McLeod, Christopher J.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

BACKGROUND: The outcomes of catheter ablation for atrial fibrillation in adults with congenital heart disease are not well described. METHODS: In a retrospective study of adult patients with congenital heart disease who underwent catheter ablation for atrial fibrillation between 2000 and 2020 at Mayo Clinic, procedural characteristics and outcomes were collected. The primary outcomes were atrial arrhythmia (AA) recurrence following a 3-month blanking period and repeat ablation. An arrhythmia clinical severity score was assessed pre- and post-ablation based on the duration of arrhythmia episodes, symptoms, cardioversion frequency, and antiarrhythmic drug use. RESULTS: One hundred forty-five patients (age, 57±12 years; 28% female; 63% paroxysmal atrial fibrillation) underwent 198 ablations with a median follow-up of 26 months (interquartile range, 14–69). One hundred ten, 26, and 9 patients had simple, moderate, and complex congenital heart disease, respectively. All patients underwent pulmonary vein isolation, and non–pulmonary vein targets were ablated in 79 (54%). AA recurrence at 12 months was 37% (95% CI, 29%–45%). On univariate analysis, increasing left atrial volume index was associated with higher odds of AA recurrence (odds ratio, 1.03 [1.00–1.06] per 1 mL/m 2 increment; P =0.05). Noninducibility of atrial flutter was predictive of decreased odds of AA recurrence (odds ratio, 0.43 [0.21–0.90]; P =0.03). A second ablation was performed in 43 patients after a median of 20 (interquartile range, 8–37) months. Arrhythmia clinical severity scores improved following ablation, reflecting a decrease in symptoms, cardioversions, and antiarrhythmic drugs. CONCLUSIONS: Catheter ablation of atrial fibrillation is feasible and effective in patients with adult congenital heart disease and reduces symptoms. Recurrence of AA frequently requires repeat ablation.

Background: Patients with ccTGA have a high incidence of atrial arrhythmias and premature failure of the systemic right ventricle. We sought to better define this risk. Methods: A retrospective review of all clinical data for patients with ccTGA followed at a large tertiary care institution was performed. The primary outcome was a composite of death and cardiac transplantation. Results: A cohort of 165 patients (age 41±16 years, 53% male) were followed for 11±8 years; 41 patients died, 7 underwent cardiac transplantation, and 4 underwent VAD placement. Male sex (HR 2.7, p=0.002), older age (HR 1.04, p<0.001), clinical heart failure symptoms (HR 7, p<0.001), heart failure hospitalization (HR 4.9, p<0.001), syncope (HR 1.8, p=0.04) and pulmonary hypertension (HR 2.77, p<0.001) were significant predictors of the primary outcome. Atrial fibrillation (HR 3.5, p=0.002), atrial flutter (HR 3.05, p=0.04), and prolonged QRS duration (HR 1.01, p=0.004) on the first ECG, as well as right and left atrial enlargement (HR 4, p<0.001 and 3.7, p<0.003) and severe systemic AV valve regurgitation (HR 3.8, p=0.03) on the first echocardiogram were associated with poor outcomes. Atrial arrhythmias occurred in 110(67%) patients: atrial fibrillation in 75(45%), atrial flutter in 50(30%), and supraventricular tachycardia in 16(9.7%) patients. Arrhythmias were largely asymptomatic in 109(66%) patients, while 25(14.5%) experienced palpitations, dizziness or presyncope, and 10(6%) presented with decompensated heart failure. Cardioversion and antiarrhythmic therapy were utilized in 53(32%) patients. Surgical pulmonary vein isolation or atrial MAZE were performed in 15(9%) patients, cryoablation of the CTI in 3 patients, and surgical ablation of an accessory pathway in 2 patients. One or more catheter ablation procedures were also performed in 18(11%) patients. Conclusions: Atrial fibrillation and flutter, heart failure, prolonged QRS duration, systemic AV valve regurgitation and pulmonary hypertension predict worse outcomes in ccTGA. Although asymptomatic in a majority of patients, atrial arrhythmias are associated with significant morbidity and mortality. Whether a rhythm control strategy even in asymptomatic patients may improve outcomes remains unknown.

