Prophylactic antibiotic bundle compliance and surgical site infections: an artificial neural network analysis

Steven Walczak,Vic Velanovich,Marbelly Davila

doi:10.1186/s13037-019-0222-4

Abstract

BackgroundBest practice “bundles” have been developed to lower the occurrence rate of surgical site infections (SSI’s). We developed artificial neural network (ANN) models to predict SSI occurrence based on prophylactic antibiotic compliance.MethodsUsing the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) Tampa General Hospital patient dataset for a six-month period, 780 surgical procedures were reviewed for compliance with SSI guidelines for antibiotic type and timing. SSI rates were determined for patients in the compliant and non-compliant groups. ANN training and validation models were developed to include the variables of age, sex, steroid use, bleeding disorders, transfusion, white blood cell count, hematocrit level, platelet count, wound class, ASA class, and surgical antimicrobial prophylaxis (SAP) bundle compliance.ResultsOverall compliance to recommended antibiotic type and timing was 92.0%. Antibiotic bundle compliance had a lower incidence of SSI’s (3.3%) compared to the non-compliant group (8.1%, p = 0.07). ANN models predicted SSI with a 69–90% sensitivity and 50–60% specificity. The model was more sensitive when bundle compliance was not used in the model, but more specific when it was. Preoperative white blood cell (WBC) count had the most influence on the model.ConclusionsSAP bundle compliance was associated with a lower incidence of SSI’s. In an ANN model, inclusion of the SAP bundle compliance reduced sensitivity, but increased specificity of the prediction model. Preoperative WBC count had the most influence on the model.

Highlights

Best practice “bundles” have been developed to lower the occurrence rate of surgical site infections (SSI’s)
2% of all patients who undergo an operation in the United States will develop an Surgical site infection (SSI) [1, 2], with some types of operations, such as colonic surgery, being much higher [3]
All patients who underwent operations at Tampa General Hospital, a large, urban, tertiary, teaching hospital located in Tampa, Florida, USA, and were subsequently submitted to the ACS-NSQIP data base and reported back to Tampa General Hospital from 1 July 2015 to 31 December 2015 were included in this study

Summary

Introduction

Best practice “bundles” have been developed to lower the occurrence rate of surgical site infections (SSI’s). We developed artificial neural network (ANN) models to predict SSI occurrence based on prophylactic antibiotic compliance. Surgical site infections (SSI’s) are a common problem after many types of operations. 2% of all patients who undergo an operation in the United States will develop an SSI [1, 2], with some types of operations, such as colonic surgery, being much higher [3]. In addition to the suffering this causes patients, it is a substantial financial burden [4]. This has led to efforts to identify risk factors for SSI’s and to reduce the incidence of SSI. The July, 2017 American College of Surgeons National Quality Improvement Program (ACS-NSQIP) SSI

Objectives

Methods

Results

Conclusion