A mathematical model for predicting the decline in estimated glomerular filtration rate at 12 months after parathyroidectomy in patients with primary hyperparathyroidism

Background. Primary hyperparathyroidism (PHPT) is an endocrine disease characterized by excessive production of parathyroid hormone (PTH) and elevated or high-normal blood calcium levels caused by primary pathology of the parathyroid glands. The "classic" complication of PHPT is a decrease in the kidneys filtration function. Parathyroidectomy (PTE) reduces the risks of further deterioration in filtration function; however, in some cases, this is not achieved.
Aim. To develop a mathematical model to predict the decline in estimated glomerular filtration rate (eGFR) 12 months after PTE in patients with PHPT, and implement it as a software.
Methods. Retrospective study included 140 patients with PHPT who underwent PTE in 1993–2010 and 2018–2020 at the National Medical Research Center of Endocrinology. Analyzed variables included sex, age, indicators of calciumphosphorus, purine, lipid, and carbohydrate metabolism, presence of PHPT complications, treatment for PHPT, histological examination of removed parathyroid tissue, development of postoperative hypocalcemia and transient hypoparathyroidism, therapy for postoperative hypocalcemia. The random forest method was used to build the mathematical model.
Results. To predict the decline in eGFR, a model using 24 predictors was built: sex, age, body mass index, PTH, ionized calcium, alkaline phosphatase, phosphorus, urea, eGFR, total cholesterol, diastolic blood pressure, SD(T-score)<-2.5/ SD(Z-score)<-2.0, CKD, duration of nephrolithiasis, use of angiotensin II receptor blockers and angiotensin-converting enzyme inhibitors, preoperative use of cholecalciferol and cinacalcet, parathyroid hyperplasia/adenoma, postoperative hypocalcemia, dose of alfacalcidol and calcium supplements, postoperative use of cholecalciferol. The resulting model (http://194.87.111.169/cfr) predicts a decline in eGFR in patients with PHPT after PTE with a probability of 56.8–86.3% and excludes – with a probability of 85.6–97.7%.
Conclusion. A mathematical model to predict the decline in eGFR 12 months after PTE in patients with PHPT was developed, with an overall accuracy of 88%, 95% CI (79%; 93%). The model was implemented as a calculator that can be used in routine clinical practice

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