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Purpose: We examined the association between the prevalence of certified RNs in CCUs on RN workgroup satisfaction with autonomy, decision making, and nurse-physician interaction and perceived unit quality of patient [...]

Background: Bayesian inference provides a formal framework for updating knowledge by combining prior knowledge with current data. Over the past 10 years, the Bayesian paradigm has become a popular analytic tool in health research. Although the nursing literature contains examples of Bayes' theorem applications to clinical decision making, it lacks an adequate introduction to Bayesian data analysis. Methods: Bayesian data analysis is introduced through a fully Bayesian model for determining the efficacy of tai chi as an illustrative example. The mechanics of using Bayesian models to combine prior knowledge, or data from previous studies, with observed data from a current study are discussed. Results: The primary outcome in the illustrative example was physical function. Three prior probability distributions (priors) were generated for physical function using data from a similar study found in the literature. Each prior was combined with the likelihood from observed data in the current study to obtain a posterior probability distribution. In each case, the posterior distribution showed that the probability that the control group is better than the tai chi treatment group was low. Discussion: Bayesian analysis is a valid technique that allows the researcher to manage varying amounts of data appropriately. As advancements in computer software continue, Bayesian techniques will become more accessible. Researchers must educate themselves on applications for Bayesian inference, as well as its methods and implications for future research. [ABSTRACT FROM AUTHOR]

The project reported here is the first in a series of cost analyses regarding the care planning process among 107 facilities. Process-based costing strategies and data envelopment analyses identified nursing facilities with efficient and less-efficient care planning processes. Having more people and more time devoted to the care planning process did not assure quality or efficiency. Efficiency varied across the nursing facilities and was not related to number of beds, profit status, or location; however, Medicare-certified facilities were less likely to be efficient. [ABSTRACT FROM AUTHOR]

Abstract Background To evaluate institutional nursing care performance in the context of national comparative statistics (benchmarks), approximately one in every three major healthcare institutions (over 1,800 hospitals) across the United States, have joined the National Database for Nursing Quality Indicators® (NDNQI®). With over 18,000 hospital units contributing data for nearly 200 quantitative measures at present, a reliable and efficient input data screening for all quantitative measures for data quality control is critical to the integrity, validity, and on-time delivery of NDNQI reports. Methods With Monte Carlo simulation and quantitative NDNQI indicator examples, we compared two ad-hoc methods using robust scale estimators, Inter Quartile Range (IQR) and Median Absolute Deviation from the Median (MAD), to the classic, theoretically-based Minimum Covariance Determinant (FAST-MCD) approach, for initial univariate outlier detection. Results While the theoretically based FAST-MCD used in one dimension can be sensitive and is better suited for identifying groups of outliers because of its high breakdown point, the ad-hoc IQR and MAD approaches are fast, easy to implement, and could be more robust and efficient, depending on the distributional property of the underlying measure of interest. Conclusion With highly skewed distributions for most NDNQI indicators within a short data screen window, the FAST-MCD approach, when used in one dimensional raw data setting, could overestimate the false alarm rates for potential outliers than the IQR and MAD with the same pre-set of critical value, thus, overburden data quality control at both the data entry and administrative ends in our setting.

Abstract Background Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings. Methods Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI®) for the 3rd quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect. Results Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter. Conclusions The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects.

Abstract Background Gastric Emptying Scintigraphy (GES) at intervals over 4 hours after a standardized radio-labeled meal is commonly regarded as the gold standard for diagnosing gastroparesis. The objectives of this study were: 1) to investigate the best time point and the best combination of multiple time points for diagnosing gastroparesis with repeated GES measures, and 2) to contrast and cross-validate Fisher's Linear Discriminant Analysis (LDA), a rank based Distribution Free (DF) approach, and the Classification And Regression Tree (CART) model. Methods A total of 320 patients with GES measures at 1, 2, 3, and 4 hour (h) after a standard meal using a standardized method were retrospectively collected. Area under the Receiver Operating Characteristic (ROC) curve and the rate of false classification through jackknife cross-validation were used for model comparison. Results Due to strong correlation and an abnormality in data distribution, no substantial improvement in diagnostic power was found with the best linear combination by LDA approach even with data transformation. With DF method, the linear combination of 4-h and 3-h increased the Area Under the Curve (AUC) and decreased the number of false classifications (0.87; 15.0%) over individual time points (0.83, 0.82; 15.6%, 25.3%, for 4-h and 3-h, respectively) at a higher sensitivity level (sensitivity = 0.9). The CART model using 4 hourly GES measurements along with patient's age was the most accurate diagnostic tool (AUC = 0.88, false classification = 13.8%). Patients having a 4-h gastric retention value >10% were 5 times more likely to have gastroparesis (179/207 = 86.5%) than those with ≤10% (18/113 = 15.9%). Conclusions With a mixed group of patients either referred with suspected gastroparesis or investigated for other reasons, the CART model is more robust than the LDA and DF approaches, capable of accommodating covariate effects and can be generalized for cross institutional applications, but could be unstable if sample size is limited.

