Search

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.

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.

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.

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.

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.

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.

Data used to determine patient eligibility for cancer clinical trials often come from disparate sources that are typically maintained by different groups within an institution, use differing technologies, and are stored in different formats. Collecting data and resolving inconsistencies across sources increase the time it takes to screen eligible patients, potentially delaying study completion. To address these challenges, the Biostatistics and Informatics Shared Resource at The University of Kansas Cancer Center developed the Curated Cancer Clinical Outcomes Database (C3OD). C3OD merges data from the electronic medical record, tumor registry, bio-specimen and data registry, and allows querying through a single unified platform. By centralizing access and maintaining appropriate controls, C3OD allows researchers to more rapidly obtain detailed information about each patient in order to accelerate eligibility screening. This case report describes the design of this informatics platform as well as initial assessments of its reliability and usability.

Heart failure (HF), the leading cause of morbidity and mortality in the US, affects 6.6 million adults with an estimated additional 3 million people by 2030. More than 50% of HF patients have heart failure with preserved left ventricular ejection fraction (HFpEF). These patients have impaired cardiac muscle relaxation and diastolic filling, which investigators have associated with cellular energetic impairment. Patients with HFpEF experience symptoms of: (1) fatigue; (2) shortness of breath; and (3) swelling (edema) of the lower extremities. However, current HF guidelines offer no effective treatment to address these underlying pathophysiologic mechanisms. Thus, we propose a biobehavioral symptom science study using ubiquinol and D-ribose (therapeutic interventions) to target mitochondrial bioenergetics to reduce the complex symptoms experienced by patients with HFpEF. Using a randomized, double-blind, placebo-controlled design, the overall objective is to determine if administering ubiquinol and/or D-ribose to HFpEF patients for 12 weeks would decrease the severity of their complex symptoms and improve their cardiac function. The measures used to assess patients’ perceptions of their health status and level of vigor (energy) will be the Kansas City Cardiomyopathy Questionnaire (KCCQ) and Vigor subscale of the Profile of Mood States. The 6-min walk test will be used to test exercise tolerance. Left ventricular diastolic function will be assessed using innovative advanced echocardiography software called speckle tracking. We will measure B-type natriuretic peptides (secreted from ventricles in HF) and lactate/ATP ratio (measure of cellular energetics). Ubiquinol (active form of Coenzyme Q10) and D-ribose are two potential treatments that can positively affect cellular energetic impairment, the major underlying mechanism of HFpEF. Ubiquinol, the reduced form of CoQ10, is more effective in adults over the age of 50. In patients with HFpEF, mitochondrial deficiency of ubiquinol results in decreased adenosine triphosphate (ATP) synthesis and reduced scavenging of reactive oxygen species. D-ribose is a substrate required for ATP synthesis and when administered has been shown to improve impaired myocardial bioenergetics. Therefore, if the biological underpinning of deficient mitochondrial ATP in HFpEF is not addressed, patients will suffer major symptoms including lack of energy, fatigue, exertional dyspnea, and exercise intolerance. ClinicalTrials.gov Identifier: NCT03133793 ; Data of Registration: April 28, 2017.

Highlights • An abnormal mammogram was positively associated with family history and hormonal contraceptives. • It was found that older patients, those who were married, are more likely to get screened. • Those who had their first pregnancy at an older age are at higher risk of having an abnormal mammogram., Breast cancer screening guidelines serve as crucial evidence-based recommendations in deciding when to begin regular screenings. However, due to developments in breast cancer research and differences in research interpretation, screening guidelines can vary between organizations and within organizations over time. This leads to significant lapses in adopting updated guidelines, variable decision making between physicians, and unnecessary screening for low to moderate risk patients (Jacobson and Kadiyala, 2017; Corbelli et al., 2014). For analysis, risk factors were assessed for patient screening behaviors and results. The outcome variable for the first analysis was whether the patient had undergone screening. The risk factors considered were age, marital status, education level, rural versus urban residence, and family history of breast cancer. The outcome variable for the second analysis was whether patients who had undergone breast cancer screening presented abnormal results. The risk factors considered were age, Body Mass Index, family history, smoking and alcohol status, hormonal contraceptive use, Hormone Replacement Therapy use, age of first pregnancy, number of pregnancies (parity), age of first menses, rural versus urban residence, and whether or not patients had at least one child. Logistic regression analysis displayed strong associations for both outcome variables. Risk of screening nonattendance was negatively associated with age as a continuous variable, age as a dichotomous variable, being married, any college education, and family history. Risk of one or more abnormal mammogram findings was positively associated with family history, and hormonal contraceptive use. This procedure will be further developed to incorporate additional risk factors and refine the analysis of currently implemented risk factors.

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce...

To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD). In this report, we describe the utilization of C3OD to optimally retrieve and dispense biospecimen samples using these 3 data sources and demonstrate how C3OD greatly increases the efficiency of obtaining biospecimen samples for the researchers.

