SenNet recommendations for detecting senescent cells in different tissues.

Once considered a tissue culture-specific phenomenon, cellular senescence has now been linked to various biological processes with both beneficial and detrimental roles in humans, rodents and other species. Much of our understanding of senescent cell biology still originates from tissue culture studies, where each cell in the culture is driven to an irreversible cell cycle arrest. By contrast, in tissues, these cells are relatively rare and difficult to characterize, and it is now established that fully differentiated, postmitotic cells can also acquire a senescence phenotype. The SenNet Biomarkers Working Group was formed to provide recommendations for the use of cellular senescence markers to identify and characterize senescent cells in tissues. Here, we provide recommendations for detecting senescent cells in different tissues based on a comprehensive analysis of existing literature reporting senescence markers in 14 tissues in mice and humans. We discuss some of the recent advances in detecting and characterizing cellular senescence, including molecular senescence signatures and morphological features, and the use of circulating markers. We aim for this work to be a valuable resource for both seasoned investigators in senescence-related studies and newcomers to the field.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies. In addition, three workflows were developed to map new experimental data into the HRA's CCF. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and demonstrates first atlas usage applications and previews.

An open source knowledge graph ecosystem for the life sciences.

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Presentation Acuity and Surgical Outcomes for Patients With Health Insurance Living in Highly Deprived Neighborhoods.

Importance: Insurance coverage expansion has been proposed as a solution to improving health disparities, but insurance expansion alone may be insufficient to alleviate care access barriers. Objective: To assess the association of Area Deprivation Index (ADI) with postsurgical textbook outcomes (TO) and presentation acuity for individuals with private insurance or Medicare. Design, Setting, and Participants: This cohort study used data from the National Surgical Quality Improvement Program (2013-2019) merged with electronic health record data from 3 academic health care systems. Data were analyzed from June 2022 to August 2023. Exposure: Living in a neighborhood with an ADI greater than 85. Main Outcomes and Measures: TO, defined as absence of unplanned reoperations, Clavien-Dindo grade 4 complications, mortality, emergency department visits/observation stays, and readmissions, and presentation acuity, defined as having preoperative acute serious conditions (PASC) and urgent or emergent cases. Results: Among a cohort of 29 924 patients, the mean (SD) age was 60.6 (15.6) years; 16 424 (54.9%) were female, and 13 500 (45.1) were male. A total of 14 306 patients had private insurance and 15 618 had Medicare. Patients in highly deprived neighborhoods (5536 patients [18.5%]), with an ADI greater than 85, had lower/worse odds of TO in both the private insurance group (adjusted odds ratio [aOR], 0.87; 95% CI, 0.76-0.99; P = .04) and Medicare group (aOR, 0.90; 95% CI, 0.82-1.00; P = .04) and higher odds of PASC and urgent or emergent cases. The association of ADIs greater than 85 with TO lost significance after adjusting for PASC and urgent/emergent cases. Differences in the probability of TO between the lowest-risk (ADI ≤85, no PASC, and elective surgery) and highest-risk (ADI >85, PASC, and urgent/emergent surgery) scenarios stratified by frailty were highest for very frail patients (Risk Analysis Index ≥40) with differences of 40.2% and 43.1% for those with private insurance and Medicare, respectively. Conclusions and Relevance: This study found that patients living in highly deprived neighborhoods had lower/worse odds of TO and higher presentation acuity despite having private insurance or Medicare. These findings suggest that insurance coverage expansion alone is insufficient to overcome health care disparities, possibly due to persistent barriers to preventive care and other complex causes of health inequities.

A Surgical Desirability of Outcome Ranking (DOOR) Reveals Complex Relationships Between Race/Ethnicity, Insurance Type, and Neighborhood Deprivation.

