Citations

Patient complaints are valuable sources of information for quality improvement and provide an avenue for patient self-advocacy. This paper outlines the development and implementation of a novel training program, termed CODE (Compassion, Operational Support, De-escalation, Empowerment), for patient experience complaint management staff. The program consists of 2 core tracks: procedural training on operational systems and resources, and interpersonal communication training. A dual-track framework ensures practical knowledge to access resources to assist complainants is paired with communication for empathy and human connection. The CODE program's dual-track structure is a key strength, bridging the gap between operational efficiency and compassionate communication. By emphasizing both procedural knowledge and interpersonal skills, it equips healthcare professionals to manage patient complaints successfully. The CODE program represents a novel approach for healthcare institutions seeking to optimize patient experience and complaint-handling performance.

Abstract Background and Aims Accurate differentiation of cardiac amyloidosis (CA) from phenotypic mimics remains challenging using current clinical and echocardiographic techniques. The accuracy of a novel artificial intelligence (AI) screening algorithm for echocardiography-based CA detection was assessed. Methods Utilizing a multisite, multiethnic dataset (n = 2612, 52% CA), a convolutional neural network was trained to differentiate CA from phenotypic controls using transthoracic apical four-chamber video clips. External validation was conducted globally across 18 sites including 597 CA cases and 2122 controls. Classification accuracy was assessed on the entire external validation dataset, and subgroup analyses were performed both on technetium pyrophosphate scintigraphy referrals, and individuals matched for age, sex, and wall thickness. Model accuracy was also compared with the transthyretin CA score and the increased wall thickness score within a subset of older heart failure with preserved ejection fraction patients with increased wall thickness. Results Cardiac amyloidosis patients and controls displayed similar age, sex, race, and comorbidities. After the removal of uncertain AI predictions (13%), model discrimination and classification were excellent for the entire external validation dataset [area under the receiver operating characteristic curve (AUROC) 0.93, sensitivity 85%, specificity 93%], irrespective of CA subtype (sensitivity: light-chain = 84%, wild-type transthyretin = 85%, and hereditary transthyretin = 86%). Performance was maintained in subgroup analysis in patients clinically referred for technetium pyrophosphate scintigraphy imaging (AUROC 0.86, sensitivity 77%, specificity 86%) and matched patients (AUROC 0.92, sensitivity 84%, specificity 91%). The AI model (AUROC 0.93) also outperformed transthyretin CA score (AUROC 0.73) and increased wall thickness (AUROC 0.80) scores. Conclusions This AI screening model—using only an apical four-chamber view—effectively differentiated CA from other causes of increased left ventricular wall thickness.

Sustained measles elimination requires high herd immunity, maintained through vaccination and verified by global serosurveillance. Comparing measles neutralizing antibody titres in US and Indian populations, we identified distinct age-specific and country-specific trends. These findings highlight the urgent need for tailored strategies to strengthen population immunity and global measles control efforts.

AbstractPatients with resectable Ewing sarcoma (ES) in select sites, such as the pelvis or chest wall, have a higher risk of positive microscopic margins following surgery. AEWS1031 was the first North American/Australasia ES cooperative group trial allowing standardized preoperative radiotherapy, intended to minimize the morbidity of combined modality local tumor control as well as improve the likelihood of clear surgical margins. In this study, we found that the use of low‐dose preoperative radiation to a smaller target volume resulted in a high rate of R0 resection. The robust pathologic response rate strengthens existing evidence illustrating the radiosensitivity of Ewing sarcoma.

AbstractINTRODUCTIONThis study investigated the role of the ubiquitin–proteasome system (UPS) in dominantly inherited Alzheimer's disease (DIAD) by examining cerebrospinal fluid (CSF) levels of UPS proteins.METHODThe SOMAscan assay was used to detect changes in UPS proteins in mutation carriers (MCs) relative to disease progression; imaging and CSF biomarkers of amyloid, tau, and neurodegeneration measures; and Clinical Dementia Rating scale.RESULTSSubtle increases in specific ubiquitin enzymes were detected in MCs up to two decades before symptom onset, with more pronounced elevations in UPS‐activating enzymes near symptom onset. Significant correlations were found between UPS proteins and Alzheimer's disease (AD) biomarkers, especially between autophagy markers and late‐stage tau biomarkers, microglia, and axonal degeneration.DISCUSSIONThe rise in UPS proteins alongside tau‐related markers suggests UPS involvement in tau neurofibrillary tangles. Elevated CSF UPS proteins in DIAD MCs may serve as indicators of disease progression, and may support the UPS as a therapeutic target in AD.Highlights This study investigates the ubiquitin‐proteasome system (UPS) in Dominantly Inherited Alzheimer's Disease (DIAD), highlighting early molecular changes linked to disease progression. Using SOMAscan proteomics, we identified significant UPS protein alterations in cerebrospinal fluid of mutation carriers, notably up to 20 years before clinical symptom onset. Correlations between UPS protein levels and Alzheimer's biomarkers, particularly tau and neurodegeneration markers, suggest a strong association between UPS dysregulation and tau pathology in DIAD. Dynamic UPS changes align with A/T biological staging: UPS proteins were shown to increase across Aβ/tau (A/T) groups, with largest increases in the A+/T+ group, reinforcing their role in late‐stage tau pathology and disease progression. These findings underscore the potential of UPS proteins as early biomarkers for Alzheimer's disease progression and as novel therapeutic targets, especially in tau‐pathology‐driven neurodegeneration. This work contributes to understanding AD pathogenesis, by emphasizing the importance of protein quality control systems and by offering avenues for future biomarker discovery and therapeutic development in Alzheimer's disease.

