Different kidney function trajectory patterns before dialysis in elderly patients: clinical implications and outcomes

Remote organ cancer induces kidney injury, inflammation, and fibrosis and adversely alters renal function

Approximately 30% of the patients with cancer experience kidney complications, which hinder optimal cancer management, imposing a burden on patients' quality of life and the healthcare system. The etiology of kidney complications in patients with cancer is often attributed to oncological therapies. However, the direct impact of cancer on kidney health is underestimated. Our previous study demonstrated that metastatic lung cancer adversely alters the kidney and exacerbates chemotherapy-induced nephrotoxicity, indicating lung cancer-kidney crosstalk. The current study examines whether this phenomenon is specific to the employed cancer model. Female and male mice of various strains were injected with different cell lines of remote organ cancer, and their kidney tissues were analyzed for toxicity and fibrosis. The impact of cancer on the kidney varied by cancer type. Breast cancer and specific subtypes of lung cancer, including KRAS- and epidermal growth factor receptor (EGFR)-mutant cancer, pathologically altered kidney physiology and function in a manner dependent on the metastatic potential of the cell line. This was independent of mouse strain, sex, and cancer cell line origin. Moreover, tumor DNA was not detected in the renal tissue, excluding metastases to the kidney as a causative factor for the observed pathological alterations. Lewis lung carcinoma and B16 melanoma did not cause nephrotoxicity, regardless of the tumor size. Our results confirm cancer-kidney crosstalk in specific cancer types. In the era of precision medicine, further research is essential to identify at-risk oncology populations, enabling early detection and management of renal complications.NEW & NOTEWORTHY Patients with cancer frequently experience kidney complications, often attributed to antineoplastic therapies. This emphasis on therapy-induced nephrotoxicity has led to the underestimation of the impact of cancer on the kidney. Our study demonstrates that distant organ cancer is sufficient to induce nephrotoxicity, highlighting the existence of cancer-kidney crosstalk. Our findings underscore a gap in our understanding of renal complications in patients with cancer and provide a rationale for identifying the underlying mechanisms for the development of nephroprotective agents.

Association between renal function trajectories and risk of cardiovascular disease: a prospective cohort study

INTRODUCTION

It is unclear whether changing trajectories of renal function will increase the risk prediction information of cardiovascular disease (CVD). This study aimed to evaluate the trajectory patterns of estimated glomerular filtration rate (eGFR) and the association between eGFR trajectories and CVD risk.

METHODS

A total of 4742 participants were included in the cohort from the 51st Regiment of Xinjiang Production and Construction Corps. The study endpoint was the occurrence of CVD events. eGFR trajectories were identified using a linear mixed-effects model in four distinct patterns. Multivariate Cox proportional hazards models analysed the correlations between eGFR trajectories and CVD.

RESULTS

During a median follow-up period of 5.7 years, a total of 559 (11.8%) CVD, 404 (8.5%) myocardial infarction (MI), 244 (5.2%) ischemic stroke (IS), and 62 (1.3%) heart failure (HF) incidents occurred. After multivariable adjustment, gradual decline trajectory increased the risk of CVD (HR 1.42, 95% CI 1.16-1.74), MI (HR 1.41, 95% CI 1.11-1.79), and IS (HR 1.41, 95% CI 1.04-1.92); gradual increase trajectory reduced the risk of CVD (HR 0.40, 95% CI 0.25-0.64) and MI (HR 0.49, 95% CI 0.29-0.81). Consistent results were obtained in sensitivity and subgroup analyses.

CONCLUSIONS

Decline and increase of renal function were related to the risk of CVD, MI, and IS in the rural areas of Xinjiang. Monitoring eGFR changing trajectory is of great significance in improving the risk of CVD.

