Development of a multivariate model predictive of post-adrenalectomy renal function decline in patients with primary aldosteronism: a large-cohort single-center study.

PURPOSE

To develop a multivariate liniear model for predicting long-term (> 3 months) post-adrenalectomy renal function decline in patients with primary aldosteronism (PA). The model aims to help identify patients who may experience a significant decline in renal function after surgery.

METHODS

We retrospectively analyzed the clinical data of 357 patients who were diagnosed with PA and underwent adrenalectomy between September 2012 and February 2023. LASSO and multivariate linear regression analyses were used to identify significant risk factors for model construction. The models were further internally validated using bootstrap method.

RESULTS

Age (P < 0.001), plasma aldosterone concentration (PAC) measured in the upright-position (PAC, P = 0.066), PAC measured after saline infusion (PAC, P = 0.010), preoperative blood adrenocorticotropic-hormone level (ACTH, P = 0.048), preoperative estimated glomerular filtration rate (eGFR, P < 0.001) and immediate postoperative eGFR (P < 0.001) were finally included in a multivariate model predictive of post-adrenalectomy renal function decline and the coefficients were adjusted by internal validation. The final model is: predicted postoperative long-term (> 3 months) eGFR decline =-70.010 + 0.416age + 6.343lg PAC+4.802lg ACTH + 7.424lg PAC+0.637preoperative eGFR-0.438immediate postoperative eGFR. The predicted values are highly related to the observed values (adjusted R = 0.63).

CONCLUSION

The linear model incorporating perioperative clinical variables can accurately predict long-term (> 3 months) post-adrenalectomy renal function decline.