Introduction: Myocardial inflammation likely contributes to the pathogenesis of arrhythmogenic cardiomyopathy (ACM), a clinically and genetically heterogenous disorder characterized by ventricular dysfunction and potentially life-threatening ventricular arrhythmias. Due to phenotypic overlap, some patients with genetic ACM may have been evaluated for an underlying inflammatory cardiomyopathy, namely cardiac sarcoidosis. However, the cardiac fludeoxyglucose (FDG) positron emission tomography (PET) findings in ACM patients have not been assessed systematically. Methods: All genotype-positive patients in the Mayo Clinic ACM registry who received a cardiac FDG PET were included in this study. The cardiac FDG PET scan consisted of a resting myocardial perfusion scan with N13-ammonia as well as a F18-FDG scan after a high-fat diet to suppress physiologic FDG uptake. Pertinent demographic, genetic, imaging, and outcome data were extracted from the medical record. Results: Collectively, 12 (75% female) genotype-positive ACM patients received a cardiac PET FDG scan as part of their clinical evaluation (median age at scan 45.3 years [IQR 38.9-62.1]). Amongst these patients, pathogenic/likely pathogenic variants were detected in LMNA (n=7), DSP (n=3), FLNC (n=1), and PLN (n=1). Of note, 6/12 (50%) had abnormal myocardial FDG uptake, including diffuse (entire myocardium) uptake in 2/6 (33%), focal (1-2 segments) uptake in 2/6 (33%) and patchy (3+ segments) in 2/6 (33%). Median myocardial standardized uptake value ratio was 2.1 (IQR 1.5-2.6). Interestingly, LMNA -positive patients accounted for 3 out of 6 (50%) positive studies (diffuse uptake in 2 and focal uptake in 1). Conclusion: Myocardial FDG uptake is common in genetic ACM patients undergoing cardiac FDG PET. This finding further supports the role of myocardial inflammation in genetic ACM and demonstrates the ability of cardiac FDG PET to readily detect this phenomenon. Further investigation is needed to determine (i) the role of cardiac FDG PET in the evaluation of genetic ACM, (ii) if FDG uptake in genetic ACM has prognostic value and (iii) whether the attenuation of myocardial inflammation with immunosuppressive agents can impact the clinical course of inflammatory genetic ACMs.

Introduction: Repeated 6MWT in clinic are inconvenient for patients and disruptive to usual clinic throughput. Digital measures may be a means to overcome these challenges and reliably measure functional capacity. Methods: As part of an observational clinical trial (NCT04191356), during an 8-week observation period, patients with heart failure (HF) utilize arm-band biosensors (Biofourmis Everion®) for at least 15 hours daily while carrying out daily activities and complete an in-clinic investigator-guided 6MWT on the first and last days of participation. Interim data (N=30, age 56.17 ± 11.26; 24 males; 3 NYHA I, 25 NYHA II, 2 NYHA III) are used for validation, where the distance of baseline 6MWT is used as reference standard. Results: Passive 6MWT, which is the maximum 6-minute step count derived from daily accelerometer data (Fig 1), is strongly correlated with the reference standard (R=0.7501, P<0.0001, N=30) (Fig 2). Further analysis (Fig 2) reveals that regardless of the duration of the step count, the digital measures consistently have strong correlations with the reference standard, with the highest correlation obtained by Passive 10MWT (R=0.7746, P<0.0001, N=30). Conclusions: Early data highlights that accelerometer data collected during daily activities appears to indicate functional capacity of HF patients. Continued data will validate digital measures against various patient outcomes. Fig 1 Aggregation of Passive 6MWT from step count recorded during daily activities Fig 2 Correlation between accelerometer-derived passive step count digital measures and distance manually measured during baseline investigator-guided 6MWT as reference standard (N=30)

Introduction: Ebstein’s anomaly (EA) is a rare congenital anomaly of the tricuspid valve associated with heart failure and arrhythmias. Currently available imaging modalities like transthoracic echocardiography, cardiac magnetic resonance imaging (MRI), or computerized tomography (CT) are not viable for screening large asymptomatic populations. Hypothesis: A deep learning algorithm, implemented using a convolutional neural network (CNN) applied to the standard 12-lead ECG can be used as an accurate screening tool for EA in children and adults. Method: We identified 1,154 EA patients and 21,430 age- and sex-matched controls (1:20 ratio) between 1987 - 2020. A CNN was trained, validated and tested using digital 12-lead ECGs in 70%, 10% and 20% of the cohort respectively, divided by random shuffling. Results: The mean age was 26.68±19.23 years (41.8% aged < 18 years, 56.41% female) for the EA cohort and 28.36±18.65 years (37.2% aged <18 years, 55.67% female) for the control cohort. Following training and validation the area under the curve (AUC) for detecting EA in the testing cohort was 0.976 (95% CI 0.962 - 0.989) with prediction accuracy of 0.959. In subgroup analysis, the AUC was comparable in males (0.970, 95% CI 0.946-0.995) and females (0.979, 95% CI 0.964-0.994), those <18 (0.980, 95% CI 0.958-1.000) and ≥18 years old (0.972, 95% CI 0.954-0.989), as well as in those with right bundle branch block (RBBB) (0.969, 95% CI 0.950-0.989) and without RBBB (0.950, 95% CI 0.922-0.978) on ECG. Conclusion: Artificial intelligence enhanced ECG based detection of EA is possible with high diagnostic accuracy. Following further refinement and external validation, this algorithm can facilitate early detection of EA using ECGs, a widely available and inexpensive tool. Figure: Receiver operating characteristic (ROC) curve for the performance of the artificial intelligence CNN in detecting EA in the test cohort stratified by (A) age (< 18 and ≥ 18 years) and sex and (B) presence of RBBB.

