Online clearance monitoring demonstrated excellent correlation with the Daugirdas formula (Spearman ρ = 0.971) despite a systematic underestimation of Kt/V by a mean of 0.055 units.
Cross-Sectional (n=97)
No
Does online clearance monitoring and machine learning accurately predict dialysis adequacy compared to the Daugirdas formula in maintenance hemodialysis patients?
Online clearance monitoring combined with machine learning models provides highly accurate, real-time prediction of dialysis adequacy in Vietnamese hemodialysis patients.
Effect estimate: Spearman ρ 0.971
Absolute Event Rate: 1.23% vs 1.29%
p-value: p=<0.001
Online clearance monitoring (OCM) offers non-invasive real-time dialysis adequacy assessment, but validation data from Southeast Asian populations are limited. We evaluated OCM performance and developed machine learning models for adequacy prediction in Vietnamese hemodialysis patients. This cross-sectional study included 97 maintenance hemodialysis patients at a regional dialysis center in Ho Chi Minh City, Vietnam. Kt/V was measured using OCM (ionic dialysance, Fresenius 4008 S) and calculated using the second-generation Daugirdas formula (reference standard). Machine learning models were developed using OCM-derived Kt/V, age, and body mass index to predict adequacy per Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines (Kt/V ≥ 1.2 or urea reduction ratio ≥ 65%). The median Kt/V was 1.29 (interquartile range IQR: 1.18–1.35) by the Daugirdas formula and 1.23 (IQR: 1.12–1.30) by OCM, with 67.0% of patients achieving adequacy targets. OCM underestimated Kt/V by a mean of 0.055 units but correlated excellently with the Daugirdas formula (Spearman ρ = 0.971, p < 0.001). Random Forest regression achieved R² = 0.877 for continuous Kt/V prediction. Logistic regression classification demonstrated perfect test-set discrimination (area under the receiver operating characteristic curve AUC = 1.000, sensitivity = 100%), with cross-validation AUC of 0.989 ± 0.010, outperforming direct OCM thresholding (sensitivity 84.6%). OCM demonstrates excellent correlation with the Daugirdas formula despite systematic underestimation, validating its use for real-time monitoring with appropriate calibration. Machine learning models incorporating patient-specific factors may provide calibrated adjustment to OCM-derived Kt/V. A freely accessible web-based calculator was developed for clinical application, though external validation is needed before widespread implementation.
Tran et al. (Tue,) conducted a cross-sectional in End-stage kidney disease on maintenance hemodialysis (n=97). Online clearance monitoring (OCM) vs. Daugirdas formula was evaluated on Kt/V measurement correlation (Spearman ρ 0.971, p=<0.001). Online clearance monitoring demonstrated excellent correlation with the Daugirdas formula (Spearman ρ = 0.971) despite a systematic underestimation of Kt/V by a mean of 0.055 units.