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Objectives:The Core Elements of Outpatient Antibiotic Stewardship provides a framework to improve antibiotic use, but cost-effectiveness data on implementation of outpatient antibiotic stewardship interventions are limited. We evaluated the cost-effectiveness of Core Element implementation in the outpatient setting.Methods:An economic simulation model from the health-system perspective was developed for patients presenting to outpatient settings with uncomplicated acute respiratory tract infections (ARI). Effectiveness was measured as quality-adjusted life years (QALYs). Cost and utility parameters for antibiotic treatment, adverse drug events (ADEs), and healthcare utilization were obtained from the literature. Probabilities for antibiotic treatment and appropriateness, ADEs, hospitalization, and return ARI visits were estimated from 16,712 and 51,275 patient visits in intervention and control sites during the pre- and post-implementation periods, respectively. Data for materials and labor to perform the stewardship activities were used to estimate intervention cost. We performed a one-way and probabilistic sensitivity analysis (PSA) using 1,000,000 second-order Monte Carlo simulations on input parameters.Results:The proportion of ARI patient-visits with antibiotics prescribed in intervention sites was lower (62% vs 74%) and appropriate treatment higher (51% vs 41%) after implementation, compared to control sites. The estimated intervention cost over a 2-year period was $133,604 (2018 US dollars). The intervention had lower mean costs ($528 vs $565) and similar mean QALYs (0.869 vs 0.868) per patient compared to usual care. In the PSA, the intervention was dominant in 63% of iterations.Conclusions:Implementation of the CDC Core Elements in the outpatient setting was a cost-effective strategy.

Objective: High-throughput metabolomics assays can generate thousands of biomarker measurements and provide novel opportunities for prognostic modeling in colorectal cancer (CRC) research. The high dimensionality of metabolic data brings unforeseen statistical challenges and traditional variable selection methods may not perform well due to simultaneous challenges of computational expediency, statistical accuracy, and algorithmic stability. The Iterative Sure Independence Screening (ISIS) has demonstrated superior theoretical properties in handling such situations, and may be a viable alternative. Methods: In a prospective study of 77 newly diagnosed CRC patients (stage I-IV), pre-surgical urinary samples were analyzed on a gas chromatography-mass spectrometry platform. After exclusion of metabolites with >30% coefficient of variation, 168 metabolites remained for statistical analysis. Raw measures were processed following a standard normalization pipeline. The primary outcome was overall survival (OS) as measured from date of cancer diagnosis. In addition to metabolomics data, the predictor set included baseline clinical characteristics, such as age, sex, body mass index, tumor site, tumor stage, and receipt of neo-adjuvant and/or adjuvant treatment. We applied the ISIS method with Lasso penalty on a Cox regression model (ISIS-Lasso) to identify features associated with OS. Cox models with either Lasso regularization or backward selection were also considered as competing methods. The performance of the models was assessed through two standard performance matrices: Uno's time-dependent Area Under the Curve (tAUC) and Brier's score, both with resample-based validation. Results: Based on bootstrapped tAUC curves, we demonstrated that the screening step in ISIS can significantly improve model performance, since its predicted mean (and median) AUC are larger across clinically relevant follow-up time points (2 and 5 years after diagnosis) relative to the corresponding measures from the other two models. The prediction error based on Brier's score with 0.632+ bootstrap from ISIS-Lasso was also noticeably lower compared to the model from backward selections. Based on the ISIS-Lasso model, we identified two features, that were predictive of OS in CRC patients: tumor stage and cystine. When fixing all other clinical measures, patients with early stage (I-III) had 52% lower risk of death, compared to late stage (IV); 1 standard deviation of increase of cystine level was associated with 62% increased risk of death. Conclusion: We have demonstrated the feasibility and effectiveness of an ISIS-based method to improve selection of prognostic models derived from metabolomics data. This may be especially useful for studies with moderate sample sizes. We have identified cystine as a potentially important prognostic biomarker. Citation Format: Tengda Lin, Biljana Gigic, Kenneth Boucher, Ben Haaland, David Liesenfeld, Robert Owen, Petra Schrotz-King, Jourgen Boehm, Anita Peoples, Augustin Scalbert, Martin Schneider, Jane Figueiredo, William Grady, Christopher Li, David Shibata, Erin Siegel, Adetunji Toriola, Alexis Ulrich, Neli Ulrich, Jincheng Shen, Jennifer Ose. Application of iterative sureindependence screening to improve urinary metabolomics-based prediction of survival in colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 892.

