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• W4366997788 abstract "ABSTRACT Objectives Outpatient parenteral antimicrobial therapy (OPAT) use has increased significantly as it provides safe and reliable administration of long-term antimicrobials for severe infections. Benefits of OPAT include fewer antibiotic or line-related complications, increased patient satisfaction, shorter hospitalizations, and lower costs. Although OPAT programs carefully screen patients for eligibility and safety prior to enrollment, complications can occur. There is a paucity of studies identifying predictors of clinical outcomes in OPAT patients. Here, we seek to identify baseline predictors of OPAT outcomes utilizing machine learning methodologies. Methods We used electronic health record data from patients treated with OPAT between February 2019 and June 2022 at a large academic tertiary care hospital in Dallas, Texas. Three primary outcomes were examined: 1) clinical improvement at 30 days without evidence of reinfection; 2) patient actively being followed at 30 days; and 3) occurrence of any adverse event while on OPAT. Potential predictors were determined a priori , including demographic and clinical characteristics, OPAT setting, intravenous line type, and antimicrobials administered. Three classifiers were used to predict each outcome: logistic regression, random forest, and extreme gradient boosting (XGBoost). Model performance was measured using AUC, F1, and accuracy scores. Results We included 664 unique patients in the study, of whom 57% were male. At 30 days, clinical improvement was present in 78% of patients. Two-thirds of patients (67%) were actively followed at 30 days, and 30% experienced an adverse event while on OPAT. The XGBoost model performed best for predicting treatment success (average AUC = 0.873), with significant predictors including ID consultation and the use of vancomycin. The logistic regression model was best for predicting adverse outcomes (average AUC = 0.710). Risk factors for adverse outcomes included management in the home setting and the use of vancomycin, daptomycin, or piperacillin-tazobactam. Conclusion Outcomes of patients undergoing OPAT can be predicted with the use of easily-obtainable clinical and demographic factors. Patients requiring certain antimicrobial therapies, such as vancomycin or daptomycin, may derive less benefit from early hospital discharge and OPAT." @default.
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• W4366997788 date "2023-04-25" @default.
• W4366997788 modified "2023-10-14" @default.
• W4366997788 title "Risk Factors for Adverse Outcomes with Outpatient Parenteral Antimicrobial Therapy" @default.
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• W4366997788 doi "https://doi.org/10.1101/2023.04.21.23288837" @default.
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