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Background Hospitals devote considerable resources to water distribution system (WS) surveillance and remediation for Legionella in an effort to reduce risk of transmitting Legionnaires disease (LD). There are no models that accurately predict periods of greatest risk for Legionella culture positivity (cx +) within a WS. Our goal was to build and validate a model based on weather and water parameters that predicted Legionella cx+ in our hospital WS. Methods One liter water samples from fixtures at 2 campuses were cultured for Legionella on BCYE plates with cysteine as part of infection prevention protocols. Logistic regression (LR) and random forest (RF) models included daily hospital WS measurements and Pittsburgh FAA weather observation station data. Training and validation used 2014–2015 and 2016–2017 data, respectively. Models predicted a first +cx within 14 day windows. Results Cxs were defined as + by loop-day, if any cx from within a unique WS loop was + for Legionella on a given day. Of the 7,272 water samples, 5,304 were collected from 16 buildings on 2 campuses in which ≥1 cx + was obtained. A total of 1,262 WS loop-days were collected over 339 unique days from these buildings. Details on training and validation data sets appear in figure. Overall, water was Legionella cx + on 3% of loop-days. Models predicted positivity if risk was >6%. The LR model comprised of independent predictors of cx + had sensitivity/specificity of 44%/80% (AUC: 0.715; misclassification error: 0.21), and PPV/NPV of 9%/97% in the validation data set. The RF model comprised of the same predictors had sensitivity/specificity of 100%/98% (AUC: 1.0; misclassification error: 0.02), and PPV/NPV of 67%/100%. The most important RF variables in the validation data set were WS temperature and minimum pH over the 7 days prior to cx. Conclusion An RF model using water and weather data was validated as an accurate predictor of new Legionella cx+ within a hospital WS. Most importantly, NPV for the model was 100%, meaning that no positive Legionella cxs were recovered during periods identified as low-risk. The RF model is a powerful tool for most efficiently directing resources to Legionella surveillance and LD prevention. Disclosures All authors: No reported disclosures.

open forum infectious diseases

1726. A Random Forest Prediction Model Accurately Identifies Periods at Increased Risk for Positive Legionella Cultures in a Hospital Water Distribution System