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We investigate the value of a two-armed Bayesian response adaptive randomization (RAR) design to investigate early preterm birth rates of high versus low dose of docosahexaenoic acid during pregnancy. Unexpectedly, the COVID-19 pandemic forced recruitment to pause at 1100 participants rather than the planned 1355. The difference in power between number of participants at the pause and planned was 87% and 90% respectively. We decided to stop the study. This paper describes how the RAR was used to execute the study. The value of RAR in two-armed studies is quite high and their use in the future is promising.

NeoSTOP study schema

Purpose:Addition of carboplatin (Cb) to anthracycline chemotherapy improves pathologic complete response (pCR), and carboplatin plus taxane regimens also yield encouraging pCR rates in triple-negative breast cancer (TNBC). Aim of the NeoSTOP multisite randomized phase II trial was to assess efficacy of anthracycline-free and anthracycline-containing neoadjuvant carboplatin regimens.Patients and Methods:Patients aged ≥18 years with stage I–III TNBC were randomized (1:1) to receive either paclitaxel (P) weekly × 12 plus carboplatin AUC6 every 21 days × 4 followed by doxorubicin/cyclophosphamide (AC) every 14 days × 4 (CbP → AC, arm A), or carboplatin AUC6 + docetaxel (D) every 21 days × 6 (CbD, arm B). Stromal tumor-infiltrating lymphocytes (sTIL) were assessed. Primary endpoint was pCR in breast and axilla. Other endpoints included residual cancer burden (RCB), toxicity, cost, and event-free (EFS) and overall survival (OS).Results:One hundred patients were randomized; arm A (n = 48) or arm B (n = 52). pCR was 54% [95% confidence interval (CI), 40%–69%] in arm A and 54% (95% CI, 40%–68%) in arm B. RCB 0+I rate was 67% in both arms. Median sTIL density was numerically higher in those with pCR compared with those with residual disease (20% vs. 5%; P = 0.25). At median follow-up of 38 months, EFS and OS were similar in the two arms. Grade 3/4 adverse events were more common in arm A compared with arm B, with the most notable differences in neutropenia (60% vs. 8%; P < 0.001) and febrile neutropenia (19% vs. 0%; P < 0.001). There was one treatment-related death (arm A) due to acute leukemia. Mean treatment cost was lower for arm B compared with arm A (P = 0.02).Conclusions:The two-drug CbD regimen yielded pCR, RCB 0+I, and survival rates similar to the four-drug regimen of CbP → AC, but with a more favorable toxicity profile and lower treatment-associated cost.

Population-level environmental and socioeconomic factors may influence cancer burden within communities, particularly in rural and urban areas that may be differentially impacted by factors related to health care access.The University of Kansas (KU) Cancer Center serves a geographically large diverse region with 75% of its 123 counties classified as rural. Using County Health Rankings data and joinpoint regression, we examined trends in four factors related to the socioeconomic environment and health care access from 2009 to 2017 in rural and urban counties across the KU Cancer Center catchment area.The adult health uninsurance rate declined significantly in rural and urban counties across the catchment area (rural annual percent change [APC]=-5.96; 95% CI=[-7.71 to -4.17]; urban APC=-5.72; 95% CI=[-8.03 to -3.35]). Childhood poverty significantly decreased in rural counties over time (APC=-2.94; 95% CI=[-4.52 to -1.33]); in contrast, urban childhood poverty rates did not significantly change before 2012 (APC=3.68; 95% CI=[-15.12 to 26.65]), after which rates declined (APC=-5.89; 95% CI=[-10.01 to -1.58]). The number of primary care providers increased slightly but significantly in both rural and urban counties (APC=0.54; 95% CI=[0.28 to 0.80]), although urban counties had more primary care providers than rural areas (76.1 per 100K population vs. 57.1 per 100K population, respectively;Our findings reveal potential disparities in systemic factors that may contribute to differences in cancer prevention, care, and survivorship in rural and urban regions.

