Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients
Background: Personalized management of secondary hyperparathyroidism is a critical part of hemodialysis patient care. We used a mathematical model of parathyroid gland (PTG) biology to predict (1) short-term peridialytic intact PTH (iPTH) changes in response to diffusive calcium (Ca) fluxes and (2)...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.704970/full |
| _version_ | 1818725940344127488 |
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| author | Markus Pirklbauer David A. Bushinsky Peter Kotanko Peter Kotanko Gudrun Schappacher-Tilp Gudrun Schappacher-Tilp |
| author_facet | Markus Pirklbauer David A. Bushinsky Peter Kotanko Peter Kotanko Gudrun Schappacher-Tilp Gudrun Schappacher-Tilp |
| author_sort | Markus Pirklbauer |
| collection | DOAJ |
| description | Background: Personalized management of secondary hyperparathyroidism is a critical part of hemodialysis patient care. We used a mathematical model of parathyroid gland (PTG) biology to predict (1) short-term peridialytic intact PTH (iPTH) changes in response to diffusive calcium (Ca) fluxes and (2) to predict long-term iPTH levels.Methods: We dialyzed 26 maintenance hemodialysis patients on a single occasion with a dialysate Ca concentration of 1.75 mmol/l to attain a positive dialysate-to-blood ionized Ca (iCa) gradient and thus diffusive Ca loading. Intradialytic iCa kinetics, peridialytic iPTH change, and dialysate-sided iCa mass balance (iCaMB) were assessed. Patient-specific PTG model parameters were estimated using clinical, medication, and laboratory data. We then used the personalized PTG model to predict peridialytic and long-term (6-months) iPTH levels.Results: At dialysis start, the median dialysate-to-blood iCa gradient was 0.3 mmol/l (IQR 0.11). The intradialytic iCa gain was 488 mg (IQR 268). Median iPTH decrease was 75% (IQR 15) from pre-dialysis 277 to post-dialysis 51 pg/ml. Neither iCa gradient nor iCaMB were significantly associated with peridialytic iPTH changes. The personalized PTG model accurately predicted both short-term, treatment-level peridialytic iPTH changes (r = 0.984, p < 0.001, n = 26) and patient-level 6-months iPTH levels (r = 0.848, p < 0.001, n = 13).Conclusions: This is the first report showing that both short-term and long-term iPTH dynamics can be predicted using a personalized mathematical model of PTG biology. Prospective studies are warranted to explore further model applications, such as patient-level prediction of iPTH response to PTH-lowering treatment. |
| first_indexed | 2024-12-17T21:50:17Z |
| format | Article |
| id | doaj.art-0c83cc0054cf427cbcb656578aad4380 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-12-17T21:50:17Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj.art-0c83cc0054cf427cbcb656578aad43802022-12-21T21:31:20ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-09-01810.3389/fmed.2021.704970704970Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis PatientsMarkus Pirklbauer0David A. Bushinsky1Peter Kotanko2Peter Kotanko3Gudrun Schappacher-Tilp4Gudrun Schappacher-Tilp5Department of Internal Medicine IV – Nephrology and Hypertension, Medical University Innsbruck, Innsbruck, AustriaDepartment of Medicine, University of Rochester School of Medicine, Rochester, NY, United StatesRenal Research Institute New York, New York, NY, United StatesIcahn School of Medicine at Mount Sinai, New York, NY, United StatesInstitute for Mathematics and Scientific Computing, University of Graz, Graz, AustriaInstitute of Electronic Engineering, FH Joanneum-University of Applied Sciences, Graz, AustriaBackground: Personalized management of secondary hyperparathyroidism is a critical part of hemodialysis patient care. We used a mathematical model of parathyroid gland (PTG) biology to predict (1) short-term peridialytic intact PTH (iPTH) changes in response to diffusive calcium (Ca) fluxes and (2) to predict long-term iPTH levels.Methods: We dialyzed 26 maintenance hemodialysis patients on a single occasion with a dialysate Ca concentration of 1.75 mmol/l to attain a positive dialysate-to-blood ionized Ca (iCa) gradient and thus diffusive Ca loading. Intradialytic iCa kinetics, peridialytic iPTH change, and dialysate-sided iCa mass balance (iCaMB) were assessed. Patient-specific PTG model parameters were estimated using clinical, medication, and laboratory data. We then used the personalized PTG model to predict peridialytic and long-term (6-months) iPTH levels.Results: At dialysis start, the median dialysate-to-blood iCa gradient was 0.3 mmol/l (IQR 0.11). The intradialytic iCa gain was 488 mg (IQR 268). Median iPTH decrease was 75% (IQR 15) from pre-dialysis 277 to post-dialysis 51 pg/ml. Neither iCa gradient nor iCaMB were significantly associated with peridialytic iPTH changes. The personalized PTG model accurately predicted both short-term, treatment-level peridialytic iPTH changes (r = 0.984, p < 0.001, n = 26) and patient-level 6-months iPTH levels (r = 0.848, p < 0.001, n = 13).Conclusions: This is the first report showing that both short-term and long-term iPTH dynamics can be predicted using a personalized mathematical model of PTG biology. Prospective studies are warranted to explore further model applications, such as patient-level prediction of iPTH response to PTH-lowering treatment.https://www.frontiersin.org/articles/10.3389/fmed.2021.704970/fullprecision medicinesecondary hyperparathyroidismparathyroid hormonepatient-level prediction modelhemodialysis |
| spellingShingle | Markus Pirklbauer David A. Bushinsky Peter Kotanko Peter Kotanko Gudrun Schappacher-Tilp Gudrun Schappacher-Tilp Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients Frontiers in Medicine precision medicine secondary hyperparathyroidism parathyroid hormone patient-level prediction model hemodialysis |
| title | Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients |
| title_full | Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients |
| title_fullStr | Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients |
| title_full_unstemmed | Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients |
| title_short | Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients |
| title_sort | personalized prediction of short and long term pth changes in maintenance hemodialysis patients |
| topic | precision medicine secondary hyperparathyroidism parathyroid hormone patient-level prediction model hemodialysis |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.704970/full |
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