Citations

Extracorporeal membrane oxygenation (ECMO) is often used in acute respiratory distress syndrome (ARDS) with refractory hypoxemia. There is limited literature highlighting the development of right ventricular (RV) failure while on ECMO. We conducted a retrospective multicenter observational study including 70 patients who were placed on veno-venous (VV)-ECMO for respiratory failure at Mayo Clinic, Jacksonville, and Mayo Clinic, Rochester, between January 2018 and June 2022 and had at least two post-ECMO transthoracic echoes. The primary outcomes were the incidence and progression of RV dysfunction and dilatation. The secondary outcome was in-patient mortality. Among 70 patients in our cohort, 60.6% had a normal RV function at the time of ECMO placement, whereas only 42% had a normal RV function at the second post-ECMO echo. On multinomial regression, a moderate decrease in RV function was associated with ECMO flow (odds ratio [OR] = 2.32, p = 0.001) and ECMO duration (OR = 1.01, p = 0.01). A moderately dilated RV size was also associated with ECMO flow (OR = 2.62, p < 0.001) and ECMO duration (OR = 1.02, p = 0.02). An increasing degree of RV dysfunction was associated with worse outcomes. Our study showed that the increasing duration and flow of VV-ECMO correlated with progressive RV dilatation and dysfunction, which were associated with poor survival.

Case: A 20-year-old woman presented with a unipolar, 1.8 × 1.8-cm osteochondral defect of the left acetabulum. Osteochondral allograft transplantation was performed using a medial tibial plateau allograft resulting in excellent clinical outcomes across 4 different outcome scores and maintenance of the joint space at 4.3 years postoperatively. Conclusion: Although previous literature has demonstrated long-term clinical success of osteochondral allograft transplantation in knee, excellent clinical outcomes can also be obtained in the hip. Thus, osteochondral allograft transplantation may be a viable treatment option for adolescents and young adults with concomitant cartilage and subchondral bone hip defects.

AbstractArtificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high‐stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real‐world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.Level of EvidenceLevel V.

Background The goal was to compare patterns of physical activity (PA) behaviors (sedentary behavior [SB], light PA, moderate‐to‐vigorous PA [MVPA], and sleep) measured via accelerometers for 7 days between patients with incident cerebrovascular disease (CeVD) (n=2141) and controls (n=73 938). Methods and Results In multivariate models, cases spent 3.7% less time in MVPA (incidence rate ratio [IRR], 0.963 [95% CI, 0.929–0.998]) and 1.0% more time in SB (IRR, 1.010 [95% CI, 1.001–1.018]). Between 12 and 24 months before diagnosis, cases spent more time in SB (IRR, 1.028 [95% CI, 1.001–1.057]). Within the year before diagnosis, cases spent less time in MVPA (IRR, 0.861 [95% CI, 0.771–0.964]). Although SB time was not associated with CeVD risk, MVPA time, both total min/d (hazard ratio [HR], 0.998 [95% CI, 0.997–0.999]) and guideline threshold adherence (≥150 min/wk) (HR, 0.909 [95% CI, 0.827–0.998]), was associated with decreased CeVD risk. Comorbid burden had a significant partial mediation effect on the relationship between MVPA and CeVD. Cases slept more during 12:00 to 17:59 hours (IRR, 1.091 [95% CI, 1.002–1.191]) but less during 0:00 to 5:59 hours (IRR, 0.984 [95% CI, 0.977–0.992]). No between‐group differences were significant at subgroup analysis. Conclusions Daily behavior patterns were significantly different in patients before CeVD. Although SB was not associated with CeVD risk, the association between MVPA and CeVD risk is partially mediated by comorbid burden. This study has implications for understanding observable behavior patterns in cerebrovascular dysfunction and may help in developing remote monitoring strategies to prevent or reduce cerebrovascular decline.