Background: Infection with SARS-CoV-2 has led to a global pandemic and has significantly impacted the care of cancer patients. Breast cancer patients receiving active systemic therapy need protection against COVID19 but the efficacy of vaccines in this population is unknown. Although specific biomarkers associated with protection from SARS-CoV-2 infection have yet to be identified, measurement of serum antibody activity is generally accepted as a surrogate of in vivo humoral response to vaccine. This study evaluates the efficiency and durability of binding antibodies to SARS-CoV-2 spike (S) protein in response to COVID19 vaccine in breast cancer patients receiving systemic treatment. Methods: Breast cancer patients, who were unvaccinated, partially or fully vaccinated with Pfizer-BioNTech BNT162b2 (PF), Moderna mRNA-1273 (Mod) or Johnson & Johnson AD26.COV2.S (J&J) were enrolled in this prospective longitudinal study. Eligible patients were on systemic treatment with cytotoxic chemotherapy, chemotherapy plus a checkpoint inhibitor (CPI), CPI alone or a CDK 4/6 inhibitor. Longitudinal blood samples are being collected at baseline, prior to vaccination in unvaccinated patients (T0), 2 weeks after the first vaccine dose and before the second dose for the mRNA vaccines (T1), 1 month (T2), 3 months (T3), 6 months (T4) and 12 month post vaccination. For J&J, there was no T1 timepoint. Roche Elecsys® Anti-SARS-CoV-2 S receptor binding domain (RBD) antibody immunoassay was used to measure antibody titers (range 0.4 to 250 U/mL). Cut points of N% Seropositive (>0.8 U/mL)Mean Antibody Levels (U/mL)T0T1T2T3T0T1T2T3All subjects7510789810012.6102.3204.4214.6Chemotherapy50577961003.3105.6200.0250CDK 4/6 inhibitors15257510010013.786.8234.7205.8CPI + Chemotherapy82583100NA*62.8121.4177.5NA*CPI therapy20100100NA*0.46.82250NA*CPI=Checkpoint Inhibitors; *Timepoint for longitudinal samples not reached Citation Format: Cory Bivona, Kevin Li, Priyanka Sharma, Jianghua He, Grace Martin, Andrew K Godwin, Anthony Rooney, Stephen Williamson, Gary Doolittle, Weijing Sun, Bruce F Kimler, Anne P O'Dea, Lauren E Nye, Joseph P McGuirk, Ziyan Pessetto, Lisa Haney, Nicole Balmaceda, Laura Mitchell, Karissa Finke, Maggie Nelson, Dinesh Pal Mudaranthakam, Natalie Streeter, Stephanie Lafaver, Jaimie Heldstab, Qamar J Khan. Immunogenicity of SARS-CoV-2 vaccination in subjects on active treatment for breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-10-03.

Lung cancer is currently the leading cause of cancer death worldwide due to its high incidence rate and low survival rate (1). Despite its high mortality, early screening of lung cancer is underemphasized in public campaigns compared to other cancers (2). Many risk factors contribute to lung cancer, with the predominant cause being the inhalation of toxic chemicals which includes tobacco smoke and industrial pollution (3). The combustive process of coal power production releases 84 different compounds that are designated as hazardous air pollutants by the United States Environmental Protection Agency (4). These compounds can cause several diseases in both humans and animals, as demonstrated by national-level research studies conducted in Southeast Asia (5). As of 2020 coal-powered power plants contributed to 34% of the overall power generation across Kansas (6). One ton of coal only generates 2,460 kWh of electricity whereas Wyandotte county alone requires 2,300 kWh (7; 8). With that context, our goal was to assess how the coal-fired power plants across the state of Kansas are related to lung cancer incidence in their surrounding area. We found that areas within the immediate vicinity of two coal-power plants had higher incidence rates of lung cancer compared to areas with no coal-power plants. Additionally, modeling lung cancer incidence based on vicinity to plants with covariates revealed a significant relationship between poverty, age, and lung cancer incidence. Individuals living in poverty are predisposed to healthcare-related bankruptcy and cost-associated treatment nonadherence (9). They are also shown to smoke more which is a known risk factor for lung cancer (10). Limiting affordable housing for these individuals to areas containing significant risk factors for lung cancer is irresponsible and potentially exploitative. Further studies on this topic should examine additional socioeconomic and lung cancer risk factors as well.

