UCLA

BACKGROUND AND OBJECTIVES: Headache syndromes are highly prevalent, disabling, and costly. Our goals were to (1) describe headache care delivery and costs in a system and (2) identify opportunities for the system to collect, organize, or analyze health care data to facilitate value-based headache care delivery. METHODS: We performed a descriptive, retrospective cohort study using data from a large integrated health system (July 2018-July 2021). We assigned individuals into a reference (REF) or headache group based on headache-related ICD diagnoses. The primary exposure variable, applied to the headache group, was the headache specialty seen most after the incident headache diagnosis: primary care (PC), neurology (NEU), or headache subspecialist (HS). Outcomes of interest were per member per month all-cause costs, per episode costs, all-cause utilization, and headache utilization. Variables included age, sex, insurance contract, and the Adjusted Clinical Groups (ACG) concurrent risk score. We calculated univariate statistics for clinical indicators and outcomes for each group. For outcome variables, we also report these statistics after adjustment for ACG risk score. RESULTS: We identified 22,700 (14%) individuals in the headache groups and 138,818 (86%) individuals in the reference group (REF). Within the headache groups, 84% received care from PC, 14% from NEU, and 2% from HS. The average ACG risk scores increased across exposure groups. In both unadjusted and after risk adjustment analyses, total cost of care (TCOC) was highest in NEU and HS, and the largest drivers of TCOC were outpatient facility costs, followed by inpatient facility costs. HS had the highest pharmacy and professional costs. After risk adjustment, all-cause inpatient admissions and headache-related ED visits were roughly similar, although there was increasing use of outpatient PC and NEU visits across exposure groups. DISCUSSION: Individuals seen by a NEU or HS had higher medical morbidity, higher health care utilization, and higher costs than those who receive care from PC. Outcome data were either not available or not structured to determine the value of neurologic expertise in headache care or within a particular headache care pathway. To clarify neurologys value in primary headache disorders, we encourage health system leaders to adopt an economic evaluation framework.

PURPOSE: To compare the discrepancies between circumpapillary retinal nerve fiber layer (RNFL) and Bruchs membrane opening-minimum rim width (BMO-MRW) thickness in glaucoma eyes. DESIGN: A cross-sectional observational study. SUBJECTS: One hundred eighty-six eyes (118 patients) with glaucoma. METHODS: OCT optic nerve head volume scans of patients enrolled in the Advanced Glaucoma Progression Study at the final available visit were exported. The RNFL and BMO-MRW measurements were averaged into corresponding 7.5° sectors, and the nasal sector data were excluded from analyses. A 2-stage screening process was used to identify true mismatches between the RNFL and BMO-MRW measurements, in which either the RNFL or BMO-MRW value was in the less than first percentile range while its counterpart was in the greater than first percentile range on the temporal-superior-nasal-inferior-temporal curve. The prevalence of these mismatches was mapped, and corresponding images were reviewed to determine the underlying cause of these discrepancies. MAIN OUTCOME MEASURES: Proportion of mismatches between RNFL and BMO-MRW, location of mismatches between RNFL and BMO-MRW, anatomical causes of mismatches between RNFL and BMO-MRW. RESULTS: Mismatch analysis revealed true mismatches between RNFL and BMO-MRW in 7.7% of sectors. High BMO-MRW with low corresponding RNFL mismatches were most frequently located at the 45° and 322.5° sectors, whereas high RNFL with corresponding low BMO-MRW mismatches peaked at the 75° sector. Large blood vessels accounted for 90.9% of high RNFL with low BMO-MRW mismatches. Small to large blood vessels accounted for 62.9% of high BMO-MRW with low RNFL mismatches; the remaining mismatches could be attributed to retinoschisis or inclusion of outer retinal layers in BMO-MRW measurements. CONCLUSIONS: Although overall agreement between RNFL and BMO-MRW measurements is good in areas with advanced damage, blood vessels and other anatomical factors can cause discrepancies between the 2 types of structural measurements and need to be considered when evaluating the utility of such measurements for detection of change. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate.