OBJECTIVE: Develop an ordinal Desirability of Outcome Ranking (DOOR) for surgical outcomes to examine complex associations of Social Determinants of Health. BACKGROUND: Studies focused on single or binary composite outcomes may not detect health disparities. METHODS: Three health care system cohort study using NSQIP (2013-2019) linked with EHR and risk-adjusted for frailty, preoperative acute serious conditions (PASC), case status and operative stress assessing associations of multilevel Social Determinants of Health of race/ethnicity, insurance type (Private 13,957; Medicare 15,198; Medicaid 2835; Uninsured 2963) and Area Deprivation Index (ADI) on DOOR and the binary Textbook Outcomes (TO). RESULTS: Patients living in highly deprived neighborhoods (ADI>85) had higher odds of PASC [adjusted odds ratio (aOR)=1.13, CI=1.02-1.25, P <0.001] and urgent/emergent cases (aOR=1.23, CI=1.16-1.31, P <0.001). Increased odds of higher/less desirable DOOR scores were associated with patients identifying as Black versus White and on Medicare, Medicaid or Uninsured versus Private insurance. Patients with ADI>85 had lower odds of TO (aOR=0.91, CI=0.85-0.97, P =0.006) until adjusting for insurance. In contrast, patients with ADI>85 had increased odds of higher DOOR (aOR=1.07, CI=1.01-1.14, P <0.021) after adjusting for insurance but similar odds after adjusting for PASC and urgent/emergent cases. CONCLUSIONS: DOOR revealed complex interactions between race/ethnicity, insurance type and neighborhood deprivation. ADI>85 was associated with higher odds of worse DOOR outcomes while TO failed to capture the effect of ADI. Our results suggest that presentation acuity is a critical determinant of worse outcomes in patients in highly deprived neighborhoods and without insurance. Including risk adjustment for living in deprived neighborhoods and urgent/emergent surgeries could improve the accuracy of quality metrics.

Limitations of Noninvasive Tests-Based Population-Level Risk Stratification Strategy for Nonalcoholic Fatty Liver Disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed. AIMS: We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis. METHODS: Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged [Formula: see text] 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites. RESULTS: Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range. CONCLUSIONS: We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups.

Independent Associations of Neighborhood Deprivation and Patient-level Social Determinants of Health with Textbook Outcomes after Inpatient Surgery.

Objective: Assess associations of Social Determinants of Health (SDoH) using Area Deprivation Index (ADI), race/ethnicity and insurance type with Textbook Outcomes (TO). Summary Background Data: Individual- and contextual-level SDoH affect health outcomes, but only one SDoH level is usually included. Methods: Three healthcare system cohort study using National Surgical Quality Improvement Program (2013-2019) linked with ADI risk-adjusted for frailty, case status and operative stress examining TO/TO components (unplanned reoperations, complications, mortality, Emergency Department/Observation Stays and readmissions). Results: Cohort (34,251 cases) mean age 58.3 [SD=16.0], 54.8% females, 14.1% Hispanics, 11.6% Non-Hispanic Blacks, 21.6% with ADI>85, and 81.8% TO. Racial and ethnic minorities, non-Private insurance, and ADI>85 patients had increased odds of urgent/emergent surgeries (aORs range: 1.17-2.83, all P<.001). Non-Hispanic Black patients, ADI>85 and non-Private insurances had lower TO odds (aORs range: 0.55-0.93, all P<.04), but ADI>85 lost significance after including case status. Urgent/emergent versus elective had lower TO odds (aOR=0.51, P<.001). ADI>85 patients had higher complication and mortality odds. Estimated reduction in TO probability was 9.9% (CI=7.2%-12.6%) for urgent/emergent cases, 7.0% (CI=4.6%-9.3%) for Medicaid, and 1.6% (CI=0.2%-3.0%) for non-Hispanic Black patients. TO probability difference for lowest-risk (White-Private-ADI≤85-elective) to highest-risk (Black-Medicaid-ADI>85-urgent/emergent) was 29.8% for very frail patients. Conclusion: Multi-level SDoH had independent effects on TO, predominately affecting outcomes through increased rates/odds of urgent/emergent surgeries driving complications and worse outcomes. Lowest-risk versus highest-risk scenarios demonstrated the magnitude of intersecting SDoH variables. Combination of insurance type and ADI should be used to identify high-risk patients to redesign care pathways to improve outcomes. Risk adjustment including contextual neighborhood deprivation and patient-level SDoH could reduce unintended consequences of value-based programs.

Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging.

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

REDCap and the National Mesothelioma Virtual Bank-a scalable and sustainable model for rare disease biorepositories.

