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Abstract: TH-PO129

Interactive Simulator for Model-Based Predictions of Parathyroid Hormone (PTH) Levels in Hemodialysis Patients

Session Information

Category: Bone and Mineral Metabolism

  • 502 Bone and Mineral Metabolism: Clinical

Authors

  • Schappacher-Tilp, Gudrun, FH Joanneum GmbH, Graz, Steiermark, Austria
  • Kaehling, Nicoletta, FH Joanneum GmbH, Graz, Steiermark, Austria
  • Teiniker, Egon, FH Joanneum GmbH, Graz, Steiermark, Austria
  • Kotanko, Peter, Renal Research Institute, New York, New York, United States
Background

Secondary hyperparathyroidism is a prevalent condition among hemodialysis (HD) patients and a significant contributor to chronic kidney disease-mineral bone disorder (CKD-MBD). CKD-MBD poses a considerable risk of vascular calcification and cardiovascular events. The KDIGO guidelines strongly emphasize the importance of managing parathyroid hormone (PTH) levels in hemodialysis patients (Kidney Int Suppl 2017). Considering this, we have built an interactive simulator to predict PTH levels based on interventions targeting phosphate and calcitriol levels tailored to exemplary patients.

Methods

We utilized a comprehensive physiology-based mathematical model of parathyroid gland biology (Schappacher-Tilp, Physiol Rep, 2019) which has been employed successfully to predict individual PTH levels in HD patients (Pirklbauer, Front Med, 2021). The simulator follows a client-server architecture (Fig 1A) and features a user-friendly graphical user interface (GUI) allowing the user to select various parameters for the simulation (Fig 1B). Within the simulator, users can set treatment targets for serum phosphate and calcitriol levels, along with specifying the target time horizon.

Results

The interactive simulator provides users with the ability to select specific treatment targets and track the response of intact parathyroid hormone (iPTH) over time, as depicted in Fig 1C.

Conclusion

Our simulator predicts iPTH levels for various patient groups based on user-specified treatment targets for phosphate and calcitriol, serving as an effective educational tool to illustrate the effect of phosphate and calcitriol on iPTH levels.

Fig 1: A The client-server architecture and B GUI. C iPTH level predictions for an exemplary patient (5 years HD, poor historic phosphate control, no calcitriol therapy history)