Background We report VA-wide trends over time in acute respiratory infection (ARI) antibiotic prescribing, and early assessment of an intervention to improve ARI management. Methods We created a retrospective cohort of ARI (sinusitis, pharyngitis, bronchitis, and URI-NOS) visits between 2009 and April 2018. Patients with complicating conditions were excluded. Antibiotic prescribing rates were calculated. A provider-directed VA-wide ARI campaign was initiated in October 2017. The Campaign was implemented locally by antibiotic stewards or regional personnel trained in academic detailing (AD). Campaign components: dashboards for tracking provider and facility prescribing metrics, printable feedback reports, and AD educational materials. Metrics include: ARI antibiotic prescribing rates, bronchitis/URI-NOS antibiotic prescribing rates, guideline-concordant antibiotic selection for sinusitis or pharyngitis, and proportion of ARI visits with a sinusitis diagnosis. A Logistic generalized estimating equation model assessed metrics over time pre-/postintervention and χ2 tests compared guideline concordant antibiotic proportions pre-/postintervention. Results There were 1,580,612 and 137,421 ARI visits pre-/postintervention, respectively. Antibiotic prescribing decreased from 2009, annual odds ratio (OR) 0.94 [95% CI 0.93, 0.96; P < 0.001]. An additional effect was observed postintervention [OR 0.88, (0.84, 0.88), P < 0.001]. Bronchitis/URI-NOS prescribing rates decreased from 2009 [annual OR 0.94 (CI 0.93, 0.95), P < 0.001]. Additional effect was observed postintervention [OR 0.86, (0.81, 0.91), P < 0.001]. Overall, the proportion of ARI visits diagnosed with sinusitis increased [annual OR 1.09 (1.08, 1.10), P < 0.01], but the proportion of sinusitis diagnoses decreased [OR 0.72 (0.69, 0.75), P < 0.001] postintervention. Guideline-concordant antibiotic selection was 61.5% vs. 71.2% for sinusitis and 63.3% vs. 67.8% for pharyngitis pre-/postintervention, respectively (both P < 0.001). Conclusion Antibiotic prescribing rates for ARIs within the VA have steadily declined since 2010. Additional decline in antibiotic prescribing was associated with the launch of a national campaign to improve ARI management. Disclosures All authors: No reported disclosures.

Background Though more often associated with persistent diarrhea, protozoal gastroenteritis may also present as an acute illness. Suspicion for gastrointestinal protozoal infection is historically based on travel or recreational water exposure and duration of illness, which can delay diagnosis and effective treatment. The use of multiplex PCR panels has the potential to improve diagnosis of protozoal illness. To better understand when testing for protozoa is warranted, we examined predictors of protozoal gastroenteritis within a prospective multicenter study of U.S. children presenting to emergency departments with acute gastroenteritis (AGE). Methods The analysis utilized data from the IMPACT study, a prospective trial of the clinical impact of a multiplex PCR panel for detecting GI pathogens in AGE in five pediatric hospitals across the United States. From these data, we evaluated 72 potential predictors, including patient demographics, medical histories, exposures, symptoms, and vital signs. Using Random Forest Algorithm, 10 variables were selected based on variable importance measure. These were then entered into a Stepwise logistic regression model. Results Out of 962 patients there were 41 (4.3%) patients with protozoal detections, including 18 for Giardia and 24 for Cryptosporidium. Of these, 21 (51%) were male and the median age was 4.3 years old. Detection rates varied by site with 76% (31) of cases at two Midwestern sites. In 23 (56%) of cases protozoa was the sole pathogen detected. Logistic regression modeling of the top 10 variables identified by Random Forest showed the strongest predictor was living in a household with a child 5 years old or younger, followed by study site and younger age (table). Notably, travel, recreational water exposure, and duration of diarrhea were not in the top 10 variables identified. Conclusion Our analysis suggests that age and household exposure predict higher likelihood of protozoal infection in children with AGE. Classic epidemiologic exposures including travel and recreational water exposure were not predictive. These data could improve appropriate test selection. Future studies are still needed for external validation of this model. Disclosures A. Pavia, BioFire Dx: Investigator, Research support.

Background Infectious diarrheal illness is a significant contributor to healthcare costs in the US pediatric population. New multi-pathogen PCR-based panels have shown increased sensitivity over previous methods; however, they are costly and clinical utility may be limited in many cases. Clinical Prediction Rules (CPRs) may help optimize the appropriate use of these tests. Furthermore, Natural Language Processing (NLP) is an emerging tool to extract clinical history for decision support. Here, we examine NLP for the validation of a CPR for pediatric diarrhea. Methods Using data from a prospective clinical trial at 5 US pediatric hospitals, 961 diarrheal cases were assessed for etiology and relevant clinical variables. Of 65 variables collected in that study, 42 were excluded in our models based on a scarcity of documentation in reviewed clinical charts. The remaining 23 variables were ranked by random forest (RF) variable importance and utilized in both an RF and stepwise logistic regression (LR) model for viral-only etiology. We investigated whether NLP could accurately extract data from clinical notes comparable to study questionnaires. We used the eHOST abstraction software to abstract 6 clinical variables from patient charts that were useful in our CPR. These data will be used to train an NLP algorithm to extract the same variables from additional charts, and be combined with data from 2 other variables coded in the EMR to externally validate our model. Results Both RF and LR models achieved cross-validated area under the receiver operating characteristic curves of 0.74 using the top 5 variables (season, age, bloody diarrhea, vomiting/nausea, and fever), which did not improve significantly with the addition of more variables. Of 270 charts abstracted for NLP training, there were 41 occurrences of bloody diarrhea annotated, 339 occurrences of vomiting, and 145 occurrences of fever. Inter-annotator agreement over 9 training sets ranged between 0.63 and 0.83. Conclusion We have constructed a parsimonious CPR involving only 5 inputs for the prediction of a viral-only etiology for pediatric diarrheal illness using prospectively collected data. With the training of an NLP algorithm for automated chart abstraction we will validate the CPR. NLP could allow a CPR to run without manual data entry to improve care. Disclosures All authors: No reported disclosures.

