Limits...
Development and Validation of a Stratification Tool for PredictingRisk of Deep Sternal Wound Infection after Coronary Artery Bypass Grafting at a Brazilian Hospital

View Article: PubMed Central - PubMed

ABSTRACT

Objective: Deep sternal wound infection following coronary artery bypass grafting is aserious complication associated with significant morbidity and mortality.Despite the substantial impact of deep sternal wound infection, there is alack of specific risk stratification tools to predict this complicationafter coronary artery bypass grafting. This study was undertaken to developa specific prognostic scoring system for the development of deep sternalwound infection that could risk-stratify patients undergoing coronary arterybypass grafting and be applied right after the surgical procedure.

Methods: Between March 2007 and August 2016, continuous, prospective surveillance dataon deep sternal wound infection and a set of 27 variables of 1500 patientswere collected. Using binary logistic regression analysis, we identifiedindependent predictors of deep sternal wound infection. Initially wedeveloped a predictive model in a subset of 500 patients. Dataset wasexpanded to other 1000 consecutive cases and a final model and risk scorewere derived. Calibration of the scores was performed using theHosmer-Lemeshow test.

Results: The model had area under Receiver Operating Characteristic (ROC) curve of0.729 (0.821 for preliminary dataset). Baseline risk score incorporatedindependent predictors of deep sternal wound infection: obesity(P=0.046; OR 2.58; 95% CI 1.11-6.68), diabetes(P=0.046; OR 2.61; 95% CI 1.12-6.63), smoking(P=0.008; OR 2.10; 95% CI 1.12-4.67), pedicled internalthoracic artery (P=0.012; OR 5.11; 95% CI 1.42-18.40), andon-pump coronary artery bypass grafting (P=0.042; OR 2.20;95% CI 1.13-5.81). A risk stratification system was, then, developed.

Conclusion: This tool effectively predicts deep sternal wound infection risk at ourcenter and may help with risk stratification in relation to public reportingand targeted prevention strategies in patients undergoing coronary arterybypass grafting.

No MeSH data available.


Receiver operating characteristic (ROC) curve for final predictivemodel.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5382897&req=5

f3: Receiver operating characteristic (ROC) curve for final predictivemodel.

Mentions: The predictive model was tested and found to predict outcome effectively in thelarger dataset (aROC curve was 0.729) (Figure3). Hosmer-Lemeshow test showed a score of 0.142. Bootstrappingvalidation confirmed a good discriminative power of the model (preliminarydataset Dxy=0.61, testing dataset Dxy=0.42).


Development and Validation of a Stratification Tool for PredictingRisk of Deep Sternal Wound Infection after Coronary Artery Bypass Grafting at a Brazilian Hospital
Receiver operating characteristic (ROC) curve for final predictivemodel.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382897&req=5

f3: Receiver operating characteristic (ROC) curve for final predictivemodel.
Mentions: The predictive model was tested and found to predict outcome effectively in thelarger dataset (aROC curve was 0.729) (Figure3). Hosmer-Lemeshow test showed a score of 0.142. Bootstrappingvalidation confirmed a good discriminative power of the model (preliminarydataset Dxy=0.61, testing dataset Dxy=0.42).

View Article: PubMed Central - PubMed

ABSTRACT

Objective: Deep sternal wound infection following coronary artery bypass grafting is aserious complication associated with significant morbidity and mortality.Despite the substantial impact of deep sternal wound infection, there is alack of specific risk stratification tools to predict this complicationafter coronary artery bypass grafting. This study was undertaken to developa specific prognostic scoring system for the development of deep sternalwound infection that could risk-stratify patients undergoing coronary arterybypass grafting and be applied right after the surgical procedure.

Methods: Between March 2007 and August 2016, continuous, prospective surveillance dataon deep sternal wound infection and a set of 27 variables of 1500 patientswere collected. Using binary logistic regression analysis, we identifiedindependent predictors of deep sternal wound infection. Initially wedeveloped a predictive model in a subset of 500 patients. Dataset wasexpanded to other 1000 consecutive cases and a final model and risk scorewere derived. Calibration of the scores was performed using theHosmer-Lemeshow test.

Results: The model had area under Receiver Operating Characteristic (ROC) curve of0.729 (0.821 for preliminary dataset). Baseline risk score incorporatedindependent predictors of deep sternal wound infection: obesity(P=0.046; OR 2.58; 95% CI 1.11-6.68), diabetes(P=0.046; OR 2.61; 95% CI 1.12-6.63), smoking(P=0.008; OR 2.10; 95% CI 1.12-4.67), pedicled internalthoracic artery (P=0.012; OR 5.11; 95% CI 1.42-18.40), andon-pump coronary artery bypass grafting (P=0.042; OR 2.20;95% CI 1.13-5.81). A risk stratification system was, then, developed.

Conclusion: This tool effectively predicts deep sternal wound infection risk at ourcenter and may help with risk stratification in relation to public reportingand targeted prevention strategies in patients undergoing coronary arterybypass grafting.

No MeSH data available.