Validation of an Electronic Health Record-Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems.

Importance: Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings. Objective: To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems. Design, Setting, and Participants: For this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019. Main Outcomes and Measures: The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site. Results: Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%]) included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance. Conclusions and Relevance: Across 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect the risk of suicidal behavior in unselected patients. This approach could facilitate the development of clinical decision support tools that inform risk reduction interventions.

The Association of Black Cardiologists (ABC) Cardiovascular Implementation Study (CVIS): A Research Registry Integrating Social Determinants to Support Care for Underserved Patients.

African Americans, other minorities and underserved populations are consistently under- represented in clinical trials. Such underrepresentation results in a gap in the evidence base, and health disparities. The ABC Cardiovascular Implementation Study (CVIS) is a comprehensive prospective cohort registry that integrates social determinants of health. ABC CVIS uses real world clinical practice data to address critical gaps in care by facilitating robust participation of African Americans and other minorities in clinical trials. ABC CVIS will include diverse patients from collaborating ABC member private practices, as well as patients from academic health centers and Federally Qualified Health Centers (FQHCs). This paper describes the rationale and design of the ABC CVIS Registry. The registry will: (1) prospectively collect socio-demographic, clinical and biospecimen data from enrolled adults, adolescents and children with prioritized cardiovascular diseases; (2) Evaluate the safety and clinical outcomes of new therapeutic agents, including post marketing surveillance and pharmacovigilance; (3) Support National Institutes of Health (NIH) and industry sponsored research; (4) Support Quality Measures standards from the Center for Medicare and Medicaid Services (CMS) and Commercial Health Plans. The registry will utilize novel data and technology tools to facilitate mobile health technology application programming interface (API) to health system or practice electronic health records (EHR). Long term, CVIS will become the most comprehensive patient registry for underserved diverse patients with cardiovascular disease (CVD) and co morbid conditions, providing real world data to address health disparities. At least 10,000 patients will be enrolled from 50 sites across the United States.

A federated EHR network data completeness tracking system.

OBJECTIVE: The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. MATERIALS AND METHODS: The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. RESULTS: The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. DISCUSSION: Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. CONCLUSIONS: The CTX has increased the network's capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.

Coxsackievirus and adenovirus receptor is essential for cardiomyocyte development.

The coxsackievirus and adenovirus receptor (CAR) is a transmembrane protein that is known to be a site of viral attachment and entry, but its physiologic functions are undefined. CAR expression is maximal in neonates and wanes rapidly after birth in organs such as heart, muscle, and brain, suggesting that CAR plays a role in the development of these tissues. Here, we show that CAR deficiency resulted in an embryonic lethal condition associated with cardiac defects. Specifically, commencing approximately 10.5 days postconception (dpc), CAR-/- cardiomyocytes exhibited regional apoptosis evidenced by both histopathologic features of cell death and positive staining for the apoptotic marker cleaved caspase 3. CAR-/- fetuses invariably suffered from degeneration of the myocardial wall and thoracic hemorrhaging, leading to death by 11.5 dpc. These findings are consistent with the view that CAR provides positive survival signals to cardiomyocytes that are essential for normal heart development.

Inhibition of growth by p205: a nuclear protein and putative tumor suppressor expressed during myeloid cell differentiation.

p205 belongs to a family of interferon-inducible proteins called the IFI-200 family, which have been implicated in the regulation of cell growth and differentiation. While p205 is induced in hematopoietic stem cells during myeloid cell differentiation, its function is not known. Therefore, the aim of this study was to determine the role of p205 in regulating proliferation in hematopoietic progenitor cells and in nonhematopoietic cell lines. We found that p205 localizes to the nucleus in hematopoietic and nonhematopoietic cell lines. Transient expression of p205 in murine IL-3-dependent BaF3 and 32D-C123 progenitor cell lines inhibited IL-3-induced growth and proliferation. The closely related IFI-200 family members, p204 and p202, similarly inhibited IL-3-dependent progenitor cell proliferation. p205 also inhibited the proliferation and growth of normal hematopoietic progenitor cells. In nonhematopoietic cell lines, p205 and p204 expression inhibited NIH3T3 cell colony formation in vitro, and microinjection of p205 expression vectors into NIH3T3 fibroblasts inhibited serum-induced proliferation. We have determined the functional domains of p205 necessary for activity, which were identified as the N-terminal domain in apoptosis and interferon response (DAPIN)/PYRIN domain, and the C-terminal retinoblastoma protein (Rb)-binding motif. In addition, we have demonstrated that a putative ataxia telangiectasia, mutated (ATM) kinase phosphorylation site specifically regulates the activity of p205. Taken together, these data suggest that p205 is a potent cell growth regulator whose activity is mediated by its protein-binding domains. We propose that during myelomonocytic cell differentiation, induction of p205 expression contributes to cell growth arrest, thus allowing progenitor cells to differentiate.

Constitutive telomerase expression promotes mammary carcinomas in aging mice.

Telomerase is up-regulated in the vast majority of human cancers and serves to halt the progressive telomere shortening that ultimately blocks would-be cancer cells from achieving a full malignant phenotype. In contrast to humans, the laboratory mouse possesses long telomeres and, even in early generation telomerase-deficient mice, the level of telomere reserve is sufficient to avert telomere-based checkpoint responses and to permit full malignant progression. These features in the mouse provide an opportunity to determine whether enforced high-level telomerase activity can serve functions that extend beyond its ability to sustain telomere length and function. Here, we report the generation and characterization of transgenic mice that express the catalytic subunit of telomerase (mTERT) at high levels in a broad variety of tissues. Expression of mTERT conferred increased telomerase enzymatic activity in several tissues, including mammary gland, splenocytes, and cultured mouse embryonic fibroblasts. In mouse embryonic fibroblasts, mTERT overexpression extended telomere lengths but did not prevent culture-induced replicative arrest, thus reinforcing the view that this phenomenon is not related to occult telomere shortening. Robust telomerase activity, however, was associated with the spontaneous development of mammary intraepithelial neoplasia and invasive mammary carcinomas in a significant proportion of aged females. These data indicate that enforced mTERT expression can promote the development of spontaneous cancers even in the setting of ample telomere reserve.