Background Audit-feedback of antibiotic prescribing rates for acute respiratory infections (ARIs) is a promising approach to reduce antibiotic use; however, the generalizability and sustainability are unknown. We describe an audit-feedback intervention and outcomes across multiple seasons in different clinic settings. Methods Two VA Medical Centers distributed audit-feedback reports targeting providers with frequent ARI visits in emergency department (ED) and primary care (PC) during 2015–2016 and 2016–2017. An academic detailing visit delivered by local peers accompanied the initial audit-feedback report. The intervention was expanded to ED and PC clinics (n = 10) in three other VA facilities in 2017–2018. Outcomes included rates of antibiotics prescribed, recurrent visits for ARIs within 30 days, and adverse events. We assessed intervention sustainability in initiating VAs, and intervention generalizability in expansion VAs. Mixed-effect logistic regression models were used to assess intervention effect on antibiotic prescribing and outcomes. Results Antibiotic prescribing for uncomplicated ARI visits (n = 7,814) declined from 53.8% to 27.9% post intervention. The intervention was associated with a reduction in odds of prescribing antibiotics in initiating facilities (odds ratio [OR] 0.6 (95% CI 0.3, 0.9), which declined further with an annual OR 0.8 [95% CI 0.7, 1.1] per year. Preliminary 6-month postintervention results were available from pilot clinics (n = 3) within two of the expansion VAs, which indicated similar effectiveness (OR 0.5 [0.4, 0.7]). Recurrent visits for ARIs (8.2% vs. 8.6%, P = 0.14) and adverse events (2.3% vs. 2.1%, P = 0.90) were not different pre-/post-intervention. Receipt of an antibiotic was not associated with recurrent visits for ARI (8.6% vs. 8.0%, P = 0.45) or adverse events (1.9% vs. 1.7%, P = 0.11). Conclusion An audit-feedback intervention sustained a reduction in antibiotic prescribing for ARIs over 3 years, and resulted in similar reductions in antibiotic use in varied ED and PC settings without affecting ARI-related return visit rates. Disclosures All authors: No reported disclosures.

Background Despite the high prevalence and global impact of knee osteoarthritis (KOA), current treatments are palliative. No disease modifying anti-osteoarthritic drug (DMOAD) has been approved. We recently demonstrated significant involvement of uric acid and activation of the innate immune response in osteoarthritis (OA) pathology and progression, suggesting that traditional gout therapy may be beneficial for OA. We therefore assess colchicine, an existing commercially available agent for gout, for a new therapeutic application in KOA. Methods/Design COLKOA is a double-blind, placebo-controlled, randomized trial comparing a 16-week treatment with standard daily dose oral colchicine to placebo for KOA. A total of 120 participants with symptomatic KOA will be recruited from a single center in Singapore. The primary end point is 30% improvement in total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score at week 16. Secondary end points include improvement in pain, physical function, and quality of life and change in serum, urine and synovial fluid biomarkers of cartilage metabolism and inflammation. A magnetic resonance imaging (MRI) substudy will be conducted in 20 participants to evaluate change in synovitis. Logistic regression will be used to compare changes between groups in an intention-to-treat analysis. Discussion The COLKOA trial is designed to evaluate whether commercially available colchicine is effective for improving signs and symptoms of KOA, and reducing synovial fluid, serum and urine inflammatory and biochemical joint degradation biomarkers. These biomarkers should provide insights into the underlying mechanism of therapeutic response. This trial will potentially provide data to support a new treatment option for KOA. Trial registration The trial has been registered at clinicaltrials.gov as NCT02176460. Date of registration: 26 June 2014. Electronic supplementary material The online version of this article (doi:10.1186/s13063-015-0726-x) contains supplementary material, which is available to authorized users.

Greater trochanter pain syndrome due to tendinopathy or bursitis is a common cause of hip pain. The previously reported magnetic resonance (MR) findings of trochanteric tendinopathy and bursitis are peritrochanteric fluid and abductor tendon abnormality. We have often noted peritrochanteric high T2 signal in patients without trochanteric symptoms. The purpose of this study was to determine whether the MR findings of peritrochanteric fluid or hip abductor tendon pathology correlate with trochanteric pain. We retrospectively reviewed 131 consecutive MR examinations of the pelvis (256 hips) for T2 peritrochanteric signal and abductor tendon abnormalities without knowledge of the clinical symptoms. Any T2 peritrochanteric abnormality was characterized by size as tiny, small, medium, or large; by morphology as feathery, crescentic, or round; and by location as bursal or intratendinous. The clinical symptoms of hip pain and trochanteric pain were compared to the MR findings on coronal, sagittal, and axial T2 sequences using chi-square or Fisher’s exact test with significance assigned as p