Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease

Chronic kidney disease (CKD) is an increasingly serious public health problem [1], with the number of patients with reduced estimated glomerular filtration rate (eGFR) increased by 70% globally from 1990 to 2016 [2]. Rapid kidney function decline (RKFD) is associated with cardiovascular disease (CVD) [3], incident CKD [4] and all-cause mortality [5]. However, due to their comorbidities, individuals at risk of cardiovascular disease usually need to take antihypertension medicine, antidiabetic drugs or antithrombotic agents, which may increase the burden on the kidney. Therefore, early identifying and treating the individuals with high risk of RKFD may help decrease the incidence of cardiovascular events among population at risk of CVD [6].

Previous studies had established several effective prediction models to identified patients at risk of worsening renal function. However, the endpoint they mainly focused on was incident CKD which was defined as incident eGFR less than 60 ml/min/1.73m² [7, 8]. Risk prediction models constructed to identify patients at risk of incident CKD were only applicable for individuals without baseline CKD. Once the patient had progressed to CKD, these prediction models were no longer applicable. In this situation, risk prediction model to identify patients at risk of RKFD, which was defined as a relative fall in eGFR, may be more practical. Therefore, in the present study, we aimed to develop and validate a prediction model to quickly identify individuals at risk of RKFD among population at risk of cardiovascular disease.

The present study maybe the first to develop a nomogram for the prediction of 5-year RKFD among the population at risk of cardiovascular disease. Our simple nomogram including 5 predictors: age, eGFR, hemoglobin, SBP, and diabetes, demonstrated good discrimination and calibration. With the cut-off point of 150, we may easily identify individuals at high risk of RKFD.

Rapid declining kidney function is associated with higher risk of myocardial infarction (MI), heart failure (HF), stroke, and peripheral arterial disease (PAD) among patients with or without CKD [10], especially for the elderly who are even more likely to suffer from CVD and CKD. The elderly and adults with chronic disease (hypertension and diabetes) are currently the key monitoring population for the China National Basic Public Health Service. Therefore, to quickly identify individuals with rapidly declining renal function among this population is very meaningful, which may help reducing the incidence of CVD and improve their quality of life. As for the definition of “RKFD”, some studies defined RKFD as annual eGFR declines of 3 ml/min per 1.73m² [10, 11] or 5 ml/min per 1.73m² [12]. Recently, a Japanese study among healthy subjects reported that eGFR decline non-linearly altered according to age [13]. Compared with these absolute values change, the percentage decrease of eGFR seems to be more personalized in defining the RKFD. Moreover, the ARIC cohort study including 13,029 participants showed that patients with annual decline (annual decline ≥5.65%) in eGFR was at significantly greater risk for coronary heart disease [3]. In year 2014, the NKF and FDA published a series article to suggest that eGFR declines of 40% could be an effective surrogate endpoint in the clinical trials [14]. Therefore, wo chose eGFR declines of 40% to be the definition of RKFD in this study.

In our study, we identified 5 independent predictors of RKFD: age, baseline eGFR, hemoglobin concentration, SBP, and diabetes. This finding was partly consistent with those of prior studies [4, 8, 15‐18]. In a community-based retrospective cohort study among 51,938 adults who underwent annual medical examinations between 1999 and 2013, aging, higher SBP, proteinuria, and smoking were related to faster loss of kidney function [16]. A prospective observational cohort study - KNOW-CKD Study [17] and a study based on Chinese population [12] also found that SBP had a greater association with adverse kidney outcomes than DBP. These findings clearly emphasize that SBP is an important correlated factor for faster loss of kidney function. Anemia is also an independent risk factor for worsening kidney function among the middle-aged and elderly population [18]. As we all known, CKD often complicated with anemia mainly due to iron deficiency and erythropoietin deficiency [19, 20]. However, on the other side, anemia could also contribute to kidney function decline, which may be due to hypoxia and/or increased oxidative stress [21]. Anemia decreases the oxygen delivery to tissues and thus affects organ function including kidney and heart [18]. The stage of baseline eGFR has been proven to be associated with incident CKD [8, 22], and we further found that the baseline eGFR could also contribute to the rapid eGFR decline (HR = 1.022, 95%CI 1.019–1.025), and this finding further confirmed that the baseline value of eGFR is essential for future kidney function development [23]. There is no doubt that diabetes is an independent risk factor for kidney function decline [24].

We noticed that, in the univariate analysis, there were some phenomena that were different from clinical cognition, such as lower serum creatinine, cholesterol, and uric acid, higher eGFR, lower smoking and drinking rate in patients developing RKFD. For the lower creatinine and higher eGFR in the patients developing RKFD, it may be caused by the endpoint of our study, which was defined by the percentage declined in eGFR. For individuals with better baseline renal function, the space for the decline in renal function was greater than that of people with baseline renal insufficiency. As for uric acid, in the community physical examination in 2015, uric acid was not routinely tested, so the rate of missing data was over 22%. Therefore, the uric acid was not included in the multivariable Cox regression analysis. And for the smoking and drinking, one reason may be that individuals who often smoking or drinking were relatively young, and, we could find that smoking and drinking were excluded after adjusting for other influencing factors through multivariate COX regression analysis. In the further research, we would carry out prospective research to improve the quality of our baseline data and verify the model.

Constructed based on the demographic, clinical and laboratory variables from electronic health records, our nomogram is clinically applicable and easy to use in primary care setting. Although our nomogram performed well in our validation cohort, further external validations are needed before clinical generalization.