A nomogram prediction model for sternal incision problems

Risk factors for sternal wound infection after open-heart operations: A systematic review and meta-analysis

We aimed to quantitatively and systematically elucidate the rationality of the examined variables as independent risk factors for sternal wound infection. We searched databases to screen studies, ascertained the variables to be analysed, extracted the data and applied meta-analysis to each qualified variable. Odds ratios and mean differences were considered to be the effect sizes for binary and continuous variables, respectively. A random-effects model was used for these procedures. The source of heterogeneity was evaluated using a meta-regression. Publication bias was tested by funnel plot and Egger's test, the significant results of which were then calculated using trim and fill analysis. We used a sensitivity analysis and bubble chart to describe their robustness. After screening all variables in the eligible literature, we excluded 55 because only one or no research found them significant after multivariate analysis, leaving 33 variables for synthesis. Two binary variables (age over 65 years, NYHA class >2) and a continuous variable (preoperative stay) were not significant after the meta-analysis. The most robust independent risk factors in our study were diabetes mellitus, obesity, use of bilateral internal thoracic arteries, chronic obstructive pulmonary disease, prolonged surgery time, prolonged ventilation and critical preoperative state, followed by congestive heart failure, atrial fibrillation, renal insufficiency, stroke, peripheral vascular disease and use of an intra-aortic balloon pump. Relatively low-risk factors were emergent/urgent surgery, smoking, myocardial infarction, combined surgery and coronary artery bypass grafting. Sternal wound infection after open-heart surgery is a multifactorial disease. The detected risk factors significantly affected the wound healing process, but some were different in strength. Anything that affects wound healing and antibacterial ability, such as lack of oxygen, local haemodynamic disorders, malnutrition condition and compromised immune system will increase the risk, and this reminds us of comprehensive treatment during the perioperative period.

Nomogram for Predicting In-hospital Mortality in Infective Endocarditis Based on Early Clinical Features and Treatment Options

Aim

The aim of this study was to develop a nomogram based on early clinical features and treatment options for predicting in-hospital mortality in infective endocarditis (IE).

Methods

We retrospectively analyzed the data of 294 patients diagnosed with IE in our hospital from June 01, 2012 to November 24, 2021, determined independent risk factors for in-hospital mortality by univariate and multivariate logistic regression analysis, and established a Nomogram prediction model based on these factors. Finally, the prediction performance of nomogram is evaluated by C-index, bootstrapped-concordance index, and calibration plots.

Results

Age, abnormal leukocyte count, left-sided IE, right-sided IE, and no surgical treatment were independent risk factors for in-hospital mortality in patients with IE, and we used these independent risk factors to construct a nomogram prediction model to predict in-hospital mortality in IE. The C-index of the model was 0.878 (95% CI: 0.824–0.931), and the internal validation of the model by bootstrap validation method showed a prediction accuracy of 0.852 and a bootstrapped-concordance index of 0.53.

Conclusion

Our nomogram can accurately predict in-hospital mortality in IE patients and can be used for early identification of high-risk IE patients.