Anticoagulants prevent thrombosis and death in patients with atrial fibrillation and venous thromboembolism (VTE) but also increase bleeding risk. The benefit/risk ratio favors anticoagulation in most of these patients. However, some will have a bleeding complication, such as the common trip-and-fall brain injury in elderly patients that results in traumatic intracranial hemorrhage. Clinicians must then make the difficult decision about when to restart the anticoagulant. Restarting too early risks making the bleeding worse. Restarting too late risks thrombotic events such as ischemic stroke and VTE, the indications for anticoagulation in the first place. There are more data on restarting patients with spontaneous intracranial hemorrhage, which is very different than traumatic intracranial hemorrhage. Spontaneous intracranial hemorrhage increases the risk of rebleeding because intrinsic vascular changes are widespread and irreversible. In contrast, traumatic cases are caused by a blow to the head, usually an isolated event portending less future risk. Clinicians generally agree that anticoagulation should be restarted but disagree about when. This uncertainty leads to long restart delays causing a large, potentially preventable burden of strokes and VTE, which has been unaddressed because of the absence of high quality evidence. Restart Traumatic Intracranial Hemorrhage (the “r” distinguished intracranial from intracerebral) (TICrH) is a prospective randomized open label blinded end-point response-adaptive clinical trial that will evaluate the impact of delays to restarting direct oral anticoagulation (1, 2, or 4 weeks) on the composite of thrombotic events and bleeding in patients presenting after traumatic intracranial hemorrhage.

BACKGROUND Studying patients’ social needs is critical to the understanding of health conditions and disparities, and to inform strategies for improving health outcomes. Studies have shown that people of color, low-income families, and those with lower educational attainment experience greater hardships related to social needs. The COVID-19 pandemic represents an event that severely impacted people’s social needs. This pandemic was declared by the World Health Organization on March 11, 2020, and contributed to food and housing insecurity, while highlighting weaknesses in the health care system surrounding access to care. To combat these issues, legislators implemented unique policies and procedures to help alleviate worsening social needs throughout the pandemic, which had not previously been exerted to this degree. We believe that improvements related to COVID-19 legislature and policy have positively impacted people’s social needs in Kansas and Missouri, United States. In particular, Wyandotte County is of interest as it suffers greatly from issues related to social needs that many of these COVID-19–related policies aimed to improve. OBJECTIVE The research objective of this study was to evaluate the change in social needs before and after the COVID-19 pandemic declaration based on responses to a survey from The University of Kansas Health System (TUKHS). We further aimed to compare the social needs of respondents from Wyandotte County from those of respondents in other counties in the Kansas City metropolitan area. METHODS Social needs survey data from 2016 to 2022 were collected from a 12-question patient-administered survey distributed by TUKHS during a patient visit. This provided a longitudinal data set with 248,582 observations, which was narrowed down into a paired-response data set for 50,441 individuals who had provided at least one response before and after March 11, 2020. These data were then bucketed by county into Cass (Missouri), Clay (Missouri), Jackson (Missouri), Johnson (Kansas), Leavenworth (Kansas), Platte (Missouri), Wyandotte (Kansas), and Other counties, creating groupings with at least 1000 responses in each category. A pre-post composite score was calculated for each individual by adding their coded responses (yes=1, no=0) across the 12 questions. The Stuart-Maxwell marginal homogeneity test was used to compare the pre and post composite scores across all counties. Additionally, McNemar tests were performed to compare responses before and after March 11, 2020, for each of the 12 questions across all counties. Finally, McNemar tests were performed for questions 1, 7, 8, 9, and 10 for each of the bucketed counties. Significance was assessed at P RESULTS The Stuart-Maxwell test for marginal homogeneity was significant (PPPPPPPPPPP=.02), and were also less likely to request help with these unmet needs (OR=0.7368, P CONCLUSIONS Post-COVID-19 responses showed improvement across almost all social needs–related questions, indicating that the federal policy response possibly had a positive impact on social needs across the populations of Kansas and western Missouri. Some counties were impacted more than others and positive outcomes were not limited to urban counties. The availability of resources, safety net services, access to health care, and educational opportunities could play a role in this change. Future research should focus on improving survey response rates from rural counties to increase their sample size, and to evaluate other explanatory variables such as food pantry access, educational status, employment opportunities, and access to community resources. Government policies should be an area of focused research as they may affect the social needs and health of the individuals considered in this analysis.

