Big Data in Nephrology

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives, an emerging research tool in medicine. In nephrology, clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy. This is especially important given the epidemiology of chronic kidney disease, renal oncology, and hypertension worldwide. However, there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Prediction of dialysis adequacy using data-driven machine learning algorithms

BACKGROUND

Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcomes in chronic hemodialysis patients. However, the need for pre- and postdialysis blood samples prevented the assessment of spKt/V in every session.

METHODS

This retrospective single-center study was performed on end-stage renal disease (ESKD) patients aged ≥ 18 years who received standard thrice-weekly chronic HD therapy. Eighty-seven variables, including general, intradialytic, and laboratory variables, were collected from the medical records for analysis. Five steps of preprocessing procedure were deployed to select only the most relevant variables. Six binary classification models were developed to predict whether spKt/V was higher than 1.4.

RESULTS

A total of 1869 HD sessions from 373 ESKD patients were included in this study. The Random Forest model showed the best prediction for dialysis adequacy, with AUROC scores of 0.860 in the validation dataset and 0.873 in the testing dataset. Notably, an accessible model that solely relied on noninvasively collected general and dialysis-related variables maintained high prediction accuracy, with AUROC scores of 0.854 and 0.868 in the validation and testing datasets, respectively. The five most significant predictive variables were vascular access, gender, body mass index, ultrafiltration volume, and dialysis duration.

CONCLUSION

The study results suggest that the development of ML models for accurately predicting dialysis adequacy based on general and intradialytic variables is feasible. These models have the potential to be utilized for noninvasive clinical assessments of dialysis adequacy.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics

The COVID-19 pandemic resulted in an unprecedented burden on intensive care units (ICUs). With increased demands and limited supply, critical care resources, including dialysis machines, became scarce, leading to the undertaking of value-based cost-effectiveness analyses and the rationing of resources to deliver patient care of the highest quality. A high proportion of COVID-19 patients admitted to the ICU required dialysis, resulting in a major burden on resources such as dialysis machines, nursing staff, technicians, and consumables such as dialysis filters and solutions and anticoagulation medications. Artificial intelligence (AI)-based big data analytics are now being utilized in multiple data-driven healthcare services, including the optimization of healthcare system utilization. Numerous factors can impact dialysis resource allocation to critically ill patients, especially during public health emergencies, but currently, resource allocation is determined using a small number of traditional factors. Smart analytics that take into account all the relevant healthcare information in the hospital system and patient outcomes can lead to improved resource allocation, cost-effectiveness, and quality of care. In this review, we discuss dialysis resource utilization in critical care, the impact of the COVID-19 pandemic, and how AI can improve resource utilization in future public health emergencies. Research in this area should be an important priority.

Persistence of tolvaptan medication for autosomal dominant polycystic kidney disease: A retrospective cohort study using Shizuoka Kokuho Database

Autosomal dominant polycystic kidney disease (ADPKD) is a rare hereditary disease leading to end-stage renal failure in approximately half of patients by seventy years of age. It is important to continuously take tolvaptan to control disease progression. However, adherence to tolvaptan in a real-world setting, rather than randomized controlled trials (RCTs), has not been sufficiently reported. We aimed to investigate tolvaptan persistence among patients with ADPKD using a large claims database. Using the Shizuoka Kokuho Database, we identified patients diagnosed with ADPKD who were prescribed tolvaptan from March 2014-September 2018 in Japan. The persistence rate of tolvaptan medication was estimated by Kaplan-Meier analysis, and patient background factors associated with treatment discontinuation were exploratively evaluated with log-rank tests. We identified 1714 eligible patients with ADPKD, and among them, 25 patients used tolvaptan medication. We followed up these patients, whose median treatment duration was 21 months. The persistence rates at 12, 24, and 36 months were estimated to be 70.8% (95% confidence interval: 48.2-93.4), 46.5% (23.2-66.9), and 38.7% (16.4-60.8), respectively. In the exploratory analysis, there were no factors that were obviously associated with tolvaptan discontinuation. The persistence rate of tolvaptan in patients with ADPKD in a real-world setting may be lower than that in previous RCTs. Our innovative method, particularly in Japan, to analyze adherence using large claims data should change the way clinical epidemiological research and health policies of rare diseases are designed in the future.