Abstract Background Mortality from breast cancer has increased among American Indian/Alaskan Native (AI/AN) women. Despite this alarming reality, AI/AN women have some of the lowest breast cancer screening rates. Only 37% of eligible AI/AN women report a mammogram within the last year and 52% report a mammogram within the last two years compared to 57% and 72% for White women. The experiences and satisfaction surrounding mammography for AI/AN women likely are different from that of women of other racial/ethnic groups, due to cultural differences and limited access to Indian Health Service sponsored mammography units. The overall goals of this study are to identify and understand the mammography experiences and experiential elements that relate to satisfaction or dissatisfaction with mammography services in an AI/AN population and to develop a culturally-tailored AI/AN mammography satisfaction survey. Methods and Design The three project aims that will be used to guide this work are: 1) To compare the mammography experiences and satisfaction with mammography services of Native American/Alaska Native women with that of Non-Hispanic White, Hispanic, and Black women, 2) To develop and validate the psychometric properties of an American Indian Mammography Survey, and 3) To assess variation among AI/AN women's assessments of their mammography experiences and mammography service satisfaction. Evaluations of racial/ethnic differences in mammography patient satisfaction have received little study, particularly among AI/AN women. As such, qualitative study is uniquely suited for an initial examination of their experiences because it will allow for a rich and in-depth identification and exploration of satisfaction elements. Discussion This formative research is an essential step in the development of a validated and culturally tailored AI/AN mammography satisfaction assessment. Results from this project will provide a springboard from which a maximally effective breast cancer screening program to benefit AI/AN population will be developed and tested in an effort to alter the current breast cancer-related morbidity and mortality trajectory among AI/AN women.

Abstract Background American Indians (AI) have the highest smoking rates of any ethnic group in the US (40.8%), followed most closely by African Americans (24.3%) and European Americans (23.6%). AI smokers also have more difficulty quitting smoking compared to other ethnic groups, evidenced by their significantly lower quit ratios, and are among the least successful in maintaining long term abstinence. While health disparities like these have existed for years among AI, the epidemiology of smoking and nicotine dependence has not been optimally described among this underserved population. Our overarching hypothesis is that the susceptibility of AI to cigarette smoking and nicotine dependence and its consequences has both an underlying nicotine metabolism component as well as psychosocial, cultural, and environment causes. We are well-positioned to explore this issue for the first time in this population. Our objective is to establish a cohort of AI tribal college/university students to determine the predictors of smoking initiation (non-use to experimentation), progression (experimentation to established use), and cessation (established use to cessation). Much of what is known about the process of smoking initiation and progression comes from quantitative studies with non-Native populations. Information related to smoking use among AI tribal college/university (TCU) students is entirely unknown and critically needs further investigation. This study will be the first of its kind among AI college students who are at the highest risk among all ethnic groups for tobacco dependence. Methods/design First year students at Haskell Indian Nations University in Kansas will be recruited over four consecutive years and will be surveyed annually and repeatedly through year 5 of the study. We will use both longitudinal quantitative surveys and qualitative focus group methods to examine key measures and determinants of initiation and use among this high risk group.

Abstract Background The National Database for Nursing Quality Indicators® (NDNQI®) was established in 1998 to assist hospitals in monitoring indicators of nursing quality (eg, falls and pressure ulcers). Hospitals participating in NDNQI transmit data from nursing units to an NDNQI data repository. Data are summarized and published in reports that allow participating facilities to compare the results for their units with those from other units across the nation. A disadvantage of this reporting scheme is that the sampling variability is not explicit. For example, suppose a small nursing unit that has 2 out of 10 (rate of 20%) patients with pressure ulcers. Should the nursing unit immediately undertake a quality improvement plan because of the rate difference from the national average (7%)? Methods In this paper, we propose approximating 95% credible intervals (CrIs) for unit-level data using statistical models that account for the variability in unit rates for report cards. Results Bayesian CrIs communicate the level of uncertainty of estimates more clearly to decision makers than other significance tests. Conclusion A benefit of this approach is that nursing units would be better able to distinguish problematic or beneficial trends from fluctuations likely due to chance.