Background: Recognition of the causes of early mortality after atrial fibrillation (AF) catheter ablation is essential for the improvement of patient safety. Objective: This study sought to determine the causes of early mortality (≤90-days) after AF ablation and to further understand the circumstances surrounding death after the procedure. Methods: We performed a retrospective analysis of AF ablation from January 1, 2013, to December 1, 2021 at the Mayo Clinic (Rochester, Phoenix, and Jacksonville). Causes of death were identified through a comprehensive chart review of the electronic health record from within the Mayo Clinic system and outside records when available. Results: A total of 6,723 patients were included in the study. The 30-day and 90-day all-cause mortality rates were 0.09% and 0.22%, respectively. Among all 90-day deaths, majority of the deaths (73.3%) did not have a direct relationship with the procedure. Sudden death was the most common cause of early death (20%), followed by peri-procedural stroke (13%), respiratory failure (13%), atrioesophageal fistula (13%), infection (7%), heart failure (7%), and traumatic brain injury (7%). The 90-day mortality rate directly due to AF ablation procedural complications was 0.06%. Most of the early deaths (93.3%) occurred after discharge from index hospitalization, among which 64.3% were readmitted before their death, 14% died at home, and 7% were discharged on comfort care and died in hospice. The main reason for readmission leading to death was respiratory failure (33%). Conclusion: Atrial fibrillation ablation procedure has a 90-day mortality of 0.22% and the most common cause of early mortality was sudden death. The majority (73.3%) of early mortality was not directly associated with a procedural complication, and the mortality rate due to complications associated with the AF ablation procedure was 0.06%.

Background: Recognition of the causes of early mortality after ventricular tachycardia (VT) ablation in patients with reduced left ventricular ejection fraction is an essential step toward improving post-procedural outcomes. Objective: This study sought to determine the causes of early mortality (≤30-days) after VT ablation in patients with reduced left ventricular ejection fraction and to understand further the circumstances surrounding death after the procedure. Methods: We performed a retrospective analysis of all patients undergoing VT ablation in patients with reduced left ventricular ejection fraction from January 1, 2013, to November 10, 2021, at the Mayo Clinic (Rochester, Phoenix, and Jacksonville). Causes of death were identified through a detailed chart review of the electronic health record within the Mayo Clinic system and outside records. Results: A total of 503 patients (mean age 63 ± 13 years, 11.2% female) with EF <50% were included in the study. The 30-day all-cause mortality rate was 5% (n=25), and the mortality rate due to a procedural complication was 0.4%. Among all 30-day deaths, recurrent VT was the most common primary cause of death (44%). This was followed by decompensated heart failure (28%), procedure-related death (8%), cerebrovascular accident (CVA) (4%), and infection (4%). Most patients (91%) who died from VT had the VT recurrence within three days of the ablation. 76% of patients with early mortality had delayed referral for VT ablation, and 72% opted for comfort care before death. Conclusions: The overall early mortality (≤30-days) rate after catheter ablation of VT in patients with reduced left ventricular ejection fraction was 5%, but the death rate directly due to a procedural complication was only 0.4%. The most common cause of death was recurrent VT, followed by heart failure. Further research into ablation strategies is vital to improving the safety, efficacy, and durability of VT ablation.

AbstractCardiac physiologic pacing (CPP), encompassing cardiac resynchronization therapy (CRT) and conduction system pacing (CSP), has emerged as a pacing therapy strategy that may mitigate or prevent the development of heart failure (HF) in patients with ventricular dyssynchrony or pacing‐induced cardiomyopathy. This clinical practice guideline is intended to provide guidance on indications for CRT for HF therapy and CPP in patients with pacemaker indications or HF, patient selection, pre‐procedure evaluation and preparation, implant procedure management, follow‐up evaluation and optimization of CPP response, and use in pediatric populations. Gaps in knowledge, pointing to new directions for future research, are also identified.

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.