Improving surgical outcomes through optimized hernia prediction

通过优化疝气预测改善手术结果

• 批准号: 10343149
• 负责人: John Patrick Fischer
• 金额: $ 55.65万
• 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/10343149
• 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; 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; Supervision; Surgeon; Surgical incisions; Techniques; Testing; United States National Institutes of Health; Validation; Variant; Work; acceptability and feasibility; base; 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; outcome prediction; 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.

项目摘要 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，每年维修153 000多个简易住房，费用超过70亿美元。 循证干预，包括术前优化、手术技术和预防性补片， 可以降低风险;然而，多层次因素阻碍了临床转化。一个关键的障碍是需要 准确、可推广的风险预测，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， 和知情同意。目前IH预测的局限性为IH预防创造了障碍。我们的建议 满足了对患者特定的、明确呈现的风险信息的需求，以加强医疗保健， 个体化风险评估，缩小疝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， 以治疗为导向的模式效率低下，确定了预防的关键患者人群， 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， 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 为这个提议做好准备。我们的方法的特点是使用一个独特的多源 数据库，机器学习的创新应用，知识产权持有人参与，以及跨学科 协作在本提案中，我们将使用以下数据识别和发现与IH相关的因素： > 130，000例患者进行纵向随访，并使用自然 语言处理机器学习将提高预测性能（目标1）。车型将 在地理时间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，从而创建了一个提供者自适应的Hernia Calc，其中包含预测模型（目标2）。 疝计算器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 * 制定一项由知识产权持有者驱动的实施议定书，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、专业的 在提供者知情的界面中集成特定的IH风险模型，并实施临床 使用.这项工作将对该领域产生广泛、重大和持续的影响，促进一个重大的转折点 致力于预防疝气，为腹部手术患者提供精确的风险预测。完成我们的 aims将增加疝气的知识，并改善手术中的健康结果，从而在实践中发挥作用 预防和调整我们的建议与国家卫生研究院的核心任务。