Three reports from the Institute of Medicine found that errors in hospital care were more common than previously thought; that health care delivery should be reorganized to improve the quality of care; and that, operationally, nurses have a critical role in securing patient safety. Now the contribution of nursing to the reduction of adverse events must be established empirically, so that nursing-sensitive indicators can be incorporated in such health care-improvement strategies as public reporting of hospital quality and performance-based payment systems. This article reviews what is known from previous nursing outcomes research and identifies gaps in the current state of knowledge. It then describes the contribution to research that can be made through the National Database of Nursing Quality Indicators TM (NDNQI®). Next it reports an NDNQI study that found three nursing workforce characteristics to be related significantly to patient outcomes: total nursing hours per patient day, percentage of hours supplied by RNs, and years of experience in nursing, and concludes with a discussion of the implications of these findings for both for nursing administrators and outcomes-based, quality-improvement initiatives. © 2007 OJIN: The Online Journal of Issues in Nursing Article published September 30, 2007 [ABSTRACT FROM AUTHOR]

Background: The combination of bazedoxifene 20 mg (BZA) and conjugated estrogens 0.45 mg (CE) marketed as Duavee® is approved for vasomotor symptom relief and osteoporosis prevention. Our pilot study suggested it had potential breast cancer risk reduction, and we proposed a multisite Phase IIB primary prevention trial assessing change in breast imaging and tissue risk biomarkers. By the time funding was acquired in February 2021, Duavee® was unavailable with an uncertain return date. A redesign was needed to salvage the study., Methods: The basic trial design was minimally altered. Women age 45-64 at elevated risk for breast cancer with vasomotor symptoms and no menses for at least 2 months have mammography, phlebotomy, and benign breast tissue sampling before and after 6 months of intervention. However, instead of Duavee® (single pill) vs placebo, women are randomized to 6 months of BZA + CE vs Waitlist. Those initially randomized to Waitlist can receive BZA + CE after 6 months. The primary endpoint is between arm difference in change in a fully automated measure of mammographic density with blood and tissue-based secondary endpoints., Outcomes: Accrual initiation was delayed due to contractual difficulties surrounding BZA importation during COVID-19 and deploying a fully automated method (Volpara®) to assess the primary endpoint. To accommodate this delay, a mid-grant no cost extension along with amended eligibility requirements were employed. 61/120 participants needed were entered in the initial 27 months of accrual and 37 months of funding. Despite a late start, accrual is likely to be completed within the funding period., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background and Purpose: Cerebral infarction remains an important cause of death or disability in patients with aneurysmal subarachnoid hemorrhage (SAH). The prevalence, trends, and outcomes of cerebral infarction in patients with aneurysmal SAH at a national level are not known., Methods: We identified the proportion of patients who develop cerebral infarction (ascertained using validated methodology) among patients with aneurysmal SAH and annual trends using the Nationwide Inpatient Sample (NIS) from 2016 to 2021. We analyzed the effect of cerebral infarction on in-hospital mortality, routine discharge without palliative care (based on discharge disposition), poor outcome defined by the NIS SAH outcome measure, and length and costs of hospitalization after adjusting for potential confounders., Results: A total of 35,305 (53.6%) patients developed cerebral infarction among 65,840 patients with aneurysmal SAH over a 6-year period. There was a trend toward an increase in the proportion of patients who developed cerebral infarction from 51.5% in 2016 to 56.1% in 2021 (p trend p<.001). Routine discharge was significantly lower (30.5% vs. 37.8%, odds ratio [OR] 0.82, 95% confidence interval [CI] 0.75-0.89, p<.001), and poor outcome defined by NIS-SAH outcome measure was significantly higher among patients with cerebral infarction compared with those without cerebral infarction (67.4% vs. 59.3%, OR 1.29, 95% CI 1.18-1.40, p<.001). There was no difference in in-hospital mortality (13.0% vs. 13.6%, OR 0.94, 95% CI 0.85-1.05, p = .30). The length of stay (median 18 days [interquartile range [IQR] 13-25] vs. 14 days [IQR 9-20]), coefficient 3.04, 95% CI 2.44-3.52 and hospitalization cost (median $96,823 vs. $71,311, coefficient 22,320, 95% CI 20,053-24,587) were significantly higher among patients who developed cerebral infarction compared with those who did not develop cerebral infarction., Conclusions: Cerebral infarction was seen in 54% of the patients with a trend toward an increase in the affected proportion of patients with aneurysmal SAH. Patients with cerebral infarction had higher rates of adverse outcomes and required higher resources during hospitalization., (© 2024 American Society of Neuroimaging.)

