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Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery.

Publication ,  Journal Article
Chang, B; Sun, Z; Peiris, P; Huang, ES; Benrashid, E; Dillavou, ED
Published in: J Surg Res
October 2020

BACKGROUND: Reduced surgical site infection (SSI) rates have been reported with use of closed incision negative pressure therapy (ciNPT) in high-risk patients. METHODS: A deep learning-based, risk-based prediction model was developed from a large national database of 72,435 patients who received infrainguinal vascular surgeries involving upper thigh/groin incisions. Patient demographics, histories, laboratory values, and other variables were inputs to the multilayered, adaptive model. The model was then retrospectively applied to a prospectively tracked single hospital data set of 370 similar patients undergoing vascular surgery, with ciNPT or control dressings applied over the closed incision at the surgeon's discretion. Objective predictive risk scores were generated for each patient and used to categorize patients as "high" or "low" predicted risk for SSI. RESULTS: Actual institutional cohort SSI rates were 10/148 (6.8%) and 28/134 (20.9%) for high-risk ciNPT versus control, respectively (P < 0.001), and 3/31 (9.7%) and 5/57 (8.8%) for low-risk ciNPT versus control, respectively (P = 0.99). Application of the model to the institutional cohort suggested that 205/370 (55.4%) patients were matched with their appropriate intervention over closed surgical incision (high risk with ciNPT or low risk with control), and 165/370 (44.6%) were inappropriately matched. With the model applied to the cohort, the predicted SSI rate with perfect utilization would be 27/370 (7.3%), versus 12.4% actual rate, with estimated cost savings of $231-$458 per patient. CONCLUSIONS: Compared with a subjective practice strategy, an objective risk-based strategy using prediction software may be associated with superior results in optimizing SSI rates and costs after vascular surgery.

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Published In

J Surg Res

DOI

EISSN

1095-8673

Publication Date

October 2020

Volume

254

Start / End Page

408 / 416

Location

United States

Related Subject Headings

  • Vascular Surgical Procedures
  • Surgery
  • Risk Assessment
  • Retrospective Studies
  • Negative-Pressure Wound Therapy
  • Middle Aged
  • Male
  • Humans
  • Groin
  • Female
 

Citation

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Chang, B., Sun, Z., Peiris, P., Huang, E. S., Benrashid, E., & Dillavou, E. D. (2020). Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery. J Surg Res, 254, 408–416. https://doi.org/10.1016/j.jss.2020.02.012
Chang, Bora, Zhifei Sun, Prabath Peiris, Erich S. Huang, Ehsan Benrashid, and Ellen D. Dillavou. “Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery.J Surg Res 254 (October 2020): 408–16. https://doi.org/10.1016/j.jss.2020.02.012.
Chang B, Sun Z, Peiris P, Huang ES, Benrashid E, Dillavou ED. Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery. J Surg Res. 2020 Oct;254:408–16.
Chang, Bora, et al. “Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery.J Surg Res, vol. 254, Oct. 2020, pp. 408–16. Pubmed, doi:10.1016/j.jss.2020.02.012.
Chang B, Sun Z, Peiris P, Huang ES, Benrashid E, Dillavou ED. Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery. J Surg Res. 2020 Oct;254:408–416.
Journal cover image

Published In

J Surg Res

DOI

EISSN

1095-8673

Publication Date

October 2020

Volume

254

Start / End Page

408 / 416

Location

United States

Related Subject Headings

  • Vascular Surgical Procedures
  • Surgery
  • Risk Assessment
  • Retrospective Studies
  • Negative-Pressure Wound Therapy
  • Middle Aged
  • Male
  • Humans
  • Groin
  • Female