Citations

Abstract Background Brief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers. Method Participants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aß)‐42 and Aß‐40 to create an Aß42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result Lower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s = .02), NfL (p’s = .003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s = .01) and NfL (p’s = .03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s = .02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s = .02), and worse Digit Symbol Coding was associated with lower Aß42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05). Conclusion Remote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

Epicardial pulsed field ablation (PFA) of ganglionated plexi (GPs) is being explored as a potential treatment for atrial fibrillation. Initial work using open-chest access with a monopolar ablation device has been completed. This study describes the early development work for a device that can be used with subxiphoid access and deliver bipolar ablation pulses. Electric field computational models have been used for the initial guidance on pulse parameters. An in vivo assessment of these ablation parameters has been performed in an open-chest canine study, while subxiphoid access and navigation of the device has been demonstrated in a porcine model. Results from this acute study have demonstrated the promising potential of this approach.

Abstract Background The choice of an appropriate strategy for intracanalicular vestibular schwannoma (ICVS) is still debated. We conducted a systematic review and meta-analysis with the aim to compare treatment outcomes amongst management strategies (conservative surveillance (CS), microsurgical resection (MR), or stereotactic radiosurgery (SRS)) aiming to inform guideline recommendations on behalf of the International Stereotactic Radiosurgery Society (ISRS). Methods Using PRISMA guidelines, we reviewed manuscripts published between January 1990 and October 2021 referenced in PubMed or Embase. Inclusion criteria were peer-reviewed clinical studies or case series reporting a cohort of ICVS managed with CS, MR, or SRS. Primary outcome measures included tumor control, the need for additional treatment, hearing outcomes, and posttreatment neurological deficits. These were pooled using meta-analytical techniques and compared using meta-regression with random effect. Results Forty studies were included (2371 patients). The weighted pooled estimates for tumor control were 96% and 65% in SRS and CS series, respectively (P < .001). Need for further treatment was reported in 1%, 2%, and 25% for SRS, MR, and CS, respectively (P = .001). Hearing preservation was reported in 67%, 68%, and 55% for SRS, MR, and CS, respectively (P = .21). Persistent facial nerve deficit was reported in 0.1% and 10% for SRS and MR series, respectively (P = .01). Conclusions SRS is a noninvasive treatment with at least equivalent rates of tumor control and hearing preservation as compared to MR, with the caveat of better facial nerve preservation. As compared to CS, upfront SRS is an effective treatment in achieving tumor control with similar rates of hearing preservation.

Importance Transoral robot-assisted surgery (TORS) continues to have a major role in the treatment of oropharyngeal cancer. As new iterations of robotic technology are increasingly utilized, it is important to share learning experiences and clinical outcomes data, to optimize technical efficiency and clinical care. Observations This was a retrospective review of a large academic institution’s initial clinical use of the da Vinci Single Port (SP) compared with the da Vinci Si (Si) system. A total of 205 TORS cases were reviewed: 109 in the SP group (November 22, 2018, through September 30, 2020), and 96 in the Si group (January 1, 2016, through November 12, 2018). Both groups had comparable operative times, rates of postoperative pharyngeal hemorrhage, length of hospital stay, and duration of nasogastric feeding tube use. There was no difference in pathological characteristics, rates of positive margins, or indications for or time to initiation of adjuvant therapy between the groups. The collective experience of 6 faculty members—who have trained 139 TORS surgeons for the SP system rollout—was compiled to provide a summary of learning experiences and technical notes on safe and efficient operation of the SP system. Conclusions and Relevance This Review found that the functional and oncologic outcomes were comparable between TORS cases performed with the Si and SP systems, and they had similar complication rates. Recognized advantages of the SP over the Si system include the availability of bipolar-energized instruments, a usable third surgical arm, and improved camera image quality.

Abstract Objectives Recent work has demonstrated that automated fluorescence flow cytometry (FLC) is a potential alternative for the detection and quantification of Plasmodium parasites. The objective of this study was to apply this novel FLC method to detect and quantify Babesia parasites in venous blood and compare results to light microscopy and polymerase chain reaction methods. Methods An automated hematology/malaria analyzer (XN-31; Sysmex) was used to detect and quantify B microti–infected red blood cells from residual venous blood samples (n = 250: Babesia positive, n = 170; Babesia negative, n = 80). As no instrument software currently exists for Babesia, qualitative and quantitative machine learning (ML) algorithms were developed to facilitate analysis. Results Performance of the ML models was verified against the XN-31 software using P falciparum–infected samples. When applied to Babesia-infected samples, the qualitative ML model demonstrated an area under the curve (AUC) of 0.956 (sensitivity, 95.9%; specificity, 83.3%) relative to polymerase chain reaction. For valid scattergrams, the qualitive model achieved an AUC of 1.0 (sensitivity and specificity, 100%), while the quantitative model demonstrated an AUC of 0.986 (sensitivity, 94.4%; specificity, 100%). Conclusions This investigation demonstrates that Babesia parasites can be detected and quantified directly from venous blood using FLC. Although promising, opportunities remain to improve the general applicability of the method.

