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Abstract: TH-PO025

Derivation and Validation of Machine Learning Models for the Prevention of Unplanned Dialysis in Advanced CKD Patients

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Klamrowski, Martin M., Carleton University, Ottawa, Ontario, Canada
  • Klein, Ran, University of Ottawa, Ottawa, Ontario, Canada
  • Green, James, Carleton University, Ottawa, Ontario, Canada
  • Mccudden, Chris, University of Ottawa, Ottawa, Ontario, Canada
  • Edwards, Cedric A.W., University of Ottawa, Ottawa, Ontario, Canada
  • Rashidi, Babak, University of Ottawa, Ottawa, Ontario, Canada
  • Akbari, Ayub, University of Ottawa, Ottawa, Ontario, Canada
  • Hundemer, Gregory L., University of Ottawa, Ottawa, Ontario, Canada
Background

A short timeframe kidney failure risk prediction model may serve to prevent unplanned dialysis starts, a detrimental outcome associated with increased morbidity, mortality, and healthcare costs. To date, no such clinical tool exists.

Methods

We developed and externally validated models for prediction of kidney failure over short timeframes of 6 and 12 months. The models were fit in 2,432 consecutive advanced CKD patients from The Ottawa Hospital. Models were externally validated in two independent advanced CKD cohorts from the Kingston General Hospital (N=724), and the Sunnybrook Health Sciences Center (N=323). All hospitals are in Ontario, Canada. Patients lost to follow-up, under conservative care management, or with <12 months of follow-up were excluded. Random forest classifiers were used to predict the 6- and 12-month probability of kidney failure for each patient at each follow-up visit. Input features included age, sex, and commonly available laboratory measurements and features characterizing their trajectory. The percentage of patients detected within clinically actionable timeframes of 3-15 months were computed for both models.

Results

Internally, upon presentation, patients had a mean±SD age 66±15 years, and eGFR 18±7 mL/min/1.73m2, and median (IQR) ACR 164 (49, 333). Internal ROC-AUCs (95% CI) of 0.88 (0.87-0.88) and 0.86 (0.86-0.87) were achieved by the 6- and 12-month models, respectively. Models were well-calibrated. Internally, at 70% precision, patients requiring dialysis were correctly identified with at least 6 months advanced notice in 20% and 34% of cases using the 6- and 12-month models. Performance did not significantly differ at either external site.

Conclusion

Machine learning-based short timeframe kidney failure risk prediction models accurately identify advanced CKD patients at high risk for imminent dialysis, including a substantial proportion destined for unplanned (or “crash”) dialysis initiation.

Funding

  • Government Support – Non-U.S.