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

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          Abstract

          Background

          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.

          Methods

          Using 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.

          Results

          Overall 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.

          Conclusions

          SAP 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.

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          Most cited references32

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          Multilayer feedforward networks are universal approximators

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            Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.

            J V Tu (1996)
            Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
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              Artificial neural networks in medical diagnosis

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                Author and article information

                Contributors
                vvelanov@health.usf.edu
                Journal
                Patient Saf Surg
                Patient Saf Surg
                Patient Safety in Surgery
                BioMed Central (London )
                1754-9493
                7 December 2019
                7 December 2019
                2019
                : 13
                : 41
                Affiliations
                [1 ]ISNI 0000 0001 2353 285X, GRID grid.170693.a, School of Information and Florida Center for Cybersecurity, , University of South Florida, ; Tampa, FL USA
                [2 ]ISNI 0000 0001 1501 0314, GRID grid.267280.9, College of Business, Information and Technology Management, , University of Tampa, ; 5 Tampa General Circle, Suite 740, Tampa, FL 33606 USA
                [3 ]ISNI 0000 0001 0504 7025, GRID grid.416892.0, Tampa General Hospital, ; Tampa, FL USA
                [4 ]ISNI 0000 0001 2353 285X, GRID grid.170693.a, Division of General Surgery, , University of South Florida, ; Tampa, FL USA
                Author information
                http://orcid.org/0000-0003-0133-948X
                Article
                222
                10.1186/s13037-019-0222-4
                6898955
                31827618
                2a4b604a-aacf-4f50-b363-2ec3881837ed
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 August 2019
                : 26 November 2019
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                Surgery
                surgical site infection,prophylactic antibiotic bundle compliance,artificial neural networks

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