Nebraska Medical Center

Bicuspid aortic valve (BAV) is the most common congenital heart lesion that can be associated with significant valve dysfunction in childhood. Surgical interventions for this condition often include pulmonary autograft (Ross procedure) or aortic valve replacement (mechanical - mAVR - or bioprosthetic - bAVR). Aortic valve repair (Rpr) was a common primary intervention for BAV with aortic stenosis at our institution during the study period. This single-center cohort study included 150 adults with BAV and aortic stenosis who underwent 197 surgical interventions during childhood. We investigated important clinical demographic differences, long-term freedom from repeat aortic valve intervention, and long-term freedom from adverse events based upon the type of definitive aortic valve surgery. We hypothesized that Ross and mAVR would demonstrate superior temporal durability, but that mAVR would have more associated adverse events, and that Ross would require more lifetime sternotomies. Ross and mAVR demonstrated superior temporal durability when compared to other aortic valve interventions by pairwise comparison (Rpr vs mAVR p < 0.001; Rpr vs Ross p < 0.001; Rpr vs bAVR p = 0.075; mAVR vs Ross p = 0.924; mAVR vs bAVR p = 0.026; Ross vs bAVR p = 0.001). Ross demonstrated superior freedom from adverse events when compared directly to bAVR (Rpr vs mAVR p = 0.755; Rpr vs Ross p = 0.033; Rpr vs bAVR p = 0.816; mAVR vs Ross p = 0.040; mAVR vs bAVR p = 0.847; Ross vs bAVR p = 0.006). Ross procedure was not associated with an increased number of lifetime sternotomies compared to mAVR. Concurrent Konno procedure was not an independent predictor of worse outcomes.

Objective.High frequency oscillations (HFOs) are a promising biomarker of epilepsy, but automated detectors have significant risk for false positives due to diverse EEG artifacts. Many of these artifacts, previously uncharacterized in HFO research, are readily visible to clinicians under standard EEG viewing conditions. We present an artifact detector trained on clinician markings that identify when HFOs were produced by artifacts.Approach.Clinicians read standard resolution (10 sec per screen, all channels visible, 0-30 Hz), intracranial EEG with 8000 HFO events marked in 35 patients. They labeled each event as brain-derived or artifactual based upon their expert interpretation of the EEG at the time of the event, creating a new gold standard of HFO labeling. We used 4000 events for training/validation and 4000 for held-out prospective testing. We extracted features at the time of the HFOs from the single intracranial HFO channel and the scalp and intracranial common average reference, then trained candidate supervised learning classifiers to distinguish artifacts and non-artifactual HFOs (naHFOs).Main results.The resulting Michigan Intracranial Artifact Filter (MIAF) uses binary logistic regression on just intracranial data at the time of the HFO detection to remove false positives. The MIAF applied on held-out patient data significantly increased positive predictive value from 86% to 98% and resulted in an area under the precision recall curve and receiver operating characteristic curve of 99% and 92% respectively. It improved the correlation between HFOs and the seizure onset zone and resected volume in 76.5% and 88.9% of patients respectively, outperformed alternative artifact detectors, and allowed HFO analysis from all states of vigilance.Significance.MIAF effectively removes false positives HFO detections while retaining sufficient naHFOs for downstream analysis. Because it relies only on raw intracranial data during HFO detections, it can be easily ported to other HFO detectors and recording environments.

The National Organization of Nurse Practitioner Faculties (NONPF) Leadership and Mentoring Program (LMP) supports early- and mid-career nurse practitioner (NP) faculty develop effective leadership competencies and skills. The leadership competencies and skills for NP faculty are constantly evolving. NONPF's LMP is grounded in a competency-based education model and uses backward design to align learning outcomes with measurable leadership behaviors. LMP participants engage in structured learning sessions organized around 5 core leadership domains: Lead Self, Engage Others, Achieve Results, Develop Coalitions, and Systems Transformation. One-to-one mentoring runs concurrently with the educational sessions, offering personalized guidance and support throughout the 12-month program. The new NONPF 12-month LMP is redesigned for emerging NP faculty leaders to strengthen their leadership skills and build confidence by applying leadership competencies in real-world academic settings.