The primary aim of this study was to identify distinct health status profiles among patients with CHD based on patient-reported outcomes (PROs), and to explore the sociodemographic and clinical factors associated with these profiles.

This study employed a cross-sectional design and was conducted between November 2023 to June 2024 in three tertiary hospitals in Sichuan Province, China. Patients diagnosed with CHD were recruited through the cardiology inpatient departments. The study was approved by the hospital's ethics committee (Ethics Approval No: S202303120-01).

Before distributing the questionnaires, researchers explained the purpose, procedures, and confidentiality principles of the study to ensure informed participation. Data collection took place during patients’ clinically stable period following hospital admission. Uniformly trained investigators conducted one-on-one questionnaire interviews in quiet inpatient wards to facilitate accurate and reliable responses.During the survey, investigators remained present to offer clarification and assistance as needed.

Participants did not receive any monetary compensation but were informed that participation could enhance their awareness of their own health status. Any missing or unclear responses were verified with the participant on-site to ensure data accuracy. Participants were reminded of the importance of providing honest and accurate responses to maintain the integrity and quality of the data.

Participants were eligible for inclusion if they met the following criteria: diagnosis of CHD confirmed by a cardiologist according to the Chinese Guidelines for the Diagnosis and Treatment of CHD, age ≥18 years; being conscious and able to communicate and comprehend basic information, with the ability to independently complete the questionnaire and voluntary participation with signed informed consent.

Exclusion criteria included severe cognitive impairment or communication barriers, acute myocardial infarction or other major cardiovascular events within the past three months, comorbid severe mental illness or malignant tumors or incomplete clinical or questionnaire data.

SPSS26.0 statistical software and R4.4.0 software were used for data analysis. LPA was performed using R software. LPA was conducted using the scores of the five dimensions of the PRO Scale as manifest variables. The model selection criteria included the following (12):

Akaik information criterion(AIC) and Bayesian information criterion(BIC) and sample-corrected Bayesian information criterion (aBIC), the smaller the value. It shows that the better the model fitting effect;

A total of 240 questionnaires were distributed in this study, and 240 valid questionnaires were recovered, with an effective recovery rate of 100%. The score of 240 patients was (59.58 ± 0.81), and the scores of each dimension were physical health (64.14 ± 14.02), mental health (63.69 ± 14.59), social health (64.83 ± 14.27), mental belief health (45.80 ± 25.84) and specific symptoms (57.39 ± 19.18).

According to the scale scores of 240 patients with CHD, the LPA was carried out. The scores of five dimensions were used as explicit indicators. From the initial model, a total of five potential profile models were established, as shown in Table 1. According to the model screening criteria, although the fourth and fifth types of models AIC and BIC are lower, the entropy value is also lower. The third type of model not only has low model fitting AIC, BIC and aBIC values, but also has an entropy value of 0.98, showing high classification clarity, indicating that the classification of the model is more accurate. The significance test LMR and BLRT test p value is less than 0.05, indicating that it has a good fitting effect. Therefore, based on the above evaluation criteria, the 3 model performs better in fitting effect, classification clarity and statistical significance. it is more reasonable and more explanatory to choose model 3 as the optimal model.

Each latent profile was named according to the scoring patterns in each dimension. Category 1 has less fluctuation in the five dimensions, so it is named Stable and Healthy Model (SHM). Category 2 has a higher score in the dimensions of mental health and social health, but there is a large fluctuation in the dimensions of mental belief health and specific symptoms, so it is named as Social Psychological fluctuation Model(SPFM). Category 3 has a very high score in the specific symptom dimension, while the scores in other dimensions are low and volatile, and the overall health status is unstable, so it is named as Symptom Prominent Instability model(SPIM). According to the scores of each dimension, a potential profile is made through the classification results, as shown in Figure 1.

A total of 240 patients participated in the study. Among them, 146 individuals (60.83%) were classified into Latent Class 1, 78 individuals (32.50%) into Class 2, and 16 individuals (6.67%) into Class 3.A comparison of the three latent classes based on the PROISCD-CHD across various dimensions revealed statistically significant differences in glycated hemoglobin (HbA1c), high-density lipoprotein (HDL), low-density lipoprotein (LDL), age, monthly income, occupation, sex, type of medical insurance, number of medications, and the presence of comorbid chronic obstructive pulmonary disease (COPD) (P < 0.05). However, no statistically significant differences were observed for other variables (all P > 0.05), as detailed in Table 2.

An unordered multinomial logistic regression analysis was conducted using the categorical of the PROISCD-CHD scale as the dependent variable. Independent variables included those indicators that showed statistically significant differences in the univariate analysis, with the stable health type used as the reference group.Categorical variables were assigned values accordingly,as shown in Table 3.

The results showed that HbA1c, age and combined COPD were independent risk factors affecting the health status of patients with social psychological fluctuation. Age and education level and not combined with COPD were independent influencing factors of Symptom Prominent Instability model (P < 0.05), as shown in Table 4.

Based on PRO, this study conducted a cross-sectional analysis of the health status of patients with CHD, and divided the health status into three categories (SHM. SPFM, SPIM).The proportion of SHM patients was the highest (60.83%). The scores of physiological, psychological, social and mental health dimensions were balanced, the overall fluctuation was small, the symptoms were mild, and the health status was stable, indicating that the health status of most patients was relatively stable, which was consistent with previous research results (13). These patients have higher scores in social health and mental health. Studies have shown that better mental health and social support networks, which can manage diseases effectively and maintain quality of life, reduce mortality and recurrence rates (14). Intervention strategies should focus on maintaining their health level, preventing degradation, and optimizing health behavior.

