Chronic low back pain (cLBP) is a prevalent condition with profound impacts on functioning and quality of life. While multiple evidence-based treatments exist, they all have modest average treatment effectsx2013potentially due to individual variation in treatment response and the diverse etiologies of cLBP. This multi-site sequential, multiple-assignment randomized trial (SMART) investigated four treatment modalities with two stages of randomization and aimed to enroll 630 protocol completers. The primary objective was to develop a precision medicine approach by estimating optimal treatment or treatment combinations based on patient characteristics and initial treatment response. The analysis strategy focuses on estimating interpretable dynamic treatment regimes and identifying subgroups most responsive to specific interventions. Broad eligibility criteria were implemented to enhance generalizability and recruitment, most notably that participants could be eligible to enroll even if they could not be assigned to one (but no more) of the study interventions. Enrolling participants with restrictions on the treatment they could be assigned necessitated modifications to standard minimization methods for balancing covariates. The BEST trial represents one of the largest SMARTs focused on clinical decision-making to date and the largest in cLBP. By collecting an extensive array of biomarker and phenotypic measures, this trial may identify potential treatment mechanisms and establish a more evidence-based approach to individualizing cLBP treatment in clinical practice.

Introduction Each year, over a million individuals undergo office cystoscopy for bladder cancer diagnosis and surveillance. About one-third of patients experience major discomfort during office cystoscopy, and over 40% of bladder cancer patients have substantial anxiety associated with the procedure. Reducing cystoscopy discomfort was among the highest areas of importance for bladder cancer patients and caregivers in the Bladder Cancer Advocacy Network Patient Survey Network. Studies have evaluated approaches to reducing this discomfort through medical or environmental methods. These various interventions have evidence of efficacy but have yet to be widely implemented or compared to each other. Thus, we initiated a quality improvement initiative that implemented various interventions to decrease pain and anxiety among patients undergoing cystoscopy. Our objectives were to assess which interventions most effectively reduced pain and to understand implementation challenges to inform design of a future comparative effectiveness trial. Methods We analyzed 110 adults who underwent office cystoscopy at the University of North Carolina and participated in this quality improvement initiative. Exclusion criteria included inability to speak English, urethral stricture, and additional procedures accompanying cystoscopy. Participants were offered 20mL intraurethral 2% lidocaine with a dwell time of ≤10 or >10 minutes and either music or visualization. Patients receiving music wore headphones playing the patient's chosen musical genre (jazz/classical). Patients receiving visualization watched their cystoscopy on a real-time video monitor. If a patient declined the offered intervention, their requested intervention was provided, and their experience was recorded.After the cystoscopy, patients completed a survey that measured pain during cystoscopy on the Visual Analog Scale (VAS; range 0-10), pain intensity, emotional distress-anxiety, satisfaction, willingness to use the interventions again, and willingness to participate in a future study. Demographics and urologic history were collected. Linear regression analyses were used to compare interventions. Results The majority of patients received cystoscopy for bladder cancer surveillance (57.3%) or hematuria evaluation (26.4%), with patients averaging 3.91 prior cystoscopies (SD 6.16). Each intervention was offered to 23-27% of patients (Table 1). 30 patients declined their offered inventions, 25 of whom were offered music and instead requested visualization (Table 2). Most declining patients presented for bladder cancer surveillance or hematuria (86.7%) and had undergone cystoscopy previously (73.3%). Multiple regression analysis controlling for age, number of prior cystoscopies, and cystoscopy indication showed that visualization (t=-3.21, p=0.002) and music (t=-2.25, p=0.027) were independently associated with decreased VAS; increased lidocaine dwell time was not (t=-1.62, p=0.109). Similarly, visualization (t=-3.79, p<0.001) and music (t=-3.72, p<0.001) were independently associated with reduced PROMIS Pain, measuring pain intensity; increased lidocaine dwell time was not (t=-0.24, p=0.813). No intervention was significantly associated with reduced emotional distress-anxiety. Conclusions Allowing patients to visualize their office cystoscopy in real-time or listen to music during the procedure are simple, accessible, and efficacious means of significantly reducing pain and discomfort related to cystoscopy. Visualization may be particularly valuable for patients presenting for bladder cancer surveillance, for potential diagnosis, or who have had a cystoscopy before, as these individuals tended to reject alternate environmental interventions in favor of visualization. Ultimately, defining the effectiveness of patient-centered evidence-based interventions to reduce cystoscopy discomfort will help millions of patients who undergo office cystoscopy tolerate an invasive procedure, reduce anxiety, and increase adherence to recommended follow-up cystoscopy procedures. Future directions include the evaluation of these interventions within a large, multi-institutional, randomized trial that compares efficacy and further assesses which interventions work best for different patient subgroups, allowing for the implementation of more tailored approaches to reduce cystoscopy discomfort in the future.

