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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Nomogram Model Based on Clinical Risk Factors and Heart Rate Variability for Predicting All-Cause Mortality in Stage
Xueyan Gao1,2, Jing Wang1, Hui Huang3
1Department of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Insights
Decreased heart rate variability (HRV) parameters, specifically SDNN and SDANN, are linked to higher mortality risk in stage 5 chronic kidney disease (CKD5) patients. A nomogram model combining HRV with clinical factors effectively predicts survival in CKD5 individuals.
Area of Science:
- Nephrology
- Cardiology
- Medical Statistics
Background:
- Heart rate variability (HRV) reflects cardiac circadian rhythm and is associated with outcomes in stage 5 chronic kidney disease (CKD5).
- The combined predictive value of CKD-related factors and HRV for mortality in CKD5 patients remains unclear.
Purpose of the Study:
- To evaluate the prognostic value of a nomogram model incorporating HRV and clinical risk factors for all-cause mortality in CKD5 patients.
- To assess the predictive ability of this model for survival rates.
Main Methods:
- Multicenter enrollment of CKD5 patients (2011-2019, China).
- Analysis of 24-h Holter-derived HRV parameters and clinical risk factors using multivariate Cox regression.
- Development of a nomogram model integrating lnSDNN, sex, age, BMI, diabetes mellitus, beta-blocker use, blood glucose, phosphorus, and ln intact parathyroid hormone (iPTH).
Main Results:
- Lower lnSDNN and lnSDANN values were significantly associated with increased all-cause mortality in CKD5 patients (p < 0.01).
- lnSDNN and lnSDANN showed a strong positive correlation.
- The nomogram model achieved high predictive accuracy, with AUC values of 79.44% for 3-year and 81.27% for 5-year survival rates.
Conclusions:
- Reduced SDNN and SDANN, key HRV metrics, are independently associated with higher all-cause mortality in CKD5 patients.
- A nomogram model integrating SDNN and established clinical risk factors provides a promising tool for prognostic evaluation in CKD5 patients.
Abstract:
Background: Heart rate variability (HRV), reflecting circadian rhythm of heart rate, is reported to be associated with clinical outcomes in stage 5 chronic kidney disease (CKD5) patients. Whether CKD related factors combined with HRV can improve the predictive ability for their death remains uncertain. Here we evaluated the prognosis value of nomogram model based on HRV and clinical risk factors for all-cause mortality in CKD5 patients. Methods: CKD5 patients were enrolled from multicenter between 2011 and 2019 in China. HRV parameters based on 24-h Holter and clinical risk factors associated with all-cause mortality were analyzed by multivariate Cox regression. The relationships between HRV and all-cause mortality were displayed by restricted cubic spline graphs. The predictive ability of nomogram model based on clinical risk factors and HRV were evaluated for survival rate. Results: CKD5 patients included survival subgroup (n = 155) and all-cause mortality subgroup (n = 45), with the median follow-up time of 48 months. Logarithm of standard deviation of all sinus R-R intervals (lnSDNN) (4.40 ± 0.39 vs. 4.32 ± 0.42; p = 0.007) and logarithm of standard deviation of average NN intervals for each 5 min (lnSDANN) (4.27 ± 0.41 vs. 4.17 ± 0.41; p = 0.008) were significantly higher in survival subgroup than all-cause mortality subgroup. On the basis of multivariate Cox regression analysis, the lnSDNN (HR = 0.35, 95%CI: 0.17-0.73, p = 0.01) and lnSDANN (HR = 0.36, 95% CI: 0.17-0.77, p = 0.01) were associated with all-cause mortality, their relationships were negative linear. Spearman's correlation analysis showed that lnSDNN and lnSDANN were highly correlated, so we chose lnSDNN, sex, age, BMI, diabetic mellitus (DM), β-receptor blocker, blood glucose, phosphorus and ln intact parathyroid hormone (iPTH) levels to build the nomogram model. The area under the curve (AUC) values based on lnSDNN nomogram model for predicting 3-year and 5-year survival rates were 79.44% and 81.27%, respectively. Conclusion: In CKD5 patients decreased SDNN and SDANN measured by HRV were related with their all-cause mortality, meanwhile, SDNN and SDANN were highly correlated. Nomogram model integrated SDNN and clinical risk factors are promising for evaluating their prognosis.
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