Kashou, Anthony H.

Introduction: Data on use of ICE guidance in TEER procedure is limited to case reports and small case series. Our study aims to assess the feasibility, safety, utilization patterns and clinical outcomes of mitral TEER procedure with ICE guidance using a nationally representative real-world cohort of patients.Hypothesis: There is no difference between ICE-guided TEER and TEE-guided TEER in the clinical outcomes and complications. Methods: This study used the National Inpatient Sample database from quarter 4 of 2015 to 2020. We used a propensity-matched analysis and adjusted odds ratios for in-hospital outcomes/complications. A P value of <0.05 was considered significant. Results: A total of 38,770 weighted cases of mitral TEER were identified. Of the included patients 665 patients underwent ICE-guided TEER while 38,105 had TEE-guided TEER. There were no differences in the in-hospital mortality between both groups (2.5% vs. 3.0%, p=0.58). Adjusted odds of in-hospital mortality (aOR 0.83, 95%CI [0.42-1.64]) were not significantly different. There were no differences in periprocedural complications including cardiac (aOR 0.85, 95%CI [0.54-1.35]), bleeding (aOR 1.45, 95%CI [0.93-2.33]), respiratory (aOR 0.88, 95%CI [0.61-1.25]), and renal (aOR 0.89, 95%CI [0.66-1.20]) complications between patients undergoing ICE-guided versus TEE-guided TEER. There was no difference in GI complications between both groups (aOR 1.11, 95%CI [0.46-2.70]). The adjusted length of stay was less among ICE-guided TEER (median: 1 vs. 2, p<0.01) with no differences in costs of hospitalization ($137,312 vs. $153,773, p=0.06), Conclusions: In conclusion, ICE-guided mitral TEER is safe when compared with TEE guided TEER with no significant differences in in-hospital mortality, cardiac, bleeding, respiratory, and renal complications. The length of stay in ICE group is shorter as compared with the TEE group.

Background: The impact of computer ECG interpretation (CEI) on medical professionals’ performance is understudied. This study aims to assess the interpretation proficiency of diverse medical professionals with and without access to CEI reports. Methods: Medical professionals from diverse backgrounds interpreted 60 12-lead ECGs with urgent and non-urgent findings. The interpretation process involved two interpretation phases: (i) 30 ECGs with clinical statements and (ii) the same 30 ECGs and clinical statements in random order along with a CEI report. Diagnostic performance was evaluated based on interpretation accuracy, time per ECG, and self-reported confidence (rated 0 [not confident], 1 [somewhat confident], or 2 [confident]). Results: Among 892 participants, there were 44 (4.9%) primary care physicians, 123 (13.8%) cardiology fellows-in-training, 259 (29.0%) resident physicians, 137 (15.4%) medical students, 56 (6.3%) advanced practice providers, 82 (9.2%) nurses, and 191 (21.4%) allied health professionals. The inclusion of the CEI significantly improved interpretation accuracy by 15.1% (95% CI, 14.3 to 16.0; P<0.001), reduced interpretation time by 52 seconds (95% CI, -56 to -48; P<0.001), and increased confidence by 0.06 (95% CI, 0.03 to 0.09; P=0.003) (Table 1) . Conclusions: CEI integration improves ECG interpretation accuracy, efficiency, and confidence across medical professionals. Nevertheless, even with CEI, significant gaps in ECG interpretation proficiency remain.

Background: Accurate ECG interpretation is vital, but variations in skills exist among healthcare professionals. This study aims to identify factors contributing to ECG interpretation proficiency. Methods: Survey data and ECG test scores from participants in the EDUCATE Trial were analyzed. The dependent variable was the test score for interpreting 30 12-lead ECGs. Independent variables included non-modifiable factors (physician status, clinical experience, patient care impact) and modifiable factors (weekly interpretations, training hours, expert supervision frequency). Bivariate and multivariate analyses generated the Comprehensive Model (all factors) and Actionable Model (modifiable factors only). Results: Among 1206 participants, there were 72 (6.0%) primary care physicians, 146 (12.1%) cardiology fellows-in-training, 353 (29.3%) resident physicians, 182 (15.1%) medical students, 84 (7.0%) advanced practice providers, 120 (9.9%) nurses, and 249 (20.7%) allied health professionals. Physicians accounted for 571 (47.3%) of participants, while 453 (37.6%) were non-physicians. Bivariate analysis showed associations between test scores and multiple variables (Table 1) . In the Comprehensive Model, scores were independently associated with weekly interpretations (9.9 score increase; 95% CI 7.9-11.8; P<0.001), physician status (9.0 score increase; 95% CI 7.2-10.8; P<0.001), and training hours (5.7 score increase; 95% CI 3.7-7.6; P<0.001). In the Actionable Model, scores were independently associated with weekly interpretations (12.0 score increase; 95% CI, 10.0-14.0; P<0.001) and training hours (4.7 score increase; 95% CI 2.6-6.7; P<0.001). The Comprehensive and Actionable Models accounted for 18.7% and 12.3% of the variance in test scores, respectively. Conclusions: Being a physician is a non-modifiable predictor, while training and regular practice are modifiable predictors of ECG interpretation performance.

