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Kidney Week

Abstract: PO1091

Use of Predictive Analytics to Inform Integrated Care Programs to Reduce Hospitalizations Among Hemodialysis Patients

Session Information

Category: Dialysis

  • 701 Dialysis: Hemodialysis and Frequent Dialysis

Authors

  • Mckeon, Katherine L., Davita Clinical Research, Minneapolis, Minnesota, United States
  • Walker, Adam G., Davita Clinical Research, Minneapolis, Minnesota, United States
  • Luo, Jiacong, Davita Clinical Research, Minneapolis, Minnesota, United States
  • Sibbel, Scott, Davita Clinical Research, Minneapolis, Minnesota, United States
  • Zywno, Meredith L., DaVita Inc, Denver, Colorado, United States
  • Becker, Bryan N., DaVita Inc, Denver, Colorado, United States
  • Bray, Tiffany L., DaVita Inc, Denver, Colorado, United States
  • Stebbins, Juliana, DaVita Integrated Kidney Care, Denver, Colorado, United States
  • Lefeber, Nick, DaVita Integrated Kidney Care, Denver, Colorado, United States
  • Roer, David A., DaVita Inc, Denver, Colorado, United States
  • Brunelli, Steven M., Davita Clinical Research, Minneapolis, Minnesota, United States
Background

Integrated care for dialysis patients could benefit from identification of those who are at high risk for poor outcomes in order to efficiently deploy clinical resources. We recently developed a hospitalization risk stratification model to triage hemodialysis (HD) patients for clinician contact and assessment within an integrated care clinical program (ICCP). In this analysis, we compared hospitalization rates before and after model implementation for patients enrolled in an ICCP and control patients who were not.

Methods

All patients received our standard level of care consistent with industry best practices and regulations. ICCP patients predicted to be medium and high risk received additional services proportional to predicted risk level. Relative differences in annualized hospitalization rates for HD patients enrolled in an ICCP were compared to controls who were not by calculating relative rate ratios (RRR) and 95% confidence intervals (CI) in the baseline (Feb 2017-April 2018) and postmodel implementation (Jan-Aug 2019) eras. Comparisons were stratified by predicted risk level.

Results

The baseline hospitalization rate was 3.0 admissions/patient-year (pt-yr) for all high-risk patients. Post implementation, hospitalization rates decreased to a greater extent among ICCP patients (-0.4 admissions/pt-yr) versus controls (-0.2 admissions/pt-yr): RRR (95% CI) = 0.94 (0.90, 0.97). The baseline hospitalization rate was 1.7 admissions/pt-yr for all medium-risk patients. Post implementation, hospitalization rates decreased to a greater extent among ICCP patients (-0.2 admissions/pt-yr) versus controls (-0.1 admissions/pt-yr): RRR (95% CI) = 0.95 (0.90, 1.00). No differences were observed among low-risk ICCP patients and low-risk control patients.

Conclusion

These results support the potential utility of predictive analytics to support programs aimed at improving clinical outcomes among HD patients.