Introduction
Coronary heart disease (CHD) is a type of ischemic heart diseases characterized by atherosclerotic plaque accumulation in the coronary arteries [
1] As one of the leading causes of hospitalization and death [
2] CHD affects over 110 million individuals worldwide [
3] and thus gives rise to a heavy burden on health expenditures [
4] Advancing age is a major risk factor for cardiovascular events,[
5,
6] previous evidence showed a higher incidence of CHD in males over 40 years old and the prevalence can be as high as 27.8% in patients over 60 years old [
7] Currently, coronary angiography (CAG) is the reference standard for diagnosing CHD. Whereas, the more complications and worse physical conditions in elder people, including renal insufficiency, coagulation abnormalities, or intolerance to CAG [
1] all of which require special attention, highlight the importance of alternative diagnostic methods that are more appropriate. Therefore, we try to establish a cardiovascular disease prediction model for this special group to provide a feasible method for early screening and diagnosis of high-risk patients.
Clinical prediction models are mathematical equations that relate multiple predictors to evaluate he probability of an outcome [
8,
9] which can be used to gain insights into causality of the outcome of interest and have been recognized as reliable tools for quantifying risk in diagnostic and prognostic analyses [
10,
11] Besides, nomogram is a prediction tool with the advantages of being graph-based and easy-to-understand, which can predict individualized specific risks for each patient in complex clinical settings [
12,
13] And they could be valuable decision support tools to assist clinicians in the complicated choices they make regarding patient management.
We therefore developed and validated a diagnostic model combining the clinical and laboratory parameters of CHD in middle-aged and elderly people based on the clinical data of 839 eligible patients, to determine whether these factors could be incorporated into the model to provide a potential auxiliary solution for patient identification of CHD.
Discussions
Recently, with the growing morbidity and mortality related to CHD in middle-aged and elderly people, the early diagnosis and treatment of CHD have received extensive attention worldwide [
3] Therefore, a new prediction model for CHD patients could be the key to early screening and diagnosis and thus improve their prognosis. In this study, we attempted to construct and verify a diagnostic model based on easily available parameters such as data on demographics, complications, clinical and laboratory indicators at baseline. A total of 932 consecutive patients with suspected CHD were retrospectively evaluated, and 839 eligible patients were enrolled in the analysis. Eight indicators were recognized as risk factors for the progression of CHD in the derivation set, of these predictors, age, HbA1c, ABI, and FMD defined to be significantly related to CHD in middle-aged and elderly people by Lasso regression analysis. Moreover, the performance of the clinical prediction model was validated in the validation set, showing high net benefit, good ability, and great clinical utility of the model according to the results of calibration plot, AUC value, DCA analysis, and CIC analysis.
This study showed that predictors such as age, HbA1c, ABI, and FMD were combined as independent risk factors in the prediction model. The main reason is that CHD is a disease caused by a variety of risk factors, which is thought to be mainly associated with age, glucose metabolism, and vascular health [
1] Additionally, the result is similar to some previous reports on the risk factors of CHD [
17‐
19] However, fewer concerned ABI and FMD, which are associated with peripheral artery disease (PAD), in previous models focusing on CHD. These indicators are easy to acquire in the electronic medical systems of inpatients, strengthening the ease of use and comprehensiveness of the prediction model.
Consistent with previous findings, our results demonstrated that the older the patients, the higher was the risk score in the nomogram. In detail, prevalent cases of CHD began to account for a large proportion of epidemic cases of cardiovascular disease in patients over 40 years old, and the prevalence rose steeply with elder age categories [
4] According to the report [
3] there were approximately 10.88 million prevalent cases of CHD in patients aged 50 to 54 years, more than three times the number of cases in patients aged 40 to 44 years.
At present, the relationship between risk factors associated with diabetes and PAD, and the occurrence and development of CHD in middle-aged and elderly people are gradually obtaining more attention [
1,
20,
21] In the derivation set, patients in the CHD group had a higher HbA1c level than those in the non-CHD group, indicating that HbA1c is positively associated with the risk of CHD. In addition, a high HbA1c level was shown to be an important risk factor for glucose metabolism progression in cardiovascular disease [
22,
23] Knowledge of glucose metabolism is significant on account of a well-established link between adverse cardiovascular outcomes and diabetes [
24‐
26] Meanwhile, ordinary measurement of HbA1c in every patient with suspected CHD was recommended [
1] As outlined in the 2019 ESC Guidelines [
21] targeting near-normal HbA1c for glycemic control will reduce cardiovascular complications in diabetic patients, and less-rigorous HbA1c goals may be more appropriate for senile patients with severe comorbidities on a personalized basis. Besides, it has been shown that a reduction of approximately 1% in HbA1c was associated with a 15% reduction in the relative risk of non-fatal myocardial infarction [
27] and proper glycemic control at an early stage is strongly related to long-term cardiovascular benefits [
28].