Background: Gaps in ECG interpretation competency among medical professionals exist, with a need for practical, evidence-based, and accessible learning solutions. We conducted an international, prospective, randomized controlled trial to assess the effectiveness of web-based, self-directed ECG learning. Methods: A total of 1206 diverse medical learners and professionals underwent an initial test, followed by random assignment to one of four groups: (i) online question-bank (questions), (ii) online lectures (lectures), (iii) online, question-bank and lectures (hybrid), or (iv) no resources (control). After 4 months, a post-test was administered to evaluate the overall change in performance. Results: Of all participants, 863 (72%) completed the trial. After the 4-month follow-up, all three online, self-directed intervention groups (questions, lectures, and hybrid) showed significant improvement in overall performance (P<0.001), while the control group did not (P=0.544). The questions, lectures, and hybrid interventions demonstrated score improvements of +11.4% (95% CI, 9.1-13.7; P<0.001), +9.8% (95% CI, 7.8-11.9; P<0.001), and +11.0% (95% CI, 9.2-12.9; P<0.001) respectively, compared to +0.8% (95% CI, -1.2-2.8; P=0.544) for the control group. These improvements were consistent across medical professional groups (Figure 1) . Conclusions: Web-based, self-directed learning resources effectively improved ECG interpretation proficiency for diverse medical learners and professionals.

Background: ECG interpretation is crucial in medical practice, but professionals struggle with achieving and maintaining competency. Identifying proficiency gaps aids in designing educational interventions. Methods: Medical professionals from various disciplines interpreted 30 12-lead ECGs containing commonly taught urgent and non-urgent findings. Performance metrics evaluated were overall adjusted score (% of correctly identified findings using a point-value score based on clinical relevance), unadjusted score (% of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on an ordinal scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]). Results: Among 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FIT), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, 249 (21%) allied health professionals, 571 (47%) physicians, and 453 (38%) non-physicians. Participants had a mean adjusted score of 46.9% (± 15.9%), unadjusted score of 56.4% (± 17.2%), interpretation time of 142 seconds (± 67 seconds), and confidence of 0.83 (± 0.53) (Table 1) . Performance varied across groups; cardiology FIT had superior performance in all metrics. Physicians generally outperformed non-physicians, with higher overall adjusted score (52% vs. 43%; p<0.01), unadjusted score (62% vs. 52%; p<0.01), and confidence (0.91 vs. 0.80; p<0.01). Unadjusted score for PCPs was higher than nurses and APPs (58% vs. 47% and 51%; p<0.01) but lower than resident physicians (58% vs. 60%; p<0.01). Allied health professionals outperformed nurses and APPs and closely matched resident physicians and PCPs. Conclusions: Significant gaps in ECG interpretation proficiency exist, emphasizing the need for comprehensive, scalable, and accessible educational tools.