Background: Clinical practice recommendations guide healthcare decisions. This study aims to evaluate the strength and quality of evidence supporting the American Heart Association (AHA)/American Stroke Association (ASA) guidelines for aneurysmal subarachnoid hemorrhage (aSAH) and spontaneous intracerebral hemorrhage (ICH)., Methods: We reviewed the current AHA/ASA guidelines for aSAH and spontaneous ICH and compared with previous guidelines. Guidelines were classified based on the Class of recommendation (COR) and Level of evidence (LOE). COR signifies recommendation strength (COR 1: Strong; COR 2a: Moderate; COR 2b: Weak; COR 3: No Benefit/Harm), while LOE denotes evidence quality (LOE A: High-Quality; LOE B-NR: Moderate-Quality, Not Randomized; LOE B-R: Moderate-Quality, Randomized; LOE C-EO: Expert Opinion; LOE C-LD: Limited Data)., Results: For aSAH, we identified 84 recommendations across 15 guideline categories. Of these, 31% were classified as COR I, 30% as COR 2a, 17% as COR 2b, and 18% as COR 3. In terms of LOE, 7% were based on LOE A, 10% on LOE B-R, 65% on LOE B-NR, 14% on LOE C-LD, and 5% on LOE C-EO. Compared to previous guidelines, there was a 46% decrease in LOE A, a 45% increase in LOE B, and an 11% decrease in LOE C. For spontaneous ICH, 124 guidelines were identified across 31 guideline categories. Of these, 28% were COR I, 32% COR 2b, and 9% COR 3. For LOE, 4% were based on LOE A, 35% on LOE B-NR, and 42% on LOE C-LD. Compared to previous guidelines, there was a 78% decrease in LOE A, an 82% increase in LOE B, and a 14% increase in LOE C. This analysis highlights that less than a third of AHA/ASA guidelines are classified as the highest class of recommendation, with less than 10% based on the highest LOE., Conclusion: Less than a third of AHA/ASA guidelines on aSAH and spontaneous ICH are classified as the highest class of recommendation with less than 10% based on highest LOE. There appears to be a decrease in proportion of guidelines based on highest LOE in most recent guidelines., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024. Published by Elsevier Inc.)

Patient Reported Outcomes (PROs) 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. We propose this R shiny web application, PROpwr, that estimates power for two-arm clinical trials with PRO measures as endpoints using Item Response Theory (GRM: Graded Response Model) and simulations. PROpwr also supports the analysis of PRO data for convenience of estimating the effect size. There are seven function tabs in PROpwr: Frequentist Analysis, Bayesian Analysis, GRM power, T-test Power Given Sample Size, T-test Sample Size Given Power, Download, and References. PROpwr is user-friendly with point-and-click functions. PROpwr can assist researchers to analyze and calculate power and sample size for PRO endpoints in clinical trials without prior programming knowledge.

Background: Excessive gestational weight gain (GWG) is related to increased offspring fat accrual, and increased fat mass (FM) is related to obesity development. Prenatal DHA supplementation has been linked to lower levels of offspring FM; however, conflicting data exist., Objectives: This study aimed to determine if there is a protective effect of prenatal DHA supplementation on offspring fat accrual and adipose tissue deposition at 24 mo in offspring born to females who gain excessive weight compared with nonexcessive weight during pregnancy. We also explored if the effect of DHA dose on FM differed by offspring sex., Methods: Infants born to females who participated in the Assessment of DHA on Reducing Early Preterm Birth randomized controlled trial (ADORE) were recruited. In ADORE, females were randomly assigned to either a high or low prenatal DHA supplement. Offspring body composition and adipose tissue distribution were measured using dual-energy x-ray absorptiometry (DXA). GWG was categorized as excessive or not excessive based on clinical guidelines., Results: For total FM, there was a significant main effect for the DHA dose ( P = 0.03); however, the dose by GWG status was nonsignificant ( P = 0.44). Therefore, a higher prenatal DHA dose was related to greater offspring FM (622.9 g greater) and unrelated to GWG status. When investigating a DHA dose by sex effect, a significant main effect for DHA dose ( P = 0.01) was detected for central FM. However, no interaction was detected ( P = 0.98), meaning that both boys and girls had greater central FM if their mother was assigned to the higher DHA dose., Conclusions: Greater prenatal DHA supplementation was associated with greater offspring FM and adipose tissue distribution at 24 mo. It will be important to understand if these effects persist into childhood.This trial was registered at clinicaltrials.gov as NCT03310983., (© 2024 The Author(s).)

