1-year AF recurrence. AUROC increased from 0.66 to 0.78 after adding procedural data in the AutoML model based on test data. For patients with AF recurrence, NRI was 32% for model with procedural data. Nine of 10 important predictive features were CARTO procedural data. From CARTO procedural data, patients with lower contact force in right inferior site, long ablation duration, and low number of left inferior and right roof lesions had a higher risk of AF recurrence. Patients with persistent AF were more likely to have AF recurrence. The machine learning model with procedural data better predicted 1-year AF recurrence than the model without procedural data. The model could be used for identification of patients with high risk of AF recurrence post ablation.

Assessment of skeletal age at the wrist in children with a new ultrasound device.

BACKGROUND: Determination of skeletal development in children is important. The most common method of evaluation uses the standards of Greulich and Pyle (G and P) to assess the left hand radiograph. Numerous assessments may be made during follow-up. OBJECTIVE: The aim of our study was to compare the accuracy of a new sonographic method with the standard radiographic method. MATERIALS AND METHODS: Seventy consecutive patients (age 6-17 years; 34 girls, 36 boys) underwent radiography of the left hand, followed by sonographic examination of the same hand using the BonAge system (Sunlight Medical Ltd., Israel). This system evaluates the relationship between the velocity of sound passing thorough the distal radial and ulna epiphysis and growth, using gender- and ethnicity-based algorithms. One experienced paediatric radiologist analysed the radiograph and assigned bone age scores based on the G and P atlas for the whole left hand and for the distal radius alone. The radiologist was blinded to the chronological age (CA), height of the patient and the BonAge result. Correlation between BonAge and G and P was undertaken. RESULTS: In 65 patients, BonAge measurement could be performed successfully. In five patients, the scanning process was impossible using the ultrasound device. The r(2) (r is the Pearson correlation coefficient) of the BonAge ultrasound measurement and the G and P method was 0.82. The averaged accuracy (i.e. absolute difference in years between G and P reading and BonAge ultrasonic results) was calculated. Results were similar for boys and girls: 1.0+/-0.8 years for the whole left hand and 0.8+/-0.7 year for the distal radius. On average, the difference between BonAge and CA is the same as the difference between G and P and CA, i.e. 1.4 years. CONCLUSIONS: The BonAge device demonstrates the ability of ultrasound to produce an accurate assessment of bone age. The results are highly correlated with skeletal age evaluated conventionally using the G and P method. Obvious advantages of the ultrasound device are objectivity, lack of ionizing radiation, and easy accessibility.