Purpose: Addition of carboplatin (Cb) to anthracycline chemotherapy improves pathologic complete response (pCR), and carboplatin plus taxane regimens also yield encouraging pCR rates in triple-negative breast cancer (TNBC). Aim of the NeoSTOP multisite randomized phase II trial was to assess efficacy of anthracycline-free and anthracycline-containing neoadjuvant carboplatin regimens. Patients and Methods: Patients aged ≥18 years with stage I–III TNBC were randomized (1:1) to receive either paclitaxel (P) weekly × 12 plus carboplatin AUC6 every 21 days × 4 followed by doxorubicin/cyclophosphamide (AC) every 14 days × 4 (CbP → AC, arm A), or carboplatin AUC6 + docetaxel (D) every 21 days × 6 (CbD, arm B). Stromal tumor-infiltrating lymphocytes (sTIL) were assessed. Primary endpoint was pCR in breast and axilla. Other endpoints included residual cancer burden (RCB), toxicity, cost, and event-free (EFS) and overall survival (OS). Results: One hundred patients were randomized; arm A (n = 48) or arm B (n = 52). pCR was 54% [95% confidence interval (CI), 40%–69%] in arm A and 54% (95% CI, 40%–68%) in arm B. RCB 0+I rate was 67% in both arms. Median sTIL density was numerically higher in those with pCR compared with those with residual disease (20% vs. 5%; P = 0.25). At median follow-up of 38 months, EFS and OS were similar in the two arms. Grade 3/4 adverse events were more common in arm A compared with arm B, with the most notable differences in neutropenia (60% vs. 8%; P < 0.001) and febrile neutropenia (19% vs. 0%; P < 0.001). There was one treatment-related death (arm A) due to acute leukemia. Mean treatment cost was lower for arm B compared with arm A (P = 0.02). Conclusions: The two-drug CbD regimen yielded pCR, RCB 0+I, and survival rates similar to the four-drug regimen of CbP → AC, but with a more favorable toxicity profile and lower treatment-associated cost.

This research study aimed to evaluate the financial burden among older cancer patients and its corresponding risk factors. Factors such as increasing treatment costs and work limitations often lead cancer patients to bankruptcy and poor quality of life. These consequences, in turn, can cause higher mortality rates among these patients.This retrospective cohort study utilized data from the Health Retirement Study (HRS), conducted by the University of Michigan (N = 18,109). Eligible participants had responses captured from years 2002 to 2016. Participants were classified according to any self-reported cancer diagnosis (yes or no) and were compared on the basis of financial, work, and health-related outcomes. Propensity score (PS) matching was applied to reduce the effects of potential confounding factors. Also only, individuals with an age ≥50 and ≤85 during Wave 6 were retained.Multivariate analysis with random effects revealed several indicators of financial burden when comparing participants with a cancer diagnosis to those with no history of cancer. Mean out-of-pocket costs associated with a cancer diagnosis were $1058 higher when compared to participants with no history of cancer, suggesting that even cancer patients with insurance coverage faced out-of-pocket costs. Respondents with cancer patients had higher odds of encountering financial hardship if they are facing Work Limitations (OR = 2.714), Regular use of Medications (OR = 2.518), Hospital Stays (OR = 2.858), Declining Health (OR = 2.349), or were being covered under government health insurance (OR = 5.803) than respondents who did not have cancer, or suffered from mental health issues such as Depression (OR = 0.901).Cancer patients contend with increasing financial costs during their treatment. However, most newly diagnosed patients are not aware of these costs and are given few resources to handle them.

PURPOSE The University of Kansas Cancer Center (KU Cancer Center) recently developed a data warehouse to Organize and Prioritize Trends to Inform KU Cancer Center (OPTIK). The OPTIK database aggregates and standardizes data collected across the bistate catchment area served by the KU Cancer Center. To improve the usability of the OPTIK database, we developed shinyOPTIK, a user-friendly, interactive web application for visualizing cancer risk factor and mortality rate data across the KU Cancer Center Catchment area. METHODS Data in the OPTIK database were first consolidated at the county level across the KU Cancer Center catchment area. Next, the shinyOPTIK development team met with the KU Cancer Center leadership to discuss the needs and priorities of the shinyOPTIK web application. shinyOPTIK was developed under the R Shiny framework and consists of a user interface (ui.R) and a web server (server.R). At present, s hinyOPTIK can be used to generate county-level geographical heatmaps; bar plots of demographic, screening, and risk factors; and line plots to visualize temporal trends at different Rural-Urban Continuum Codes (RUCCs), rural-urban status, metropolitan, or county levels across the KU Cancer Center catchment area. RESULTS Two examples, adult obesity prevalence and lung cancer mortality, are presented to illustrate how researchers can use shinyOPTIK. Each example is accompanied by post hoc visualizations to help explain key observations in terms of rural-urban disparities. CONCLUSION Although shinyOPTIK was developed to improve understanding of spatial and temporal trends across the population served by the KU Cancer Center, our hope is that the description of the steps involved in the creation of this tool along with open-source code for our application provided herein will serve as a guide for other research centers in the development of similar tools.

