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Original Research

Development and Validation of a Risk Nomogram Model for Predicting Revascularization After Percutaneous Coronary Intervention in Patients with Acute Coronary Syndrome

ORCID Icon, ORCID Icon, , , ORCID Icon, , , , ORCID Icon, , ORCID Icon & show all
Pages 1541-1553 | Published online: 20 Aug 2021

Figures & data

Figure 1 Study flow diagram.

Abbreviations: ACS, acute coronary syndrome; CABG, coronary artery bypass grafting.
Figure 1 Study flow diagram.

Table 1 Participant Characteristics

Table 2 Univariate Logistic Regression Analysis of Revascularization Based on Preoperative Data in the Training Cohort

Table 3 Multivariate Logistic Regression Analysis of Revascularization Based on Preoperative Data in the Training Cohort

Figure 2 Nomogram used for predicting revascularization after PCI in ACS patients. The final score (ie, total points) is calculated as the sum of the individual scores of each of the ten variables included in the nomogram.

Abbreviations: PCI, percutaneous transluminal coronary intervention; ACS, acute coronary syndrome; DM, diabetes mellitus; SCr, serum creatinine; SUA, serum uric acid; LDL-C2, low-density lipoprotein cholesterol at re-admission; Lp(a), Lipoprotein-a; CTO, chronic total occlusion.
Figure 2 Nomogram used for predicting revascularization after PCI in ACS patients. The final score (ie, total points) is calculated as the sum of the individual scores of each of the ten variables included in the nomogram.

Figure 3 ROC curve of the nomogram for predicting revascularization after PCI in ACS patients. (A) ROC curve in the training set; (B) ROC curve in the validation set.

Abbreviations: AUC, area under the ROC curve; ROC, receiver operating characteristic; PCI, percutaneous transluminal coronary intervention; ACS, acute coronary syndrome.
Figure 3 ROC curve of the nomogram for predicting revascularization after PCI in ACS patients. (A) ROC curve in the training set; (B) ROC curve in the validation set.

Figure 4 Calibration curve of the nomogram for the training set (A) and the validation set (B). The X-axis represents the overall predicted probability of revascularization after PCI and the Y-axis represents the actual probability. Model calibration is indicated by the degree of fitting of the curve and the diagonal.

Figure 4 Calibration curve of the nomogram for the training set (A) and the validation set (B). The X-axis represents the overall predicted probability of revascularization after PCI and the Y-axis represents the actual probability. Model calibration is indicated by the degree of fitting of the curve and the diagonal.

Figure 5 Decision curve analysis for the training set (A) and the validation set (B). A horizontal line indicates that all samples are negative and not treated, with a net benefit of zero. An oblique line indicates that all samples are positive. The net benefit has a negative slope.

Figure 5 Decision curve analysis for the training set (A) and the validation set (B). A horizontal line indicates that all samples are negative and not treated, with a net benefit of zero. An oblique line indicates that all samples are positive. The net benefit has a negative slope.