Background: Atrial septal defects (ASD) and ventricular septal defects (VSD) are among the most common forms of congenital heart disease (CHD). Early detection and intervention for these defects are crucial to prevent complications. Given advancements in AI technology, the development of AI-enabled ECG models for detecting these defects is of significant interest. Aims: This study aimed to create AI models using pediatric ECG data for the detection of ASD, VSD, and a combined model detecting either ASD or VSD. Methods: We analyzed a dataset of 7,795 pediatric patients from Mayo Clinic who had undergone echocardiography between 2002 and 2022, with corresponding 10-second, 12-lead surface ECGs taken within 14 days of echocardiography. This dataset included 259 patients with ASDs and 139 patients with VSDs (single or multiple). The control group was comprised of 7,397 patients without significant or complex CHD. For cases and controls, we included patients with all forms of electrocardiographic abnormalities and those with valvular lesions who had less than moderate stenosis or regurgitation. The dataset was divided into training, validation, and testing subsets to develop convolutional neural network (CNN) models for ASD, VSD, and either ASD or VSD detection. Results: Our ASD and VSD detection models achieved test AUCs of 0.82 (95% CI 0.76, 0.88) and 0.80 (95% CI 0.70, 0.90), respectively. Meanwhile, a combined model that detected either ASD or VSD demonstrated a test AUC of 0.81 (95% CI, 0.76, 0.86), sensitivity of 0.73, specificity of 0.76, PPV of 0.14, and NPV of 0.98 (Figure 1). Conclusions: AI-enabled ECG models demonstrate promising performance in detecting ASD and VSD in pediatric patients. A combined model that detects either ASD or VSD also shows robust results. Future research should focus on multi-center validation of these models, refining their diagnostic capacities, and investigating their potential for early intervention in CHDs.

Introduction: Left and right ventricular systolic dysfunction (LVSD and RVSD respectively) contribute to considerable pediatric morbidity. Both LVSD and RVSD can occur in children before symptom onset, emphasizing the need for early detection. Existing deep-learning algorithms can identify LVSD in adults using 12-lead ECG; however, their efficacy in children is uncertain. Aims: We aimed to create a deep-learning model for LVSD and RVSD detection in pediatric patients using an AI-enabled ECG and compare novel pediatric models to a previously validated adult LVSD model. Methods: We identified 10,214 pediatric patients at the Mayo Clinic with a transthoracic echocardiogram (TTE) and a 10-second, 12-lead surface ECG within 14 days of TTE, performed from 2002 -2022. Our cohort comprised the first TTE-ECG pair from each patient, totaling 138 with EF ≤ 35%, 312 with EF ≤ 50%, 482 with RVSD, and 9,567 without any LVSD or RVSD. The cohort was split into training, validation, and testing datasets to develop neural networks for systolic dysfunction detection. Results: The cohort was 52.3% male, with an average age of 10.3 years. Novel models generated in the pediatric cohort demonstrated excellent performance in systolic dysfunction detection (Figure 1). Pediatric models for EF ≤ 35% and EF ≤ 50% achieved test AUCs of 0.92 (95% CI 0.89, 0.95) and 0.89 (95% CI 0.85, 0.94) respectively. The RVSD model reached a test AUC of 0.91 (95% CI 0.88, 0.94). A combined model for EF ≤ 35% or RVSD exhibited a test AUC of 0.90 (95% CI 0.87, 0.93), sensitivity 0.88, specificity 0.82, PPV 0.20, and NPV 0.95. A combined model for EF ≤ 50% or RVSD exhibited a test AUC of 0.88 (95% CI 0.85, 0.91). The previously validated adult data-derived model achieved an AUC of 0.86 (95% CI 0.83, 0.89) for EF ≤ 50% in children. Conclusions: AI-enabled ECGs demonstrate efficient detection of both LVSD and RVSD in pediatric patients. Future multi-site validation is needed to further establish the reliability of these models.

