Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI
To identify risk factors and develop a risk-prediction nomogram model for 1-year readmission due to major adverse cardiovascular events (MACEs) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI). This was a single-center,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-11-01
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| Series: | Clinical and Applied Thrombosis/Hemostasis |
| Online Access: | https://doi.org/10.1177/10760296221137847 |
| _version_ | 1811226021583650816 |
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| author | Wensen Yao MD Jie Li PhD |
| author_facet | Wensen Yao MD Jie Li PhD |
| author_sort | Wensen Yao MD |
| collection | DOAJ |
| description | To identify risk factors and develop a risk-prediction nomogram model for 1-year readmission due to major adverse cardiovascular events (MACEs) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI). This was a single-center, retrospective cohort study. A total of 526 eligible participants were enrolled, which included 456 non-readmitted and 70 readmitted patients. Multivariate logistical regressions were performed to identify the independent risk factors for readmission, and a prediction nomogram model was developed based on the results of the regression analysis. The receiver operating characteristic curve, Hosmer-Lemeshow test, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Female (OR = 2.426; 95% CI: 1.395–4.218), hypertension (OR = 1.898; 95% CI: 1.100–3.275), 3-vessel disease (OR = 2.632; 95% CI: 1.332–5.201), in-hospital Ventricular arrhythmias (VA) (OR = 3.143; 95% CI: 1.305–7.574), peak cTnI (OR = 1.003; 95% CI: 1.001–1.004) and baseline NT-proBNP (OR = 1.001; 95% CI: 1.000–1.002) were independent risk factors for readmission (all P < 0.05). The nomogram exhibited good discrimination with the area under the curve (AUC) of 0.723, calibration (Hosmer-Lemeshow test; χ 2 = 15.396, P = 0.052), and clinical usefulness. Female gender, hypertension, in-hospital VA, 3-vessel disease, baseline NT-proBNP, and peak cTnI were independent risk factors for readmission. The nomogram helped clinicians to identify the patients at risk of readmission before their hospital discharge, which may help reduce readmission rates. |
| first_indexed | 2024-04-12T09:17:05Z |
| format | Article |
| id | doaj.art-6fceb5ffc66a4f52a2a15c2936783373 |
| institution | Directory Open Access Journal |
| issn | 1938-2723 |
| language | English |
| last_indexed | 2024-04-12T09:17:05Z |
| publishDate | 2022-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Clinical and Applied Thrombosis/Hemostasis |
| spelling | doaj.art-6fceb5ffc66a4f52a2a15c29367833732022-12-22T03:38:47ZengSAGE PublishingClinical and Applied Thrombosis/Hemostasis1938-27232022-11-012810.1177/10760296221137847Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCIWensen Yao MDJie Li PhDTo identify risk factors and develop a risk-prediction nomogram model for 1-year readmission due to major adverse cardiovascular events (MACEs) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI). This was a single-center, retrospective cohort study. A total of 526 eligible participants were enrolled, which included 456 non-readmitted and 70 readmitted patients. Multivariate logistical regressions were performed to identify the independent risk factors for readmission, and a prediction nomogram model was developed based on the results of the regression analysis. The receiver operating characteristic curve, Hosmer-Lemeshow test, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Female (OR = 2.426; 95% CI: 1.395–4.218), hypertension (OR = 1.898; 95% CI: 1.100–3.275), 3-vessel disease (OR = 2.632; 95% CI: 1.332–5.201), in-hospital Ventricular arrhythmias (VA) (OR = 3.143; 95% CI: 1.305–7.574), peak cTnI (OR = 1.003; 95% CI: 1.001–1.004) and baseline NT-proBNP (OR = 1.001; 95% CI: 1.000–1.002) were independent risk factors for readmission (all P < 0.05). The nomogram exhibited good discrimination with the area under the curve (AUC) of 0.723, calibration (Hosmer-Lemeshow test; χ 2 = 15.396, P = 0.052), and clinical usefulness. Female gender, hypertension, in-hospital VA, 3-vessel disease, baseline NT-proBNP, and peak cTnI were independent risk factors for readmission. The nomogram helped clinicians to identify the patients at risk of readmission before their hospital discharge, which may help reduce readmission rates.https://doi.org/10.1177/10760296221137847 |
| spellingShingle | Wensen Yao MD Jie Li PhD Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI Clinical and Applied Thrombosis/Hemostasis |
| title | Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI |
| title_full | Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI |
| title_fullStr | Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI |
| title_full_unstemmed | Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI |
| title_short | Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI |
| title_sort | risk factors and prediction nomogram model for 1 year readmission for major adverse cardiovascular events in patients with stemi after pci |
| url | https://doi.org/10.1177/10760296221137847 |
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