TY - JOUR
T1 - Development and Validation of a Model for Predicting Surgical Site Infection After Pelvic Organ Prolapse Surgery
AU - Sheyn, David
AU - Gregory, W. Thomas
AU - Osazuwa-Peters, Oyomoare
AU - Jelovsek, J. Eric
N1 - Publisher Copyright:
Copyright © 2022 American Urogynecologic Society. All rights reserved.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - IMPORTANCE: Surgical site infection (SSI) is a common and costly complication. Targeted interventions in high-risk patients may lead to a reduction in SSI; at present, there is no method to consistently identify patients at increased risk of SSI. OBJECTIVE: The aim of this study was to develop and validate a model for predicting risk of SSI after pelvic organ prolapse surgery. STUDY DESIGN: Women undergoing surgery between 2011 and 2017 were identified using Current Procedural Terminology codes from the Centers for Medicare and Medicaid Services 5% Limited Data Set. Surgical site infection ≤90 days of surgery was the primary outcome, with 41 candidate predictors identified, including demographics, comorbidities, and perioperative variables. Generalized linear regression was used to fit a full specified model, including all predictors and a reduced penalized model approximating the full model. Model performance was measured using the c-statistic, Brier score, and calibration curves. Accuracy measures were internally validated using bootstrapping to correct for bias and overfitting. Decision curves were used to determine the net benefit of using the model. RESULTS: Of 12,334 women, 4.7% experienced SSI. The approximated model included 10 predictors. Model accuracy was acceptable (bias-corrected c-statistic [95% confidence interval], 0.603 [0.578-0.624]; Brier score, 0.045). The model was moderately calibrated when predicting up to 5-6 times the average risk of SSI between 0 and 25-30%. There was a net benefit for clinical use when risk thresholds for intervention were between 3% and 12%. CONCLUSIONS: This model provides estimates of probability of SSI within 90 days after pelvic organ prolapse surgery and demonstrates net benefit when considering prevention strategies to reduce SSI.
AB - IMPORTANCE: Surgical site infection (SSI) is a common and costly complication. Targeted interventions in high-risk patients may lead to a reduction in SSI; at present, there is no method to consistently identify patients at increased risk of SSI. OBJECTIVE: The aim of this study was to develop and validate a model for predicting risk of SSI after pelvic organ prolapse surgery. STUDY DESIGN: Women undergoing surgery between 2011 and 2017 were identified using Current Procedural Terminology codes from the Centers for Medicare and Medicaid Services 5% Limited Data Set. Surgical site infection ≤90 days of surgery was the primary outcome, with 41 candidate predictors identified, including demographics, comorbidities, and perioperative variables. Generalized linear regression was used to fit a full specified model, including all predictors and a reduced penalized model approximating the full model. Model performance was measured using the c-statistic, Brier score, and calibration curves. Accuracy measures were internally validated using bootstrapping to correct for bias and overfitting. Decision curves were used to determine the net benefit of using the model. RESULTS: Of 12,334 women, 4.7% experienced SSI. The approximated model included 10 predictors. Model accuracy was acceptable (bias-corrected c-statistic [95% confidence interval], 0.603 [0.578-0.624]; Brier score, 0.045). The model was moderately calibrated when predicting up to 5-6 times the average risk of SSI between 0 and 25-30%. There was a net benefit for clinical use when risk thresholds for intervention were between 3% and 12%. CONCLUSIONS: This model provides estimates of probability of SSI within 90 days after pelvic organ prolapse surgery and demonstrates net benefit when considering prevention strategies to reduce SSI.
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U2 - 10.1097/SPV.0000000000001222
DO - 10.1097/SPV.0000000000001222
M3 - Article
C2 - 35830590
AN - SCOPUS:85185223589
SN - 2151-8378
VL - 28
SP - 658
EP - 666
JO - Urogynecology
JF - Urogynecology
IS - 10
ER -