Advancing Precision Medicine for Hypertensive Nephropathy: A Novel Prognostic Model Incorporating Pathological...

This study aimed to assess the long-term renal prognosis of patients with hypertensive nephropathy (HN) diagnosed through renal biopsy, utilizing the random survival forest (RSF) algorithm.

From December 2010 to December 2022, HN patients diagnosed by renal biopsy in Xijing Hospital were enrolled and randomly divided into training set and testing set at a ratio of 7∶3. The study's composite endpoint was defined as a ≥50% decline in estimated glomerular filtration rate (eGFR), end-stage renal disease, or death. RSF and Cox regression were used to establish a renal prognosis prediction model based on the factors screened by the RSF algorithm. The Concordance index (C-index), integrated Brier score, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate discrimination, calibration, and risk classification, respectively.

A total of 225 patients were included in this study, with 72 (32.0%) patients experiencing combined events after a median follow-up of 29.9 (16.6, 52.1) months. Six eligible variables (overall chronicity grade of renal pathology, eGFR, high-density lipoprotein cholesterol, hematocrit, monocyte, and stroke volume) were selected from clinical data and introduced into the RSF model. The RSF model had a higher C-index in both the training set (0.904 [95% CI: 0.842-0.938] vs. 0.831 [95% CI: 0.768-0.894], p < 0.001) and the testing set (0.893 [95% CI: 0.770-0.944] vs. 0.841 [95% CI: 0.751-0.931], p = 0.021) compared to the Cox model. NRI and IDI indicated that the RSF model outperformed the Cox model regarding risk classification.

In this study, the RSF algorithm was employed to identify the risk factors affecting the prognosis of HN patients, and a clinical prognostic RSF model was constructed to predict the adverse outcomes of HN patients based on renal pathology. Compared to the traditional Cox regression model, the RSF model offers superior performance and can provide valuable new insights for clinical diagnosis and treatment strategies.