Development and validation of a risk prediction model for worsening renal function in patients with incident heart failure
Authors: Wang H , Tao Y , Hussain M , Oswald A S , Win M L , Liew Y J , Gao C , Guignard-Duff M , Cole C , Hall C , Das S , Baruah R , Mordi I R , Lang C C 
Published: October 2024

Abstract

Background
Worsening renal function (WRF) is one of the strongest predictors of outcome in patients with heart failure (HF). The presence of WRF often influences the decision to start, up-titrate, or discontinue disease modifying HF therapies and is one of the key determinants of suboptimal guideline-directed medical therapy. Existing WRF risk scores were developed in hospitalised HF patients to predict in-hospital WRF. Their performance among incident HF is far from ideal. It is therefore important to develop a new WRF risk prediction model in people with newly diagnosed HF.

Purpose
This study was to develop and validate a clinical risk prediction model for 180-day WRF post diagnosis of incident HF using data from a population-based longitudinal cohort.

Methods
We developed and validated a multivariable logistic regression model for WRF in the 180 days post diagnosis of incident HF. Data for individuals with incident HF between 2016 to 2021 were extracted from the NELSON study conducted in Tayside, Scotland. WRF was defined as a composite event of (i) having two consecutive eGFRs declined by 40% or greater; (ii) having an eGFR &lt; 15mL/min/1.73m2; (iii) initiation of sustained dialysis; (iv) development of end-stage kidney disease; (v) receiving kidney transplantation; or (vi) kidney related death. Four candidate models included one model using literature knowledge; one multivariable fractional polynomial (MFP) model; and two stepwise selected models based on either Akaike or Bayesian information criterion (AIC or BIC). Calibration and discrimination were assessed, and performance stability were evaluated using optimism corrected performance via 1000 times bootstrapping.

Results
4076 individuals (mean age 72.8 ± 13.2; 58% male; 1081 HF with reduced ejection fraction (HFrEF), 646 with HF with mildly reduced ejection fraction (HFmrEF), 1348 HF with preserved ejection fraction (HFpEF), and 1001 HF with unknown ejection fraction (EF)) who were WRF-free when diagnosed with HF were identified. Of these, 2095 (51.4%) were in-patients, and 1981 (48.6%) were out-patients. 285 (7%) patients developed WRF within 180 days post HF diagnosis. The selected MFP model (comprising 12 predictors, Table 1) showed good calibration (slope = 1.00 [0.86, 1.14]; intercept = 0.0 [-0.13, 0.13]) discrimination (C-statistic = 0.69 [95% CI: 0.65, 0.72]), and stability (optimism corrected: calibration slope = 0.94 [0.80, 1.07]; calibration intercept = -0.14 [-0.50, 0.20]; C-statistic = 0.67 [0.64, 0.71]). Discrimination of the developed model significantly outperformed two existing risk scores (Forman and Basel, both with a C-statistic = 0.62 [0.59 – 0.65]).

Conclusions
We have shown that the developed model performed better in predicting WRF in individuals with newly diagnosed HF. This could have utility in identifying at risk patients with HF early in the disease trajectory for therapies that can reduce renal decline.Calibration of predicted riskModel estimates for predicting 180d WRF