A Health Survey-Based Prediction Equation for Incident CKD

Prediction models for earlier stages of chronic kidney disease

PURPOSE OF REVIEW

Identifying patients with risk of developing progressive chronic kidney disease (CKD) early is an important step in improving kidney care. This review discusses four recently developed models, two which predict risk of new onset disease, and two which predict progression earlier in the course of disease.

RECENT FINDINGS

Several models predicting CKD incidence and progression have been recently developed and externally validated. A connecting theme across these models is the use of data beyond estimated glomerular filtration rate, allowing for greater accuracy and personalization. Two models were developed with stratification by diabetes status, displaying excellent model fit with and without variables like use of diabetes medication and hemoglobin A1C. Another model was designed to be patient facing, not requiring the knowledge of any laboratory values for use. The final model was developed using lab data and machine learning. These models demonstrated high levels of discrimination and calibration in external validation, suggesting suitability for clinical use.

SUMMARY

Models that predict risk of CKD onset and progression have the potential to significantly reduce disease burden, financial cost, and environmental output from CKD through upstream disease prevention and slowed progression. These models should be implemented and evaluated prospectively in primary care settings.

Strategies to Improve Long-Term Outcomes for Patients with Chronic Kidney Disease in China

Background

Chronic kidney disease (CKD) is an incurable disease requiring lifelong management. China has a high prevalence of CKD, which disproportionately affects older adults and those with chronic risk factors for CKD development. The rising prevalence of CKD in China places a substantial burden on the general population and the healthcare system.

Summary

In China, there are currently many unmet needs for patients with CKD and high-risk individuals, resulting from a lack of education and support to reduce risk factors, delayed diagnoses, limited knowledge of CKD among primary-care physicians, and poor access to treatments among some patient populations. An integrated, nationwide approach is required to improve the current situation of CKD management in China. There are currently several national healthcare frameworks in place that focus on new major health policies to prevent disease and encourage people to adopt healthier lifestyles, and while they do not directly target CKD, they may have a positive indirect impact. We explore the unmet needs for patients with CKD in China and discuss the potential strategies that may be required to overcome them. Such strategies include improving physician and patient education, establishing a targeted screening programme, supporting patients to improve self-management behaviours, accelerating the creation of medical consortia and medical satellite centres, and migrating from hospital- to community-based management. In addition to policy-driven strategies, development of novel therapies will be key to providing new solutions for the long-term management of CKD.

Key Messages

An integrated, nationwide approach is required, incorporating policy-driven changes to the clinical management of CKD, as well as the development of novel CKD treatments.

Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes

PURPOSE OF REVIEW

Recent years have witnessed the development of kidney risk prediction models which diverge from traditional model designs to incorporate novel approaches along with a focus on earlier outcomes. This review summarizes these recent advances, evaluates their pros and cons, and discusses their potential implications.

RECENT FINDINGS

Several kidney risk prediction models have recently been developed utilizing machine learning rather than traditional Cox regression. These models have demonstrated accurate prediction of kidney disease progression, often beyond that of traditional models, in both internal and external validation. On the opposite end of the spectrum, a simplified kidney risk prediction model was recently developed which minimized the need for laboratory data and instead relies primarily on self-reported data. While internal testing showed good overall predictive performance, the generalizability of this model remains uncertain. Finally, there is a growing trend toward prediction of earlier kidney outcomes (e.g., incident chronic kidney disease [CKD]) and away from a sole focus on kidney failure.

SUMMARY

Newer approaches and outcomes now being incorporated into kidney risk prediction modeling may enhance prediction and benefit a broader patient population. However, future work should address how best to implement these models into practice and assess their long-term clinical effectiveness.