BACKGROUND The National Clinical Trials Network program conducts phase 2 or phase 3 treatment trials across all National Cancer Institute’s designated cancer centers. Participant accrual across these clinical trials is a critical factor in deciding their success. Cancer centers that cater to rural populations, such as The University of Kansas Cancer Center, have an additional responsibility to ensure rural residents have access and are well represented across these studies. OBJECTIVE There are scant data available regarding the factors that act as barriers to the accrual of rural residents in these clinical trials. This study aims to use electronic screening logs that were used to gather patient data at several participating sites in The Kansas University of Cancer Center’s Catchment area. METHODS Screening log data were used to assess what clinical trial participation barriers are faced by these patients. Additionally, the differences in clinical trial participation barriers were compared between rural and urban participating sites. RESULTS Analysis revealed that the hospital location rural urban category, defined as whether the hospital was in an urban or rural setting, had a medium effect on enrolment of patients in breast cancer and lung cancer trials (Cohen d=0.7). Additionally, the hospital location category had a medium effect on the proportion of recurrent lung cancer cases at the time of screening (d=0.6). CONCLUSIONS In consideration of the financially hostile nature of cancer treatment as well as geographical and transportation barriers, clinical trials extended to rural communities are uniquely positioned to alleviate the burden of nonmedical costs in trial participation. However, these options can be far less feasible for patients in rural settings. Since the number of patients with cancer who are eligible for a clinical trial is already limited by the stringent eligibility criteria required of such a complex disease, improving accessibility for rural patients should be a greater focus in health policy.

Background The University of Kansas Alzheimer’s Disease Center (KU ADC) maintains several large databases to track participant recruitment, enrollment, and capture various research-related activities. It is challenging to manage and coordinate all the research-related activities. One of the crucial activities involves generating a consensus diagnosis and communicating with participants and their primary care providers. Process To effectively manage the cohort, the KU ADC utilizes a combination of open-source electronic data capture (EDC) (i.e. REDCap), along with other homegrown data management and analytic systems developed using R-studio and Shiny. Process evaluation In this article, we describe the method and utility of the user-friendly dashboard that was developed for the rapid reporting of dementia evaluations which allows clinical researchers to summarize recruitment metrics, automatically generate letters to both participants and healthcare providers, which ultimately help optimize workflows. Conclusions We believe this general framework would be beneficial to any institution that build reports and summarizing key metrics of their research from longitudinal databases.

Objective Managing registries with continual data collection poses challenges, such as following reproducible research protocols and guaranteeing data accessibility. The University of Kansas (KU) Alzheimer’s Disease Center (ADC) maintains one such registry: Curated Clinical Cohort Phenotypes and Observations (C3PO). We created an automated and reproducible process by which investigators have access to C3PO data. Materials and Methods Data was input into Research Electronic Data Capture. Monthly, data part of the Uniform Data Set (UDS), that is data also collected at other ADCs, was uploaded to the National Alzheimer’s Coordinating Center (NACC). Quarterly, NACC cleaned, curated, and returned the UDS to the KU Data Management and Statistics (DMS) Core, where it was stored in C3PO with other quarterly curated site-specific data. Investigators seeking to utilize C3PO submitted a research proposal and requested variables via the publicly accessible and searchable data dictionary. The DMS Core used this variable list and an automated SAS program to create a subset of C3PO. Results C3PO contained 1913 variables stored in 15 datasets. From 2017 to 2018, 38 data requests were completed for several KU departments and other research institutions. Completing data requests became more efficient; C3PO subsets were produced in under 10 seconds. Discussion The data management strategy outlined above facilitated reproducible research practices, which is fundamental to the future of research as it allows replication and verification to occur. Conclusion We created a transparent, automated, and efficient process of extracting subsets of data from a registry where data was changing daily.

