Friedman, Paul A.

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P < .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.

Abstract Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk–benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Background The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new‐onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non‐POAF. Methods and Results This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non‐POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non‐POAF; 12‐month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non‐POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non‐POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non‐POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87–2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04–1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82–0.85]; P <0.0001) than patients who saw a surgeon or other specialties. Conclusions The use of and adherence to OAC were higher in non‐POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non‐POAF.

Key Points Measuring blood potassium has always required access to blood. The surface electrocardiogram, analyzed using an artificial intelligence algorithm, can detect hyperkalemia bloodlessly. The artificial intelligence-analyzed electrocardiogram exhibited a high negative predictive value but substantially lower positive predictive value. Background Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results The ED cohort ( N =40,128) had a mean age of 60 years, 48% were male, and 1% ( N =351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort ( N =2636) had a mean age of 65 years, 60% were male, and 3% ( N =87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Abstract Background The extravascular implantable cardioverter defibrillator (EV ICD) has recently emerged as a new option for guideline indicated ICD patients. Previous study results from the EV ICD Pilot and Pivotal studies have demonstrated that the device can provide safe and effective therapy through chronic follow up. However, less has been reported regarding EV ICD lead removal. Purpose To report on the experience of lead removal in the EV ICD Pivotal Study. Methods The EV ICD Pivotal study was an international, prospective, single-arm, premarket clinical study. Patients with a class I or IIa indication for a single-chamber ICD per ESC or ACC/AHA/HRS guidelines were enrolled. Lead removal summary data was reported. Results Of 316 patients with an implant attempt, (74.7% male, age 53.8±13.1 years, 82% primary prevention, LVEF of 38.9%±15.4%, and NYHA Class I [23.7%] or II/III [65.5%]) 299 were fully implanted; 17 (5.7%) of which had a lead removal procedure (14–752 days post-implant, at 13 sites) with 3 of them having been implanted for more than 1 year. Reasons for lead removal were lead dislodgement (n=7), infection (n=5), high impedance alert due to lead fracture (n=2), pacing indication (n=1), cardiac transplant (n=1), or scheduled sternotomy (n=1). New EV ICD leads were placed in 7/17 (41.2%) patients. No serious adverse events were adjudicated as related to the explant procedures. There was a single observation of incision site haemorrhage, that was resolved with wound care. Additional details on lead removal will be included at the time of presentation. Conclusions The EV ICD lead was successfully removed 14-752 days post implant. Nine patients were re-implanted with either extravascular or transvenous systems. Longer term extraction data collection is ongoing through the EV ICD Pivotal study and through the EV ICD post-approval study and will continue to provide additional insights.Lead removed 24 months post-implant.

Introduction: LMNA encodes the nuclear envelope proteins lamin A/C that are involved in ageing processes. Mutations in LMNA cause a spectrum of phenotypes which include muscular dystrophy, arrhythmogenic dilated cardiomyopathy, peripheral neuropathy, progeroid syndrome and overlap syndrome. Early diagnosis is crucial to prevent sudden cardiac death. We hypothesized that LMNA mutations result in an ECG biological age older than chronological age. Methods: In this study, we used AI-enabled ECG network to investigate standard digital 12-lead ECGs in genetically characterized LMNA patients. Results: We identified 33 LMNA patients with 331, 12-lead ECG available for analysis. Median age at symptoms onset was 23.5 (range: <1 to 61 years;15 females). Mean follow up time was 86 months (range: <1 to 367 months). Symptoms at onset included muscle weakness (n=10) or cardiac symptoms (n=18) and asymptomatic (n=5). We included a total of 1532 age sex matched controls. We found significant differences in positive age-gap (biologically older than chronological age) comparing LMNA patients (median of 17.5) versus controls (4.9 years; p<0.001, Figure 1 ). AI-ECG age with 10 years or higher age-gap was observed in 73% of LMNA patients compared to 27% in the controls (p<0.001). Consecutive serial analysis for all ECG’s in the study versus predicted age showed faster aging when comparing the rates of age-gap overtime in the LMNA group compared to controls (p<0.0001, Figure 2 ). Moreover, the subset analysis of 23 ECG in the 5 asymptomatic patients versus controls also showed significantly higher AI-ECG age-gap (p=0.004) Conclusions: An AI-ECG algorithm to detect biological age effectively demonstrates accelerated aging in patients with LMNA mutations related to physiological aging. Our findings further validate the role of AI-ECG to detect accelerated aging, expanding previous studies demonstrating higher mortality among individuals with in increased positive age-gap.

