Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model

The global prevalence of heart failure is estimated to exceed 37.7 million. The risk of mortality is higher in patients with CHF comorbid with AF, but few risk prediction tools are available for this subgroup.

Using patients’ routine clinical variables, we designed and evaluated a risk stratification system based on the LightGBM model to effectively predict all-cause mortality in patients with CHF comorbid with AF. In this study, the LightGBM model performed better than the traditional risk prediction model.

Chronic heart failure (CHF) refers to a syndrome of ventricular filling or contraction disorders caused by damage to the structure and/or function of the heart under the influence of various pathogenic factors, leading to a series of complex clinical symptoms. In developed countries, patients with heart failure constitute about 1% to 2% of all adults, and this proportion increases to > 10% of adults aged > 70 years [1]. The global prevalence of heart failure is estimated to exceed 37.7 million. In the United States, the total medical cost of patients with heart failure was US$20.9 billion in 2012 and is expected to increase to US$53.1 billion by 2030 [2]. The high prevalence rate and poor prognosis of heart failure seriously affect patients’ physical and mental health and quality of life, and heart failure has become a global public health problem that threatens human health.

Atrial fibrillation (AF) is the most common arrhythmia in heart failure. AF increases the risk of thromboembolism (especially stroke) and may damage cardiac function, leading to deterioration of high-frequency symptoms. In the Framingham Heart Study, patients with heart failure comorbid with atrial fibrillation have a higher risk of mortality than those with only one disease [3]. The combination of AF and CHF is a major clinical burden because of the common pathophysiology, common risk factors, mutual causality, and poor prognosis of these concomitant diseases.

Accurate risk prediction can promote patient classification, assist clinicians in understanding individual patients’ disease risk, and preserve medical resources for patients with potential life-threatening needs in emergency care, thus delaying disease progression and improving the prognosis. However, the existing risk prediction models of heart failure have some shortcomings. First, traditional risk prediction models are based on the assumption that a linear relationship exists between variables and outcomes, which often limits their ability to model complex relationships. Second, the performance of the risk scores is still limited. For example, in the long-term heart failure registry of the European Society of Cardiology, the Meta-Analysis Global Group in Chronic Heart Failure risk score overestimated mortality while the Seattle Heart Failure Model underestimated mortality [4]; this limits their clinical application. Therefore, more accurate prognostic tools are needed.

The machine learning model can overcome the conditional limitations of the traditional survival prediction model, deal with high-dimensional interactions and nonlinear relationships between variables, improve the prediction ability of the model, and show better performance in identifying personalized outcome predictions [5]. It has been effectively used in heart disease research [6, 7], including the prediction of hospital readmission and mortality, etc. However, few prognostic studies have focused on the outcome of CHF comorbid with AF. Therefore, the goal of the present study was to identify the risk factors for all-cause mortality in patients with CHF comorbid with AF and to design and evaluate a LightGBM-based risk stratification model to predict 1- to 3-year all-cause mortality based on the patient’s baseline parameters at admission. It can help clinicians identify and manage high- and low-risk patients and carry out more targeted intervention measures to realize precision medicine and the optimal allocation of health care resources.

In this study, we designed and evaluated a risk stratification system based on the LightGBM model to predict 1- to 3-year all-cause mortality in patients with CHF comorbid with AF. The risk stratification system showed moderate predictive performance with an average AUC of 0.740. CHF and AF are causes and effects of each other. Damage to the cardiac structure or function, abnormal activation of neurohumoral mechanisms, and remodeling of ion channels in patients with CHF can lead to myocardial remodeling, enlarge the atrium, change the electrical activity characteristics of atrial myocytes, and promote the occurrence and persistence of AF. Similarly, regular atrial contraction loss and irregular electrocardiographic conduction in patients with AF can lead to the impairment of left ventricular diastolic and systolic function and promote the occurrence and development of heart failure. Previous studies have shown that the existence of AF is related to the poor prognosis of CHF [11]. However, identifying the risk factors of adverse prognosis of CHF comorbid with AF and taking effective control and treatment measures will help to reduce the incidence of adverse events such as death.

Consideration of the relationship between multiple clinical variables of a single patient and mortality is a great challenge for clinicians, and it is often easy to ignore the potential relationship between variables. ML methods can handle complex interactions and nonlinear relationships between predictors, allowing the selection of unknown variables and the best predictive subset of the model through continuous iteration. As an emerging algorithm in machine learning, the LightGBM algorithm overcomes the limitations of traditional boosting algorithms. LightGBM algorithm has the following advantages: first, it has faster training speed, higher efficiency and better accuracy; Second, it has lower memory consumption and can process large-scale data; Third, it supports parallel, distributed, and GPU learning. Experiments show that LightGBM algorithm can speed up the training process of traditional gradient boosting decision tree (GBDT) by more than 20 times, while achieving almost the same accuracy, and LightGBM can be significantly better than the extreme gradient boosting (XGBoost) algorithm and the stochastic gradient boosting (SGB) algorithm in computing speed and memory consumption [10]. Therefore, we chose LightGBM for this research.

We screened the most important predictors of all-cause mortality in the cohort of this study. According to the feature importance ranking, older age; a higher NT-proBNP level, NYHA class, AST level, TBIL level; a lower DBP level, albumin level, cyscGFR and LVEF; combined with COPD; and not taking beta-blockers had a relatively large contribution to prediction of the risk of death in patients with CHF comorbid with AF.