Background: Interventions to prevent excessive gestational weight gain (GWG) have had a limited impact on maternal and infant outcomes. Dietary fiber is a nutrient with benefits that counters many of the metabolic and inflammatory changes that occur during pregnancy. We will determine if a high dietary fiber (HFib) intervention provides benefit to maternal and infant outcomes., Methods and Design: Pregnant women will be enrolled in an 18-week intervention and randomized in groups of 6-10 women/group into the intervention or control group. Weekly lessons will include information on high-dietary fiber foods and behavior change strategies. Women in the intervention group will be given daily snacks high in dietary fiber (10-12 g/day) to facilitate increasing dietary fiber intake. The primary aim will assess between-group differences for the change in maternal weight, dietary fiber intake, dietary quality, and body composition during pregnancy and up to two months post-partum. The secondary aim will assess between-group differences for the change in maternal weight, dietary fiber intake, and dietary quality from two months to one year post-partum and infant body composition from birth to one-year-old., Discussion: Effective and simple intervention strategies to improve maternal and offspring outcomes are lacking. Changes during the perinatal period are related to the risk of disease development in the mother and offspring. However, it is unknown which changes can be successfully targeted to have a meaningful impact. We will test the effect of an intervention designed to counter many of the metabolic and inflammatory changes that occur during pregnancy., Ethics and Dissemination: The University of Kansas Medical Center Institutional Review Board (IRB) approved the study protocol (STUDY00145397). The results of the trial will be disseminated at conferences and in peer reviewed publications., Trial Registration: ClinicalTrials.gov ID: NCT04868110., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: The Symbol Digit Modalities Test (SDMT) is a highly sensitive neuropsychological tool used for the assessment of information processing speed (IPS) in various neurological disorders., State of the Art: In this review, we have focused on the current knowledge regarding the use of SDMT selectively in the evaluation of progressive multiple sclerosis (PMS) patients. A literature review was performed regarding the application of SDMT in PMS, with a focus on the primary progressive and secondary progressive subtypes. Relationships of diverse disease-associated factors with SDMT have been described, including disease course, imaging findings, molecular biomarkers, treatment and others., Clinical Implications: SDMT is a very useful and easily applicable instrument in the diagnostic armamentarium of neurologists and neuropsychologists. It is especially valuable in the evaluation of PMS patients, in whom the prevalence of IPS deficits is higher than in relapsing-remitting multiple sclerosis subjects or in healthy individuals., Future Directions: An emphasis should be laid on larger study groups and differentiating between individual PMS subtypes and their separate analysis in the context of cognitive assessment.

Background: Lung cancer is the leading cause of cancer related deaths. In Kansas, where coal-fired power plants account for 34% of power, we investigated whether hosting counties had higher age-adjusted lung cancer incidence rates. We also examined demographics, poverty levels, percentage of smokers, and environmental conditions using spatial analysis., Methods: Data from the Kansas Health Matters, and the Behavioral Risk Factor Surveillance System (2010-2014) for 105 counties in Kansas were analyzed. Multiple Linear Regression (MLR) assessed associations between potential risk factors and age-adjusted lung cancer incidence rates while Geographically Weighted Regression (GWR) examined regional risk factors., Results: Moran's I test confirmed spatial autocorrelation in age-adjusted lung cancer incidence rates (p<0.0003). MLR identified percentage of smokers, population size, and proportion of elderly population as significant predictors of age-adjusted lung cancer incidence rates (p<0.05). GWR showed positive associations between percentage of smokers and age-adjusted lung cancer incidence rates in over 50% of counties., Conclusion: Contrary to our hypothesis, proximity to a coal-fired power plant was not a significant predictor of age-adjusted lung cancer incidence rates. Instead, percentage of smokers emerged as a consistent global and regional risk factor. Regional lung cancer outcomes in Kansas are influenced by wind patterns and elderly population., Competing Interests: Competing interests: The authors report there are no competing interests to declare.

Background and Objectives : The aim of this study was to assess the relationship between habitual physical activity, body composition, serum myokine concentration, and all-cause mortality in chronic hemodialysis patients. Materials and Methods : A prospective cohort study with a 7-year follow-up was conducted in a group of 38 patients (24 men, 14 women, mean age 65.6 ± 13.9 years, dialysis vintage 1.17 ± 1.25 years). Baseline serum concentrations of myokines-follistatin and myostatin-were assessed along with a measurement of physical activity with multidimensional accelerometery, body composition, and the force of forearm muscle contraction. Survival analysis was performed using the Kaplan-Meier method for tertiles of follistatin, serum myostatin, body composition, and physical activity expressed in metabolic equivalents (MET). Results : The mean physical activity among patients was 81 min/24 h (median 38.5 min), and the mean weekly 3MET activity was 493 min (median 218 min). The probability of survival of patients was significantly lower in the subgroup with 3MET/24 h less than 26 min/24 h and 3METt less than 148 min per week compared to the other subgroup ( p = 0.006 and p = 0.006, respectively). During the 70-month follow-up, the subgroup with the lowest baseline follistatin concentration showed a significantly lower risk of death ( p = 0.02). Baseline myostatin levels were not significant risk factors for mortality, nor were BMI or lean and fat tissue index categories. Conclusions : Physical activity and low plasma follistatin, but not body composition indexes or plasma myostatin, could serve as predictors of all-cause mortality in hemodialysis patients.

