Scholarlink

Background <p>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.</p> Methods <p>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&#xa0;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%).</p> Results <p>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, <i>p</i> &lt; 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%).</p> Conclusions <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Validation of online clearance monitoring and machine learning-based prediction of dialysis adequacy in Vietnamese hemodialysis patients: a cross-sectional study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

查看全文
  • Xuan Chanh Tran,
  • Tuan Anh Pham,
  • Thinh Chi Phan,
  • Anh Thuy Tram Nguyen,
  • Huy Quoc Tran,
  • Thanh Kien Lam

摘要

Background

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.

Methods

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%).

Results

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%).

Conclusions

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.