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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development of prediction models for cardiovascular disease mortality risk in maintenance hemodialysis patients based
Insights
Maintenance hemodialysis patients have high cardiovascular death risk. This study developed prediction models using serum albumin, procalcitonin, and myoglobin to identify high-risk individuals for better clinical management.
Area of Science:
- Nephrology
- Cardiology
- Biostatistics
Background:
- Patients undergoing maintenance hemodialysis (MHD) exhibit a 10-20 fold increased risk of cardiovascular death compared to the general population.
- This elevated risk necessitates advanced strategies for accurate mortality prediction and preemptive clinical management.
Purpose of the Study:
- To develop and validate prediction models for cardiovascular disease (CVD) mortality in MHD patients.
- To identify key clinical and biochemical indicators associated with CVD mortality in this high-risk cohort.
Main Methods:
- Retrospective data collection from MHD patients (2016-2021) including demographics, medical history, biochemical, and echocardiogram data.
- Development of a nomogram model and a Classification and Regression Tree (CART) decision tree model.
- Evaluation of model discrimination, calibration, and clinical utility using training and validation sets.
Main Results:
- The nomogram model identified systolic blood pressure, uric acid, total cholesterol, diabetes, myoglobin, serum albumin, and procalcitonin as predictors (AUC=0.947).
- The CART model identified serum albumin, procalcitonin, and myoglobin, categorizing patients into risk groups (AUCs: 0.933 training, 0.774 validation).
- Both models demonstrated strong predictive performance, with serum albumin, procalcitonin, and myoglobin consistently identified as key factors.
Conclusions:
- Serum albumin, procalcitonin, and myoglobin are key predictors of CVD mortality in MHD patients.
- Developed nomogram and CART models show promising predictive capabilities for CVD mortality in this population.
- External validation in larger, multi-center studies is recommended for clinical implementation.
Background:
Patients on maintenance hemodialysis (MHD) face a dramatically elevated risk of cardiovascular death, which is 10 - 20 times higher than in the general population. To address this high risk, we developed and validated a prediction model to accurately estimate cardiovascular disease (CVD) mortality and guide preemptive clinical management.
Materials And Methods:
This study retrospectively collected data from MHD patients at the First Affiliated Hospital of Chengdu Medical College from 2016 to 2021 (Approval No. CYFYEC-C-005), including demographic characteristics, medical history, biochemical indicators, and echocardiogram indices. Variables were screened using univariate logistic regression and stepwise regression to construct a nomogram model. The dataset was randomly divided (6 : 4) into training and validation sets, and a classification and regression tree (CART) decision tree model was also constructed. Both models' discrimination, calibration, and clinical utility were evaluated.
Results:
The nomogram identified systolic blood pressure, uric acid, total cholesterol, diabetes, myoglobin, serum albumin, and procalcitonin as predictors, with an AUC of 0.947 (95% CI: 0.903 - 0.991) and good clinical applicability. The CART model identified serum albumin, procalcitonin, and myoglobin as predictors, categorizing the population into four groups. AUC values were 0.933 (95% CI: 0.851 - 1.000) in the training set and 0.774 (95% CI: 0.612 - 0.936) in the validation set.
Conclusion:
In conclusion, this study consistently identified serum albumin, procalcitonin, and myoglobin as key factors associated with CVD mortality risk in MHD patients. Both models demonstrated promising predictive performance in our cohort. These findings suggest the potential of such models to inform clinical risk assessment. However, external validation in larger, multi-center studies is necessary before these tools can be considered for direct clinical decision-making.
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