Background Monitoring subject recruitment is key to the success of a clinical trial. Accordingly, researchers have developed accrual-monitoring tools to support the design and conduct of trials. At an institutional level, delays in identifying studies with high risk of accrual failure can lead to inefficient and costly trials with little chances of meeting study objectives. Comprehensive accrual monitoring is necessary to the success of the research enterprise. Methods This article describes the design and implementation of the University of Kansas Cancer Center Accrual Prediction Program, a web-based platform was developed to support comprehensive accrual monitoring and prediction for all active clinical trials. The Accrual Prediction Program provides information on accrual, including the predicted completion date, predicted number of accrued subjects during the pre-specified accrual period, and the probability of achieving accrual targets. It relies on a Bayesian accrual prediction model to combine protocol information with real-time trial enrollment data and disseminates results via web application. Results First released in 2016, the Accrual Prediction Program summarizes enrollment information for active studies categorized by various trial attributes. The web application supports real-time evidence-based decision making for strategic resource allocation and study management of over 120 ongoing clinical trials at the University of Kansas Cancer Center. Conclusion The Accrual Prediction Program makes accessing comprehensive accrual information manageable at an institutional level. Cancer centers or even entire institutions can reproduce the Accrual Prediction Program to achieve real-time comprehensive monitoring and prediction of subject accrual to aid investigators and administrators in the design, conduct, and management of clinical trials.

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR. Here, we propose Relevant Word Order Vectorization (RWOV) to aid with structuring. RWOV is based on finding the positional relationship between the most relevant words to predicting the class of a text. This facilitates machine learning algorithms to use the interaction of not just keywords but positional dependencies (e.g. a relevant word occurs 5 relevant words before some term of interest). As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center, comparing RWOV to other methods using the F1 score and AUC. RWOV performed as well as, or better than other methods in all but one case. For F1 score, RWOV had a clear edge on most tasks. AUC tended to be closer, but for HER2, RWOV was significantly better for most comparisons. These results suggest RWOV should be further developed for EHR-related NLP.

BACKGROUND As the cost of clinical trials continues to rise, novel approaches are required to ensure ethical allocation of resources. Multisite trials have been increasingly utilized in phase 1 trials for rare diseases and in phase 2 and 3 trials to meet accrual needs. The benefits of multisite trials include easier patient recruitment, expanded generalizability, and more robust statistical analyses. However, there are several problems more likely to arise in multisite trials, including accrual inequality, protocol nonadherence, data entry mistakes, and data integration difficulties. OBJECTIVE The Biostatistics & Data Science department at the University of Kansas Medical Center developed a clinical trial management system (comprehensive research information system [CRIS]) specifically designed to streamline multisite clinical trial management. METHODS A National Institute of Child Health and Human Development–funded phase 3 trial, the ADORE (assessment of docosahexaenoic acid [DHA] on reducing early preterm birth) trial fully utilized CRIS to provide automated accrual reports, centralize data capture, automate trial completion reports, and streamline data harmonization. RESULTS Using the ADORE trial as an example, we describe the utility of CRIS in database design, regulatory compliance, training standardization, study management, and automated reporting. Our goal is to continue to build a CRIS through use in subsequent multisite trials. Reports generated to suit the needs of future studies will be available as templates. CONCLUSIONS The implementation of similar tools and systems could provide significant cost-saving and operational benefit to multisite trials. CLINICALTRIAL ClinicalTrials.gov NCT02626299; https://tinyurl.com/j6erphcj

INTRODUCTION Maternal-infant equilibrium occurs when cord blood docosahexaenoic acid (DHA) is less than or equal to maternal DHA at delivery. Equilibrium may be an indicator of sufficient DHA for optimal fetal and infant neurodevelopment. The purpose of this study was to test the effect of maternal DHA supplementation on equilibrium status and fetal neurodevelopment. METHODS Women enrolled between 12 and 20 weeks gestation and were randomized to 200 or 800 mg DHA/day until delivery. Maternal red blood cell (RBC) phospholipids were measured at enrollment, 32 weeks, delivery, and in cord blood at delivery. Fetal neurodevelopment was measured at 32 and 36 weeks gestation. Intent-to-treat analyses were conducted to test differences in equilibrium status by group. Fetal outcomes were assessed by equilibrium status and group. RESULTS Three hundred women enrolled and 262 maternal-infant dyads provided blood samples at delivery. No maternal-infant dyads with maternal RBC-DHA ≤ 6.96% at delivery achieved equilibrium. The incidence of equilibrium was significantly higher in the 800 mg group. There was no effect of maternal group or equilibrium status on fetal neurodevelopment. CONCLUSION The significance of maternal-infant DHA equilibrium remains unknown. Ongoing research will test the effect of treatment group, equilibrium, and nutrient status on infant behavior and brain function. IMPACT Pregnant women who received a higher dose of docosahexaenoic acid (DHA) were more likely to achieve maternal-infant DHA equilibrium at delivery. Equilibrium status had no effect on fetal neurodevelopment in this sample. While DHA is crucial for early life neurodevelopment, the significance of achieving maternal-infant equilibrium above the lower threshold is uncertain. There is a lower threshold of maternal DHA status where maternal-infant DHA equilibrium never occurs. The lack of equilibrium associated with low maternal DHA status may indicate insufficient maternal status for optimal placental transfer.