Importance Cryptogenic sensory polyneuropathy (CSPN) is a common generalized slowly progressive neuropathy, second in prevalence only to diabetic neuropathy. Most patients with CSPN have significant pain. Many medications have been tried for pain reduction in CSPN, including antiepileptics, antidepressants, and sodium channel blockers. There are no comparative studies that identify the most effective medication for pain reduction in CSPN. Objective To determine which medication (pregabalin, duloxetine, nortriptyline, or mexiletine) is most effective for reducing neuropathic pain and best tolerated in patients with CSPN. Design, Setting, and Participants From December 1, 2014, through October 20, 2017, a bayesian adaptive, open-label randomized clinical comparative effectiveness study of pain in 402 participants with CSPN was conducted at 40 neurology care clinics. The trial included response adaptive randomization. Participants were patients with CSPN who were 30 years or older, with a pain score of 4 or greater on a numerical rating scale (range, 0-10, with higher scores indicating a higher level of pain). Participant allocation to 1 of 4 drug groups used the utility function and treatment’s sample size for response adaptation randomization. At each interim analysis, a decision was made to continue enrolling (up to 400 participants) or stop the whole trial for success (80% power). Patient engagement was maintained throughout the trial, which helped guide the study and identify ways to communicate and disseminate information. Analysis was performed from December 11, 2015, to January 19, 2018. Interventions Participants were randomized to receive nortriptyline (n = 134), duloxetine (n = 126), pregabalin (n = 73), or mexiletine (n = 69). Main Outcomes and Measures The primary outcome was a utility function that was a composite of the efficacy (participant reported pain reduction of ≥50% from baseline to week 12) and quit (participants who discontinued medication) rates. Results Among the 402 participants (213 men [53.0%]; mean [SD] age, 60.1 [13.4] years; 343 White [85.3%]), the utility function of nortriptyline was 0.81 (95% bayesian credible interval [CrI], 0.69-0.93; 34 of 134 [25.4%] efficacious; and 51 of 134 [38.1%] quit), of duloxetine was 0.80 (95% CrI, 0.68-0.92; 29 of 126 [23.0%] efficacious; and 47 of 126 [37.3%] quit), pregabalin was 0.69 (95% CrI, 0.55-0.84; 11 of 73 [15.1%] efficacious; and 31 of 73 [42.5%] quit), and mexiletine was 0.58 (95% CrI, 0.42-0.75; 14 of 69 [20.3%] efficacious; and 40 of 69 [58.0%] quit). The probability each medication yielded the highest utility was 0.52 for nortriptyline, 0.43 for duloxetine, 0.05 for pregabalin, and 0.00 for mexiletine. Conclusions and Relevance This study found that, although there was no clearly superior medication, nortriptyline and duloxetine outperformed pregabalin and mexiletine when pain reduction and undesirable adverse effects are combined to a single end point. Trial Registration ClinicalTrials.gov Identifier:NCT02260388

1. Abstract Early phase clinical trials are the first step in testing new medications and therapeutics developed by clinical and biomedical investigators. These trials aim to find a safe dose of a newly targeted drug (phase I) or find out more about the side effects and early signals of treatment efficacy (phase II). In a research institute, many biomedical investigators in oncology are encouraged to initiate such trials early in their careers as part of developing their research portfolio. These investigator-initiated trials (IITs) are funded internally by the University of Kansas Cancer Center or partially funded by pharmaceutical companies. As financial, administrative, and practical considerations play an essential role in the successful completion of IITs, it is imperative to efficiently allocate resources to plan, design, and execute these studies within the allotted time. This manuscript describes monitoring tools and processes to improve the efficiency, cost-effectivness, and reliability of IITs. The contributions of this team to processes such as: participant recruitment, feasibility analysis, clinical trial design, accrual monitoring, data management, interim analysis support, and final analysis and reporting are described in detail. This manuscript elucidates how, through the aid of technology and dedicated personnel support, the efficiency of IIT-related processes can be improved. Early results of these initiatives look promising, and the Biostatistics and Informatics team intends to continue fostering innovative methodologies to enhance cancer research by improving the efficiency of IITs.

In the last few decades, the number of trials using Bayesian methods has grown rapidly. Publications prior to 1990 included only three clinical trials that used Bayesian methods, but that number quickly jumped to 19 in the 1990s and to 99 from 2000 to 2012. While this literature provides many examples of Bayesian Adaptive Designs (BAD), none of the papers that are available walks the reader through the detailed process of conducting a BAD. This paper fills that gap by describing the BAD process used for one comparative effectiveness trial (Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations) that can be generalized for use by others. A BAD was chosen with efficiency in mind. Response-adaptive randomization allows the potential for substantially smaller sample sizes, and can provide faster conclusions about which treatment or treatments are most effective. An Internet-based electronic data capture tool, which features a randomization module, facilitated data capture across study sites and an in-house computation software program was developed to implement the response-adaptive randomization. A process for adapting randomization with minimal interruption to study sites was developed. A new randomization table can be generated quickly and can be seamlessly integrated in the data capture tool with minimal interruption to study sites. This manuscript is the first to detail the technical process used to evaluate a multisite comparative effectiveness trial using adaptive randomization. An important opportunity for the application of Bayesian trials is in comparative effectiveness trials. The specific case study presented in this paper can be used as a model for conducting future clinical trials using a combination of statistical software and a web-based application. ClinicalTrials.gov Identifier: NCT02260388 , registered on 6 October 2014