However, the health dimension scores of patients with SPFM, SPIM instability fluctuate greatly, suggesting that these patients may face more challenges in health management and need to develop more targeted intervention strategies. Patients with SPFM had higher scores in the dimensions of mental health and social health, but the dimensions of mental belief and specific symptoms fluctuated greatly. Despite having strong social support and psychological adaptability, the lack of spiritual health may limit their psychological resilience in coping with chronic illness. Studies have shown that the lack of spiritual belief health can easily lead to negative emotions such as anxiety and depression, and increase the burden of disease (15). Therefore, interventions for this profile should emphasize a combination of spiritual support and psychological counseling to enhance the patients’ disease adaptation abilities.

Patients with SPIM are the most serious in terms of specific symptoms, other health dimensions have lower scores, and the overall health status is poor. Such patients not only face significant physiological challenges, but also are accompanied by increased psychological burden and lack of social support. This dual physiological and psychological pressure may form a vicious circle, further exacerbating health instability. The multidimensional health indicators of these patients were significantly lower than those of other types, suggesting that religious beliefs or spiritual support may alleviate psychological stress and improve physiological symptoms, which is consistent with previous studies (16). Consequently, intervention should focus on symptom control, supplemented by multidisciplinary collaboration to improve mental health and social function.

HbA1c level is an independent risk factor affecting the health status of patients with psychosocial fluctuation. The patients in this group had the highest level of HbA1c, suggesting that poor blood glucose control may increase the psychological burden and disease symptoms. Studies have shown that (17, 18) long-term poor glucose control can aggravate atherosclerosis and microvascular damage, further worsening specific symptoms and overall health levels. Significant differences were observed in HDL and LDL levels across the three profiles. The levels of HDL and LDL in patients with unstable symptoms were significantly increased, suggesting that lipid metabolism disorder is an important cause of unstable health. Although HDL is generally considered to be good cholesterol, high levels may reflect chronic inflammation and aggravate vascular damage (19); elevated LDL increases the risk of cardiovascular events by promoting atherosclerotic plaque formation and inflammatory response (20). Chinese blood lipid management guidelines (21) require that the primary goal should be to reduce LDL levels, combined with HDL functional assessment, to develop personalized interventions, including optimizing diet structure, increasing physical activity and alleviating psychological stress, in order to improve lipid metabolism disorders and improve patient health outcomes.

The influence of age and gender on health status is also significant, and the proportion of young and middle-aged patients (≤44 years old) in patients with prominent and unstable symptoms is higher. This may be related to greater occupational stress, unhealthy lifestyles, and underestimation of disease risk (22). However, due to the strong compensatory ability of young patients, the symptoms may be hidden but progress rapidly, which may lead to increased instability of health status. In addition, the proportion of males in this group is significantly higher, and studies have shown that male patients are more likely to show severe symptoms and unstable health status (23, 24).

The monthly income of patients with unstable symptoms was significantly higher than that of the other two groups, but high income did not directly translate into better health management. High-income people tend to be accompanied by greater professional responsibilities and stress, resulting in irregular work and rest and unhealthy eating habits, thereby offsetting the health benefits of income advantages (25, 26).At the same time, high-income groups may have higher expectations for physical health status. When health conditions are poor, they are prone to stronger psychological frustration and stress reactions, which affect disease management compliance (27). In terms of medical insurance payment methods, most patients rely on resident insurance, but the reimbursement ratio is low, which makes it difficult to meet the needs of high-quality medical services and affects the overall improvement of health status (28).

The data indicate that occupational type also shows significant differences in health status classification. Mental workers accounted for a significantly higher proportion of unstable symptoms. Studies have shown that (4), mental workers may lead to energy metabolism disorders and increased psychological stress due to long-term sedentary, high work stress and insufficient physical activity. These factors may lead to increased risk of coronary heart disease and decreased compliance with disease management. Moderate physical activity of manual workers may have a protective effect on cardiovascular health (29). For mental workers, intervention should be carried out by improving lifestyle, increasing physical activity and alleviating occupational stress; for manual workers, occupational health monitoring should be strengthened to avoid health risks caused by excessive physical activity.

The influence of education level on health status can not be ignored. Highly educated patients have a higher proportion of unstable symptoms. Studies have shown that (30), patients with high education have more cardiovascular risk factors, including overweight/obesity, diabetes, dyslipidemia, lack of exercise and unhealthy diet, which increase the risk of cardiovascular events. The age of first acute myocardial infarction is about 56.3 years old, about 10 years earlier than those with low education, which affects the stability of their health status. The type of medication is an important factor affecting the health status of patients.

The proportion of patients taking 6 or more drugs in unstable patients with prominent symptoms is higher. These patients may need to take more drugs because of the complexity of the disease and the severity of complications, which in turn may affect their treatment compliance and disease control. Studies have shown that (31), multi-drug therapy can effectively control cardiovascular risk through synergy, but too many types of drugs may increase the economic burden of patients, the complexity of medication, and the risk of drug interactions, which in turn affects the patient 's treatment compliance and disease control effect. Therefore, it is necessary to optimize the drug combination, streamline unnecessary multi-drug combination therapy, and strengthen medication education to help patients understand the importance of multi-drug treatment and improve medication compliance.

In addition, patients with COPD are more concentrated in the symptoms of prominent instability, suggesting that COPD has a significant negative impact on the health status of coronary heart disease. Dyspnea caused by COPD can increase the cardiopulmonary burden and form a vicious circle with coronary heart disease (32), resulting in a decline in the quality of life and increasing the complexity of health management. For patients with COPD, multidisciplinary collaborative management mode should be adopted to integrate cardiovascular and respiratory treatment, and mental health intervention and health education should be strengthened to improve their overall health status.