Multilevel interventions (MLIs) hold promise for reducing health inequities by intervening at multiple types of social determinants of health consistent with the socioecological model of health. In spite of their potential, methodological challenges related to study design compounded by a lack of tools for sample size calculation inhibit their development. We help address this gap by proposing the Multilevel Intervention Stepped Wedge Design (MLI-SWD), a hybrid experimental design which combines cluster-level (CL) randomization using a Stepped Wedge design (SWD) with independent individual-level (IL) randomization. The MLI-SWD is suitable for MLIs where the IL intervention has a low risk of interference between individuals in the same cluster, and it enables estimation of the component IL and CL treatment effects, their interaction, and the combined intervention effect. The MLI-SWD accommodates cross-sectional and cohort designs as well as both incomplete (clusters are not observed in every study period) and complete observation patterns. We adapt recent work using generalized estimating equations for SWD sample size calculation to the multilevel setting and provide an R package for power and sample size calculation. Furthermore, motivated by our experiences with the ongoing NC Works 4 Health study, we consider how to apply the MLI-SWD when individuals join clusters over the course of the study. This situation arises when unemployment MLIs include IL interventions that are delivered while the individual is unemployed. This extension requires carefully considering whether the study interventions will satisfy additional causal assumptions but could permit randomization in new settings.

You have accessJournal of UrologyHealth Services Research: Quality Improvement & Patient Safety III (PD62)1 May 2024PD62-01 INTERVENTIONS TO REDUCE CYSTOSCOPY DISCOMFORT: A MULTI-SITE QUALITY IMPROVEMENT PILOT Avani P. Desai, Ram Sankar Basak, Yair Lotan, John Sperger, Florian Schroeck, Iftach Chaplain, Hannah Roberson, Perla Lopez, Whitney Jenkins, John L. Gore, Michael R. Kosorok, and Angela Smith Avani P. DesaiAvani P. Desai , Ram Sankar BasakRam Sankar Basak , Yair LotanYair Lotan , John SpergerJohn Sperger , Florian SchroeckFlorian Schroeck , Iftach ChaplainIftach Chaplain , Hannah RobersonHannah Roberson , Perla LopezPerla Lopez , Whitney JenkinsWhitney Jenkins , John L. GoreJohn L. Gore , Michael R. KosorokMichael R. Kosorok , and Angela SmithAngela Smith View All Author Informationhttps://doi.org/10.1097/01.JU.0001008656.89655.67.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Each year, over a million individuals undergo office cystoscopy to evaluate the lower urinary tract. About one-third of patients experience major discomfort during this procedure, and over 40% of bladder cancer patients experience substantial anxiety associated with it. A quality improvement pilot explored interventions to decrease pain and anxiety among cystoscopy patients. Our objectives were to assess intervention efficacy and implementation challenges to inform design of a future comparative effectiveness trial. METHODS: Office cystoscopy was performed on 190 adults (University of North Carolina (n=110), University of Texas Southwestern (n=80)). Patients were offered 20mL intraurethral 2% lidocaine with a dwell time of ≤10 or >10 minutes and either listening to music on headphones or real-time visualization of cystoscopy. If a patient declined the offered intervention, their requested intervention was provided and noted. After the procedure, patients recorded cystoscopy pain on the Visual Analog Scale (VAS; range 0-10), pain intensity, and emotional distress-anxiety. We performed a linear regression analysis that controlled for age and number of prior cystoscopies and accounted for correlation among patients nested within institutions. Pain intensity and anxiety were log-transformed. RESULTS: Visualization was significantly associated with decreased VAS, pain intensity, and anxiety (Table 1). Conversely, music was significantly associated with increased anxiety. Increased lidocaine dwell time was not associated with any change in pain or anxiety. 35% of patients declined their offered inventions, 88% of whom were offered music and requested visualization instead of or in addition to music (Figure 1). Most declining patients presented for bladder cancer surveillance (87%) or had undergone prior cystoscopy (84%). CONCLUSIONS: Allowing patients to visualize their cystoscopy in real time during the procedure is an accessible, efficacious, and desired means of significantly reducing pain and anxiety related to cystoscopy. Download PPT Source of Funding: Carolina Medical Student Research Program (University of North Carolina Medical Alumni Fund) © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e1285 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Avani P. Desai More articles by this author Ram Sankar Basak More articles by this author Yair Lotan More articles by this author John Sperger More articles by this author Florian Schroeck More articles by this author Iftach Chaplain More articles by this author Hannah Roberson More articles by this author Perla Lopez More articles by this author Whitney Jenkins More articles by this author John L. Gore More articles by this author Michael R. Kosorok More articles by this author Angela Smith More articles by this author Expand All Advertisement PDF downloadLoading ...

