Developing a calculable risk prediction model for sternal wound infection after median sternotomy: a retrospective
1Department of Burns and Cutaneous Surgery, Xijing Hospital of Air Force Medical University, Xi'an, 710032, Shaanxi, People's Republic of China.
Background:
Diagnosing sternal wound infection (SWI) following median sternotomy remains laborious and troublesome, resulting in high mortality rates and great harm to patients. Early intervention and prevention are critical and challenging. This study aimed to develop a simple risk prediction model to identify high-risk populations of SWI and to guide examination programs and intervention strategies.
Methods:
A retrospective analysis was conducted on the clinical data obtained from 6715 patients who underwent median sternotomy between January 2016 and December 2020. The least absolute shrink and selection operator (LASSO) regression method selected the optimal subset of predictors, and multivariate logistic regression helped screen the significant factors. The nomogram model was built based on all significant factors. Area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to assess the model's performance.
Results:
LASSO regression analysis selected an optimal subset containing nine predictors that were all statistically significant in multivariate logistic regression analysis. Independent risk factors of SWI included female [odds ratio (OR) = 3.405, 95% confidence interval (CI) = 2.535-4.573], chronic obstructive pulmonary disease (OR = 4.679, 95% CI = 2.916-7.508), drinking (OR = 2.025, 95% CI = 1.437-2.855), smoking (OR = 7.059, 95% CI = 5.034-9.898), re-operation (OR = 3.235, 95% CI = 1.087-9.623), heart failure (OR = 1.555, 95% CI = 1.200-2.016) and repeated endotracheal intubation (OR = 1.975, 95% CI = 1.405-2.774). Protective factors included bone wax (OR = 0.674, 95% CI = 0.538-0.843) and chest physiotherapy (OR = 0.446, 95% CI = 0.248-0.802). The AUC of the nomogram was 0.770 (95% CI = 0.745-0.795) with relatively good sensitivity (0.798) and accuracy (0.620), exhibiting moderately good discernment. The model also showed an excellent fitting degree on the calibration curve. Finally, the DCA presented a remarkable net benefit.
Conclusions:
A visual and convenient nomogram-based risk calculator built on disease-associated predictors might help clinicians with the early identification of high-risk patients of SWI and timely intervention.