It confirmed that the risk of death derived from cardiovascular causes is at a higher level in patients with large-vessel PAD [
29] The ABI is a sensitive marker for arterial stiffness [
30] and the FMD can be tested noninvasively to evaluate vascular endothelial function associated with atherosclerosis [
31] In the derivation set, patients in the CHD group had lower ABI and FMD levels than those in the non-CHD group, indicating that the ABI and FMD levels were negatively correlated with the risk of CHD. Meanwhile, this study showed that the lower the levels, the higher was the risk score in the nomogram. Indeed, impaired ABI and FMD have been reported as early biomarkers of the development of atherosclerotic disease in previous studies, and higher values generally predict better coronary vascular outcomes [
32,
33] A previous report found that patients with an FMD ≥ 10% were less strongly associated with fewer cardiovascular risk factors than those with an FMD < 10% [
31] Moreover, ABI may be defined as a risk modifier in the assessment of cardiovascular risk [
34] According to the American Heart Association, [
35] ABI is an independent predictor of the cardiovascular event risk, even in the absence of PAD symptoms. The degree of the increased risk related to a low ABI is approximately two to three times greater in patients with diagnosed cardiovascular disease than in healthy individuals, and a decline in ABI of > 0.15 over time is related to a twofold increase in mortality [
30,
35]
Among the present studies, cardiovascular risk prediction models involving traditional risk factors such as sex, age, smoking history, hypertension, diabetes mellitus, and hypercholesterolemia have been utilized to evaluate risk of future cardiovascular events [
36] These prediction models, however, have limited comprehensiveness and accuracy [
37] resulting in the assessment of other risk predictors such as electrocardiogram [
38] or parameters of obesity such as waist circumference, [
17] used in combination with other traditional risk factors or alone. More accurate recognization of high-risk individuals could facilitate the development of appropriate targeted aggressive risk reduction therapies, but more proper assessments for this strategy are still required in the future.
Furthermore, both the Hosmer–Lemeshow test and the calibration curve showed a good consistency between the actual and predicted risk of CHD, which ensures the reliability and repeatability of the CHD prediction model. Meanwhile, the discrimination of the model was assessed by the AUC value. The AUC value was 0.722 in the derivation set for the model to predict functional outcome and 0.783 in the validation set, showing that the clinical prediction model had a good predictive ability [
39] Besides, the results of DCA and CIC analysis illustrated that the clinical prediction model had remarkable predictive power. DCA analysis was utilized to assess the clinical utility of the prediction models, in which the net benefit is defined as the difference between the expected benefit and the expected harm of each prediction model [
40] The plots indicated that the clinical prediction model showed a greater net benefit with a wider range of threshold probabilities for predicting CHD in the two sets. The DCA for the derivation set indicated that the net benefit was maximized with threshold probabilities of 0% to 40% by the “predict all” method. Moreover, the CIC was further mapped on the basis of the DCA to assess the clinical impact, presenting the approximate number of patients with predicted CHD and the number of those who were in actual situation of illness at each risk threshold. When the risk threshold is greater than 60%, the estimated value is closer to the true number. Meanwhile, a similar trend was seen in the validation set.
The study is the first to construct and validate a clinical prediction model of CHD and investigate the value of easily accessible clinical and laboratory predictors to predict the development of CHD in middle-aged and elderly people. In addition, the nomogram we developed is beneficial to the early diagnosis of CHD, especially those who are not suitable for CAG or constrained by patients' characteristics such as those with severe comorbidities or the absence of medical conditions. Its purpose is to help clinicians make appropriate clinical treatment decisions based on individual conditions.
Limitations
Nevertheless, certain limitations of the study need to be mentioned. Firstly, this study is a retrospective single-center study with a single population source leads to a certain degree of selection bias. The study only included patients with suspected CHD, which may restrict the generalizability of the findings to patients with confirmed CHD or those without suspected CHD. Secondly, this study adopted a single time-node data modeling, which could not avoid the impact of dynamic changes in the physiological and pathological states of patients over time on the model. Subsequently, the design could be further improved, and multi-time-node data could be collected and analyzed to improve and update the model. Thirdly, the clinical performance of the constructed model was only evaluated by internal validation, therefore, the clinical value in external application needs to be further verified. The study did not assess the impact of the prediction model on patient outcomes, such as mortality or morbidity, which could help providing a more comprehensive evaluation of the model's clinical utility. We look forward to more large-scale, multi-center and prospective studies with rigorous and standardized design to verify and improve the results of this study.
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