Improving surgical outcomes through optimized hernia prediction

项目总结 切口疝气(IH)是一种常见的、在众多患者中被忽视的外科健康问题。 和利益相关者。在美国，每年有超过15.3万个IHS需要维修，花费超过70亿美元。 循证干预，包括术前优化、手术技术和预防网眼， 可以降低风险；然而，多层次的因素阻碍了临床翻译。一个关键障碍是需要 准确、可概括的风险预测，将风险识别、行为变化和结果联系起来。术前 风险评估使提供者能够利用风险信息来指导决策、手术规划、 和知情同意。目前对IH预测的局限性给IH的预防造成了障碍。我们的建议 满足对患者特定、清晰呈现的风险信息的需求，以加强医疗保健， 个性化风险评估，缩小疝气最佳实践与实际临床护理之间的差距 预防。我们的初步研究已经确定了IH的临床和经济负担，特点是 以治疗为导向的范例效率低下，确定了用于预防的关键患者群体，以及 证明了有效的降低风险的外科技术。我们还展示了使用电子健康的好处 基于记录的对行政索赔数据集的预测和机器学习的能力，最大限度地 模特表演。最近，我们创建了一个试验性、便携的临床决策支持--移动用户界面 为了预测，为这项提议做好准备。我们的方法的特点是使用了一个独特的多源 数据库、机器学习的创新应用、利益相关者参与和跨学科 协作。在本提案中，我们将使用以下数据确定和发现与IH相关的因素 对130,000名患者进行纵向随访，并使用Natural 语言处理。机器学习将提高预测性能(目标1)。模特们将成为 在地理时间上不同的数据源和最终用户输入上进行测试，将指导功能、格式 和功能，导致创建一个提供商适应的疝气计算容纳预测模型(目标2)。 疝气计算将在现实世界的实践中进行评估，以评估上下文决定因素并创建 利益攸关方主导的执行议定书，以确定支持广泛传播的战略(目标3)。 我们的方法通过开发优化的、经过验证的专业- 在提供商告知的界面中集成的特定IH风险模型和临床实施策略 使用。这项工作将对该领域产生广泛、重大和持续的影响，催化一个重要的支点 用于预防腹股沟疝气，使腹部手术患者能够准确预测风险。完成我们的 AIMS将增加有关疝气的知识，并改善手术中的健康结果，从而在实践中发挥关键作用 使我们的建议与美国国立卫生研究院的核心任务保持一致。

PROJECT SUMMARY Incisional hernia (IH) is a common, overlooked surgical health problem spanning a broad range of patients and stakeholders. In the U.S., over 153,000 IHs are repaired per year with expenditures exceeding $7 billion. Evidence-based interventions, including preoperative optimization, surgical techniques, and prophylactic mesh, can reduce risk; however, multi-level factors impede clinical translation. One critical barrier is the need for accurate, generalizable risk prediction to link risk recognition, behavior change, and outcomes. Pre-operative risk assessment enables providers to leverage risk information to guide decision-making, surgical planning, and informed consent. Current limitations of IH prediction have created barriers to IH prevention. Our proposal addresses the need for patient-specific, clearly presented risk information to enhance health care, enable individualized risk assessment, and close the gap between optimal practice and actual clinical care in hernia prevention. Our preliminary research has defined the clinical and economic burden of IH, characterized inefficiencies in treatment-oriented paradigms, defined key patient populations for prevention, and demonstrated effective risk reductive surgical techniques. We also show the benefit of using electronic health record-based prediction over administrative claims datasets and the power of machine learning to maximize model performance. Most recently, we created a pilot, portable, clinical decision support-mobile user interface for prediction, setting the stage for this proposal. Our approach is hallmarked by use of a unique multi-source database, innovative applications of machine learning, stake-holder engagement, and inter-disciplinary collaboration. In this proposal, we will identify and discover factors associated with IH using data from >130,000 patients with longitudinal follow-up and characterize intra-operative risk factors using natural language processing. Machine learning will enable improved predictive performance (Aim 1). Models will be tested on a geo-temporally diverse data source and end-user input will guide and prioritize features, format, and functionality, leading to creation of a provider-adapted Hernia Calc housing the predictive models (Aim 2). Hernia Calc will be evaluated in real-world practice to assess contextual determinants and to create a stakeholder-driven implementation protocol to identify strategies to support widespread dissemination (Aim 3). Our approach addresses barriers to IH prevention through development of optimized, validated, specialty- specific IH risk models integrated within a provider-informed interface and implementation strategies for clinical use. This work will lead to a broad, significant, and sustained impact on the field, catalyzing a major pivot towards hernia prevention, enabling precise risk prediction for abdominal surgery patients. Completion of our aims will augment knowledge of hernia and improve health outcomes in surgery allowing a pivot in practice towards prevention and aligning our proposal with Core Missions of the NIH.