A nomogram prediction model for sternal incision problems

Abstract

Presently, the incidence and mortality rates of sternal incision problems (SIPs) after thoracotomy remain high, and no effective preventive measures are available. The data on 23 182 patients at Xinqiao Hospital, Army Medical University treated with median sternotomy from 1 August 2009 to 31 July 2019 were retrospectively reviewed. A prediction model of SIPs after median thoracotomy was established using R software and then validated using the bootstrap method. Next, the validity and accuracy of the model were tested and evaluated. In total, 15 426 cases met the requirements of the present study, among which 309 cases were diagnosed with SIPs, with an incidence rate of 2%. The body mass index (BMI), intensive care unit (ICU) time, diabetes mellitus, and revision for bleeding were identified as independent risk factors for postoperative SIPs. The nomogram model achieved good discrimination (73.9%) and accuracy (70.2%) in predicting the risk of SIPs after median thoracotomy. Receiver operating characteristic curve analysis showed that the area under curve of the model was 0.705 (95% confidence interval [CI]: 0.746-0.803); the Hosmer-Lemeshow test showed that χ² = 6.987 and P = 0.538, and the fitting degree of the calibration curve was good. Additionally, the clinical decision curve showed that the net benefit of the model was greater than 0, and the clinical application value was high. The nomogram based on BMI, ICU time, diabetes mellitus, and revision for bleeding can predict the individualised risk of SIPs after median sternotomy, showing good discrimination and accuracy, and has high clinical application value. It also provides significant guidance for screening high-risk populations and developing intervention strategies.

Keywords: median thoracotomy; nomogram; prediction model; sternal incision problems.