P1060USING ARTIFICIAL INTELLIGENCE TO PREDICT HOME THERAPY CANDIDATES. (6th June 2020)

• Record Type: Journal Article
• Authors: Yu, Jerry; Long, Andrew; Hanson, Maria; Ellis, Aleetha; Macarthur, Michael; Chatoth, Dinesh; Turk, Joseph; Buck, Michael; Usvyat, Len A; Kossmann, Robert J; Maddux, Franklin
• Is Part Of: Nephrology dialysis transplantation. Volume 35(2020)Supplement 3
• Publication Date: 2020-06-06
• Subjects: Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfaa142.P1060
• Languages: English
• ISSNs: 0931-0509

Abstract

Abstract: Background and Aims: There are many benefits for performing dialysis at home including more flexibility and more frequent treatments. A possible barrier to election of home therapy (HT) by in-center patients is a lack of adequate HT education. To aid efficient education efforts, a predictive model was developed to help identify patients who are more likely to switch from in-center and succeed on HT. Method: We developed a model using machine learning to predict which patients who are treated in-center without prior HT history are most likely to switch to HT in the next 90 days and stay on HT for at least 90 days. Training data was extracted from 2016–2019 for approximately 300, 000 patients. We randomly sampled one in-center treatment date per patient and determined if the patient would switch and succeed on HT. The input features consisted of treatment vitals, laboratories, absence history, comprehensive assessments, facility information, county-level housing, and patient characteristics. Patients were excluded if they had less than 30 days on dialysis due to lack of data. A machine learning model (XGBoost classifier) was deployed monthly in a pilot with a team of HT educators to investigate the model's utility for identifying HT candidates. Results: There were approximately 1, 200 patients starting a home therapy per month in a large dialysis provider, with approximately one-third being in-center patients. The prevalence of switching and succeeding to HT in this population was 2.54%. The predictive model achieved an area under the curve of 0.87, sensitivity of 0.77, and a specificity of 0.80 on a hold-out test dataset. The pilot was successfully executed for several months and two major lessons were learned: 1) some patients who reappeared on each month's list should be removed from the list after expressing no interest in HT, and 2) a data collection mechanism should be put in place to capture the reasons why patients are not interested in HT. Conclusion: This quality-improvement initiative demonstrates that predictive modeling can be used to identify patients likely to switch and succeed on home therapy. Integration of the model in existing workflows requires creating a feedback loop which can help improve future worklists.