The current economic burden of cardiovascular (CV)-related hospitalizations grouped by diagnoses and procedures in the United States has not been well characterized. The objective was to identify current trends in CV-related hospitalizations, procedural utilization, and health care costs using the most recent 6 years of hospitalization data. A retrospective analysis of discharge data from the National Inpatient Sample database was conducted to determine trends in CV-related hospitalizations, costs, and procedures for each year from 2016 to the most recent available dataset, 2021. Total CV-related costs were adjusted to and reported in 2023 dollars. In 2021, there were 4,687,370 CV-related hospitalizations at a cost of $108 billion. Heart failure hospitalizations accounted for the highest costs at $18.5 billion, followed by non-ST-elevation myocardial infarction at $11.2 billion and stroke at $10.9 billion. Significant upward trends in costs from 2016 to 2021 were observed for heart failure, stroke, atrial fibrillation, ST-elevation myocardial infarction, chest pain, hypertensive emergency, ventricular tachycardia, aortic dissection, sudden cardiac death, pericarditis, supraventricular tachycardia, and pulmonary heart disease. Over the 6 observational years, total costs increased by over $10 billion, representing a 10% increase. However, the increases were not linear, as there was a significant increase of 6.5% from 2018 to 2019, then a decrease of over 7% from 2019 to 2020, followed by an increase of approximately 6% from 2020 to 2021. By 2030, total CV-related costs are projected to reach $131.3 billion. For all years, coronary procedures were the most performed, followed by extracorporeal membrane oxygenation, non-bypass peripheral vascular surgery, pacemaker placement, and coronary artery bypass graft surgery. Both transcatheter aortic valve replacement and MitraClip procedures demonstrated significant upward trends from 2016 to 2021. Overall, from the years 2016 to 2021, CV-related hospitalizations, costs, and procedures demonstrated upward trends. In conclusion, CV disease remains a high burden in the hospital setting with tremendous health care costs.

OBJECTIVES: Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms. MATERIALS AND METHODS: We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring. RESULTS: We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P = .01) and weight (87.5% vs 69.4%, P = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity. DISCUSSION AND CONCLUSION: Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events.

BACKGROUND: Meditation practices have demonstrated numerous psychological and physiological benefits, but capturing the neural correlates of varying meditative depths remains challenging. In this study, we aimed to decode self-reported time-varying meditative depth in expert practitioners using electroencephalography (EEG). METHODS: Expert Vipassana meditators (n = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information. RESULTS: We achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The spontaneous emergence method yielded improved decoding performance compared with traditional probing and correlated more strongly with postsession outcome measures. Best performance was achieved by a novel machine learning method that fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and preselected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths. CONCLUSIONS: This study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce spontaneous emergence as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices.

As medical schools move to integrate the Core Entrustable Professional Activities for Entering Residency (EPAs) into curricula and address the transition from student to resident, residency preparatory courses have become more prevalent. The authors developed an experiential learning EPA-based capstone course for assessment to determine impact on learner self-assessed ratings of readiness for residency and acquisition of medical knowledge. All fourth-year students from the classes of 2018-2020 completed a required course in the spring for assessment of multiple EPAs, including managing core complaints, performing basic procedures, obtaining informed consent, and providing patient handoffs. Learners selected between three specialty-based parallel tracks - adult medicine, surgery, or pediatrics. Students completed a retrospective pre-post questionnaire to provide self-assessed ratings of residency preparedness and comfort in performing EPAs. Finally, the authors studied the impact of the course on knowledge acquisition by comparing student performance in the adult medicine track on multiple choice pre- and post-tests. Four hundred and eighty-one students were eligible for the study and 452 (94%) completed the questionnaire. For all three tracks, there was a statistically significant change in learner self-assessed ratings of preparedness for residency from pre- to post-course (moderately or very prepared: adult medicine 61.4% to 88.6% [p-value < 0.001]; surgery 56.8% to 81.1% [p-value < 0.001]; pediatrics 32.6% to 83.7% [p-value 0.02]). A similar change was noted in all tracks in learner self-assessed ratings of comfort from pre- to post-course for all studied EPAs. Of the 203 students who participated in the adult medicine track from 2019-2020, 200 (99%) completed both the pre- and post-test knowledge assessments. The mean performance improved from 65.0% to 77.5% (p-value < 0.001). An experiential capstone course for the assessment of EPAs can be effective to improve learner self-assessed ratings of readiness for residency training and acquisition of medical knowledge.

Aim

Materials and methods

Results

Limitations

Conclusion