Older adults are characterized by profound clinical heterogeneity. When designing and delivering interventions, there exist multiple approaches to account for heterogeneity. We present the results of a systematic review of data-driven, personalized interventions in older adults, which serves as a use case to distinguish the conceptual and methodologic differences between individualized intervention delivery and precision health-derived interventions. We define individualized interventions as those where all participants received the same parent intervention, modified on a case-by-case basis and using an evidence-based protocol, supplemented by clinical judgment as appropriate, while precision health-derived interventions are those that tailor care to individuals whereby the strategy for how to tailor care was determined through data-driven, precision health analytics. We discuss how their integration may offer new opportunities for analytics-based geriatric medicine that accommodates individual heterogeneity but allows for more flexible and resource-efficient population-level scaling.

Background: Each year, nearly 300,000 women and 5 million fetuses or neonates die during childbirth or shortly thereafter, a burden concentrated disproportionately in low- and middle-income countries. Identifying women and their fetuses at risk for intrapartum-related morbidity and death could facilitate early intervention. Methods: The Limiting Adverse Birth Outcomes in Resource-Limited Settings (LABOR) Study is a multi-country, prospective, observational cohort designed to exhaustively document the course and outcomes of labor, delivery, and the immediate postpartum period in settings where adverse outcomes are frequent. The study is conducted at four hospitals across three countries in Ghana, India, and Zambia. We will enroll approximately 12,000 women at presentation to the hospital for delivery and follow them and their fetuses/newborns throughout their labor and delivery course, postpartum hospitalization, and up to 42 days thereafter. The co-primary outcomes are composites of maternal (death, hemorrhage, hypertensive disorders, infection) and fetal/neonatal adverse events (death, encephalopathy, sepsis) that may be attributed to the intrapartum period. The study collects extensive physiologic data through the use of physiologic sensors and employs medical scribes to document examination findings, diagnoses, medications, and other interventions in real time. Discussion: The goal of this research is to produce a large, sharable dataset that can be used to build statistical algorithms to prospectively stratify parturients according to their risk of adverse outcomes. We anticipate this research will inform the development of new tools to reduce peripartum morbidity and mortality in low-resource settings.

