• Srisuda Assawapalanggool, Nongyao Kasatpibal, Supatra Sirichotiyakul, Rajin Arora, and Watcharin Suntornlimsiri.
• 1 Infection Control Section, Mae Sot Hospital, Tak Province, Thailand .
• Surg Infect (Larchmt). 2017 Aug 1; 18 (6): 694-701.

BackgroundOrgan/space surgical site infections (SSIs) are serious complications after cesarean delivery. However, no scoring tool to predict these complications has yet been developed. This study sought to develop and validate a prognostic scoring tool for cesarean organ/space SSIs.MethodsData for case and non-case of cesarean organ/space SSI between January 1, 2007 and December 31, 2012 from a tertiary care hospital in Thailand were analyzed. Stepwise multivariable logistic regression was used to select the best predictor combination and their coefficients were transformed to a risk scoring tool. The likelihood ratio of positive for each risk category and the area under receiver operating characteristic (AUROC) curves were analyzed on total scores. Internal validation using bootstrap re-sampling was tested for reproducibility.ResultsThe predictors of 243 organ/space SSIs from 4,988 eligible cesarean delivery cases comprised the presence of foul-smelling amniotic fluid (four points), vaginal examination five or more times before incision (two points), wound class III or greater (two points), being referred from local setting (two points), hemoglobin less than 11 g/dL (one point), and ethnic minorities (one point). The likelihood ratio of cesarean organ/space SSIs with 95% confidence interval among low (total score of 0-1 point), medium (total score of 2-5 points), and high risk (total score of ≥6 points) categories were 0.11 (0.07-0.19), 1.03 (0.89-1.18), and 13.25 (10.87-16.14), respectively. Both AUROCs of the derivation and validation data were comparable (87.57% versus 86.08%; p = 0.418).ConclusionsThis scoring tool showed a high predictive ability regarding cesarean organ/space SSIs on the derivation data and reproducibility was demonstrated on internal validation. It could assist practitioners prioritize patient care and management depending on risk category and decrease SSI rates in cesarean deliveries.

21 keywords...

hide…