Our study found that NT-proBNP is an important predictor of prognosis in patients with CHF comorbid with AF. The NT-proBNP level is positively correlated with the severity of heart failure, and is closely related to NYHA class, end-diastolic pressure, and degree of hemodynamic disturbances, and can be used as an effective means of prognostic evaluation [12, 13]. Abnormal liver enzymes often appear in patients with heart failure, with a prevalence of 30–60% [14]. Increased venous congestion and impaired hemodynamics are common causes of abnormal liver enzymes in patients with heart failure. Abnormal liver function may lead to increased fluid overload due to hypoalbuminemia and low-osmolality state, which may lead to deterioration of heart failure. This explains the liver function indicators as powerful predictors of prognosis in patients with CHF comorbid with AF in our study, and is consistent with other studies [15‐17].

Renal dysfunction is a common complication of CHF, the pathophysiology of cardiorenal syndrome is closely related to decreased cardiac output and increased central venous pressure. About 40% of hospitalized patients with heart failure showed elevated serum creatinine and decreased glomerular filtration rate (GFR) [18]. Cystatin C is considered to be a more sensitive blood marker of renal function than creatinine and is less strongly affected by muscle mass, age, sex, or race. The CyscGFR is closer to the directly measured glomerular filtration rate and has better prognostic value [19]. Renal function parameters were found to be predictors of adverse events in patients with CHF comorbid with AF in the present study, consistent with previous reports [20‐23].

Diabetes (28.12%) and COPD (24.05%) are common complications and predictors of poor prognosis in the present study. COPD and AF have common risk factors and therefore often coexist. COPD greatly limits the survival of patients. Previous study has found that patients with concurrent AF and COPD have higher cardiovascular mortality and all-cause mortality [24]. The prevalence of diabetes is 12–44% in heart failure patients, depending on the severity of heart failure and whether the left ventricular ejection fraction is reduced. Diabetes is a powerful independent predictor of death in patients with advanced heart failure [25]. Type II diabetes can cause inflammation of adipose tissue, and the resulting systemic inflammation can lead to the expansion of epicardial adipose tissue and proinflammatory transformation. In one study, patients with multiple non-cardiovascular comorbidities had a higher risk of competitive death [26]. The white blood cell count is elevated in patients with AF and CHF, and the increase of inflammatory markers in patients with cardiovascular disease (especially heart failure) can be considered a factor for a poor prognosis [27]. There is a lot of evidence that systemic inflammation is present in COPD patients [28]. Combined with the above, it may explain to some extent the cause of high mortality in patients with COPD or diabetes. The RDW is the coefficient of variation of the red blood cell volume and reflects the heterogeneity of the red blood cell volume. It is a proven predictor of adverse outcomes of heart failure [29].

In the present study, patients with a higher BMI had a lower prognostic risk. This seems to reflect the obesity paradox but laterally indicates that the BMI is not an independent predictor of CHF comorbid with AF. Lower DBP is associated with an increased risk of adverse cardiovascular events in patients with heart failure with a preserved LVEF [30, 31], and our study revealed a similar relationship in patients with CHF comorbid with AF. Oral beta-blocker therapy is helpful to control the heart rate. In Chinese elderly patients with heart failure, admission without beta-blocker therapy is a specific independent risk factor for readmission or death within 1 year [32]. Previous studies have also shown a moderate association between the use of ACEI/ARB and lower mortality [33].

Based on the above predictive variables, we conducted subgroup analysis, but found that LightGBM model had a low discrimination for patients aged ≥ 75 years. We speculate that the possible reason is that patients aged ≥ 75 years have more complicated conditions, more complications, and are more likely to have some complex clinical emergencies. The prediction model constructed by conventional inspection indexes can not achieve good prediction results, and we have found the same results in another machine learning study[34].

Compared with previous reports, the innovations of this study are the use of patients with CHF comorbid with AF as the target population; the use of the LightGBM as a new machine learning prediction model; inclusion of a variety of non-cardiac clinical variables such as COPD, diabetes, and liver and kidney function in the model; and the fact that the patients’ clinical variables were easy to obtain. Compared with the traditional risk prediction model, the LightGBM model performs better in predicting all-cause mortality in patients with CHF comorbid with AF. It can risk-stratify individuals and identify patients with a high risk of death during the whole follow-up period. Patients in the high-risk group had a significantly higher hazard of death than those in the low-risk group, the hazard ratio was 12.68, 13.13, 14.82 in our study, respectively. Clinicians can carry out active intervention programs for high-risk patients and controllable variables to improve patients’ quality of life and reduce mortality. In addition, we performed 100 random splits on the data set to ensure that the prediction results of the model were more robust.

Our study has two main limitations. First, we did not perform external verification; the external accuracy of the model may need to be confirmed by further research. Second, we did not include genetic biomarkers (such as microRNA); thus, the clinical data and biomarkers of patients with CHF should be combined in future studies to establish a new predictive model with comprehensive patient information to improve prognostic risk assessment.

Using patients’ routine clinical variables, we designed and evaluated a risk stratification system based on the LightGBM model to effectively predict all-cause mortality in patients with CHF comorbid with AF and identify subgroups of patients with a high risk of death. It can help clinicians identify and manage high- and low-risk patients and carry out more targeted intervention measures to realize precision medicine and the optimal allocation of health care resources.