BACKGROUND:To set therapeutic benchmarks, in 2009 the Society for Vascular Surgery defined objective performance goals (OPG) for treatment of patients with chronic limb threatening ischemia (CLTI) with either open surgical bypass or endovascular intervention. The goal of these OPGs are to set standards of care from a revascularization standpoint and to provide performance benchmarks for 1 year patency rates for new endovascular therapies. While OPGs are useful in this regard, a critical decision point in the treatment of patients with CLTI is determining when revascularization is necessary. There is little guidance in the comprehensive treatment of this patient population, especially in the nonoperative cohort. Guidelines are needed for the CLTI patient population as a whole and not just those revascularized, and our aim was to assess whether CLTI OPGs could be attained with nonoperative management alone.METHODS:Our cohort included patients with an incident diagnosis of CLTI (by hemodynamic and symptomatic criteria) at our institution from 2013-2017. The primary outcome measured was mortality. Secondary outcomes were limb loss and failure of amputation-free survival. Descriptive statistics were used to define the 2 groups - patients undergoing primary revascularization and patients undergoing primary wound management. The risk difference in outcomes between the 2 groups was estimated using collaborative-targeted maximum likelihood estimation.RESULTS:Our cohort included 349 incident CLTI patients; 60% male, 51% white, mean age 63 +/- 13 years, 20% Rutherford 4, and 80% Rutherford 5. Most patients (277, 79%) underwent primary revascularization, and 72 (21%) were treated with wound care alone. Demographics and presenting characteristics were similar between groups. Although the revascularized patients were more likely to have femoropopliteal disease (72% vs. 36%), both groups had a high rate of infrapopliteal disease (62% vs. 57%). Not surprisingly, the patients in the revascularization group were less likely to have congestive heart failure (34% vs. 42%), complicated diabetes (52% vs. 79%), obesity (19% vs. 33%), and end stage renal disease (14% vs. 28%). In the wound care group, 2-year outcomes were 65% survival, 51% amputation free survival, 19% major limb amputation, and 17% major adverse cardiac event. The wound care cohort had a 13% greater risk of death at 2 years; however, the risk of limb loss at 2 years was 12% less in the wound care cohort.CONCLUSIONS:A comprehensive set treatment goals and expected amputation free survival outcomes can guide revascularization, but also assure that appropriate outcomes are achieved for patients treated without revascularization. The 2-year outcomes achieved in this cohort provide an estimate of outcomes for nonrevascularized CLTI patients. Although multi-center or prospective studies are needed, we demonstrate that equal, even improved, limb salvage rates are possible.

In the twenty years since Dr. Leo Breiman's incendiary paper Statistical Modeling: The Two Cultures was first published, algorithmic modeling techniques have gone from controversial to commonplace in the statistical community. While the widespread adoption of these methods as part of the contemporary statistician's toolkit is a testament to Dr. Breiman's vision, the number of high-profile failures of algorithmic models suggests that Dr. Breiman's final remark that "the emphasis needs to be on the problem and the data" has been less widely heeded. In the spirit of Dr. Breiman, we detail an emerging research community in statistics - data-driven decision support. We assert that to realize the full potential of decision support, broadly and in the context of precision health, will require a culture of social awareness and accountability, in addition to ongoing attention towards complex technical challenges.

ObjectivesDevelop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD).Design and settingThis study developed and validated prognostic penalised logistic regression models using reports to the international Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease voluntary registry from March to October 2020. Model development was done using a training data set (85% of cases reported 13 March–15 September 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported 16 September–20 October 2020).ParticipantsWe included 2709 cases from 59 countries (mean age 41.2 years (SD 18), 50.2% male). All submitted cases after removing duplicates were included.Primary and secondary outcome measuresCOVID-19 related: (1) Hospitalisation+: composite outcome of hospitalisation, ICU admission, mechanical ventilation or death; (2) Intensive Care Unit+ (ICU+): composite outcome of ICU admission, mechanical ventilation or death; (3) Death. We assessed the resulting models’ discrimination using the area under the curve of the receiver operator characteristic curves and reported the corresponding 95% CIs.ResultsOf the submitted cases, a total of 633 (24%) were hospitalised, 137 (5%) were admitted to the ICU or intubated and 69 (3%) died. 2009 patients comprised the training set and 700 the test set. The models demonstrated excellent discrimination, with a test set area under the curve (95% CI) of 0.79 (0.75 to 0.83) for Hospitalisation+, 0.88 (0.82 to 0.95) for ICU+ and 0.94 (0.89 to 0.99) for Death. Age, comorbidities, corticosteroid use and male gender were associated with a higher risk of death, while the use of biological therapies was associated with a lower risk.ConclusionsPrognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of patients with IBD. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with patients with IBD.

