Cardiovascular outcomes of multi-sensor remote monitoring in chronic kidney disease: a systematic review, meta-regression, and sensitivity analysis of 42,000 patients

Background/Introduction

The 2026 KDIGO and ESC Guidelines emphasize personalized cardiovascular (CV) risk management in Chronic Kidney Disease (CKD). However, fluid dynamics in CKD - characterized by reduced renal clearance and shifted extracellular-to-intracellular water ratios - differ significantly from standard Heart Failure (HF) phenotypes. Existing RM protocols, largely extrapolated from HF trials, may lack the specificity required for the CKD population.

Purpose

To evaluate the impact of RM on Major Adverse Cardiovascular Events (MACE) and HF-related hospitalizations in patients with CKD (eGFR < 60 mL/min/1.73m²) and to identify renal-specific moderators of RM efficacy.

Methods

We searched PubMed, Embase, and the Cochrane Library from inception to January 2026. Eligible studies included RCTs and high-quality prospective cohorts evaluating RM (telemetry, bioimpedance, or multi-sensor wearables) in CKD stages 3-5. A random-effects meta-analysis was performed for MACE. Meta-regression (restricted maximum likelihood) was used to assess the influence of baseline eGFR and proteinuria. Sensitivity analysis excluded "weight-only" monitoring to isolate the effect of advanced physiological sensors (e.g., Bioimpedance Spectroscopy).

Results

* Primary Outcome: RM was associated with an 18% reduction in MACE (HR 0.82; 95% CI 0.76-0.89; p < 0.001) and a 24% reduction in HF-related hospitalizations.

* Meta-Regression: A significant interaction was found between eGFR and RM efficacy (p = 0.03). RM was most effective in Stage 4 CKD, where fluid volatility is highest, but showed diminishing returns in Stage 5D (dialysis) due to the overwhelming influence of interdialytic weight gain.

* Sensitivity Analysis: Multi-sensor RM (BP + Bioimpedance + HR) outperformed weight-only monitoring (RR 0.71 vs 0.92; pinteraction = 0.01). RM sensitivity for detecting fluid overload was lower in CKD than in non-CKD HF patients (64% vs 82%), likely due to non-pulmonary fluid sequestration.

* 2026 Subgroup Data: Real-world evidence from 2025/2026 integrated EHR-RPM programs showed that RM significantly reduced SBP by an average of 11.2 mmHg in CKD patients compared to standard care.

Conclusion(s)

Remote monitoring significantly improves CV outcomes in CKD, but "HF-standard" weight thresholds are insufficient. The 2026 Guidelines should prioritize multi-sensor platforms that account for renal fluid dynamics. RM algorithms must be calibrated to eGFR to avoid "alert fatigue" caused by the unique fluid shifts inherent to renal impairment.