Chang Bora, Sun Zhifei, Peiris Prabath, Huang Erich S, Benrashid Ehsan, Dillavou Ellen D
KelaHealth, Durham, North Carolina.
KelaHealth, Durham, North Carolina; Department of Surgery, Duke University Medical Center, Durham, North Carolina.
J Surg Res. 2020 Oct;254:408-416. doi: 10.1016/j.jss.2020.02.012. Epub 2020 Mar 17.
Reduced surgical site infection (SSI) rates have been reported with use of closed incision negative pressure therapy (ciNPT) in high-risk patients.
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.
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.
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.
据报道，在高危患者中使用封闭切口负压疗法（ciNPT）可降低手术部位感染（SSI）率。
基于深度学习的风险预测模型是从一个包含72435例接受涉及大腿上部/腹股沟切口的股下血管手术患者的大型国家数据库中开发出来的。患者的人口统计学信息、病史、实验室检查值和其他变量作为多层自适应模型的输入。然后将该模型回顾性应用于一个前瞻性跟踪的单中心数据集，该数据集包含370例接受血管手术的类似患者，由外科医生酌情决定在封闭切口上应用ciNPT或对照敷料。为每位患者生成客观预测风险评分，并用于将患者分类为SSI的“高”或“低”预测风险。
高危ciNPT组与对照组的实际机构队列SSI率分别为10/148（6.8%）和28/134（20.9%）（P<0.001），低危ciNPT组与对照组分别为3/31（9.7%）和5/57（8.8%）（P=0.99）。将该模型应用于机构队列表明，205/370（55.4%）的患者在封闭手术切口上得到了适当的干预（高危患者使用ciNPT，低危患者使用对照），165/370（44.6%）的患者匹配不当。将该模型应用于队列中，完美利用时预测的SSI率为27/370（7.3%），而实际率为12.4%，估计每位患者可节省成本231 - 458美元。
与主观实践策略相比，使用预测软件的基于客观风险的策略在优化血管手术后的SSI率和成本方面可能会产生更好的结果。
