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Abstract: TH-PO408

A Machine Learning Model to Predict Patient Risk of Peritonitis Episodes

Session Information

Category: Dialysis

  • 703 Dialysis: Peritoneal Dialysis

Authors

  • Blanchard, Tommy C., Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Willetts, Joanna, Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • O'Connell, Michael Ryan, Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Chaudhuri, Sheetal, Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Usvyat, Len A., Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Ellison, Brian Christopher, Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Moran, Judith, Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Herman, Melissa, Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Dunphy, Susan M., Fresenius Medical Care North America, Waltham, Massachusetts, United States
  • Maddux, Franklin W., Fresenius Medical Care North America, Waltham, Massachusetts, United States
Background

Peritonitis infections are one of the primary complications in the use of peritoneal dialysis. Predicting what patients are at a higher risk of peritonitis is of great interest so that cases of peritonitis can be caught early, or patients can be given additional training to prevent peritonitis infections altogether.

Methods

We analyzed data of 36,329 peritoneal dialysis patients who were treated by a large dialysis provider from 2016-2017. We had 10,522 cases of peritonitis over this period. We trained a machine learning model (XGBoost boosted tree model) to predict which patients will be diagnosed with a peritonitis infection in the next month based on patients’ history of peritonitis, symptoms noted by nurses during assessments, routine clinical laboratory values, and demographic data.

Results

Our machine learning model achieved an area under the ROC curve of 0.736. The features that were found to be most important for prediction were: whether the patient has had a previous peritoneal infection, how long the patient has been on peritoneal dialysis days since previous infection, changes in potassium levels, and albumin levels (Table 1).

Conclusion

We built a machine learning model that was able to predict which patients will have a peritonitis infection in the next month. This model can be used to allocate resources to try to catch infections early or prevent them. Future work can expand the features the model has access to in order to improve the model performance.

VariableMean value for peritonitisMean value for uninfected
Vintage647 days621 days
Previous infection32%13%
Days since infection102153
Change in potassium.052.028
Albumin3.343.48

Funding

  • Commercial Support