Design and Development of a Community-Based, Interdisciplinary, Collaborative Dementia Care Program.

OBJECTIVE: To describe the design, development, and baseline characteristics of enrollees of a home-based, interdisciplinary, dyadic, pilot dementia care program. DESIGN: Single-arm, dementia care intervention in partnership with primary care providers delivered by Health Coaches to persons with dementia and caregiver "dyads" and supervised by an interdisciplinary team. SETTING: Home- and virtual-based dyad support. PARTICIPANTS: Persons with mild cognitive impairment or dementia diagnosis and/or who were prescribed antidementia medications; had an identified caregiver willing to participate; were under the care of a partner primary care provider; and had health insurance through the affiliated accountable care organization (Banner Health Network). INTERVENTION: Provision of personalized dementia education and support in the home or virtually by Health Coaches supported by an interdisciplinary team. MEASUREMENTS: Cognition, function, mood, and behavior of persons with dementia; caregiver stress and program satisfaction; primary care provider satisfaction. RESULTS: Served dyads from three primary care clinics with a total of 87 dyads enrolled between December 2018 and June 2020. CONCLUSION: A pilot Dementia Care Partners demonstrated feasibility and suggested acceptability, and high satisfaction among primary care providers and caregivers.

Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system.

BACKGROUND: A barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS). METHODS: Actionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File - Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm. RESULTS: As of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%. CONCLUSIONS: Successful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact.

A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.

OBJECTIVE: To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. MATERIALS AND METHODS: Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology "N-of-1-pathways." The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided. RESULTS: Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). DISCUSSION: Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes. CONCLUSION: The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway-level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.

Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM.

In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will transition to ICD-10-CM (the tenth revision) diagnosis codes. One challenge posed to clinical researchers and other analysts is conducting diagnosis-related queries across datasets containing both coding schemes. Further, healthcare administrators will manage growth, trends, and strategic planning with these dually-coded datasets. The majority of the ICD-9-CM to ICD-10-CM translations are complex and nonreciprocal, creating convoluted representations and meanings. Similarly, mapping back from ICD-10-CM to ICD-9-CM is equally complex, yet different from mapping forward, as relationships are likewise nonreciprocal. Indeed, 10 of the 21 top clinical categories are complex as 78% of their diagnosis codes are labeled as "convoluted" by our analyses. Analysis and research related to external causes of morbidity, injury, and poisoning will face the greatest challenges due to 41 745 (90%) convolutions and a decrease in the number of codes. We created a web portal tool and translation tables to list all ICD-9-CM diagnosis codes related to the specific input of ICD-10-CM diagnosis codes and their level of complexity: "identity" (reciprocal), "class-to-subclass," "subclass-to-class," "convoluted," or "no mapping." These tools provide guidance on ambiguous and complex translations to reveal where reports or analyses may be challenging to impossible.Web portal: http://www.lussierlab.org/transition-to-ICD9CM/Tables annotated with levels of translation complexity: http://www.lussierlab.org/publications/ICD10to9.

Evidence for Clinical Implementation of Pharmacogenomics in Cardiac Drugs.

OBJECTIVE: To comprehensively assess the pharmacogenomic evidence of routinely used drugs for clinical utility. METHODS: Between January 2, 2011, and May 31, 2013, we assessed 71 drugs by identifying all drug/genetic variant combinations with published clinical pharmacogenomic evidence. Literature supporting each drug/variant pair was assessed for study design and methods, outcomes, statistical significance, and clinical relevance. Proposed clinical summaries were formally scored using a modified AGREE (Appraisal of Guidelines for Research and Evaluation) II instrument, including recommendation for or against guideline implementation. RESULTS: Positive pharmacogenomic findings were identified for 51 of 71 cardiovascular drugs (71.8%), representing 884 unique drug/variant pairs from 597 publications. After analysis for quality and clinical relevance, 92 drug/variant pairs were proposed for translation into clinical summaries, encompassing 23 drugs (32.4% of drugs reviewed). All were recommended for clinical implementation using AGREE II, with mean ± SD overall quality scores of 5.18±0.91 (of 7.0; range, 3.67-7.0). Drug guidelines had highest mean ± SD scores in AGREE II domain 1 (Scope) (91.9±6.1 of 100) and moderate but still robust mean ± SD scores in domain 3 (Rigor) (73.1±11.1), domain 4 (Clarity) (67.8±12.5), and domain 5 (Applicability) (65.8±10.0). Clopidogrel (CYP2C19), metoprolol (CYP2D6), simvastatin (rs4149056), dabigatran (rs2244613), hydralazine (rs1799983, rs1799998), and warfarin (CYP2C9/VKORC1) were distinguished by the highest scores. Seven of the 9 most commonly prescribed drugs warranted translation guidelines summarizing clinical pharmacogenomic information. CONCLUSION: Considerable clinically actionable pharmacogenomic information for cardiovascular drugs exists, supporting the idea that consideration of such information when prescribing is warranted.

