Key result
A nomogram incorporating age, eGFR, hemoglobin, systolic blood pressure, and diabetes successfully predicted 5-year rapid kidney function decline with an AUC of 0.763 in the development cohort and 0.740 in the validation cohort.
Why the study?
A reliable model to predict rapid kidney function decline among individuals at risk of cardiovascular disease was needed.
Can a nomogram based on age, eGFR, hemoglobin, systolic blood pressure, and diabetes accurately predict rapid kidney function decline in a population at risk of cardiovascular disease?
Cohort (n=8,455)
No
Can a nomogram based on age, eGFR, hemoglobin, systolic blood pressure, and diabetes accurately predict rapid kidney function decline in a population at risk of cardiovascular disease?
Effect estimate: AUC 0.763
A simple nomogram using five readily available clinical variables can effectively identify primary care patients at high risk for rapid kidney function decline, aiding in early intervention to prevent cardiovascular and renal events.
BACKGROUND: To develop a reliable model to predict rapid kidney function decline (RKFD) among population at risk of cardiovascular disease. METHODS: In this retrospective study, key monitoring residents including the elderly, and patients with hypertension or diabetes of China National Basic Public Health Service who underwent community annual physical examinations from January 2015 to December 2020 were included. Healthy records were extracted from regional chronic disease management platform. RKFD was defined as the reduction of estimated glomerular filtration rate (eGFR) ≥ 40% during follow-up period. The entire cohort were randomly assigned to a development cohort and a validation cohort in a 2:1 ratio. Cox regression analysis was used to identify the independent predictors. A nomogram was established based on the development cohort. The concordance index (C-index) and calibration plots were calculated. Decision curve analysis was applied to evaluate the clinical utility. RESULTS: A total of 8455 subjects were included. During the median follow-up period of 3.72 years, the incidence of RKFD was 11.96% (n = 1011), 11.98% (n = 676) and 11.92% (n = 335) in the entire cohort, development cohort and validation cohort, respectively. Age, eGFR, hemoglobin, systolic blood pressure, and diabetes were identified as predictors for RKFD. Good discriminating performance was observed in both the development (C-index, 0.73) and the validation (C-index, 0.71) cohorts, and the AUCs for predicting 5-years RKFD was 0.763 and 0.740 in the development and the validation cohort, respectively. Decision curve analysis further confirmed the clinical utility of the nomogram. CONCLUSIONS: Our nomogram based on five readily accessible variables (age, eGFR, hemoglobin, systolic blood pressure, and diabetes) is a useful tool to identify high risk patients for RKFD among population at risk of cardiovascular disease in primary care. Whereas, further external validations are needed before clinical generalization.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2022) conducted a cohort in At risk of cardiovascular disease (n=8,455). Risk factors for rapid kidney function decline (age, eGFR, hemoglobin, systolic blood pressure, and diabetes) was evaluated on Rapid kidney function decline (reduction of estimated glomerular filtration rate ≥ 40%) (AUC 0.763). A nomogram incorporating age, eGFR, hemoglobin, systolic blood pressure, and diabetes successfully predicted 5-year rapid kidney function decline with an AUC of 0.763 in the development cohort and 0.740 in the validation cohort.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: