All youth must transition from pediatric to adult-centered medical care. This process is especially difficult for youth with special health care needs. Many youth do not receive the age-appropriate medical care they need and are at risk during this vulnerable time. Previous research has identified barriers that may prevent effective transition, and protocols have been developed to improve the process. Health outcomes related to successful transition have yet to be fully defined. Health care transition can also be influenced by education of providers, but there are gaps in medical education at the undergraduate, graduate, and postgraduate levels. Current changes in federal health policy allow improved health care coverage, provide some new financial incentives, and test new structures for transitional care, including the evolution of accountable care organizations (ACO). Future work must test how these systems changes will affect quality of care. Finally, transition protocols exist in various medical subspecialties; however, national survey results show no improvement in transition readiness, and there are no consistent measures of what constitutes transition success. In order to advance the field of transition, research must be done to integrate transition curricula at the undergraduate, graduate, and postgraduate levels; to provide advance financial incentives and pilot the ACO model in centers providing care to youth during transition; to define outcome measures of importance to transition; and to study the effectiveness of current transition tools on improving these outcomes.

IMPORTANCE There is a lack of agreement on what constitutes successful outcomes for the process of health care transition (HCT) among adolescent and young adults with special health care needs. OBJECTIVE To present HCT outcomes identified by a Delphi process with an interdisciplinary group of participants. DESIGN, SETTING, AND PARTICIPANTS A Delphi method involving 3 stages was deployed to refine a list of HCT outcomes. This 18-month study (from January 5, 2013, of stage 1 to July 3, 2014, of stage 3) included an initial literature search, expert interviews, and then 2 waves of a web-based survey. On this survey, 93 participants from outpatient, community-based, and primary care clinics rated the importance of the top HCT outcomes identified by the Delphi process. Analyses were performed from July 5, 2014, to December 5, 2014. EXPOSURES Health care transition outcomes of adolescents and young adults with special health care needs. MAIN OUTCOMES AND MEASURES Importance ratings of identified HCT outcomes rated on a Likert scale from 1 (not important) to 9 (very important). RESULTS The 2 waves of surveys included 117 and 93 participants as the list of outcomes was refined. Transition outcomes were refined by the 3 waves of the Delphi process, with quality of life being the highest-rated outcome with broad agreement. The 10 final outcomes identified included individual outcomes (quality of life, understanding the characteristics of conditions and complications, knowledge of medication, self-management, adherence to medication, and understanding health insurance), health services outcomes (attending medical appointments, having a medical home, and avoidance of unnecessary hospitalization), and a social outcome (having a social network). Participants indicated that different outcomes were likely needed for individuals with cognitive disabilities. CONCLUSIONS AND RELEVANCE Quality of life is an important construct relevant to HCT. Future research should identify valid measures associated with each outcome and further explore the role that quality of life plays in the HCT process. Achieving consensus is a critical step toward the development of reliable and objective comparisons of HCT outcomes across clinical conditions and care delivery locations.

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HighlightsWe created a validation method for the evaluation of automated classification of interictal spikes.We used a modified version of Wave_clus (WC) to automatically classify the data of 5 patients.WC classification was similar to EEG reviewers providing an unbiased evaluation of the clinical data.

The quantification of brain dynamics is essential to its understanding. However, the brain is a system operating on multiple time scales, and characterization of dynamics across time scales remains a challenge. One framework to study such dynamics is that of fractal geometry; and currently there exist several methods for the study of brain dynamics using fractal geometry. We aim to highlight some of the practical challenges of applying fractal geometry to brain dynamics—and as a putative feature for machine learning applications, and propose solutions to enable its wider use in neuroscience. Using intracranially recorded electroencephalogram (EEG) and simulated data, we compared monofractal and multifractal methods with regards to their sensitivity to signal variance. We found that both monofractal and multifractal properties correlate closely with signal variance, thus not being a useful feature of the signal. However, after applying an epoch-wise standardization procedure to the signal, we found that multifractal measures could offer non-redundant information compared to signal variance, power (in different frequency bands) and other established EEG signal measures. We also compared different multifractal estimation methods to each other in terms of reliability, and we found that the Chhabra-Jensen algorithm performed best. Finally, we investigated the impact of sampling frequency and epoch length on the estimation of multifractal properties. Using epileptic seizures as an example event in the EEG, we show that there may be an optimal time scale (i.e., combination of sampling frequency and epoch length) for detecting temporal changes in multifractal properties around seizures. The practical issues we highlighted and our suggested solutions should help in developing robust methods for the application of fractal geometry in EEG signals. Our analyses and observations also aid the theoretical understanding of the multifractal properties of the brain and might provide grounds for new discoveries in the study of brain signals. These could be crucial for the understanding of neurological function and for the developments of new treatments.

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