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6568 Background: Few studies have been performed addressing nutritional interventions & mitigation strategies to address side effects such as weight gain, anorexia, dysgeusia, in patients (pts) undergoing chemotherapy. We aim to study the effect of nutritional counseling on body mass index (BMI) and other side effects in pts undergoing systemic chemotherapy. Methods: Prospective randomized study of 50 pts into two groups (A & B). Group A was randomized to standard of care: oncologist visits once per month x 3 months, routine physician counseling & dietary recommendations. Group B: standard of care + nutritionist intervention which included 3 visits x 30 mins each. Nutritionists performed an initial assessment of pts at the first session (template provided in poster) then tailored dietary counseling monthly x3 months which includes dietary strategies to mitigate side effects, pt & caregiver education, recipes, & handouts (provided in poster). Inclusions: medically insured male & female adults with active malignancies (hematologic &boncologic) on chemotherapy (neoajuvant, adjuvant, palliative). Exclusions: pts with head & neck, stomach, esophagus, pancreatic tumors & pts with pre-existing cachexia. Data collected: pt gender, age, race, ethnicity, cancer subtype, TNM staging, chemotherapy regimen. Primary objective: compare change in BMI at visit 1, 2, &3. Secondary endpoints: anorexia, nausea, vomiting, depression, etc. reported in poster. Mean change in BMI, secondary endpoints compared using students T-test with ANOVA using SAS software version 8.0 & minitab version 17. Results: Group A initial BMI = 28.85 vs group B initial BMI = 27.34 (p = 0.05). Change in BMI group A vs B at visit 1 (0.4 vs 0.09, p = 0.002), visit 2 (4.6 vs -0.52, p = 0.001), visit 3 (6.5 vs 0.46, p = 0.003). Demographics & secondary endpoints reported in poster. Conclusions: We found dietary counseling in addition to standard of care was associated with significant mitigation of weight gain in patients on systemic chemotherapy. Dietary counseling is a relatively simple, inexpensive, individualized, & reproducible method to mitigate chemotherapy related weight gain.

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Background Carbapenem-resistant Enterobacterales are a growing threat globally. Early detection of CRE is necessary for appropriate treatment and infection control measures. Many hospital labs can test for carbapenemase production; however, in some regions, including South Texas, the majority of CRE are non-carbapenemase producing (NCPE). This study had two interrelated aims to develop decision rules tailored to a region with high prevalence of NCPE to predict 1) antimicrobial resistance (AMR) from whole genome sequencing (WGS) data and 2) CRE treatment outcomes. Methods To better understand links between resistome, phenotypic AMR, and prediction of outcomes for CRE, we developed decision rules to build machine learning prediction models. We conducted WGS and antibiotic susceptibility testing (21 antibiotics) on CRE isolates from unique patients across 5 hospitals in the South Texas region between 2013 and 2020. Day 30 outcomes were based on desirability of outcome ranking (DOOR). The overall classification accuracies of the models are reported. Results Overall 146 CRE isolates were included, 97 were used to train each model, and 49 were used for validation. Among the K. pneumoniae and E. coli CRE isolates that were available with susceptibility data, the majority (62%) were NCPE. For the clinical recovery model (DOOR), the combination of admission ICU status, piperacillin-tazobactam (PT) MIC > 16, presence of sul gene, and polymyxin-sparring regimens associated with an overall accuracy of 95% for having a worse DOOR. Majority (60%) of patients were empirically treated with piperacillin-tazobactam; notably, less than 33% isolates had PT MIC ≤ 16. Interestingly, combined effects of isolates that did not harbor carbapenemases, blaOXA-1, blaCTX-M-15, blaCMY, or aac(6’)ib-cr genes resulted in a decision rule with a 95.7% overall accuracy for susceptibility to PT (MIC < 16 ug/mL). Conclusion Herein, we used machine learning approaches to predict AMR and treatment-based outcomes linked with WGS data in a region with predominantly NCPE infections. Machine learning can obtain a model that can make repeatable predictions, further data can improve the accuracy and guide tailored clinical decision-making. Disclosures Grace Lee, PharmD, PhD, BCPS, Merck Co. (Grant/Research Support)NIA/NIH (Research Grant or Support)