AbstractINTRODUCTIONWe previously demonstrated that single‐domain amnestic mild cognitive impairment (SDAm‐MCI) detected through longitudinal neuropsychological testing in our research cohort is less severe than in clinically incident symptomatic individuals. This study aims to replicate and expand this work using a National Alzheimer's Coordinating Center dataset.METHODSParticipants were classified into two groups: those enrolled with normal cognition (Normal Enrollees) and those enrolled with SDAm‐MCI (Amnestic Enrollees). Groups were compared at the time of diagnosis and on rates of cognitive decline post‐diagnosis.RESULTSAt SDAm‐MCI diagnosis, Normal Enrollees were 17% to 39% less impaired on memory tasks, after which the slope of change over time steepened compared to their pre‐symptomatic change and was steeper than in Amnestic Enrollees.DISCUSSIONIncident MCI detected through longitudinal neuropsychological testing is initially milder but may progress more steeply than in clinically established MCI. Sensitive assessment of cognitive decline is essential for capturing early changes that are more likely to be amenable to therapeutic treatment.Highlights Longitudinal cognitive evaluation detects symptomatic Alzheimer's disease earlier than mental status exams. At the time of diagnosis, converters were less impaired compared to initially symptomatic patients. Converters’ cognition declined more steeply than initially symptomatic patients. Sensitive cognitive tests are essential to capture early changes, given new disease‐modifying therapies.

Introduction Infectious intracranial aneurysms (IIAs) are rare but serious complications of systemic infections, particularly infective endocarditis. These aneurysms are prone to rupture, leading to significant morbidity and mortality. Management strategies lack consensus due to the rarity of the condition and reliance on small case series. This study examines the clinical management of IIAs using data from a large multicenter cohort. Methods A retrospective registry-based cohort study was conducted across 11 tertiary care centers in the USA between 2018 and 2023. Patients with IIAs were identified based on clinical and radiographic criteria. The primary outcome was treatment failure defined as persistence, growth, or rupture of the aneurysm. Secondary outcomes were mortality and the modified Rankin Scale (mRS) score at 90 days and 1 year. Multivariate logistic regressions were used to identify outcome predictors. Results A total of 104 patients with 166 aneurysms were included, with a median age of 43 years. Medical management was successful in 56% of cases, with failure often within 18 days of initiation. Predictors of failure included younger age, larger aneurysm size, and rupture at presentation. Surgical and endovascular interventions achieved higher success rates with better outcomes. At 90 days, 57% of patients achieved functional independence (mRS 0–2), while the mortality rate was 24%. Conclusion This study highlights the limitations of medical management for IIAs and underscores the need for early surgical or endovascular intervention in high-risk patients. Outcome predictors aid clinical decision-making, optimizing patient management. Further research is needed to standardize management guidelines for IIAs.

Abstract Cortico‐cortical evoked potentials (CCEPs) are an active electrophysiological technique used during intracranial electroencephalography to evaluate the effective connectivity and influence of therapeutic stimulation between distinct cortical regions and pinpoint epileptogenic zones (EZs) in patients with epilepsy. Various methodologies have been implemented to analyze CCEPs and characterize the epileptogenic networks for EZ localization. Despite its promise, their interpretation remains challenging due to the large volumes of spatially and temporally complex data generated. Early studies focused largely on qualitative descriptors and predefined, semi‐quantitative features such as waveform morphology and peak latencies. However, these methods are limited by the significant heterogeneity in CCEP waveform conformations across patients and cortical regions. The specific technique used for extraction of features, such as the spectral band power and root mean squared values, remains open to empirical refinement, as does choice of appropriate latency windows, with no consensus reached regarding the optimal approach. Graph theoretical metrics such as degree centrality, betweenness centrality, and clustering coefficients can provide a rich representation of epileptogenic network connectivity. However, these metrics are often abstract and difficult to interpret in a clinical setting or to the non‐expert, and their neuroscientific substrates remains poorly understood. The lack of standardization in stimulation protocol and data‐processing pipelines has further contributed to inconsistency in reported findings. Emerging machine learning approaches have been increasingly applied to CCEP data, offering a more data‐driven and potentially generalizable way to identify electrophysiological biomarkers of the epileptogenic effective connectivity. In this article, we discuss qualitative, quantitative, and spectral features; network‐analytical metrics; and more recently, data driven methodologies aimed at improving the interpretability and clinical utility of CCEP data.