Introduction: Biologic sex and hormonal concentrations shape ECG parameters due to sex hormone effects on cardiac function. AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. Aims: We aimed to test the ability of AI-enabled ECGs to predict pediatric patient sex and explore the influence of pubertal development. Methods: Two AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead surface ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 89,063 pediatric patients (aged <19 years) recorded from 1988-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), pubertal (8-14 years), and post-pubertal (15-18 years) patients. Results: The cohort consisted of 46.6% males, with 21,434 prepubertal, 26,300 pubertal, and 41,329 post-pubertal children. No significant performance difference was observed between the two models. Both models demonstrated 81% accuracy and an overall AUC of 0.91 in the testing cohorts, with high discriminatory ability in post-pubertal teenagers (AUC = 0.98). (Figure 1) Conclusions: AI-ECG predicts pediatric patient sex with varying performance across pubertal stages. The highest discriminatory ability is observed post-puberty, decreasing in pubertal and prepubertal children. The results suggest hormonal and physiological changes during puberty may influence cardiac electrophysiology measurably. More research is required to interpret CNN-predicted sex discordance with actual sex and its implications for future cardiovascular risk.

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have been demonstrated to improve outcomes in congestive heart failure (HF). However, their effect in hypertrophic cardiomyopathy (HCM) is unknown. Research Question: Can SGLT2i improve clinical outcomes in HCM patients? Methods: Using TriNetX Global Research Network from March 2013 to February 2021, patients diagnosed with HCM (ICD-10 codes I42.1, I42.2) were identified and categorized into 2 groups: those on SGLT2i vs. those who are not. To avoid inclusion of other causes of left ventricular hypertrophy, patients with aortic stenosis or systemic hypertension were excluded. Propensity score matching (PSM) was used to limit confounders. Primary outcome was all-cause mortality and secondary outcomes were HF exacerbation, all-cause hospitalization, and documented cardiovascular symptoms (chest pain, dyspnea, palpitations, or leg edema) over a 2-year follow-up period. Results: A total of 27,561 HCM patients were identified; 408 (1.5%) on SGLT2i and 27,153 (98.5%) were not. Patients on SGLT2i were older (55 ± 15 vs. 48 ± 19, p<0.01) and had more comorbidities, including a nearly 6-fold higher prevalence of diabetes mellitus (44% vs. 7%, p<0.01) and congestive HF (65% vs. 10%, p<0.01). After PSM, each group consisted of 355 patients. HCM patients on SGLT2i had lower rates of all-cause mortality (OR 0.22 [95% CI: 0.12, 0.39]; p<0.01), hospitalizations (OR 0.72 [95% CI: 0.53, 0.98]; p=0.04), and cardiovascular symptoms (OR 0.71 [95% CI: 0.52, 0.97]; p=0.029) when compared to patients not on SGLT2i. There was no statistical difference in the rate of HF exacerbation (OR 0.82 [95% CI: 0.54, 1.24]; p=0.3). Conclusion: In this large dataset, SGLT2i use in a select subset of HCM patients was associated with improved survival, less hospitalizations, and fewer cardiovascular symptoms. These data support future randomized trials to evaluate the efficacy and safety of SGLT2i in more diverse HCM patient populations.

Background: Up to 70% of patients with cardiogenic shock (CS) will survive hospitalization, yet studies on the outcomes of CS survivors are scarce. Hypothesis: We hypothesized that CS survivors would be at high risk of rehospitalization, particularly CS without acute myocardial infarction (AMI) who may have chronic heart failure (HF). Methods: We used the Nationwide Readmissions Database (NRD) to examine adult patients who were discharged alive after an index CS admission from 2016-2019. We compared demographic and clinical features during the index admission and the readmission mortality rate between the AMI-CS versus non-AMI-CS groups. We analyzed the cumulative hazard for all-cause readmission within 90 days after index hospitalization using Kaplan-Meier curves. Results: We included 134,793 survivors of CS hospitalization from 2016 to 2019 who were discharged to home or a long-term facility, including 43.9% with AMI-CS and 56.1% with non-AMI-CS. The demographics and clinical features differed between the AMI-CS and non-AMI-CS groups, with more HF and non-cardiac comorbidities in the non-AMI-CS group. The non-AMI-CS patients were more likely to be readmitted after discharge, with a higher 90-day readmission rate compared to the AMI-CS group (31.6% vs 25.9%, p <0.001; Figure). During the readmission, the non-AMI-CS group had a higher rate of repeat CS presentation compared to the AMI-CS group (8.9% vs.. 6.1%, p < 0.001). In-hospital mortality during readmission was higher in the non-AMI-CS group (8.6% vs.7.2%, p < 0.001). Conclusions: Nearly one-third of CS survivors require readmission within 90 days after their index hospitalization. Non-AMI-CS patients have a higher re-admission rate after their index hospitalization and worse outcomes during readmission. This analysis identifies an unmet need and an important opportunity to improve long-term outcomes for CS survivors.