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| Predictive value analysis of STS risk evaluation system for prolonged mechanical ventilation after off-pump coronary artery bypass grafting in single-center |
| Wang Zi-yu, Fu Qiang, Wang Shu-ying, Zhang Bin, Liu Jun-ling, Sun He-yuan, Yan Wei-qing |
| Tianjin Medical University Graduate School, Tianjin 300070, China |
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Abstract Objective To construct a risk prediction model for prolonged mechanical ventilation (PMV) after off-pump coronary artery bypass grafting (OPCABG) in single-center, and to evaluate the predictive performance for PMV after OPCABG compared with the American Society of Thoracic Surgeons (STS) risk evaluation system. Methods The clinical information of 110 patients underwent OPCABG were retrospectively analyzed. The patients were divided into non-PMV group (mechanical ventilation time ≤24 h) and PMV group (mechanical ventilation time>24 h). The risk factors of PMV was established by univariate analysis and binary Logistic regression analysis. The area under the receiver operating characteristic (ROC) area (AUC) was used to assess model discrimination, and the calibration was assessed by the Hosmer-Lemeshow (H-L) statistics and observed versus expected (O∶E). Results There were 28 cases of PMV in the entire cohort, and the realized rate was 25.45%. Lower height (OR=0.932, 95%CI0.876-0.991), COPD (OR=18.894, 95%CI 3.410-104.678),preoperative arrhythmia (OR=4.645, 95%CI 1.430-15.086) were independent risk factors for PMV after OPCABG. The PMV risk prediction model achieved good discrimination in the entire cohort (AUC=0.770>0.75), the discrimination of the high-risk group was acceptable (AUC=0.733>0.70), and the discrimination of the low-risk group was poor (AUC=0.592<0.70). It achieved good calibration in both entire cohort (O∶E=0.999) and high-risk group (O∶E=1.113),while the rate of PMV was overestimated in the low-risk group（O∶E=0.734）. The STS risk evaluation system achieved acceptable discrimination the entire cohort (AUC=0.736>0.70), and the calibration was poor (O∶E=3.615), which seriously underestimated the rate of PMV in the entire cohort. The discrimination were poor in low-risk group (AUC=0.614<0.7) and high-risk group (AUC=0.567<0.7), which the rates of PMV were seriously underestimated in both groups. Conclusion The PMV risk prediction model can be used to predict the risk of PMV after OPCABG in the entire cohort and high-risk group, whereas the STS risk evaluation system is not suitable temporarily for the risk assessment of PMV after OPCABG in our center.
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Corresponding Authors:
Fu Qiang, E-mail: 13920864938@163.com
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