Sleep disorders are frequent in hospitalized patients and harmful to the health. A meta-analysis of 47 articles suggested that the incidence of sleep disorders in the elderly over 60 years old was 35.9% (95% CI: 30.6–41.2%) in the Chinese population [9]. According to the results of this study, the incidence of sleep disorders in AMI patients during hospitalization was 35.16%. Meanwhile, the use rate of sleeping pills was 12.89%. We conducted a retrospective study to explore the independent risk factors of sleep disorders in patients with AMI during hospitalization and construct a related risk prediction model in order to achieve early identification and intervention of high-risk patients. Our study found that age, smoking, NYHA class, anxiety or depression status at admission, and strangeness of environment were the independent risk factors for sleep disorders in AMI patients during hospitalization. Our study identified depression status at admission (OR = 7.934) and strangeness of environment (OR = 7.958) as the most significant risk factors for sleep disorders. More importantly, this study constructed a nomogram model of predictive variables. The nomogram model, combining six selected predictors, could be easily assessed within a few hours after admission. In addition, C-index, calibration curve, DCA, and clinical practicability show good predictive performance. To guarantee practical convenience in clinical practice, we used the routine variables, which could provide an effective basis for early identification of AMI.

This study confirmed that age and smoking are independent risk factors for sleep disorders in patients with AMI during hospitalization. Previous studies have shown that sleep becomes more fragmented and lighter as the number of arousals and awakenings increases during the natural aging process of the human body [10, 11]. Meanwhile, the elderly’s immune system function is degraded, accompanied by a variety of chronic diseases and increased drug use, which will further contribute to sleep disorders [12]. Smoking is associated with an increased risk of hypertension, coronary heart disease, and peripheral vascular disease. A cross-sectional study of 189,970 participants applied urinary cotinine to verify smoking intensity and demonstrated that smoking was associated with sleep problems [13]. Another analysis indicated a relationship between sleep disturbance and both active and passive exposure to high levels of tobacco smoke [14]. Smoking cessation is recommended for patients with AMI.

The analysis found a relationship between sleep disorders and the NYHA class in AMI patients. Heart failure is usually characterized by progressive worsening and recurrent symptoms such as dyspnea, limitation of activity, and fluid retention. Patients with heart failure are more likely to have sleep-disordered breathing [15]. Nasir et al. defined PSQI > 5 as sleep disorders, and reported that nearly 92.5% of patients with chronic heart failure had sleep quality problems [16]. Patients with heart failure are prone to sleep disruption and arterial oxyhemoglobin desaturation [17], which can easily lead to the occurrence of anxiety and depression. Therefore, sleep intervention is necessary for patients with a high NYHA class. A previous study suggested that raising the head of the bed and oxygen therapy could relieve the symptoms of respiratory disorders and therefore improve sleep quality [18]. Left ventricular ejection fraction (LVEF) and B-type natriuretic peptide (BNP) serve as the important objective items for diagnosing heart failure in clinical practice. A decline in LVEF or an increase in BNP may be related to sleep disorders in patients with AMI. Meanwhile, myocardial enzymes and troponin are indeed the core indicators for diagnosing and assessing the severity of AMI [2]. Therefore, it is necessary to conduct echocardiographic screening and relevant serological tests at admission.

Anxiety manifests as restlessness, panic, and somatic manifestations such as nausea and palpitations. Depression was characterized by a depressed mood, delayed thinking, and changes in sleep habits. In this study, anxiety status and depression status were the important risk factors for sleep disorders. Patients with anxiety or depression often have decreased appetite, which increases the risk of malnutrition and immunodeficiency [19]. Meanwhile, patients with sleep disorders experience daytime fatigue and decreased physical strength, which further aggravates the occurrence of negative emotions. Luo et al. showed that insomnia symptoms could increase the risk of depression and anxiety one year later [20]. There are bidirectional associations between sleep disorders and anxiety/depression status. Nurses should evaluate the psychological status of anxiety and depression in hospitalized patients with AMI, relieve their anxiety and depression status individually, and intervene in sleep disorders. Additionally, this analysis suggested that environmental strangeness was a risk factor for sleep disorders in patients with AMI. Noise, smell, humidity, and bed may make patients feel unfamiliar. Therefore, nurses need to pay attention to the psychological counselling and increase the patient’s ability to accept the new environment.

To our knowledge, this report represents the first such comprehensive study to develop and validate a prediction nomogram for sleep disorders in hospitalized patients with AMI. This analysis demonstrated a good overall performance in prediction and could help achieve the goals of early diagnosis and early treatment in clinical nursing practice. However, our study had some limitations. Firstly, this analysis was a retrospective study from a single institution. Future research should focus on updating the model by expanding the sample size and conducting external validation using a multi-center patient population. Secondly, the outcome indicator was obtained during the short-term hospitalization period, and there was a lack of long-term follow-up with patients. Thirdly, we did not include different laboratory data at admission that may indicate an association with sleep disorders. It is necessary to incorporate relevant serum biomarkers, including creatine kinase (CK), Troponin (cTnI or cTnT), and BNP, in order to further improve the prediction nomogram in the future research.