ArticleBMC cardiovascular disorders2024
Development and validation of a prediction nomogram for sleep disorders in hospitalized patients with acute myocardial infarction.
Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevalence and influencing factors of insomnia in patients with coronary heart disease: a systematic review and meta-analysis.Frontiers in psychiatry · 2026Pooled it
- The diagnostic efficacy of unenhanced CT for detecting acute central pulmonary embolism.BMC cardiovascular disorders · 2025Article
- The effects of high-quality nursing accompaniment on the sleep quality, negative psychological moods and quality of life of patients with acute myocardial infarct.Frontiers in cardiovascular medicine · 2025Article
- The influencing factors of postoperative sleep disorders in elderly patients undergoing hip replacement surgery under general anesthesia and the construction and validation of a nomogram prediction model.Frontiers in neurology · 2025Article
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6 authors.
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Abstract
purposeSleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders.
methodsClinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted.
resultsA total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance.
conclusionsWe identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice.
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