Background: Two randomized trials found women with low blood docosahexaenoic acid (DHA; an omega 3 fatty acid) had fewer early preterm births (<34 weeks gestation) if they were assigned to high dose DHA supplementation, however, there is currently no capacity for clinicians who care for pregnancies to obtain a blood assessment of DHA. Determining a way to identify women with low DHA intake whose risk could be lowered by high dose DHA supplementation is desired., Objective: To determine if assessing DHA intake can identify pregnancies that benefit from high dose DHA supplementation., Study Design: This secondary analysis used birth data from 1310 pregnant women who completed a 7-question food frequency questionnaire (DHA-FFQ) at 16.8 ± 2.5 weeks gestation that is validated to assess DHA status. They were then randomly assigned to a standard (200 mg/day) or high dose (800 or 1000 mg/day) DHA supplement for the remainder of pregnancy. Bayesian logistic regressions were fitted for early preterm birth and preterm birth as a function of DHA intake and assigned DHA dose., Results: Participants who consumed less than 150 mg/day DHA prior to 20 weeks' gestation (n = 810/1310, 58.1%) had a lower Bayesian posterior probability (pp) of early preterm birth if they were assigned to high dose DHA supplementation (1.4% vs 3.9%, pp = 0.99). The effect on preterm birth (<37 weeks) was also significant (11.3% vs 14.8%, pp = 0.97)., Conclusion: The DHA-FFQ can identify pregnancies that will benefit most from high dose DHA supplementation and reduce the risk of preterm birth. The DHA-FFQ is low burden to providers and patients and could be easily implemented in obstetrical practice., Competing Interests: Declaration of competing interest SEC has received honorariums for presentations about DHA in infancy and pregnancy. KMG was the PI of R01HD086001. SEC, BJG and CJV were PIs of R01HD083292, CJV was an employee of RB Nutrition, which produces infant formulas and supplements with DHA at the time the study was conducted, however, RB was not involved 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., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Intention-to-treat analyses do not address adherence. Per protocol analyses treat nonadherence as a protocol deviation and assess if the intervention is effective if followed., Objective: To determine the rate of early preterm birth (EPTB, <34 weeks gestation) and preterm birth (PTB, <37 weeks gestation) in participants who adhered to a randomly assigned docosahexaenoic acid (DHA) dose of 1000 mg/day., Study Design: Eleven hundred women with a singleton pregnancy were enrolled before 20-weeks' gestation, provided a capsule with 200 mg/day DHA and randomly assigned to two additional capsules containing a placebo or 800 mg of DHA. In the Bayesian Adaptive Design, new randomization schedules were determined at prespecified intervals. In each randomization, the group with the most EPTB was assigned fewer participants than the other group. Adherence was defined a priori as a postpartum red blood cell phospholipid DHA (RBC-PL-DHA) ≥5.5%.and post hoc as ≥8.0% RBC-PL-DHA, the latter after examination of postpartum RBC-PL-DHA. Bayesian mixture models were fitted for gestational age and dichotomized for EPTB and PTB as a function of baseline RBC-PL-DHA and dose-adherence. Bayesian hierarchical models were also fitted for EPTB by dose adherence and quartiles of baseline RBC-PL-DHA., Results: Adherence to the high dose using both RBC-PL-DHA cut points resulted in less EPTB compared to 200 mg [Bayesian posterior probability (pp) = 0.93 and 0.92, respectively]. For participants in the two lowest quartiles of baseline DHA status, adherence to the higher dose resulted in lower EPTB (≥5.5% RBC-PL-DHA, quartiles 1 and 2, pp = 0.95 and 0.96; ≥8% RBC-PL-DHA, quartiles 1 and 2, pp = 0.94 and 0.95). Using the Bayesian model, EPTB was reduced by 65%, from 3.45% to 1.2%, using both cut points. Adherence also reduced PTB before 35, 36 and 37 weeks using both cut points (pp ≥ 0.95). In general, performance of the nonadherent subgroup mirrored that of participants assigned to 200 mg., Conclusion: Adherence to high dose DHA reduced EPTB and PTB. The largest effect of adherence on reducing EPTB was observed in women with low baseline DHA levels., Clinicaltrials: gov (NCT02626299)., Competing Interests: Conflict of interest SEC has received honorariums for presentations about DHA in infancy and pregnancy. SEC, BJG and CJV were PIs of R01HD083292, CJV was an employee of RB Nutrition, which produces infant formulas and supplements with DHA at the time the study was conducted, however, RB was not involved 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., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Studies that investigate the performance of prognostic and predictive biomarkers are commonplace in medicine. Evaluating the performance of biomarkers is challenging in traumatic brain injury (TBI) and other conditions when both the time factor (i.e. time from injury to biomarker measurement) and different levels or doses of treatments are in play. Such factors need to be accounted for when assessing the biomarker's performance in relation to a clinical outcome. The Hyperbaric Oxygen in Brain Injury Treatment (HOBIT) trial, a phase II randomized control clinical trial seeks to determine the dose of hyperbaric oxygen therapy (HBOT) for treating severe TBI that has the highest likelihood of demonstrating efficacy