Gaining Insights Into Patients' Experiences of Remote Diagnostic Screening for Chronic Kidney Disease in Patients With Diabetes

Introduction: Chronic kidney disease (CKD) affects a significant portion of the UK population and is a pressing public health issue. Current screening methods have a low patient uptake rate. This retrospective study explores the patient experience of remote diagnostic screening. Objective: This article retrospectively analyses patient-reported experiences, focusing on at-home urinary screening to detect CKD. Intervention: Our primary care network commissioned a remote diagnostic service for adult patients with diabetes (Types I and II) who had not taken urinary albumin: creatinine ratio test within 12 months. Patients were provided with an at-home kit and guided by a smartphone application. Qualitative clinical data was collected during screening, with a questionnaire capturing patients' experiences. Impact: A total of 60% of eligible patients performed testing, and 35% were detected to have abnormal results. A total of 80% of patients preferred remote screening. Conclusions: This study provides evidence for remote CKD screening and opens avenues for innovation. Most patients reported a positive experience, underscoring the potential of this approach to improve health outcomes, especially in higher-risk populations.

The Assessment of the Association of Proton Pump Inhibitor Usage with Chronic Kidney Disease Progression through a Process Mining Approach

Previous studies have suggested an association between Proton Pump Inhibitors (PPIs) and the progression of chronic kidney disease (CKD). This study aims to assess the association between PPI use and CKD progression by analysing estimated glomerular filtration rate (eGFR) trajectories using a process mining approach. We conducted a retrospective cohort study from 1 January 2006 to 31 December 2011, utilising data from the Stockholm Creatinine Measurements (SCREAM). New users of PPIs and H2 blockers (H2Bs) with CKD (eGFR < 60) were identified using a new-user and active-comparator design. Process mining discovery is a technique that discovers patterns and sequences in events over time, making it suitable for studying longitudinal eGFR trajectories. We used this technique to construct eGFR trajectory models for both PPI and H2B users. Our analysis indicated that PPI users exhibited more complex and rapidly declining eGFR trajectories compared to H2B users, with a 75% increased risk (adjusted hazard ratio [HR] 1.75, 95% confidence interval [CI] 1.49 to 2.06) of transitioning from moderate eGFR stage (G3) to more severe stages (G4 or G5). These findings suggest that PPI use is associated with an increased risk of CKD progression, demonstrating the utility of process mining for longitudinal analysis in epidemiology, leading to an improved understanding of disease progression.

Mathematical expansion and clinical application of chronic kidney disease stage as vector field

There are cases in which CKD progression is difficult to evaluate, because the changes in estimated glomerular filtration rate (eGFR) and proteinuria sometimes show opposite directions as CKD progresses. Indices and models that enable the easy and accurate risk prediction of end-stage-kidney disease (ESKD) are indispensable to CKD therapy. In this study, we investigated whether a CKD stage coordinate transformed into a vector field (CKD potential model) accurately predicts ESKD risk. Meta-analysis of large-scale cohort studies of CKD patients in PubMed was conducted to develop the model. The distance from CKD stage G2 A1 to a patient's data on eGFR and proteinuria was defined as r. We developed the CKD potential model on the basis of the data from the meta-analysis of three previous cohort studies: ESKD risk = exp(r). Then, the model was validated using data from a cohort study of CKD patients in Japan followed up for three years (n = 1,564). Moreover, the directional derivative of the model was developed as an index of CKD progression velocity. For ESKD prediction in three years, areas under the receiver operating characteristic curves (AUCs) were adjusted for baseline characteristics. Cox proportional hazards models with spline terms showed the exponential association between r and ESKD risk (p<0.0001). The CKD potential model more accurately predicted ESKD with an adjusted AUC of 0.81 (95% CI 0.76, 0.87) than eGFR (p<0.0001). Moreover, the directional derivative of the model showed a larger adjusted AUC for the prediction of ESKD than the percent eGFR change and eGFR slope (p<0.0001). Then, a chart of the transformed CKD stage was developed for implementation in clinical settings. This study indicated that the transformed CKD stage as a vector field enables the easy and accurate estimation of ESKD risk and CKD progression and suggested that vector analysis is a useful tool for clinical studies of CKD and its related diseases.