OBJECTIVE: Rare disease research requires data sharing networks to power translational studies. We describe novel use of Research Electronic Data Capture (REDCap), a web application for managing clinical data, by the National Mesothelioma Virtual Bank, a federated biospecimen, and data sharing network. MATERIALS AND METHODS: National Mesothelioma Virtual Bank (NMVB) uses REDCap to integrate honest broker activities, enabling biospecimen and associated clinical data provisioning to investigators. A Web Portal Query tool was developed to source and visualize REDCap data in interactive, faceted search, enabling cohort discovery by public users. An AWS Lambda function behind an API calculates the counts visually presented, while protecting record level data. The user-friendly interface, quick responsiveness, automatic generation from REDCap, and flexibility to new data, was engineered to sustain the NMVB research community. RESULTS: NMVB implementations enabled a network of 8 research institutions with over 2000 mesothelioma cases, including clinical annotations and biospecimens, and public users' cohort discovery and summary statistics. NMVB usage and impact is demonstrated by high website visits (>150 unique queries per month), resource use requests (>50 letter of interests), and citations (>900) to papers published using NMVB resources. DISCUSSION: NMVB's REDCap implementation and query tool is a framework for implementing federated and integrated rare disease biobanks and registries. Advantages of this framework include being low-cost, modular, scalable, and efficient. Future advances to NVMB's implementations will include incorporation of -omics data and development of downstream analysis tools to advance mesothelioma and rare disease research. CONCLUSION: NVMB presents a framework for integrating biobanks and patient registries to enable translational research for rare diseases.

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research.

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

Association of Frailty and the Expanded Operative Stress Score with Preoperative Acute Serious Conditions, Complications, and Mortality in Males Compared to Females: A Retrospective Observational Study.

OBJECTIVE: The aim of this study was to expand Operative Stress Score (OSS) increasing procedural coverage and assessing OSS and frailty association with Preoperative Acute Serious Conditions (PASC), complications and mortality in females versus males. SUMMARY BACKGROUND DATA: Veterans Affairs male-dominated study showed high mortality in frail veterans even after very low stress surgeries (OSS1). METHODS: Retrospective cohort using NSQIP data (2013-2019) merged with 180-day postoperative mortality from multiple hospitals to evaluate PASC, 30-day complications and 30-, 90-, and 180-day mortality. RESULTS: OSS expansion resulted in 98.2% case coverage versus 87.0% using the original. Of 82,269 patients (43.8% male), 7.9% were frail/very frail. Males had higher odds of PASC [adjusted odds ratio (aOR) = 1.31, 95% confidence interval (CI) = 1.21-1.41, P < 0.001] and severe/life-threatening Clavien-Dindo IV (CDIV) complications (aOR = 1.18, 95% CI = 1.09-1.28, P < 0.001). Although mortality rates were higher (all time-points, P < 0.001) in males versus females, mortality was similar after adjusting for frailty, OSS, and case status primarily due to increased male frailty scores. Additional adjustments for PASC and CDIV resulted in a lower odds of mortality in males (30-day, aOR = 0.81, 95% CI = 0.71-0.92, P = 0.002) that was most pronounced for males with PASC compared to females with PASC (30-day, aOR = 0.75, 95% CI = 0.56-0.99, P = 0.04). CONCLUSIONS: Similar to the male-dominated Veteran population, private sector, frail patients have high likelihood of postoperative mortality, even after low-stress surgeries. Preoperative frailty screening should be performed regardless of magnitude of the procedure. Despite males experiencing higher adjusted odds of PASC and CDIV complications, females with PASC had higher odds of mortality compared to males, suggesting differences in the aggressiveness of care provided to men and women.

Tissue registration and exploration user interfaces in support of a human reference atlas.

Seventeen international consortia are collaborating on a human reference atlas (HRA), a comprehensive, high-resolution, three-dimensional atlas of all the cells in the healthy human body. Laboratories around the world are collecting tissue specimens from donors varying in sex, age, ethnicity, and body mass index. However, harmonizing tissue data across 25 organs and more than 15 bulk and spatial single-cell assay types poses challenges. Here, we present software tools and user interfaces developed to spatially and semantically annotate ("register") and explore the tissue data and the evolving HRA. A key part of these tools is a common coordinate framework, providing standard terminologies and data structures for describing specimen, biological structure, and spatial data linked to existing ontologies. As of April 22, 2022, the "registration" user interface has been used to harmonize and publish data on 5,909 tissue blocks collected by the Human Biomolecular Atlas Program (HuBMAP), the Stimulating Peripheral Activity to Relieve Conditions program (SPARC), the Human Cell Atlas (HCA), the Kidney Precision Medicine Project (KPMP), and the Genotype Tissue Expression project (GTEx). Further, 5,856 tissue sections were derived from 506 HuBMAP tissue blocks. The second "exploration" user interface enables consortia to evaluate data quality, explore tissue data spatially within the context of the HRA, and guide data acquisition. A companion website is at https://cns-iu.github.io/HRA-supporting-information/ .

DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients.

Identifying patients with high risk of suicide is critical for suicide prevention. We examined lab tests together with medication use and diagnosis from electronic medical records (EMR) data for prediction of suicide-related events (SREs; suicidal ideations, attempts and deaths) in post-traumatic stress disorder (PTSD) patients, a population with a high risk of suicide. We developed DeepBiomarker, a deep-learning model through augmenting the data, including lab tests, and integrating contribution analysis for key factor identification. We applied DeepBiomarker to analyze EMR data of 38,807 PTSD patients from the University of Pittsburgh Medical Center. Our model predicted whether a patient would have an SRE within the following 3 months with an area under curve score of 0.930. Through contribution analysis, we identified important lab tests for suicide prediction. These identified factors imply that the regulation of the immune system, respiratory system, cardiovascular system, and gut microbiome were involved in shaping the pathophysiological pathways promoting depression and suicidal risks in PTSD patients. Our results showed that abnormal lab tests combined with medication use and diagnosis could facilitate predicting SRE risk. Moreover, this may imply beneficial effects for suicide prevention by treating comorbidities associated with these biomarkers.

An atomic approach to the design and implementation of a research data warehouse.

OBJECTIVE: As a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research. MATERIALS AND METHODS: We designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies. RESULTS: Neptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (HIPAA) Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of institutional review board-approved research projects by local investigators and for national projects. DISCUSSION: The design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement. CONCLUSION: We describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.

Open-source Software Sustainability Models: Initial White Paper From the Informatics Technology for Cancer Research Sustainability and Industry Partnership Working Group.

BACKGROUND: The National Cancer Institute Informatics Technology for Cancer Research (ITCR) program provides a series of funding mechanisms to create an ecosystem of open-source software (OSS) that serves the needs of cancer research. As the ITCR ecosystem substantially grows, it faces the challenge of the long-term sustainability of the software being developed by ITCR grantees. To address this challenge, the ITCR sustainability and industry partnership working group (SIP-WG) was convened in 2019. OBJECTIVE: The charter of the SIP-WG is to investigate options to enhance the long-term sustainability of the OSS being developed by ITCR, in part by developing a collection of business model archetypes that can serve as sustainability plans for ITCR OSS development initiatives. The working group assembled models from the ITCR program, from other studies, and from the engagement of its extensive network of relationships with other organizations (eg, Chan Zuckerberg Initiative, Open Source Initiative, and Software Sustainability Institute) in support of this objective. METHODS: This paper reviews the existing sustainability models and describes 10 OSS use cases disseminated by the SIP-WG and others, including 3D Slicer, Bioconductor, Cytoscape, Globus, i2b2 (Informatics for Integrating Biology and the Bedside) and tranSMART, Insight Toolkit, Linux, Observational Health Data Sciences and Informatics tools, R, and REDCap (Research Electronic Data Capture), in 10 sustainability aspects: governance, documentation, code quality, support, ecosystem collaboration, security, legal, finance, marketing, and dependency hygiene. RESULTS: Information available to the public reveals that all 10 OSS have effective governance, comprehensive documentation, high code quality, reliable dependency hygiene, strong user and developer support, and active marketing. These OSS include a variety of licensing models (eg, general public license version 2, general public license version 3, Berkeley Software Distribution, and Apache 3) and financial models (eg, federal research funding, industry and membership support, and commercial support). However, detailed information on ecosystem collaboration and security is not publicly provided by most OSS. CONCLUSIONS: We recommend 6 essential attributes for research software: alignment with unmet scientific needs, a dedicated development team, a vibrant user community, a feasible licensing model, a sustainable financial model, and effective product management. We also stress important actions to be considered in future ITCR activities that involve the discussion of the sustainability and licensing models for ITCR OSS, the establishment of a central library, the allocation of consulting resources to code quality control, ecosystem collaboration, security, and dependency hygiene.

Using the Unified Medical Language System to Expand the Operative Stress Score - First Use Case.