Background Several meta analyses have concluded n-3 fatty acids, including docosahexaenoic acid (DHA), reduce early preterm birth (EPB, 0.90). Interpretation Clinicians could consider prescribing 1000 mg DHA daily during pregnancy to reduce EPB in women with low DHA status if they are able to screen for DHA. Funding The National Institutes of Health Child Health and Human Development (NICHD) funded the study. Life's DHA™-S oil, DSM Nutritional Products LLC, Switzerland provided all capsules.

Objective : Patient reported outcomes (PRO) are widely used in quality of life (QOL) studies, health outcomes research, and clinical trials. The importance of PRO has been advocated by health authorities. Patient Reported Outcomes Measurement Information System (PROMIS®) is a collection of standardized measures of PROs using Item Response Theory (IRT). However, in clinical trials with PROs as endpoints, observed scores are routinely used for power estimation rather than IRT scores. This paper aims to fill this gap and estimate power in a two-arm clinical trials with PROMIS measures as endpoints with IRT model. Study Design and Setting : We conducted a series of simulations to study the IRT power with validated PROMIS measures controlling factors including sample size, effect size, number of items, and missing data proportion. Results : Our results showed that sample size, effect size, and number of items are important indicators of IRT based power estimation for PROMIS measures. When effect size is small and sample size is limited, IRT model provides higher power than the closed form formula. Conclusion : IRT based simulation should be used for power estimation in two-armed clinical, especially when there is small effect size or small sample size.

Background: Several meta analyses have concluded n-3 fatty acids, including docosahexaenoic acid (DHA), reduce early preterm birth (0.90). Interpretation: Physicians should consider prescribing 1000 mg DHA daily during pregnancy to reduce early preterm birth, particularly in women entering pregnancy with low DHA status. Trial Registration: The study is registered with ClinicalTrials.gov (NCT02626299) and closed to enrollment. Funding Statement: National Institutes of Health Child Health and Human Development (NICHD) R01HD083292. Declaration of Interests: SEC has received honorariums for presentations about DHA in infancy and pregnancy. CJV is an employee of RB Nutrition, which produces infant formulas and supplements, however, RB had no involvement in the study execution or analysis. She conducted this study through her role as an Adjunct Professor at The University of Cincinnati. The other authors have no competing interests. Ethics Approval Statement: The University of Kansas Medical Center granted approval under a central IRB with reliance by the other institutions (STUDY00003455). All participants gave written consent.

One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant information to the publications is done manually. This manuscript describes the efforts to transition KU Cancer Center’s publication gathering process from a heavily manual to a more automated and efficient process. To achieve this transition in the most customized and cost-effective manner, a homegrown, automated system was developed using open source API among other software. When comparing the automated and the manual processes over several years of data, publication search and retrieval time dropped from an average of 59 h to 35 min, which would amount to a cost savings of several thousand dollars per year. The development and adoption of an automated publications search process can offer research centers great potential for less-error prone results with a savings in time and cost.

The primary goal for any clinical trial after it receives a funding notification is to receive regulatory approval and initiate the trial for recruitment. Every trial must go through documentation and regulatory process before it can start recruiting participants and collecting data; this initial process of review and approval is known as the study start-up process (SSU). We evaluated the average time taken for studies to receive approvals. Using data from clinical trials conducted at the University of Kansas Medical Center, various times to reach the start of the study were calculated based on the dates of individual study. The results of this analysis showed that chart review studies and investigator-initiated trials had a shorter time to activation than other types of studies. Additionally, single-center studies had a shorter activation time than multi-center studies. The analysis also demonstrated that the overall processing time consistently had been reduced over time., Highlights • The 2018 year’s trend shows reduced time to study start. • SSU process for non-cancer trial on an average requires four to six months. • The activation time of the SSU process varied for different study types and scopes.

