Limits...
Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest.

Miao F, Cai YP, Zhang YX, Li Y, Zhang YT - Comput Math Methods Med (2015)

Bottom Line: The simplified risk model also achieved a good accuracy of 0.799.Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model).As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China ; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing 100049, China.

ABSTRACT
Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy for predicting 1-year mortality in cardiac arrhythmias patients using random survival forest (RSF), a robust approach for survival analysis. 10,488 cardiac arrhythmias patients available in the public MIMIC II clinical database were investigated, with 3,452 deaths occurring within 1-year followups. Forty risk factors including demographics and clinical and laboratory information and antiarrhythmic agents were analyzed as potential predictors of all-cause mortality. RSF was adopted to build a comprehensive survival model and a simplified risk model composed of 14 top risk factors. The built comprehensive model achieved a prediction accuracy of 0.81 measured by c-statistic with 10-fold cross validation. The simplified risk model also achieved a good accuracy of 0.799. Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model). Moreover, various factors are observed to have nonlinear impact on cardiac arrhythmias prognosis. As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.

No MeSH data available.


Related in: MedlinePlus

(a) Ensemble survival function for each individual. Red line is overall ensemble survival, while green line is Nelson-Aalen estimator. (b) Comparison of the population ensemble survival function and the Nelson-Aalen estimator.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4562335&req=5

fig3: (a) Ensemble survival function for each individual. Red line is overall ensemble survival, while green line is Nelson-Aalen estimator. (b) Comparison of the population ensemble survival function and the Nelson-Aalen estimator.

Mentions: In order to improve the availability of the proposed model, we reduced the comprehensive models to include the most important 14 risk factors selected from the comprehensive RSF analysis and developed a simplified model. The error rates for ensemble cumulative hazard function and VIMP for predictors are presented in Figure 2 with an estimated c-statistic of 0.799 (the detailed method for calculating prediction error and VIMP is presented in Section 2). Figure 3 gives the correlation between ensemble survival function and nonparametric Nelson-Aalen estimator, which is an alternative estimator for Kaplan-Meier. From the figure we can see that the ensemble survival function is very close to the curve with Nelson-Aalen estimator (r = 0.999, p < 0.001). In other words, the estimated survival function using RSF basically conforms to the real survival curve.


Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest.

Miao F, Cai YP, Zhang YX, Li Y, Zhang YT - Comput Math Methods Med (2015)

(a) Ensemble survival function for each individual. Red line is overall ensemble survival, while green line is Nelson-Aalen estimator. (b) Comparison of the population ensemble survival function and the Nelson-Aalen estimator.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4562335&req=5

fig3: (a) Ensemble survival function for each individual. Red line is overall ensemble survival, while green line is Nelson-Aalen estimator. (b) Comparison of the population ensemble survival function and the Nelson-Aalen estimator.
Mentions: In order to improve the availability of the proposed model, we reduced the comprehensive models to include the most important 14 risk factors selected from the comprehensive RSF analysis and developed a simplified model. The error rates for ensemble cumulative hazard function and VIMP for predictors are presented in Figure 2 with an estimated c-statistic of 0.799 (the detailed method for calculating prediction error and VIMP is presented in Section 2). Figure 3 gives the correlation between ensemble survival function and nonparametric Nelson-Aalen estimator, which is an alternative estimator for Kaplan-Meier. From the figure we can see that the ensemble survival function is very close to the curve with Nelson-Aalen estimator (r = 0.999, p < 0.001). In other words, the estimated survival function using RSF basically conforms to the real survival curve.

Bottom Line: The simplified risk model also achieved a good accuracy of 0.799.Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model).As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China ; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing 100049, China.

ABSTRACT
Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy for predicting 1-year mortality in cardiac arrhythmias patients using random survival forest (RSF), a robust approach for survival analysis. 10,488 cardiac arrhythmias patients available in the public MIMIC II clinical database were investigated, with 3,452 deaths occurring within 1-year followups. Forty risk factors including demographics and clinical and laboratory information and antiarrhythmic agents were analyzed as potential predictors of all-cause mortality. RSF was adopted to build a comprehensive survival model and a simplified risk model composed of 14 top risk factors. The built comprehensive model achieved a prediction accuracy of 0.81 measured by c-statistic with 10-fold cross validation. The simplified risk model also achieved a good accuracy of 0.799. Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model). Moreover, various factors are observed to have nonlinear impact on cardiac arrhythmias prognosis. As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.

No MeSH data available.


Related in: MedlinePlus