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Abstract: PO0907

Prediction of Severe Gastrointestinal Bleeding Events in Hemodialysis: Collaborative Development of Machine Learning Model Within INSPIRE

Session Information

Category: Dialysis

  • 701 Dialysis: Hemodialysis and Frequent Dialysis

Authors

  • Lama, Suman Kumar, Fresenius Medical Care, Global Medical Office, Waltham, Massachusetts, United States
  • Chaudhuri, Sheetal, Fresenius Medical Care, Global Medical Office, Waltham, Massachusetts, United States
  • Willetts, Joanna, Fresenius Medical Care, Global Medical Office, Waltham, Massachusetts, United States
  • Larkin, John W., Fresenius Medical Care, Global Medical Office, Waltham, Massachusetts, United States
  • Winter, Anke, Fresenius Medical Care, Global Medical Office, Bad Homburg, Germany
  • Stauss-Grabo, Manuela, Fresenius Medical Care, Global Medical Office, Bad Homburg, Germany
  • Usvyat, Len A., Fresenius Medical Care, Global Medical Office, Waltham, Massachusetts, United States
  • Hymes, Jeffrey L., Fresenius Medical Care, Global Medical Office, Waltham, Massachusetts, United States
  • Maddux, Franklin W., Fresenius Medical Care AG und Co KGaA, Bad Homburg, Hessen, Germany
  • Wheeler, David C., University College London, London, United Kingdom
  • Stenvinkel, Peter, Karolinska Institutet, Stockholm, Stockholm, Sweden
  • Floege, Jürgen, RWTH Aachen University Hospital, Aachen, Germany

Group or Team Name

  • On behalf of the INSPIRE Core Group
Background

INitiativeS on advancing Patients’ outcomes In REnal disease (INSPIRE) is an academia and industry collaboration set forth to identify critical investigations/models needed to advance the practice of nephrology. As an inaugural effort, INSPIRE group aims to develop a machine learning (ML) model that can identify a hemodialysis (HD) patient’s 30-day risk for hospitalization due to gastrointestinal (GI) bleeding.

Methods

We used data from adult (age ≥18 years) HD patients (Jan 2017-Dec 2020) in the United States to build a XGBoost model considering 2,292 variables for classification of 30-day GI bleed hospitalization risk. Data were randomly split in 50%:20%:30% ratio for model training, validation, and testing. Unseen data by model (testing) was used for assessing performance via area under the curve (AUC) and feature importance of predictors via Shapley (SHAP) values.

Results

Among 58,187 HD patients included in the testing dataset, 1150 had a GI bleed hospitalization. ML model showed AUC=0.67 and top predictors of a GI bleed hospitalization in 30 days were the minimum hemoglobin level in prior 180 days, time since prior GI bleed hospitalization, and higher vitamin D levels (Figure 1).

Conclusion

ML model appears to have suitable performance for identifying a patient’s 30-day risk for GI bleed hospitalization. Albeit further model iterations/tuning are needed, ML techniques that account for collinearity and missingness hold promise for early detection of potentially avoidable GI bleeding admissions. Model identified an important association between higher vitamin D levels and GI bleeding events, which is consistent with the increasing evidence suggesting antithrombotic and anticoagulant actions of vitamin D derivatives.

Funding

  • Commercial Support