Background: Background: An investigational pharmacy is responsible for all tasks related to receiving, storing, and dispensing of any investigational drugs. Traditional methods of inventory and protocol tracking on paper binders are very tedious and could be error-prone. Objective: To evaluate the utilization of the IDS to efficiently manage the inventory within an investigational Pharmacy. We hypothesize that the IDS will reduce the drug processing time. Methods: Our pharmacy tracked the drug processing time before and after using the IDS including the receiving, dispensing, and inventory. As part of the receiving the study drug pharmacists tracked the time it took a pharmacist to complete the tasks of logging the study drug before and after the implementation of the IDS system. In addition, the pharmacy also timed the process for drug dispensing and a full investigational drug inventory check. Wilcoxon signed-rank test was used to compare the difference in the meantime of total processing before and after the IDS. Results: Utilization of the IDS system showed significant reduction in processing time, and improvement of efficiency in inventory management. Additionally, the usability survey of the IDS demonstrated that the IDS system helped pharmacists capture data consistently across every clinical trial. Conclusion: Our results demonstrates how technology helps pharmacists to focus on their actual day to day medication-related tasks rather than worrying about other operational aspects. Informatics team continues to further enhance the features such as monitor portal, and features related to finance – generation of invoices, billing reconciliation, etc.

Anticoagulant-associated traumatic intracranial hemorrhage (tICrH) is a devastating injury with high morbidity and mortality. For survivors, treating clinicians face the dilemma of restarting oral anticoagulation with scarce evidence to guide them. Thromboembolic risk is high from the bleeding event, patients’ high baseline risks, that is, the pre-existing indication for anticoagulation, and the risk of immobility after the bleeding episode. This must be balanced with potentially devastating hematoma expansion or new hemorrhagic lesions. Retrospective evidence and expert opinion support restarting oral anticoagulants in most patients with tICrH, but timing is uncertain. Researchers have failed to make clear distinctions between tICrH and spontaneous intracranial hemorrhage (sICrH), which have differing natural histories. While both appear to benefit from restarting, sICrH has a higher rebleeding risk and similar or lower thrombotic risk. Clinical equipoise on restarting is also divergent. In sICrH, equipoise is centered on whether to restart. In tICrH, it is centered on when. Several prospective randomized clinical trials are ongoing or about to start to examine the risk–benefit of restarting. Most of them are restricted to patients with sICrH, with antiplatelet control groups. Most are also restricted to direct oral anticoagulants (DOACs), as they are associated with a lower overall risk of ICrH. There is some overlap with tICrH via subdural hematoma, and one trial is specific to restart timing with DOACs in only traumatic cases. This is a narrative review of the current evidence for restarting anticoagulation and restart timing after tICrH along with a summary of the ongoing and planned clinical trials.

An increasingly diversified demographic landscape in rural and urban America warrants the attention of The University of Kansas Cancer Center (KU Cancer Center) researchers, clinicians, outreach staff and administrators as the institution assesses ways to reach its expansive, bi-state catchment area. Within the counties of the KU Cancer Center catchment area, patient level and public health data are available and categorized by varying geographic regional boundaries. Multiple data sources and different data collection processes complicate summarizing catchment area data. A curated data warehouse that retrieves and structures the data, with a common denominator, can support meaningful use of the data in a standard and consistent format. The KU Cancer Center built a data warehouse to Organize and Prioritize Trends to Inform KU Cancer Center (OPTIK), which functions to streamline the process of synthesizing data regarding Kansas and Missouri demographics, cancer risk factors and incidence and mortality rates. OPTIK standardizes these diverse data sources to enable analyses of the cancer burden at local, regional and national levels while upholding a strict standard of patient privacy. The OPTIK database enables researchers to use available data and create heat maps and other visualizations to aid in funding proposals, presentations and research activities. Furthermore, using knowledge provided by OPTIK, the KU Cancer Center is able to prioritize action items for research and outreach and more effectively communicate the impact of those efforts.