BACKGROUND: The Unified Medical Language System (UMLS) maps relationships between and within >100 biomedical vocabularies, including Current Procedural Terminology (CPT) codes, creating a powerful knowledge resource which can accelerate clinical research. METHODS: We used synonymy and concepts relating hierarchical structure of CPT codes within the UMLS, (1) guiding surgical experts in expanding the Operative Stress Score (OSS) from 565 originally rated CPT codes to additional, 1,853 related procedures; (2) establishing validity of the association between the added OSS ratings and 30-day outcomes in VASQIP (2015-2018). RESULTS: The UMLS Metathesaurus and Semantic Network was converted into an interactive graph database (https://github.com/dbmi-pitt/UMLS-Graph) delineating ontology relatedness. From this UMLS-graph, the CPT hierarchy was queried obtaining all paths from each code to the hierarchical apex. Of 1,853 added ratings, 43% and 76% were siblings and cousins of original OSS CPT codes. Of 857,577 VASQIP cases (mean age, 64±11years; 91% male; 75% white), 786,122 (92%) and 71,455 (8%) were rated in the original and added OSS. Compared to original, added OSS cases included more females (14% versus 9%) and frail patients (25% versus 19%) undergoing high stress procedures (11% versus 8%; all P <.001). Postoperative mortality consistently increased with OSS. Very low stress procedures had <0.5% (original, 0.4% [95%CI, 0.4%-0.5%] versus added, 0.9% [95%CI, 0.6%-1.2%]) and very high 3.8% (original, 3.5% [95%CI, 3.0%-4.0%] versus added, 5.8% [95%CI, 4.6-7.3%]) mortality rates. CONCLUSIONS: The synonymy and concepts relating biomedical data within the UMLS can be abstracted and efficiently used to expand the utility of existing clinical research tools.

An Emulation of Randomized Trials of Administrating Antipsychotics in PTSD Patients for Outcomes of Suicide-Related Events.

Post-traumatic stress disorder (PTSD) is a prevalent mental disorder marked by psychological and behavioral changes. Currently, there is no consensus of preferred antipsychotics to be used for the treatment of PTSD. We aim to discover whether certain antipsychotics have decreased suicide risk in the PTSD population, as these patients may be at higher risk. A total of 38,807 patients were identified with a diagnosis of PTSD through the ICD9 or ICD10 codes from January 2004 to October 2019. An emulation of randomized clinical trials was conducted to compare the outcomes of suicide-related events (SREs) among PTSD patients who ever used one of eight individual antipsychotics after the diagnosis of PTSD. Exclusion criteria included patients with a history of SREs and a previous history of antipsychotic use within one year before enrollment. Eligible individuals were assigned to a treatment group according to the antipsychotic initiated and followed until stopping current treatment, switching to another same class of drugs, death, or loss to follow up. The primary outcome was to identify the frequency of SREs associated with each antipsychotic. SREs were defined as ideation, attempts, and death by suicide. Pooled logistic regression methods with the Firth option were conducted to compare two drugs for their outcomes using SAS version 9.4 (SAS Institute, Cary, NC, USA). The results were adjusted for baseline characteristics and post-baseline, time-varying confounders. A total of 5294 patients were eligible for enrollment with an average follow up of 7.86 months. A total of 157 SREs were recorded throughout this study. Lurasidone showed a statistically significant decrease in SREs when compared head to head to almost all the other antipsychotics: aripiprazole, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone (p < 0.0001 and false discovery rate-adjusted p value < 0.0004). In addition, olanzapine was associated with higher SREs than quetiapine and risperidone, and ziprasidone was associated with higher SREs than risperidone. The results of this study suggest that certain antipsychotics may put individuals within the PTSD population at an increased risk of SREs, and that careful consideration may need to be taken when prescribed.

Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder.

Around 800,000 people worldwide die from suicide every year and it's the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Sertraline, Fentanyl, Aripiprazole, Lamotrigine, and Tramadol were strong indicators for no SREs within one year. The use of Haloperidol, Trazodone and Citalopram, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, predicted the onset of SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.

Technology-Enabled Clinical Trials: Transforming Medical Evidence Generation.

The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.

The Global academic research organization network: Data sharing to cure diseases and enable learning health systems.

INTRODUCTION: Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. METHODS: The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. RESULTS: This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. CONCLUSIONS: The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients.