Improving surgical outcomes through optimized hernia prediction

通过优化疝气预测改善手术结果

• 批准号: 10532801
• 负责人: John Patrick Fischer
• 金额: $ 70.25万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10532801
• 关键词: Abdomen; Address; Adoption; Affect; Algorithms; Behavior; Biological Factors; Biomechanics; Caring; Clinical; Clinical Data; Code; Consensus; Data; Data Set; Data Sources; Databases; Decision Making; Development; Diagnostic; Dissemination and Implementation; Economic Burden; Effectiveness; Electronic Health Record; Enabling Factors; Epidemiology; Evidence based intervention; Expenditure; Feedback; Focus Groups; Future; Genetic Diseases; Health; Healthcare; Hernia; Housing; Informed Consent; Intervention; Knowledge; Laboratories; Link; Machine Learning; Measurable; Measures; Methods; Mission; Modeling; Natural Language Processing; Nutritional; Operative Surgical Procedures; Outcome; Patients; Performance; Perioperative; Physiological; Pilot Projects; Prevalence; Prevention; Provider; Qualitative Methods; Reporting; Research; Risk; Risk Assessment; Risk Factors; Risk Reduction; Software Engineering; Source; Surgeon; Surgical incisions; Techniques; Testing; United States National Institutes of Health; Validation; Variant; Work; acceptability and feasibility; behavior change; clinical care; clinical decision support; clinical practice; clinical translation; design; diverse data; effectiveness trial; follow-up; future implementation; implementation protocol; implementation science; implementation strategy; improved; improved outcome; innovation; interdisciplinary collaboration; medical specialties; patient population; personalized predictions; portability; pragmatic implementation; predictive modeling; prophylactic; repaired; risk prediction; risk prediction model; stakeholder perspectives; supervised learning; surgery outcome; usability; virtual

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.

项目概要 切口疝 (IH) 是一种常见的、被忽视的外科健康问题，涉及广泛的患者 和利益相关者。在美国，每年修复超过 153,000 个 IH，支出超过 70 亿美元。 基于证据的干预措施，包括术前优化、手术技术和预防性网片， 可以降低风险；然而，多层次因素阻碍了临床转化。一个关键障碍是需要 准确、可概括的风险预测，将风险识别、行为改变和结果联系起来。术前 风险评估使提供者能够利用风险信息来指导决策、手术计划、 并知情同意。目前 IH 预测的局限性给 IH 预防造成了障碍。我们的建议 满足对患者特定的、清晰呈现的风险信息的需求，以加强医疗保健，使 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，其特点是 以治疗为导向的模式效率低下，确定了预防的关键患者群体，以及 证明了有效的降低风险的手术技术。我们还展示了使用电子健康的好处 对行政索赔数据集进行基于记录的预测以及机器学习的力量最大化 模型性能。最近，我们创建了一个试点、便携式临床决策支持移动用户界面 为了进行预测，为该提案奠定了基础。我们的方法的特点是使用独特的多源 数据库、机器学习的创新应用、利益相关者参与和跨学科 合作。在本提案中，我们将使用以下数据来识别和发现与 IH 相关的因素： 对超过 130,000 名患者进行纵向随访，并使用自然方法描述术中危险因素 语言处理。机器学习将提高预测性能（目标 1）。型号将是 在地理、时间上多样化的数据源和最终用户输入上进行测试将指导和优先考虑功能、格式、 和功能，从而创建一个适合提供商的 Hernia Calc，其中包含预测模型（目标 2）。 Hernia Calc 将在现实世界的实践中进行评估，以评估背景决定因素并创建一个 利益相关者驱动的实施协议，以确定支持广泛传播的策略（目标 3）。 我们的方法通过开发优化的、经过验证的、专业的方法来解决 IH 预防的障碍。 将特定的 IH 风险模型集成到提供者知情的界面和临床实施策略中 使用。这项工作将对该领域产生广泛、重大和持续的影响，推动重大转型 预防疝气，为腹部手术患者提供精确的风险预测。完成我们的 目标将增加对疝气的了解并改善手术中的健康结果，从而在实践中发挥作用 旨在预防并使我们的提案与 NIH 的核心使命保持一致。