Randomized clinical trial of an Internet-based depression prevention program for adolescents (Project CATCH-IT) in primary care: 12-week outcomes.

OBJECTIVE: The authors sought to evaluate 2 approaches with varying time and complexity in engaging adolescents with an Internet-based preventive intervention for depression in primary care. The authors conducted a randomized controlled trial comparing primary care physician motivational interview (MI, 5-10 minutes) + Internet program versus brief advice (BA, 1-2 minutes) + Internet program. SETTING: Adolescent primary care patients in the United States, aged 14 to 21 years. PARTICIPANTS: Eighty-four individuals (40% non-white) at increased risk for depressive disorders (subthreshold depressed mood >3-4 weeks) were randomly assigned to either the MI group (n = 43) or the BA group (n = 40). MAIN OUTCOME MEASURES: Patient Health Questionnaire-Adolescent and Center for Epidemiologic Studies Depression Scale (CES-D). RESULTS: Both groups substantially engaged the Internet site (MI, 90.7% vs BA 77.5%). For both groups, CES-D-10 scores declined (MI, 24.0 to 17.0, p < .001; BA, 25.2 to 15.5, p < .001). The percentage of those with clinically significant depression symptoms based on CES-D-10 scores declined in both groups from baseline to 12 weeks, (MI, 52% to 12%, p < .001; BA, 50% to 15%, p < .001). The MI group demonstrated declines in self-harm thoughts and hopelessness and was significantly less likely than the BA group to experience a depressive episode (4.65% vs 22.5%, p = .023) or to report hopelessness (MI group of 2% vs 15% for the BA group, p = .044) by 12 weeks. CONCLUSIONS: An Internet-based prevention program in primary care is associated with declines in depressed mood and the likelihood of having clinical depression symptom levels in both groups. Motivational interviewing in combination with an Internet behavior change program may reduce the likelihood of experiencing a depressive episode and hopelessness.

Integrative internet-based depression prevention for adolescents: a randomized clinical trial in primary care for vulnerability and protective factors.

BACKGROUND: Adolescent depression is both a major public health and clinical problem, yet primary care physicians have limited intervention options. We developed two versions of an Internet-based behavioral intervention to prevent the onset of major depression and compared them in a randomized clinical trial in 13 US primary care practices. METHODS: We enrolled 84 adolescents at risk for developing major depression and randomly assigned them to two groups: brief advice (BA; 1-2 minutes) + Internet program versus motivational interview (MI; 5-15 minutes) + Internet program. We compared pre/post changes and between group differences for protective and vulnerability factors (individual, family, school and peer). RESULTS: Compared with pre-study values, both groups demonstrated declines in depressed mood; [MI: 21.2 to 16.74 (p < 0.01), BA: 23.34 to 16.92 (p < 0.001)]. Similarly, both groups demonstrated increases in social support by peers [MI: 8.6 to 12.1 (p = 0.002), BA: 7.10 to 12.5 (p < 0.001)] and reductions in depression related impairment in school [MI: 2.26 to 1.76 (p = 0.06), BA: 2.16 to 1.93 (p = 0.07)]. CONCLUSIONS: Two forms of a primary care/Internet-based behavioral intervention to prevent adolescent depression may lower depressed mood and strengthen some protective factors for depression.