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Abstract Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub’s EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%–98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Objective:. To assess the association of Private, Medicare (MC), and Medicaid/Uninsured (MU) insurance type with 30-day emergency department visits/observation stays (EDOS), readmissions, and costs in a safety-net hospital (SNH) serving diverse socioeconomic status patients. Background:. MC’s hospital readmission reduction program (HRRP) disproportionately penalizes SNHs. Methods:. This retrospective cohort study used inpatient National Surgical Quality Improvement Program (2013–2019) data merged with cost data. Frailty, expanded operative stress score, case status, and insurance type were used to predict odds of EDOS and readmissions, as well as index hospitalization costs. Results:. The cohort had 1477 Private; 1164 MC; and 3488 MU cases with a patient mean age 52.1 years [SD = 14.7] and 46.8% of the cases were performed on male patients. MU [adjusted odds ratio (aOR) = 2.69, 95% confidence interval (CI) = 2.38–3.05, P < 0.001] and MC (aOR = 1.32, 95% CI = 1.11–1.56, P = 0.001) had increased odds of urgent/emergent surgeries and complications versus Private patients. Despite having similar frailty distributions, MU compared to Private patients had higher odds of EDOS (aOR = 1.71, 95% CI = 1.39–2.11, P < 0.001), and readmissions (aOR = 1.35, 95% CI = 1.11–1.65, P = 0.004), after adjusting for frailty, OSS, and case status, whereas MC patients had similar odds of EDOS and readmissions versus Private. Hospitalization variable cost %change was increased for MC (12.5%) and MU (5.9%), but MU was similar to Private after adjusting for urgent/emergent cases. Conclusions:. Increased rates and odds of urgent/emergent cases in MU patients drive increased odds of complications and index hospitalization costs versus Private. SNHs care for higher cost populations while receiving lower reimbursements and are further penalized by the unintended consequences of HRRP. Increasing access to care, especially for MU patients, could reduce urgent/emergent surgeries resulting in fewer complications, EDOS/readmissions, and costs.

Objective:. Assess associations of social determinants of health (SDoH) using area deprivation index (ADI), race/ethnicity and insurance type with textbook outcomes (TO). Background:. 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 (adjusted odds ratios [aORs] range: 1.17–2.83, all P < 0.001). Non-Hispanic Black patients, ADI >85 and non-Private insurances had lower TO odds (aORs range: 0.55–0.93, all P < 0.04), but ADI >85 lost significance after including case status. Urgent/emergent versus elective had lower TO odds (aOR = 0.51, P < 0.001). ADI >85 patients had higher complication and mortality odds. Estimated reduction in TO probability was 9.9% (95% confidence interval [CI] = 7.2%–12.6%) for urgent/emergent cases, 7.0% (95% CI = 4.6%–9.3%) for Medicaid, and 1.6% (95% CI = 0.2%–3.0%) for non-Hispanic Black patients. TO probability difference for lowest-risk (White-Private-ADI 85-urgent/emergent) was 29.8% for very frail patients. Conclusion:. Multilevel 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.

Abstract Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011–2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.

Objective:. Association of insurance type with colorectal surgical complications, textbook outcomes (TO), and cost in a safety-net hospital (SNH). Background:. SNHs have higher surgical complications and costs compared to low-burden hospitals. How does presentation acuity and insurance type influence colorectal surgical outcomes? Methods:. Retrospective cohort study using single-site National Surgical Quality Improvement Program (2013–2019) with cost data and risk-adjusted by frailty, preoperative serious acute conditions (PASC), case status and open versus laparoscopic to evaluate 30-day reoperations, any complication, Clavien-Dindo IV (CDIV) complications, TO, and hospitalization variable costs. Results:. Cases (Private 252; Medicare 207; Medicaid/Uninsured 619) with patient mean age 55.2 years (SD = 13.4) and 53.1% male. Adjusting for frailty, open abdomen, and urgent/emergent cases, Medicaid/Uninsured patients had higher odds of presenting with PASC (adjusted odds ratio [aOR] = 2.02, 95% confidence interval [CI] = 1.22–3.52, P = 0.009) versus Private. Medicaid/Uninsured (aOR = 1.80, 95% CI = 1.28–2.55, P < 0.001) patients were more likely to undergo urgent/emergent surgeries compared to Private. Medicare patients had increased odds of any and CDIV complications while Medicaid/Uninsured had increased odds of any complication, emergency department or observations stays, and readmissions versus Private. Medicare (aOR = 0.51, 95% CI = 0.33–0.88, P = 0.003) and Medicaid/Uninsured (aOR = 0.43, 95% CI = 0.30–0.60, P < 0.001) patients had lower odds of achieving TO versus Private. Variable cost %change increased in Medicaid/Uninsured patients to 13.94% (P = 0.005) versus Private but was similar after adjusting for case status. Urgent/emergent cases (43.23%, P < 0.001) and any complication (78.34%, P < 0.001) increased %change hospitalization costs. Conclusions:. Decreasing the incidence of urgent/emergent colorectal surgeries, possibly by improving access to care, could have a greater impact on improving clinical outcomes and decreasing costs, especially in Medicaid/Uninsured insurance type patients.

