Prescriber Uncertainty as Opportunity to Improve Care of Type 2 Diabetes with Chronic Kidney Disease: Mixed Methods Study

Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review

BACKGROUND

Approximately 37 million individuals in the United States (US) have chronic kidney disease (CKD). Patients with CKD have a substantial morbidity and mortality, which contributes to a huge economic burden to the healthcare system. A limited number of clinical pathways or defined workflows exist for CKD care delivery in the US, primarily due to a lower prioritization of CKD care within health systems compared with other areas (e.g., cardiovascular disease [CVD], cancer screening). CKD is a public health crisis and by the year 2040, CKD will become the fifth leading cause of years of life lost. It is therefore critical to address these challenges to improve outcomes in patients with CKD.

METHODS

The CKD Leaders Network conducted a virtual, 3 h, multidisciplinary roundtable discussion with eight subject-matter experts to better understand key factors impacting CKD care delivery and barriers across the US. A premeeting survey identified topics for discussion covering the screening, diagnosis, risk stratification, and management of CKD across the care continuum. Findings from this roundtable are summarized and presented herein.

RESULTS

Universal challenges exist across health systems, including a lack of awareness amongst providers and patients, constrained care team bandwidth, inadequate financial incentives for early CKD identification, non-standardized diagnostic classification and triage processes, and non-centralized patient information. Proposed solutions include highlighting immediate and long-term financial implications linked with failure to identify and address at-risk individuals, identifying and managing early-stage CKD, enhancing efforts to support guideline-based education for providers and patients, and capitalizing on next-generation solutions.

CONCLUSIONS

Payers and other industry stakeholders have opportunities to contribute to optimal CKD care delivery. Beyond addressing the inadequacies that currently exist, actionable tactics can be implemented into clinical practice to improve clinical outcomes in patients at risk for or diagnosed with CKD in the US.

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.