P A Baltodano1, Y Webb-Vargas2, K C Soares3, C W Hicks3, C M Cooney4, P Cornell3, K K Burce3, T M Pawlik3, F E Eckhauser5. 1. Ravitch Division of GI and Minimally Invasive Surgery, Department of Surgery, The Johns Hopkins University School of Medicine, Blalock 618, 600 N. Wolfe Street, Baltimore, MD, 21287, USA. baltodp@mail.amc.edu. 2. Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 3. Ravitch Division of GI and Minimally Invasive Surgery, Department of Surgery, The Johns Hopkins University School of Medicine, Blalock 618, 600 N. Wolfe Street, Baltimore, MD, 21287, USA. 4. Plastic and Reconstructive Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA. 5. Ravitch Division of GI and Minimally Invasive Surgery, Department of Surgery, The Johns Hopkins University School of Medicine, Blalock 618, 600 N. Wolfe Street, Baltimore, MD, 21287, USA. feckhau2@jhmi.edu.
Abstract
BACKGROUND/ PURPOSE: To present a validated model that reliably predicts unplanned readmission after open ventral hernia repair (open-VHR). STUDY DESIGN: A total of 17,789 open-VHR patients were identified using the 2011-2012 ACS-NSQIP databases. This cohort was subdivided into 70 and 30% random testing and validation samples, respectively. Thirty-day unplanned readmission was defined as unexpected readmission for a postoperative occurrence related to the open-VHR procedure. Independent predictors of 30-day unplanned readmission were identified using multivariable logistic regression on the testing sample (n = 12,452 patients). Subsequently, the predictors were weighted according to β-coefficients to generate an integer-based Clinical Risk Score (CRS) predictive of readmission, which was validated using receiver operating characteristics (ROC) analysis of the validation sample (n = 5337 patients). RESULTS: The rate of 30-day unplanned readmission was 4.7%. Independent risk factors included inpatient status at time of open-VHR, operation time, enterolysis, underweight, diabetes, preoperative anemia, length of stay, chronic obstructive pulmonary disease, history of bleeding disorders, hernia with gangrene, and panniculectomy (all P < 0.05). ROC analysis of the validation cohort rendered an area under the curve of 0.71, which demonstrates the accuracy of this prediction model. Predicted incidence within each 5 risk strata was statistically similar to the observed incidence in the validation sample (P = 0.18), further highlighting the accuracy of this model. CONCLUSION: We present a validated risk stratification tool for unplanned readmissions following open-VHR. Future studies should determine if implementation of our CRS optimizes safety and reduces readmission rates in open-VHR patients.
BACKGROUND/ PURPOSE: To present a validated model that reliably predicts unplanned readmission after open ventral hernia repair (open-VHR). STUDY DESIGN: A total of 17,789 open-VHRpatients were identified using the 2011-2012 ACS-NSQIP databases. This cohort was subdivided into 70 and 30% random testing and validation samples, respectively. Thirty-day unplanned readmission was defined as unexpected readmission for a postoperative occurrence related to the open-VHR procedure. Independent predictors of 30-day unplanned readmission were identified using multivariable logistic regression on the testing sample (n = 12,452 patients). Subsequently, the predictors were weighted according to β-coefficients to generate an integer-based Clinical Risk Score (CRS) predictive of readmission, which was validated using receiver operating characteristics (ROC) analysis of the validation sample (n = 5337 patients). RESULTS: The rate of 30-day unplanned readmission was 4.7%. Independent risk factors included inpatient status at time of open-VHR, operation time, enterolysis, underweight, diabetes, preoperative anemia, length of stay, chronic obstructive pulmonary disease, history of bleeding disorders, hernia with gangrene, and panniculectomy (all P < 0.05). ROC analysis of the validation cohort rendered an area under the curve of 0.71, which demonstrates the accuracy of this prediction model. Predicted incidence within each 5 risk strata was statistically similar to the observed incidence in the validation sample (P = 0.18), further highlighting the accuracy of this model. CONCLUSION: We present a validated risk stratification tool for unplanned readmissions following open-VHR. Future studies should determine if implementation of our CRS optimizes safety and reduces readmission rates in open-VHRpatients.
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