Background: Inflammatory bowel disease (IBD) is a chronic inflammation of the gastrointestinal tract with rising incidence and no effective cure.IBD increases the risk of colon cancer through a Colitis-Associated-Cancer (CAC) pathway.Despite available anti-inflammatory or immunosuppressive drugs, many patients fail or lose response over time and many others experience drug-induced adverse events.Natural plant products are increasingly used by IBD patients and reported to have some efficacy for IBD in experimental models and clinical trials.The gut microbiota is implicated in IBD, and clinical and animal studies suggest that S-329 AGA Abstracts gut bacteria trigger and perpetuate chronic colitis.Saffron (Crocus sativus), has been reported to play a key role in treatment of different digestive system disorders but its influence on gut microbiota or its preventive role in IBD has not been explored.Aim: To investigate whether saffron treatment influences gut microbiota in relation to susceptibility to experimental colitis.Methods: Mice were pre-treated with either saffron (10 or 20 mg/kg body weight) or vehicle through daily oral gavage for 4 days before dextran sulfate sodium (DSS) administration.Then, we induced acute colitis in C57BL/6 mice with 2.5% DSS.On day 11, mice were euthanized and analyzed for gross and microscopic inflammation.Distal colon segments were collected for analyzing TNF-α, IL-1β, and IL6 expression by ELISA.The gut microbiome composition was also assessed in the mice by deep sequencing.Results: Saffron pre-treatment improved gross and histopathological characteristics of colonic mucosa in experimental colitis mice.Saffron dose 10 and 20 mg/kg body weight showed a significant improvement in body weight, disease activity index (DAI), colon length and histology score, when compared to vehicle treated DSS mice group.Pre-treatment with saffron significantly decreased pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 in the colon tissues indicating saffron mediates its preventive effect through its antiinflammatory activity.The gut microbiome analysis revealed distinct clusters in the saffron treated and non-treated-mice in DSS-colitis by visualization of Bray-Curtis diversity by principal coordinate ordination (PCoA).In addition, we observed that at the Operational Taxonomic Unit (OTU) level Cyanobacteria that are known to increase during colitis were depleted in saffron-treated mice. Conclusion:These data demonstrate that Saffron modulates gut microbiota composition and reduces the susceptibility to colitis.In addition to revealing the therapeutic potential of Saffron in experimental colitis this study gives insights in developing natural products as alternative treatment for IBD-associated colitis.

We discuss "Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study" by Qian, Klasnja and Murphy. In this discussion, we study when the linear mixed effects models with endogenous covariates are feasible to use by providing examples and diagnostic tools as well as discussing potential extensions. This includes evaluating feasibility of partial likelihood-based inference, checking the conditional independence assumption, estimation of marginal effects, and kernel extensions of the model.

In the applied sciences, the ultimate goal is not just to acquire knowledge but to turn knowledge into action. The next wave for data disciplines may be experimental designs and analytical methods for closing the gap between the “real‐world” situations faced by decision‐makers and their idealized representations in optimization problems, and the health sciences are poised to be the discipline where these developments substantially improve lives. We discuss three recent trends in research—experimental designs and analytical methods for precision medicine and pragmatic trials; technological developments in sensors, wearables, and smartphones for measuring health data; and methods addressing algorithmic bias and model interpretability—and argue that these seemingly disparate trends point to a future where data‐driven decision support tools are increasingly used to promote wellbeing.

This paper highlights the importance of developing accurate flood hazard maps to price insurance effectively and to communicate flood risk to interested parties. Risk-based insurance premiums can encourage insurance purchase and investment in cost effective mitigation measures. We undertake a study using light imaging detection and ranging (LIDAR) technology and depth damage curves to determine risk-based rates for residential structures in three counties in the state of North Carolina. We then compare these prices with current premiums charged to homeowners by the National Flood Insurance Program (NFIP) for 11,915 single-family residences. NFIP premiums are significantly higher than risk-based premiums for over 90 percent of the homes in each of the counties in our study. Risk-based prices are higher than NFIP premiums only in instances where buildings are predicted to suffer damage from more frequent, shallow floods that are currently not considered explicitly in NFIP premium calculations. Accurate flood maps are needed to determine cost-effective loss reduction measures and to address issues of affordability and fairness for homeowners currently living in flood-prone areas.