ArticlePatient safety in surgery2019
Prophylactic antibiotic bundle compliance and surgical site infections: an artificial neural network analysis.
Article in Patient safety in surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic evaluation of machine learning models for postoperative surgical site infection prediction.PloS one · 2024Pooled it
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Appropriateness of choice and duration of surgical antibiotic prophylaxis and the incidence of surgical site infections: A prospective study.Journal of Taibah University Medical Sciences · 2023Article
- Article
- Using Artificial Neural Networks to Predict Intra-Abdominal Abscess Risk Post-Appendectomy.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2022Article
- Incidence and root causes of surgical site infections after gastrointestinal surgery at a public teaching hospital in Sudan.Patient safety in surgery · 2020Article
- The Role of Bacterial Colonization of the Suture Thread in Early Identification and Targeted Antibiotic Treatment of Surgical Site Infections: A Prospective Cohort Study.International journal of environmental research and public health · 2020Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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%,
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.
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