Abstract Background Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. Results We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. Conclusion Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

Each year our Clinical and Translational Science Award pilot projects program awards approximately $500,000 in translational pilot funding to advance health in South Texas. We identified needs to improve the timeliness, transparency, and efficiency of the review process by surveying applicants. Lean six sigma methodologies, following a “Define, Measure, Analyze, Improve, Control” approach, were used to streamline the pilot project application and review by identifying and removing bottlenecks from process flows. We evaluated the impact of our reorganized review process by surveying applicants and reviewers. Process mapping identified pilot project review as the main source of delay, leading to the implementation of a study section-style review mechanism. After one cycle, 90.3% of pilot applicants and 100% of reviewers were highly satisfied with the new processes and time to award notice was reduced by 2 months. All reviewers familiar with both review processes preferred the study section. We demonstrated how lean six sigma, a methodology not commonly applied in research administration, can be used to evaluate processes in translational science in academic health centers. Through our efforts, we were able to improve timeliness, transparency, and efficiency of the review process.

The unprecedented increase in preclinical studies necessitates high-throughput, inexpensive, and straightforward methods for evaluating diseased tissues. Near-infrared imaging of live subjects is a versatile, cost-effective technology that can be effectively used in a variety of pathologic conditions. We have characterized an inexpensive optoelectronic chemical, IR-820, as an infrared blood pool contrast agent to detect and quantify diseased tissue in live animals. IR-820 has maximal excitation and emission wavelengths of 710 and 820 nm, respectively. IR-820 emission is significantly improved in vivo on serum binding to albumin, and elimination occurs predominantly via the gastrointestinal tract. We demonstrate the utility of this contrast agent for serially imaging of traumatized tissue (muscle), tissue following reperfusion (eg, stroke), and tumors. IR-820 can also be employed to map regional lymph nodes. This novel contrast agent is anticipated to be a useful and an inexpensive tool for screening a wide variety of preclinical models of human diseases.

Background Extremity vascular injury (EVI) causes significant disability in Veterans of the Afghanistan/Iraq conflicts. Advancements in acute trauma care improved survival and decreased amputations. The study of wartime EVI has relied on successful limb salvage as a surrogate for vascular repair. We used imaging studies as a specific measure of arterial repair durability.Methods Service members with EVI were identified using the Department of Defense Trauma Registry and validated by chart abstraction. Inclusion criteria for the arterial patency subgroup included an initial repair attempt with subsequent imaging reports (duplex ultrasound, CT angiography, and angiogram) documenting initial patency.Results The cohort of 527 included 140 Veterans with available imaging studies for 143 arterial repairs; median follow-up from injury time to last available imaging study was 19 months (Q1–Q3: 3–58; range: 1–175). Injury mechanism was predominantly explosions (52%) and gunshot wounds (42%). Of the 143 arterial repairs, 81% were vein grafts. Eight repairs were occluded, replaced or included in extremity amputations. One upper extremity and three transtibial late amputations were performed for chronic pain and poor function averaging 27 months (SD: 4; range: 24–32). Kaplan-Meier analysis estimated patency rates of 99%, 97%, 95%, 91% and 91% at 3, 6, 12, 24, and 36 months, respectively, with similar results for upper and lower extremity repairs. Explosive and gunshot wound injury mechanisms had similar patency rates and upper extremity injuries repaired with vein grafts had increased patency.Conclusions Arterial repair mid-term patency in combat-related extremity injuries is excellent based on imaging studies for 143 repairs. Assertive attempts at acute limb salvage and vascular repair are justified with decisions for amputation versus limb salvage based on the overall condition of the patient and degree of concomitant nerve, orthopedic and soft tissue injuries rather than the presence of arterial injuries.Level of evidence Therapeutic/care management, level IV.