Abstract
Objective: To explore the application of the Cox model based on extreme learning machine in the survival analysis of patients with chronic heart failure.Methods: The medical records of 5,279 inpatients diagnosed with chronic heart failure in two grade 3 and first-class hospitals in Taiyuan from 2014 to 2019 were collected; with death as the outcome and after the feature selection, the Lasso Cox, random survival forest (RSF), and the Cox model based on extreme learning machine (ELM Cox) were constructed for survival analysis and prediction; the prediction performance of the three models was explored based on simulated data with three censoring ratios of 25, 50, and 75%.Results: Simulation results showed that the prediction performance of the three models decreased with increasing censoring proportion, and the ELM Cox model performed best overall; the ELM Cox model constructed with 21 highly influential survival predictors screened from actual chronic heart failure data showed the best performance with C-index and Integrated Brier Score (IBS) of 0.775(0.755, 0.802) and 0.166(0.150, 0.182), respectively.Conclusion: The ELM Cox model showed good discrimination performance in the survival analysis of patients with chronic heart failure; it performs consistently for data with a high proportion of censored survival time; therefore, the model could help physicians identify patients at high risk of poor prognosis and target therapeutic measures to patients as early as possible.
Highlights
Chronic heart failure (CHF), one of the most severe cardiovascular diseases of the 21st century [1], is a complex clinical syndrome manifested when the heart does not pump enough blood for tissue and metabolic needs [2]
Doctors can prescribe more aggressive treatment plans for high-risk patients based on accurate risk prediction, and patients will follow the treatment more because they have confidence in the treatment plan prescribed by the doctor [5]
The prediction performance of the three models under three survival time data censoring ratios was compared, and the results showed that the prediction performance of the three models gradually decreases as the censoring ratio increases
Summary
Chronic heart failure (CHF), one of the most severe cardiovascular diseases of the 21st century [1], is a complex clinical syndrome manifested when the heart does not pump enough blood for tissue and metabolic needs [2]. An accurate prediction model can help clinical researchers design clinical trials to target high-risk patients with heterogeneous characteristics and change treatment interventions [6]. The above survival prediction model data comes from clinical trials These data have a small sample size, strict test conditions, lack of heterogeneity in the patient population, and poor population representation [9]. These models based on clinical trials are not derived from real-world data. Even if such a model is constructed with high accuracy, it is not very useful for real-world research [10]. As electronic medical records (EHRs) become more common in clinical research, methods for predicting the prognosis of HF using EHRs instead of clinical trial data have become necessary [11, 12]
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