Introduction: Data on clinical outcomes of percutaneous closure of iatrogenic atrial septal defect (PC-ASD) in patients undergoing transcatheter edge-to-edge repair (TEER) or mitral valve replacement (TMVR) remains limited. We used a real-world USA database to evaluate the differences in the in-hospital outcomes between both groups. Hypothesis: Is there a clinical significance for PC-ASD in patients undergoing TEER or TMVR? Methods: We used the National Inpatient Sample (2015-2020) by utilizing ICD-10 codes to identify all patients who underwent TEER or TMVR with or without PC-ASD. Multivariate logistic regression was used for adjusted outcomes (adjusted odds ratio [aOR]). Results: A total of 44,065 eligible weighted hospitalizations were included: 39,625 (89.9%) of the patients underwent TEER and 4,440 (10.1%) underwent TMVR. Rates of PC-ASD were higher in TMVR group compared to TEER group (10.7% vs. 2.0%, p<0.01). In patients who underwent TEER without PC-ASD, PC-ASD was more associated with acute heart failure (AHF) (aOR 1.42 [1.19-1.68], p<0.01) and longer hospitalizations (aOR 1.64 [1.41-1.89], p<0.01), without significant difference in inpatient mortality (aOR 0.97 [0.61-1.54], p<0.01), cardiogenic shock (aOR 1.13 [0.85-1.51], p=0.21), need for mechanical circulatory support (MCS) (aOR 0.98 [0.61-1.54], p=0.18), and stroke. Compared to patients who underwent TMVR without PC-ASD, PC-ASD was associated with higher inpatient morality (aOR 1.65 [1.13-2.4], p<0.01), need for MCS (aOR 1.91 [1.33-2.73], p<0.01) without significant difference in AHF (aOR 1.19 [0.94-1.51], p=0.18] and cardiogenic shock (aOR 0.83 [0.56-1.22], p=0.43). Remarkably, PC-ASD was less associated with stroke (aOR 0.27 [0.10-0.72], p<0.01). Conclusions: Rates of PC-ASD after TEER were lower than after TMVR and were associated with worse in-hospital mortality and need for MCS in TMVR but not in TEER. Also, PC-ASD was less associated with stroke after TMVR.

Introduction: Data on clinical outcomes of early transcatheter aortic valve replacement (TAVR) (<48 hours) compared to late (≥48 hours) TAVR in patients hospitalized with acute heart failure (AHF) and aortic stenosis (AS) remains limited. We aim to evaluate the differences between early vs. late TAVR regarding major clinical outcomes using a real-world US database. Hypothesis: There is no differences in the clinical outcomes based on the timing of TAVR procedural intervention in patients with acute heart failure. Methods: We used the National Inpatient Sample database by utilizing ICD-10 codes to identify all hospitalizations with a diagnosis of AHF and TAVR (2015-2020). The associations between the time of TAVR and outcomes were examined using multivariable logistic regression. Propensity-score matched (PSM) analysis was utilized to determine the adjusted odds ratio (aOR) of major clinical outcomes. Results: A total of 25,285 weighted hospitalizations of AHF were identified, of which 6,855 patients (27.1%) underwent early TAVR and 18,430 (72.9%) had late TAVR. Compared with early TAVR, Late TAVR was associated with less need for mechanical circulatory support (MCS) (aOR 0.97 [0.96-0.98], p<0.01) and acute hypoxic respiratory failure (aOR 0.99 [0.98-0.99], p<0.01), but higher odds of developing septic shock (aOR 2.06 [1.52-2.80], p <0.01). However, there was no significant difference between both procedures regarding in-hospital mortality (aOR 1.01 [1.00-1.02], p=0.14), cardiac arrest (aOR 0.99 [0.98-1.00], p=0.21), and stroke (aOR 1.01 [1.00-1.02], p=0.18). Conclusions: Most patients with AHF and AS undergo TAVR after 48 hours of their presentation. After PSM, there was no difference in inpatient mortality, cardiac arrest, and stroke between both procedures. Late TAVR was associated with lower odds for MCS and acute hypoxic respiratory failure. The optimal timing of TAVR in patients with AHF and AS needs further study.

AbstractThe discrimination of ventricular tachycardia (VT) versus supraventricular wide complex tachycardia (SWCT) via 12‐lead electrocardiogram (ECG) is crucial for achieving appropriate, high‐quality, and cost‐effective care in patients presenting with wide QRS complex tachycardia (WCT). Decades of rigorous research have brought forth an expanding arsenal of applicable manual algorithm methods for differentiating WCTs. However, these algorithms are limited by their heavy reliance on the ECG interpreter for their proper execution. Herein, we introduce the Mayo Clinic ventricular tachycardia calculator (MC‐VTcalc) as a novel generalizable, accurate, and easy‐to‐use means to estimate VT probability independent of ECG interpreter competency. The MC‐VTcalc, through the use of web‐based and mobile device platforms, only requires the entry of computerized measurements (i.e., QRS duration, QRS axis, and T‐wave axis) that are routinely displayed on standard 12‐lead ECG recordings.

