Related Experiment Video
Updated: Mar 24, 2026
5/6 Nephrectomy Using Sharp Bipolectomy Via Midline Laparotomy in Rats
Published on: April 4, 2025
Development and validation of early-stage and progression prediction models for chronic kidney disease: a
Tongyuan Wan1, Qi Chen1, Yiming Gao1
1National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, China.
Insights
This study developed accurate prediction and progression models for chronic kidney disease (CKD) using clinical laboratory data. These tools aid in early CKD detection and risk assessment for better patient management.
Area of Science:
- Nephrology
- Medical Diagnostics
- Biostatistics
Background:
- Chronic kidney disease (CKD) represents a substantial global health challenge.
- Effective early detection and risk stratification are crucial for managing CKD.
- Existing models may require enhancement for improved predictive accuracy.
Purpose of the Study:
- To identify clinical laboratory indices associated with CKD.
- To develop and validate predictive models for early-stage CKD.
- To create prognostic models for assessing CKD progression.
Main Methods:
- Logistic regression analysis to identify independent predictors.
- Development of visual nomograms for risk prediction and progression.
- Validation using area under the receiver operating characteristic curves (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curves (CIC).
Main Results:
- The early-stage CKD prediction nomogram achieved high AUC values (0.981 training, 0.969 validation).
- The CKD progression nomogram also showed excellent performance (0.984 training, 0.972 validation).
- DCA and CIC analyses confirmed the clinical utility and applicability of both developed models.
Conclusions:
- Validated prediction and progression models for CKD demonstrate high discriminative ability and calibration.
- These nomograms offer significant clinical utility for early CKD detection and dynamic risk assessment.
- The developed models can potentially improve CKD management strategies.
Objectives:
Chronic kidney disease (CKD) poses a significant public health burden. This study aimed to evaluate the associations between clinical laboratory indices and CKD and to develop prediction and prognostic models for CKD risk assessment and disease progression.
Design & Methods:
Between January 2008 and June 2018, we enrolled 500 healthy controls, 445 patients with early-stage CKD (G1-G2), and 527 patients with CKD G5 at the First Hospital of China Medical University. Logistic regression analyses were performed to identify independent predictors for the presence of CKD and progression to advanced disease, which were subsequently incorporated into visual nomograms. Model performance was evaluated using area under the receiver operating characteristic curves (AUC) and calibration plots. Clinical utility was assessed using decision curve analysis (DCA) and clinical impact curves (CIC).
Results:
The early-stage CKD prediction nomogram achieved an AUC of 0.981 in the training set and 0.969 in the validation set. The progression nomogram demonstrated AUC values of 0.984 and 0.972 in the training and validation sets, respectively. DCA and CIC analyses further confirmed the clinical relevance and potential applicability of both models.
Conclusions:
We developed and validated early-stage prediction and progression assessment for CKD, demonstrating high discriminative ability, good calibration, and significant clinical utility. These models may facilitate early detection and dynamic risk assessment in CKD management.
More Related Videos
Related Concept Videos
01:25Chronic Kidney Disease I: Introduction
01:28Chronic Kidney Disease III: Interprofessional Care
01:28Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
01:18Chronic Kidney Disease IV: Nursing Management
01:30Acute Kidney Injury IV: Diagnostic Studies and Prevention
01:17Factors Affecting Renal Clearance: Renal Impairment
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...