AbstractWith the discovery of an expanding number of neural autoantibodies, autoimmune etiologies of seizures have been increasingly recognized. Clinical phenotypes have been identified in association with specific underlying antibodies, allowing an earlier diagnosis. These phenotypes include faciobrachial dystonic seizures with LGI1 encephalitis, neuropsychiatric presentations associated with movement disorders and seizures in NMDA‐receptor encephalitis, and chronic temporal lobe epilepsy in GAD65 neurologic autoimmunity. Prompt recognition of these disorders is important, as some of them are highly responsive to immunotherapy. The response to immunotherapy is highest in patients with encephalitis secondary to antibodies targeting cell surface synaptic antigens. However, the response is less effective in conditions involving antibodies binding intracellular antigens or in Rasmussen syndrome, which are predominantly mediated by cytotoxic T‐cell processes that are associated with irreversible cellular destruction. Autoimmune encephalitides also may have a paraneoplastic etiology, further emphasizing the importance of recognizing these disorders. Finally, autoimmune processes and responses to novel immunotherapies have been reported in new‐onset refractory status epilepticus (NORSE) and febrile infection‐related epilepsy syndrome (FIRES), warranting their inclusion in any current review of autoimmune‐associated seizure disorders.

AbstractPurposeTo study implant lead tip heating because of the RF power deposition by developing mathematical models and comparing them with measurements acquired at 1.5 T and 3 T, especially to predict resonant length.Theory and MethodsA simple exponential model and an adapted transmission line model for the electric field transfer function were developed. A set of wavenumbers, including that calculated from insulated antenna theory (King wavenumber) and that of the embedding medium were considered. Experiments on insulated, capped wires of varying lengths were performed to determine maximum temperature rise under RF exposure. The results are compared with model predictions from analytical expressions derived under the assumption of a constant electric field, and with those numerically calculated from spatially varying, simulated electric fields from body coil transmission. Simple expressions for the resonant length bounded between one‐quarter and one‐half wavelength are developed based on the roots of transcendental equations.ResultsThe King wavenumber for both models more closely matched the experimental data with a maximum root mean square error of 9.81°C at 1.5 T and 5.71°C at 3 T compared to other wavenumbers with a maximum root mean square error of 27.52°C at 1.5 T and 22.01°C for 3 T. Resonant length was more accurately predicted compared to values solely based on the embedding medium.ConclusionAnalytical expressions were developed for implanted lead heating and resonant lengths under specific assumptions. The value of the wavenumber has a strong effect on the model predictions. Our work could be used to better manage implanted device lead tip heating.

Abstract Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Background: Infective endocarditis (IE) management is challenging, usually requiring multidisciplinary collaboration from cardiologists, infectious disease specialists, interventional cardiologists, and cardiovascular surgeons, as more than half of the cases will require surgical procedures. Therefore, it is essential for all healthcare providers involved in managing IE to understand the disease’s characteristics, potential complications, and treatment options. While systemic embolization is one of the most frequent complications of IE, the coronary localization of emboli causing acute myocardial infarction (AMI) is less common, with an incidence ranging from 1% to 10% of cases, but it has a much higher rate of morbidity and mortality. There are no guidelines for this type of AMI management in IE. Methods: This narrative review summarizes the current knowledge regarding septic coronary embolization in patients with IE. Additionally, this paper highlights the diagnosis and management challenges in such cases, particularly due to the lack of protocols or consensus in the field. Results: Data extracted from case reports indicate that septic coronary embolization often occurs within the first two weeks of the disease. The aortic valve is most commonly involved with vegetation, and the occluded vessel is frequently the left anterior descending artery. Broad-spectrum antibiotic therapy followed by targeted antibiotic therapy for infection control is essential, and surgical treatment offers promising results through surgical embolectomy, concomitant with valve replacement or aspiration thrombectomy, with or without subsequent stent insertion. Thrombolytics are to be avoided due to the increased risk of bleeding. Conclusions: All these aspects should constitute future lines of research, allowing the integration of all current knowledge from multidisciplinary team studies on larger patient cohorts and, subsequently, creating a consensus for assessing the risk and guiding the management of this potentially fatal complication.