Background Physiological monitoring systems, like Masimo, used during inpatient hospitalisation, offer a non-invasive approach to capture critical vital signs data. These systems trigger alarms when measurements deviate from preset parameters. However, often non-urgent or potentially false alarms contribute to ‘alarm fatigue,’ a form of sensory overload that can have adverse effects on both patients and healthcare staff. The Joint Commission, in 2021, announced a target to mitigate alarm fatigue-related fatalities through improved alarm management. Yet, no established guidelines are presently available. This study aims to address alarm fatigue at the Mayo Clinic to safeguard patient safety, curb staff burnout and improve the sensitivity of oxygen saturation monitoring to promptly detect emergencies. Methods A quality improvement project was conducted to combat minimise the false alarm burden, with data collected 2 months prior to intervention commencement. The project’s goal was to decrease the total alarm value by 20% from 55%–85% to 35%–75% within 2 months, leveraging quality improvement methodologies. Interventions February to April 2021, we implemented a two-pronged intervention: (1) instituting a protocol to evaluate patients’ continuous monitoring needs and discontinuing it when appropriate, and (2) introducing educational signage for patients and Mayo Clinic staff on monitoring best practices. Results Baseline averages of red alarms (158.6), manual snoozes (37.8) and self-resolves (120.7); the first postintervention phase showed reductions in red alarms (125.5), manual snoozes (17.8) and self-resolves (107.8). Second postintervention phase recorded 138 red alarms, 13 manual snoozes and 125 self-resolves. Baseline comparison demonstrated an average of 16.92% reduction of alarms among both interventions (p value: 0.25). Conclusion Simple interventions like education and communication techniques proved instrumental in lessening the alarm burden for patients and staff. The findings underscore the practical use and efficacy of these methods in any healthcare setting, thus contributing to mitigating the prevalent issue of alarm fatigue.

BACKGROUND: Differentiation of wide QRS complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide QRS tachycardia (SWCT) through 12-lead electrocardiogram (ECG) interpretation is one of the most critical yet challenging tasks in clinical practice. Recent research highlights novel automated methods that successfully diagnose WCTs. We sought to directly compare the diagnostic efficacy of these novel automated WCT differentiation methods (i.e., WCT Formula, VT Prediction Model, and WCT Formula II) to traditional ECG interpretation approaches (i.e., Brugada and Vereckei aVR algorithms). METHODS: A collection of paired WCT and baselined ECGs were retrospectively analyzed. Next, an electrophysiologist blindly and prospectively applied the Brugada and Vereckei aVR algorithms. Separately, computerized measurements were used to apply the three automated WCT differentiation methods. The diagnostic performance of each method was then evaluated. RESULTS: 213 WCTs (111 VT and 102 SWCT) from 105 patients were analyzed. The WCT Formula demonstrated superior overall accuracy (84.7% vs. 64.8%), specificity (86.2% vs. 35.3%), positive predictive value (PPV) (85.7% vs. 60.7%), and positive (+) likelihood ratio (LR) (6.06 vs. 1.42) compared to Vereckei aVR algorithm. Similarly, the WCT Formula attained higher specificity (86.2% vs. 61.8%), PPV (85.7% vs. 71.9%), and (+) LR (6.06 vs. 2.36) than the Brugada algorithm. The WCT Formula II demonstrated superior overall accuracy (89.2% vs. 64.8%), specificity (84.3 % vs. 35.3%), PPV (86.7% vs. 60.7%), and positive (+) LR (5.97 vs.1.42) compared to Vereckei aVR algorithm as well as higher specificity (84.3 % vs. 61.8 %), PPV (86.7% vs. 71.9%), and (+) LR (5.97 vs. 2.36) than the Brugada algorithm. The VT prediction model yielded superior specificity (79.4% vs. 35.3%), PPV (79.8 vs. 60.7%) and (+) LR (3.63 vs.1.42) compared to the Vereckei aVR algorithm but did not demonstrate superior diagnostic performance to the Brugada algorithm. CONCLUSIONS: Novel automated WCT differentiation methods achieved similar or superior diagnostic performance metrics compared to traditional ECG interpretation approaches.