{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:47:29Z","timestamp":1790635649923,"version":"4.1.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Surgical Infections"],"published-print":{"date-parts":[[2024,5,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Surgical site complications (SSCs) are common, yet preventable hospital-acquired conditions. Single-use negative pressure wound therapy (sNPWT) has been shown to be effective in reducing rates of these complications. In the era of value-based care, strategic allocation of sNPWT is needed to optimize both clinical and financial outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>We conducted a retrospective analysis using data from the Premier Healthcare Database (2017\u20132021) for 10 representative open procedures in orthopedic, abdominal, cardiovascular, cesarean delivery, and breast surgery. After separating data into training and validation sets, various machine learning algorithms were used to develop pre-operative SSC risk prediction models. Model performance was assessed using standard metrics and predictors of SSCs were identified through feature importance evaluation. Highest-performing models were used to simulate the cost-effectiveness of sNPWT at both the patient and population level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The prediction models demonstrated good performance, with an average area under the curve of 76%. Prominent predictors across subspecialities included age, obesity, and the level of procedure urgency. Prediction models enabled a simulation analysis to assess the population-level cost-effectiveness of sNPWT, incorporating patient and surgery-specific factors, along with the established efficacy of sNPWT for each surgical procedure. The simulation models uncovered significant variability in sNPWT's cost-effectiveness across different procedural categories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>This study demonstrates that machine learning models can effectively predict a patient's risk of SSC and guide strategic utilization of sNPWT. This data-driven approach allows for optimization of clinical and financial outcomes by strategically allocating sNPWT based on personalized risk assessments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/sur.2023.274","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T14:08:51Z","timestamp":1714658931000},"page":"315-321","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Precision in Prevention: Tailoring Single-Use Negative Pressure Wound Therapy Utilization Through Artificial Intelligence-Based Surgical Site Complications Risk and Cost Modeling"],"prefix":"10.1177","volume":"25","author":[{"given":"Barrett J.","family":"Larson","sequence":"first","affiliation":[{"name":"Smith + Nephew, Inc., Pittsburgh, Pennsylvania, USA."},{"name":"Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashley","family":"Roakes","sequence":"additional","affiliation":[{"name":"Smith + Nephew, Inc., Pittsburgh, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Yurick","sequence":"additional","affiliation":[{"name":"Smith + Nephew, Inc., Pittsburgh, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan A.","family":"Netravali","sequence":"additional","affiliation":[{"name":"Smith + Nephew, Inc., Pittsburgh, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2017.0904"},{"key":"e_1_3_4_3_2","unstructured":"World Union of Wound Healing Societies. Closed Surgical Incision Management: Understanding the Role of NPWT. Wounds International. 2016."},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajic.2008.12.010"},{"key":"e_1_3_4_5_2","unstructured":"Agency for Healthcare Research and Quality U.S. Department of Health and Human Services Patient Safety Network. Surgical Site Infections. 2019. Available from: https:\/\/psnet.ahrq.gov\/primer\/surgical-site-infections [Last accessed: June5 2023]."},{"key":"e_1_3_4_6_2","unstructured":"Centers for Disease Control. Surgical Site Infection Event (SSI). 2023. Available from: https:\/\/www.cdc.gov\/nhsn\/pdfs\/pscmanual\/9pscssicurrent.pdf [Last Accessed: June5 2023]."},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1089\/sur.2017.156"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11845-020-02283-7"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2022.05.020"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00268-019-05335-x"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1111\/1471-0528.16963"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1177\/1553350619893222"},{"issue":"10","key":"e_1_3_4_13_2","first-page":"456","article-title":"Preliminary outcome of treatment of postoperative primarily closed sternotomy wounds treated using negative pressure wound therapy","volume":"86","author":"Witt-Majchrzak A","year":"2014","unstructured":"Witt-Majchrzak A, , \u017belazny P, , Snarska J. Preliminary outcome of treatment of postoperative primarily closed sternotomy wounds treated using negative pressure wound therapy. Przeglad Chirugiczny, 2014; 86(10):456\u2013465; doi: 10.2478\/pjs-2014-0082","journal-title":"Przeglad Chirugiczny"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.771608"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2021.03.026"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.3171\/2019.5.FOCUS19241"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1097\/DCR.0000000000002559"},{"key":"e_1_3_4_18_2","first-page":"104956","article-title":"Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis","volume":"84","author":"Wu G","year":"2022","unstructured":"Wu G, , Huang Y, , He M, et al. Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis. Ann Med Surg (Lond), 2022; 84:104956; doi: 10.1016\/j.amsu.2022.104956","journal-title":"Ann Med Surg (Lond)"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1089\/sur.2017.072"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2013.2246"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhin.2017.03.004"},{"key":"e_1_3_4_22_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0232960"},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000002098"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.1302\/2046-3758.58.BJR-2016-0022.R1"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhin.2017.10.022"},{"key":"e_1_3_4_26_2","doi-asserted-by":"publisher","DOI":"10.12968\/jowc.2021.30.Sup5.S23"},{"key":"e_1_3_4_27_2","unstructured":"Centers for Medicare & Medicaid Services. Medicare Part B Average Sales Price. 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