Reports of self-compassion and affect regulation in psilocybin-assisted therapy for alcohol use disorder: An interpretive phenomenological analysis.

OBJECTIVE: The primary aim of this qualitative study was to delineate psychological mechanisms of change in the first randomized controlled trial of psilocybin-assisted psychotherapy to treat alcohol use disorder (AUD). Theories regarding psychological processes involved in psychedelic therapy remain underdeveloped. METHOD: Participants (N = 13) mostly identified as non-Hispanic and White, with approximately equal proportions of cisgender men and women. Participants engaged in semistructured interviews about their subjective experiences in the study. Questions probed the nature of participants' drinking before and after the study as well as coping patterns in response to strong emotions, stress, and cravings for alcohol. Verbatim transcripts were coded using Dedoose software, and content was analyzed with interpretive phenomenological analysis. RESULTS: Participants reported that the psilocybin treatment helped them process emotions related to painful past events and helped promote states of self-compassion, self-awareness, and feelings of interconnectedness. The acute states during the psilocybin sessions were described as laying the foundation for developing more self-compassionate regulation of negative affect. Participants also described newfound feelings of belonging and an improved quality of relationships following the treatment. CONCLUSION: Our results support the assertion that psilocybin increases the malleability of self-related processing, and diminishes shame-based and self-critical thought patterns while improving affect regulation and reducing alcohol cravings. These findings suggest that psychosocial treatments that integrate self-compassion training with psychedelic therapy may serve as a useful tool for enhancing psychological outcomes in the treatment of AUD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Predictive Modeling to Identify Children With Complex Health Needs At Risk for Hospitalization.

BACKGROUND: Identifying children at high risk with complex health needs (CCHN) who have intersecting medical and social needs is challenging. This study's objectives were to (1) develop and evaluate an electronic health record (EHR)-based clinical predictive model ("model") for identifying high-risk CCHN and (2) compare the model's performance as a clinical decision support (CDS) to other CDS tools available for identifying high-risk CCHN. METHODS: This retrospective cohort study included children aged 0 to 20 years with established care within a single health system. The model development/validation cohort included 33 months (January 1, 2016-September 30, 2018) and the testing cohort included 18 months (October 1, 2018-March 31, 2020) of EHR data. Machine learning methods generated a model that predicted probability (0%-100%) for hospitalization within 6 months. Model performance measures included sensitivity, positive predictive value, area under receiver-operator curve, and area under precision-recall curve. Three CDS rules for identifying high-risk CCHN were compared: (1) hospitalization probability ≥10% (model-predicted); (2) complex chronic disease classification (using Pediatric Medical Complexity Algorithm [PMCA]); and (3) previous high hospital utilization. RESULTS: Model development and testing cohorts included 116 799 and 27 087 patients, respectively. The model demonstrated area under receiver-operator curve = 0.79 and area under precision-recall curve = 0.13. PMCA had the highest sensitivity (52.4%) and classified the most children as high risk (17.3%). Positive predictive value of the model-based CDS rule (19%) was higher than CDS based on the PMCA (1.9%) and previous hospital utilization (15%). CONCLUSIONS: A novel EHR-based predictive model was developed and validated as a population-level CDS tool for identifying CCHN at high risk for future hospitalization.

A Systematic Approach to Standardizing Drinking Outcomes From Timeline Followback Data.

Objective: The timeline followback (TLFB) interview is the gold standard for the quantitative assessment of alcohol use. However, self-reported "drinks" can vary in alcohol content. If this variability is not accounted for, it can compromise the reliability and validity of TLFB data. To improve the precision of the TLFB data, we developed a detailed standard operating procedure (SOP) to calculate standard drinks more accurately from participant reports. Method: For the new SOP, the volume and alcohol content by volume (ABV) of distinct types of alcoholic beverages were determined based on product websites and other reliable sources. Recipes for specific cocktails were constructed based on recipes from bartending education websites. One standard drink was defined as 0.6 oz (14 g) of absolute alcohol. Standard drink totals were contrasted for the new SOP approach and the standard procedure, which generally assumed that one self-reported drink was equivalent to one standard drink. Results: Relative to the standard TLFB procedure, higher numbers of standard drinks were reported after implementing the TLFB SOP. Conclusions: Variability in procedures for conversion of self-reported alcohol consumption to standard drinks can confound the interpretation of TLFB data. The use and reporting of a detailed SOP can significantly reduce the potential for such inconsistencies. Detailed and consistent procedures for calculation of standard drinks can enhance the quality of TLFB drinking data.

Feasibility of Implementation of a Mobile Digital Personal Health Record to Coordinate Care for Children and Youth With Special Health Care Needs in Primary Care: Protocol for a Mixed Methods Study.

