A Clinical Prediction Model for Surgical Site Infections in Dermatological Surgery

Xiaomeng Liu*, Nicole W. J. Kelleners-Smeets, Melissa Sprengers, Vishal Hira, Klara Mosterd, Patty J. Nelemans

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Web of Science)

Abstract

To adequately identify patients at risk for surgical site infection in dermatological surgery and effectively prescribe antibiotic prophylaxis, a prediction model may be helpful. Such a model was developed using data from 1,407 patients who underwent dermatological surgery without antibiotic prophylaxis. The multivariable logistic regression model included type of closure, tumour location and defect size as risk factors. Bootstrapping was used for internal validation. The overall performance of the model was good, with an area under the curve of 84.1%. The decision curve analysis showed that the model is potentially useful if one is willing to treat more than 8 patients with antibiotic prophylaxis to avoid one infection. For those who prefer more restrictive use of antibiotic prophylaxis, a default strategy of treating no patients at all with prophylaxis would be the best choice. External validation of the model is required before it can be widely applied.
Original languageEnglish
Pages (from-to)683-688
Number of pages6
JournalActa Dermato-Venereologica
Volume98
Issue number7
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • surgical site infection
  • antibiotic prophylaxis
  • dermatological surgery
  • prediction model
  • MOHS MICROGRAPHIC SURGERY
  • ANTIBIOTIC-PROPHYLAXIS
  • WOUND INFECTIONS
  • SKIN SURGERY
  • COMPLICATIONS
  • GUIDELINES
  • ABSENCE
  • EVENTS

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