Conservative Management in End-Stage Kidney Disease between the Dialysis Myth and Neglected Evidence-Based Medicine

In the last few decades, the aging of the general population has significantly increased the number of elderly patients with end-stage kidney disease (ESKD) who require renal replacement therapy. ESKD elders are often frail and highly comorbid with social issues and seem to not benefit from dialysis in terms of survival and quality of life. Conservative management (CM) could represent a valid treatment option, allowing them to live for months to years with a modest impact on their habits. Despite these possible advantages, CM remains underused due to the myth of dialysis as the only effective treatment option for all ESKD patients regardless of its impact on quality of life and survival. Both CM and dialysis remain valid alternatives in the management of ESKD. However, assessing comorbidities, disabilities, and social context should drive the choice of the best possible treatment for ESKD, while in elderly patients with short life expectancies, referring them to palliative care seems the most reasonable choice.

Predialysis trajectories of estimated GFR and concurrent trends of Chronic Kidney Disease-relevant biomarkers

Background

The glomerular filtration rate (GFR) decline varies in patients with advanced chronic kidney disease (CKD), and the concurrent changes in CKD-related biomarkers are unclear.

Objectives

This study aimed to examine the changes in CKD-related biomarkers along with the kidney function decline in various GFR trajectory groups.

Design

This study was a longitudinal cohort study originated from the pre-end-stage renal disease (pre-ESRD) care program in a single tertiary center between 2006 and 2019.

Methods

We adopted a group-based trajectory model to categorize CKD patients into three trajectories according to estimated glomerular filtration rate (eGFR) changes. A repeated-measures linear mixed model was used to estimate the concurrent biomarker trends in a 2-year period before dialysis and to examine the differences among trajectory groups. A total of 15 biomarkers were analyzed, including urine protein, serum uric acid, albumin, lipid, electrolytes, and hematologic markers.

Results

Using longitudinal data from 2 years before dialysis initiation, 1758 CKD patients were included. We identified three distinct eGFR trajectories: persistently low eGFR levels, progressive loss of eGFR, and accelerated loss of eGFR. Eight of the 15 biomarkers showed distinct patterns among the trajectory groups. Compared with the group with persistently low eGFR values, the other two groups were associated with a more rapid increase in the blood urea nitrogen (BUN) level and urine protein-creatinine ratio (UPCR), especially in the year before dialysis initiation, and a more rapid decline in hemoglobin and platelet counts. A rapid eGFR decline was associated with lower levels of albumin and potassium, and higher levels of mean corpuscular hemoglobin concentration (MCHC) and white blood cell (WBC). The albumin level in the group with an accelerated loss of eGFR was below the normal range.

Conclusion

Using longitudinal data, we delineated the changes in CKD biomarkers with disease progression. The results provide information to clinicians and clues to elucidate the mechanism of CKD progression.

Machine Learning Case Study: Patterns of Kidney Function Decline and Their Association With Clinical Outcomes Within 90 Days After the Initiation of Renal Dialysis

A case study explores patterns of kidney function decline using unsupervised learning methods first and then associating patterns with clinical outcomes using supervised learning methods. Predicting short-term risk of hospitalization and death prior to renal dialysis initiation may help target high-risk patients for more aggressive management. This study combined clinical data from patients presenting for renal dialysis at Fresenius Medical Care with laboratory data from Quest Diagnostics to identify disease trajectory patterns associated with the 90-day risk of hospitalization and death after beginning renal dialysis. Patients were clustered into 4 groups with varying rates of estimated glomerular filtration rate (eGFR) decline during the 2-year period prior to dialysis. Overall rates of hospitalization and death were 24.9% (582/2341) and 4.6% (108/2341), respectively. Groups with the steepest declines had the highest rates of hospitalization and death within 90 days of dialysis initiation. The rate of eGFR decline is a valuable and readily available tool to stratify short-term (90 days) risk of hospitalization and death after the initiation of renal dialysis. More intense approaches are needed that apply models that identify high risks to potentially avert or reduce short-term hospitalization and death of patients with a severe and rapidly progressive chronic kidney disease.