Introduction: Point source ablation may be effective in eliminating stable ventricular tachycardia (VT), where actual mapping and ablation are possible. Ablating patients with unstable VT is more complicated and may require aggressive intervention. While small linear lesions have been utilized, the actual utility of longer linear homogenization for eliminating ventricular arrhythmias has not been examined. Methods: Therefore, the results of 86 patients undergoing VT ablation at Mayo Clinic were examined. 42 had underlying ischemic heart disease with an EF of 33.5 ± 16.8 had VT with a cycle length of 326 ± 165 ms, and had 2.5± 1.6 episodes of VT in the preceding three months. 32 required shocks for that tachycardia. Results: Of these, 21 patients (16 male, 5 female Age 67±18) underwent detailed linear ablation at the scar margin in an effort to homogenize that region. This was drawn into the infarct scar by decreasing the upper voltage boundary to 2.0. In these patients, 18 showed elimination of clinical VT. In addition, 12 showed a decrease in VT from 10 ± 8.7 episodes down to 2.5 ± 1.2 episodes of actual ventricular arrhythmias. The shock requirement decreased from 8.6 ±4.7 to 1.5 ±2.7. These patients had 7 ± 3.5 different VTs at the time of ablation. These data suggest not only a decrease the occurrence of VT, but an improved control of the arrhythmia as manifested by decreased frequency of episodes and a decrease in shock. This suggests a straightforward alternative to point source ablation or to the requirement of detailed LAVA intervention. Conclusion: Linear infarct edge homogenization is a straightforward means of ablating ventricular tachycardia’s, even when unstable. This approach may be simpler than seen with LAVA interventions.

Introduction: The treatment options for high and extreme-risk patients with severe symptomatic aortic stenosis have changed significantly over the past decade because of advances in transcatheter aortic valve replacement (TAVR). However new permanent pacemaker implant (PPI) remains an impending concern after TAVR. Hypothesis: We sought to determine the frequency, predictors and outcomes of patients requiring PPI after TAVR. Methods: We queried the National Inpatient Sample (NIS) database using the ICD-9-CM procedure code of 35.05 for transfemoral/transaortic and 35.06 for transapical replacement of aortic valve .We identified TAVR procedures associated with new permanent pacemaker implants using appropriate validated ICD-9-CM codes. Using multivariate analysis we further identified predictors of PPM implants Results: An estimated 8828 TAVR procedures were performed in United States between 2011-2012. 773 (8.7%) TAVR procedures were associated with PPIs. PPI were higher in older (age>80 years), White race, diabetics, left bundle branch block (LBBB), right bundle branch block (RBBB), transfemoral approach. The inhospital mortality ( 5.2% vs.4.6% ) , mean length of stay ( 9.8 vs. 8.3 days ) disposition to short term nursing facility was higher (38.2% vs. 32.1%) and mean total costs (82217$ vs. 52283$) was higher in TAVR + PPI group . On multivariate analysis, Age in 5 year increment [Odds ratio (OR) 1.11; Confidence interval (CI) 1.05-1.17, p<0.001], diabetes [OR 1.26; CI 1.07-1.49, p<0.001], LBBB [OR 1.99; CI 1.55-2.56, p<0.001], RBBB [OR 5.36; CI 3.80-7.55, p<0.001] were independent predictors of PPI. Transfemoral approach was associated with more PPI implants [OR 1.41; CI 1.12 -1.78, p<0.001] Conclusion: Age, baseline conduction system disturbances, diabetes, transfemoral approach emerged as independent predictors of PPI after TAVR. This study provides useful tools to identify high-risk patients for PPI after TAVR.

Introduction: The subcutaneous implantable cardioverter defibrillator (S-ICD) is an attractive option for patients with congenital heart disease (CHD) in whom a transvenous system is contraindicated. However, more than 10% of adult patients with CHD fail S-ICD screening due to a large T- to R-wave ratio. We hypothesized right-sternal electrodes may permit S-ICD placement in patients who fail left-sternal screen. Methods: We prospectively screened 40 patients with CHD for S-ICD using the standard proprietary approach. We also screened with right sternal electrodes (with left can electrode position). Results: The mean age of the cohort was 46+/-15 years; 24 males were enrolled. CHD anomalies consisted of tetralogy of Fallot (n=11), Ebstein’s anomaly (n=7), atrial septal defect (n=6), dextro-looped transposition of the great artery (n=5) and other (n=11). Left side screening ECG failed in 9 patients (23%), five of whom passed with right sternal electrodes, increasing the pass rate from 77% to 90%. Figure shows the screening ECG and chest x-ray of a patient who failed in all three vectors on the left side but passed in the alternative vector on the right side and underwent right sided implantation. Conclusions: The addition of right sternal electrodes during screening increases S-ICD eligibility and may avoid thoracotomy for high risk patients with CHD.