in a phase III trial. Hyperbaric Oxygen in Brain Injury Treatment will study up to 200 participants with severe TBI. This paper discusses the statistical approaches to assess the prognostic and predictive performance of the biomarkers studied in this trial, where prognosis refers to the association between a biomarker and the clinical outcome while the predictiveness refers to the ability of the biomarker to identify patient subgroups that benefit from therapy. Analyses based on initial biomarker levels accounting for different levels of HBOT and other baseline clinical characteristics, and analyses of longitudinal changes in biomarker levels are discussed from a statistical point of view. Methods for combining biomarkers that are of complementary nature are also considered and the relevant algorithms are illustrated in detail along with an extensive simulation study that assesses the performance of the statistical methods. Even though the discussed approaches are motivated by the HOBIT trial, their applications are broader. They can be applied in studies assessing the predictiveness and prognostic ability of biomarkers in relation to a well-defined therapeutic intervention and clinical outcome., Competing Interests: Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: The study startup process for interventional clinical trials is a complex process that involves the efforts of many different teams. Each team is responsible for their startup checklist in which they verify that the necessary tasks are done before a study can move on to the next team. This regulatory process provides quality assurance and is vital for ensuring patient safety [10]. However, without having this startup process centralized and optimized, study approval can take longer than necessary as time is lost when it passes through many different hands., Objective: This manuscript highlights the process and the systems that were developed at The University of Kansas Comprehensive Cancer Center regarding the study startup process. To facilitate this process the regulatory management, site development, cancer center administration, and the Biostatistics & Informatics Shared Resources (BISR) teams came together to build a platform aimed at streamlining the startup process and providing a transparent view of where a study is in the startup process., Process: Ensuring the guidelines are clearly articulated for the review criteria of each of the three review boards, i.e., Disease Working Group (DWG), Executive Resourcing Committee (ERC), and Protocol Review and Monitoring Committee (PRMC) along with a system that can track every step and its history throughout the review process., Results: Well-defined processes and tracking methodologies have allowed the operations teams to track each study closely and ensure the 90-day and 120-day deadlines are met, this allows the operational team to dynamically prioritize their work daily. It also provides Principal investigators a transparent view of where their study stands within the study startup process and allows them to prepare for the next steps accordingly., Conclusion/future Work: The current process and technology deployment has been a significant improvement to expedite the review process and minimize study startup delays. There are still a few opportunities to fine-tune the study startup process; an example of which includes automatically informing the operational managers or the study teams to act upon deadlines regarding study review rather than the current manual communication process which involves them looking it up in the system which can add delays., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Introduction/aims: Peripheral neuropathies commonly affect quality of life of patients due to pain, sleep disturbances, and fatigue, although trials have not adequately explored these domains of care. The aim of this study was to assess the impact of nortriptyline, duloxetine, pregabalin, and mexiletine on pain, sleep, and fatigue in patients diagnosed with cryptogenic sensory polyneuropathy (CSPN)., Methods: We implemented a Bayesian adaptive design to perform a 12-wk multisite, randomized, prospective, open-label comparative effectiveness study in 402 CSPN patients. Participants received either nortriptyline (n = 134), duloxetine (n = 126), pregabalin (n = 73), or mexiletine (n = 69). At prespecified analysis timepoints, secondary outcomes, Patient Reported Outcomes Measurement Information System (PROMIS) surveys including Short Form (SF)-12, pain interference, fatigue, and sleep disturbance, were collected., Results: Mexiletine had the highest quit rate (58%) due to gastrointestinal side effects, while nortriptyline (38%) and duloxetine (38%) had the lowest quit rates. If tolerated for the full 12 wk of the study, mexiletine had the highest probability (>90%) of positive outcomes for improvements in pain interference and fatigue. There was no significant difference among the medications for sleep disturbance or SF-12 scores. Adverse events and lack of efficacy were the two most common reasons for cessation of therapy., Discussion: Physicians caring for patients with CSPN should consider mexiletine to address pain and fatigue, although nortriptyline and duloxetine are better medications to trial first since they are better tolerated. Future research should compare other commonly used medications for CSPN to determine evidence-based treatment strategies., (© 2022 Wiley Periodicals LLC.)

