RESUMO
Rationale & Objective: The Kidney Failure Risk Equations have been proven to perform well in multinational databases, whereas validation in Asian populations is lacking. This study sought to externally validate the equations in a community-based chronic kidney disease cohort in China. Study Design: A retrospective cohort study. Setting & Participants: Patients with and estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 dwelling in an industrialized coastal city of China. Exposure: Age, sex, eGFR, and albuminuria were included in the 4-variable model, whereas serum calcium, phosphate, bicarbonate, and albumin levels were added to the previously noted variables in the 8-variable model. Outcome: Initiation of long-term dialysis treatment. Analytical Approach: Model discrimination, calibration, and clinical utility were evaluated by Harrell's C statistic, calibration plots, and decision curve analysis, respectively. Results: A total of 4,587 participants were enrolled for validation of the 4-variable model, whereas 1,414 were enrolled for the 8-variable model. The median times of follow-up were 4.0 (interquartile range: 2.6-6.3) years for the 4-variable model and 3.4 (2.2-5.6) years for the 8-variable model. For the 4-variable model, the C statistics were 0.750 (95% CI: 0.615-0.885) for the 2-year model and 0.766 (0.625-0.907) for the 5-year model, whereas the values were 0.756 (0.629-0.883) and 0.774 (0.641-0.907), respectively, for the 8-variable model. Calibration was acceptable for both the 4-variable and 8-variable models. Decision curve analysis for the models at the 5-year scale performed better throughout different net benefit thresholds than the eGFR-based (<30 mL/min/1.73 m2) strategy. Limitations: A large proportion of patients lack albuminuria measurements, and only a subset of population could provide complete data for the 8-variable equation. Conclusions: The kidney failure risk equations showed acceptable discrimination and calibration and better clinical utility than the eGFR-based strategy for incidence of kidney failure among community-based urban Chinese patients with chronic kidney disease.
Accurate and reliable risk evaluation of chronic kidney disease (CKD) prognosis can be helpful for physicians to make decisions concerning treatment opportunity and therapeutic strategy. The kidney failure risk equation is an outstanding model for predicting risk of kidney failure among patients with CKD. However, the equation is lacking validation among Chinese populations. In the current study, we demonstrated that the equation had good discrimination among an urban community-based cohort of patients with CKD in China. The calibration was also acceptable. Decision curve analysis also showed that the equation performed better than a traditional kidney function-based strategy. The results provide the basis for using predictions derived from the kidney failure risk equation to improve the management of patients with CKD in community settings in China.
RESUMO
Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.