Abstract：
Background: 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. Materials and Methods: We conducted a retrospective analysis using data from the Premier Healthcare Database (2017-2021) 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. Results: 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. Conclusions: 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.
摘要：
背景:手术部位并发症(SSC)很常见,但可预防的医院获得性疾病。单次使用负压伤口治疗（sNPWT）已被证明可有效降低这些并发症的发生率。在以价值为基础的关怀时代,需要sNPWT的战略分配来优化临床和财务结果。材料和方法：我们使用PremierHealthcare数据库（2017-2021）的数据对骨科10个代表性开放手术进行了回顾性分析，腹部，心血管,剖宫产,乳房手术。将数据分为训练集和验证集后，各种机器学习算法被用来开发术前SSC风险预测模型。使用标准指标评估模型性能，并通过特征重要性评估确定SSC的预测因子。最高性能的模型用于模拟患者和人群水平的sNPWT的成本效益。结果：预测模型表现出良好的性能，曲线下的平均面积为76%。各个亚专业的突出预测因素包括年龄，肥胖，以及程序的紧急程度。预测模型使模拟分析能够评估sNPWT的人口水平成本效益，结合患者和手术特定因素，以及sNPWT对每种外科手术的既定疗效。仿真模型揭示了sNPWT在不同程序类别中的成本效益的显着差异。结论：这项研究表明，机器学习模型可以有效地预测患者的SSC风险，并指导sNPWT的战略利用。这种数据驱动的方法允许通过基于个性化风险评估战略性地分配sNPWT来优化临床和财务结果。