Smoking during pregnancy and postpartum remains an important public health problem. No known prior study has prospectively examined mutual changes in risk factors and women's smoking trajectory across pregnancy and postpartum. The objective of this study was to report methods used to implement a prospective cohort (Msgs4Moms), present participant baseline characteristics, and compare our sample characteristics to pregnant women from national birth record data. The cohort study was designed to investigate smoking patterns, variables related to tobacco use and abstinence, and tobacco treatment quality across pregnancy through 1-year postpartum. Current smokers or recent quitters were recruited from obstetrics clinics. Analyses included Chi-square and independent sample t-tests using Cohen's d . A total of 62 participants (41 smokers and 21 quitters) were enrolled. Participants were Black (45.2%), White (35.5%), and multiracial (19.3%); 46.8% had post-secondary education; and most were Medicaid-insured (64.5%). Compared with quitters, fewer smokers were employed (65.9 vs 90.5%, Cohen's d = 0.88) and more reported financial strain (61.1% vs 28.6%; Cohen's d = 0.75). Women who continue to smoke during pregnancy cope with multiple social determinants of health. Longitudinal data from this cohort provide intensive data to identify treatment gaps, critical time points, and potential psychosocial variables warranting intervention.

Background: Pediatric population presents several barriers for clinical trial design and analysis, including ethical constraints on the sample size and slow accrual rate. Bayesian adaptive design methods could be considered to address these challenges in pediatric clinical trials., Methods: We developed an innovative Bayesian adaptive design method and demonstrated the approach as a re-design of a published phase III pediatric trial. The innovative design used early success criteria based on skeptical prior and early futility criteria based on enthusiastic prior extrapolated from a historical adult trial, and the early and late stopping boundaries were calibrated to ensure a one-sided type I error of 2.5%. We also constructed several alternative designs which incorporated only one type of prior belief and the same stopping boundaries. To identify a preferred design, we compared operating characteristics including power, expected trial size and trial duration for all the candidate adaptive designs via simulation when performing an increasing number of equally spaced interim analyses., Results: When performing an increasing number of equally spaced interim analyses, the innovative Bayesian adaptive trial design incorporating both skeptical and enthusiastic priors at both interim and final analyses outperforms alternative designs which only consider one type of prior belief, because it allows more reduction in sample size and trial duration while still offering good trial design properties including controlled type I error rate and sufficient power., Conclusions: Designing a Bayesian adaptive pediatric trial with both skeptical and enthusiastic priors can be an efficient and robust approach for early trial stopping, thus potentially saving time and money for trial conduction., (© 2022. The Author(s).)

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., (©Dinesh Pal Mudaranthakam, Byron Gajewski, Hope Krebill, James Coulter, Michelle Springer, Elizabeth Calhoun, Dorothy Hughes, Matthew Mayo, Gary Doolittle. Originally published in JMIR Cancer (https://cancer.jmir.org), 21.04.2022.)

Objective: In this secondary analysis of the Rural Engagement in Primary Care for Optimizing Weight Reduction (RE-POWER) randomized trial, the authors determined the effectiveness of weight-loss interventions in people with diabetes compared with those without diabetes living in rural areas., Methods: The RE-POWER study was a randomized trial designed to determine the effectiveness of nonpharmacological behavioral weight-loss interventions in rural participants with obesity, comparing the individual in-clinic visit model to in-person group sessions and phone group sessions over 24 months. In this secondary analysis, weight loss was compared in participants with and without diabetes. The effects of factors such as medications, insulin, and behavioral factors were compared., Results: Participants with diabetes were less likely to lose weight during the study compared with those without diabetes up to 18 months (4.12% vs. 5.31%; net difference = 1.46%; 95% CI: 0.63%-2.28%). Participants with diabetes on insulin lost less weight than patients with diabetes not on insulin at 6 months (4.52% vs. 6.88%; net difference = 2.35%; 95% CI: 0.55%-4.16%). The group with diabetes had significantly lower changes in blood pressure and lipid parameters versus the group without diabetes., Conclusions: Patients with diabetes in rural areas were less likely to lose weight, and metabolic parameters were less responsive to weight loss, compared with patients without diabetes., (© 2022 The Obesity Society (TOS). This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

The secondary analyses of two large, recently completed randomized clinical trials of DHA supplementation in pregnancy found that women with a low baseline DHA status benefited from randomization to a higher dose (800 vs 0 and 1000 vs 200 mg/day DHA). To obtain DHA status, it is necessary to obtain a blood sample and conduct an analysis using gas chromatography (GC) or GC-mass spectrometry (GCMS), both barriers to clinics where pregnant women receive advice on nutrition. Participants consuming less than 150 mg/day of DHA at baseline in our recent trial had a lower risk of early preterm birth and preterm birth when assigned to 1000 vs 200 m/day DHA. DHA intake was determined using a 7-question food frequency questionnaire administered by a trained nutritionist. Because the need for trained personnel to administer the questionnaire would be a barrier to implementing this finding in clinical management of pregnancy, the goal of this study was to determine if an online version of the questionnaire could be validly completed without assistance., (Copyright © 2022. Published by Elsevier Ltd.)