BACKGROUND: Electronic health record (EHR)-integrated digital personal health records (PHRs) via Fast Healthcare Interoperability Resources (FHIR) are promising digital health tools to support care coordination (CC) for children and youth with special health care needs but remain widely unadopted; as their adoption grows, mixed methods and implementation research could guide real-world implementation and evaluation. OBJECTIVE: This study (1) evaluates the feasibility of an FHIR-enabled digital PHR app for CC for children and youth with special health care needs, (2) characterizes determinants of implementation, and (3) explores associations between adoption and patient- or family-reported outcomes. METHODS: This nonrandomized, single-arm, prospective feasibility trial will test an FHIR-enabled digital PHR app's use among families of children and youth with special health care needs in primary care settings. Key app features are FHIR-enabled access to structured data from the child's medical record, families' abilities to longitudinally track patient- or family-centered care goals, and sharing progress toward care goals with the child's primary care provider via a clinician dashboard. We shall enroll 40 parents or caregivers of children and youth with special health care needs to use the app for 6 months. Inclusion criteria for children and youth with special health care needs are age 0-16 years; primary care at a participating site; complex needs benefiting from CC; high hospitalization risk in the next 6 months; English speaking; having requisite technology at home (internet access, Apple iOS mobile device); and an active web-based EHR patient portal account to which a parent or caregiver has full proxy access. Digital prescriptions will be used to disseminate study recruitment materials directly to eligible participants via their existing EHR patient portal accounts. We will apply an intervention mixed methods design to link quantitative and qualitative (semistructured interviews and family engagement panels with parents of children and youth with special health care needs) data and characterize implementation determinants. Two CC frameworks (Pediatric Care Coordination Framework; Patient-Centered Medical Home) and 2 evaluation frameworks (Consolidated Framework for Implementation Research; Technology Acceptance Model) provide theoretical foundations for this study. RESULTS: Participant recruitment began in fall 2022, before which we identified >300 potentially eligible patients in EHR data. A family engagement panel in fall 2021 generated formative feedback from family partners. Integrated analysis of pretrial quantitative and qualitative data informed family-centered enhancements to study procedures. CONCLUSIONS: Our findings will inform how to integrate an FHIR-enabled digital PHR app for children and youth with special health care needs into clinical care. Mixed methods and implementation research will help strengthen implementation in diverse clinical settings. The study is positioned to advance knowledge of how to use digital health innovations for improving care and outcomes for children and youth with special health care needs and their families. TRIAL REGISTRATION: ClinicalTrials.gov NCT05513235; https://clinicaltrials.gov/study/NCT05513235. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46847.

Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.

Importance: Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. Objective: To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. Design, Setting, and Participants: This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Main Outcomes and Measures: Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Results: Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. Conclusions and Relevance: In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.

Machine learning functional impairment classification with electronic health record data.

BACKGROUND: Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment. METHODS: We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome. RESULTS: Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states. CONCLUSION: A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.

Defining the Need for Causal Inference to Understand the Impact of Social Determinants of Health: A Primer on Behalf of the Consortium for the Holistic Assessment of Risk in Transplantation (CHART).

Objective: This study aims to introduce key concepts and methods that inform the design of studies that seek to quantify the causal effect of social determinants of health (SDOH) on access to and outcomes following organ transplant. Background: The causal pathways between SDOH and transplant outcomes are poorly understood. This is partially due to the unstandardized and incomplete capture of the complex interactions between patients, their neighborhood environments, the tertiary care system, and structural factors that impact access and outcomes. Designing studies to quantify the causal impact of these factors on transplant access and outcomes requires an understanding of the fundamental concepts of causal inference. Methods: We present an overview of fundamental concepts in causal inference, including the potential outcomes framework and direct acyclic graphs. We discuss how to conceptualize SDOH in a causal framework and provide applied examples to illustrate how bias is introduced. Results: There is a need for direct measures of SDOH, increased measurement of latent and mediating variables, and multi-level frameworks for research that examine health inequities across multiple health systems to generalize results. We illustrate that biases can arise due to socioeconomic status, race/ethnicity, and incongruencies in language between the patient and clinician. Conclusions: Progress towards an equitable transplant system requires establishing causal pathways between psychosocial risk factors, access, and outcomes. This is predicated on accurate and precise quantification of social risk, best facilitated by improved organization of health system data and multicenter efforts to collect and learn from it in ways relevant to specialties and service lines.

Association of Primary Care Providers' Beliefs of Statins for Primary Prevention and Statin Prescription.

Background The 2013 American College of Cardiology/American Heart Association Cholesterol Treatment Guideline increased the number of primary prevention patients eligible for statin therapy, yet uptake of these guidelines has been modest. Little is known of how primary care provider ( PCP ) beliefs influence statin prescription. Methods and Results We surveyed 164 PCP s from a community-based North Carolina network in 2017 about statin therapy. We evaluated statin initiation among the PCP s' statin-eligible patients between 2014 and 2015 without a previous prescription. Seventy-two PCP s (43.9%) completed the survey. The median estimate of the relative risk reduction for high-intensity statins was 45% (interquartile range, 25%-50%). A minority of providers (27.8%) believed statins caused diabetes mellitus, and only 16.7% reported always/very often discussing this with patients. Most PCPs (97.2%) believed that statins cause myopathy, and 72.3% reported always/very often discussing this with patients. Most (77.7%) reported always/very often using the 10-year atherosclerotic cardiovascular disease risk calculator, although many reported that in most cases other risk factors or patient preferences influenced prescribing (59.8% and 43.1%, respectively). Of 6172 statin-eligible patients, 22.3% received a prescription for a moderate- or high-intensity statin at follow-up. Providers reporting greater reliance on risk factors beyond atherosclerotic cardiovascular disease risk were less likely to prescribe statins. Conclusions Although beliefs and approaches to statin discussions vary among community PCP s, new prescription rates are low and minimally associated with those beliefs. These results highlight the complexity of increasing statin prescriptions for primary prevention and suggest that strategies to facilitate standardized discussions and to address external influences on patient beliefs warrant future study.