Background Heart failure (HF) is a growing public health burden. Systemic inflammation is commonly observed in patients with HF and believed to be related to disease pathogenesis. Infection is a potential acute trigger of chronic inflammation. Our objective was to examine the relationship between infection‐related hospitalization (IRH) and HF. Methods and Results We studied beneficiaries with at least 15 months of continuous enrollment in the US‐based MarketScan databases during 2013 to 2019. We included n=152 008 patients with an inpatient or outpatient HF International Classification of Diseases, Ninth Revision/Tenth Revision ( ICD‐9/10 ) code in the primary position. Among patients with HF, IRH was identified using select ICD‐9/10 codes. We used a case‐crossover design to compare the frequency of IRH occurring within 3 months of the index HF event (case period) versus the frequency of IRH occurring 12 to 15 months before the index HF event (control period). Logistic models regressed the log‐odds of having an IRH during the case versus control periods; odds ratios (ORs; 95% CIs) are presented. Among 152 008 beneficiaries, 53% were male with a mean±SD age of 56(±11) years. The odds of having an IRH during the case (versus control) period was elevated for both the 3‐month case period (OR, 4.39 [95% CI, 4.18–4.60]), and 1‐month case period (OR, 7.39 [95% CI, 6.88–7.94]), after adjusting for the total number of hospitalizations. This relationship persisted across different types of infections. Conclusions IRH was associated with incident HF after both 1 and 3 months and may represent a modifiable risk factor for HF.

Importance Nontraumatic subarachnoid hemorrhage (SAH) represents the third most common stroke type with unique etiologies, risk factors, diagnostics, and treatments. Nevertheless, epidemiological studies often cluster SAH with other stroke types leaving its distinct burden estimates obscure. Objective To estimate the worldwide burden of SAH. Design, Setting, and Participants Based on the repeated cross-sectional Global Burden of Disease (GBD) 2021 study, the global burden of SAH in 1990 to 2021 was estimated. Moreover, the SAH burden was compared with other diseases, and its associations with 14 individual risk factors were investigated with available data in the GBD 2021 study. The GBD study included the burden estimates of nontraumatic SAH among all ages in 204 countries and territories between 1990 and 2021. Exposures SAH and 14 modifiable risk factors. Main Outcomes and Measures Absolute numbers and age-standardized rates with 95% uncertainty intervals (UIs) of SAH incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) as well as risk factor–specific population attributable fractions (PAFs). Results In 2021, the global age-standardized SAH incidence was 8.3 (95% UI, 7.3-9.5), prevalence was 92.2 (95% UI, 84.1-100.6), mortality was 4.2 (95% UI, 3.7-4.8), and DALY rate was 125.2 (95% UI, 110.5-142.6) per 100 000 people. The highest burden estimates were found in Latin America, the Caribbean, Oceania, and high-income Asia Pacific. Although the absolute number of SAH cases increased, especially in regions with a low sociodemographic index, all age-standardized burden rates decreased between 1990 and 2021: the incidence by 28.8% (95% UI, 25.7%-31.6%), prevalence by 16.1% (95% UI, 14.8%-17.7%), mortality by 56.1% (95% UI, 40.7%-64.3%), and DALY rate by 54.6% (95% UI, 42.8%-61.9%). Of 300 diseases, SAH ranked as the 36th most common cause of death and 59th most common cause of DALY in the world. Of all worldwide SAH-related DALYs, 71.6% (95% UI, 63.8%-78.6%) were associated with the 14 modeled risk factors of which high systolic blood pressure (population attributable fraction [PAF] = 51.6%; 95% UI, 38.0%-62.6%) and smoking (PAF = 14.4%; 95% UI, 12.4%-16.5%) had the highest attribution. Conclusions and Relevance Although the global age-standardized burden rates of SAH more than halved over the last 3 decades, SAH remained one of the most common cardiovascular and neurological causes of death and disabilities in the world, with increasing absolute case numbers. These findings suggest evidence for the potential health benefits of proactive public health planning and resource allocation toward the prevention of SAH.