SUpPress SSI - Single Use Negative Pressure Wound Therapy (NPWT) to Reduce Surgical Site Infections

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SUpPress SSI – Single Use Negative Pressure Wound Therapy (NPWT) to Reduce Surgical Site Infections The proposed work Specific Aim 1 is to evaluate the effect of single-use NPWT on SSI rates after C- section, abdominal hysterectomy, and colon procedures in at risk patients. To address this aim, we will conduct a multicenter, stepped-wedge, quasi-experimental trial evaluating use of the PrevenaTM with 125 mm Hg negative pressure for 7 days among obese (BMI >30) and/or diabetic patients undergoing the procedures of interest. This is an effectiveness trial evaluating the effect of single-use NPWT on SSI rates among high-risk patients using a randomized stepped wedge design. Six Epicenter hospitals will be included: University of Illinois Hospital (UIH), University of Iowa Hospital and Clinics (UIHC), University of Utah Hospital (UUH), Emory University Hospital Midtown (EUHM), Grady Memorial Hospital (GMH), and University of Maryland Medical Center (UMMC). If proposed improvement in SSI rates are demonstrated this will provide more clear guidance for the use of the NPWT. Specific Aim 2 is to investigate the patients’ experience of using the NPWT. A sub-set of patients will be surveyed to assess their knowledge of post-operative care, to identify complications associated with NPWT use, and to learn how patients evaluated the device’s ease of use, ease of removal, and comfort. This will identify barriers to implementation. Specific Aim 3 is to assess whether real-time decision support through machine-learning modeling can help surgeons identify patients at high risk of SSI who could benefit from NWPT or other post-surgical preventive measures. This will evaluate whether boosted tree modeling techniques can be used “at the bedside” via electronic medical record data feeds to tailor post-operative care and preventive care for specific patients. Participating hospitals will be surveyed to assess their readiness to submit data to the datamart. We will sequentially establish the data feed for each hospital. From the datamart a model will be built for SSIs that could be prevented by the NPWT. In this way, it will refine the predictive value of the model for this specific intervention. The use of this modeling will help provide more personalized care plans for patients at greatest risk of SSI.