Development and Validation of a Cirrhosis-specific Surgical Risk Calculator (C-SuRC)

肝硬化特异性手术风险计算器 (C-SuRC) 的开发和验证

• 批准号: 10237196
• 负责人: George Ioannou
• 金额: --
• 依托单位: VA PUGET SOUND HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10237196
• 关键词: Address; Alcoholic Liver Diseases; Algorithms; Calibration; Cardiovascular system; Caring; Characteristics; Cirrhosis; Clinic; Clinical; Complex; Data; Data Set; Development; Diabetes Mellitus; Discrimination; Epidemic; Feedback; Healthcare Systems; Hepatic; Hepatitis B; Hepatitis C; High Prevalence; Hospitalization; Impairment; Improve Access; Individual; Linear Models; Liver Dysfunction; Logistic Regressions; Medical; Methods; Modeling; Morbidity - disease rate; Obesity; Online Systems; Operative Surgical Procedures; Patient Selection; Patients; Performance; Perioperative complication; Population; Portal Hypertension; Postoperative Period; Predictive Analytics; Prevalence; Process; Reporting; Resources; Risk; Risk Assessment; Risk Factors; Series; Structure; Testing; Time; Trees; United States Department of Veterans Affairs; Validation; Veterans; base; clinical application; comorbidity; design; fatty liver disease; gradient boosting; high risk; improved; improved outcome; individual patient; innovation; instrument; machine learning algorithm; machine learning method; machine learning model; machine learning prediction; machine learning prediction algorithm; military veteran; modifiable risk; mortality; mortality risk; novel; perioperative morbidity; perioperative mortality; predictive modeling; prevent; programs; prospective; risk prediction model; surgery outcome; surgical risk; tool; user centered design

Background: Perioperative mortality is 2-4 times higher in patients with cirrhosis compared to patients without cirrhosis due to cirrhosis-related factors such as portal hypertension and impaired hepatic synthetic function. Currently no models exist that accurately estimate peri-operative mortality and morbidity in patients with cirrhosis. Our overarching aim is to develop and validate a Cirrhosis-specific Surgical Risk Calculator (C- SuRC) that accurately estimates perioperative mortality and complications in patients with cirrhosis. Significance/Impact: C-SuRC will improve the selection of patients with cirrhosis for surgical procedures, improve access to elective surgery for patients with low mortality, prevent surgeries in patients with high mortality and identify modifiable risk factors that could be optimized prior to surgery in order to improve outcomes. Innovation: • C-SuRC will be the first surgical risk calculator specifically designed for patients with cirrhosis that incorporates all three major classes of predictors that contribute to operative mortality in patients with cirrhosis, that is cirrhosis-related, surgery-related and comorbidity-related predictors. • C-SuRC will be developed using a unique, dataset that we developed by merging VASQIP and CDW data. This is a nationally-representative VA dataset of cirrhotic patients undergoing surgical procedures with prospectively collected baseline characteristics and surgical outcomes. • We will develop and compare both traditional logistic regression models as well as state-of-the-art, gradient-boosted (XGBoost) machine learning algorithms. • We will use a novel method for interpreting the predictions of machine learning algorithms (SHAP), which assigns the contribution of each risk factor to the mortality predicted by the model. This has profound implications for “interpretable AI” in medical predictive analytics. SHAP values can be used to “explain” a prediction and to identify potentially modifiable factors that can be improved prior to surgery. • We will apply user-centered design to develop web-based and app-based tools that execute C-SuRC. Specific Aims: SA1. Develop and externally validate a model (C-SuRC) that accurately estimates 30-day postoperative mortality and complications in patients with cirrhosis using routinely available cirrhosis-related, comorbidity-related and surgery-related predictors. SA2. Use a novel method (the SHapley Additive exPlanations or “SHAP”) to calculate the contribution of each risk factor to the mortality risk predicted by our C-SuRC gradient boosted, machine learning models in individual patients. SA3. Incorporate feedback from users and apply best practices in user-centered design to develop web- based and app-based tools that execute C-SuRC and display predictions of surgical outcomes in individual patients and the contribution of each key risk factor to the predicted risk using SHAP values. Methods: We will use conventional logistic regression models and state-of-the-art, gradient-boosted machine learning models for C-SuRC development. We will test the discrimination, calibration and accuracy of C-SuRC, externally validate it and compare it to existing surgical risk calculators. We will use SHAP values to calculate the contribution each risk factor to the mortality predicted by the machine learning models. We will incorporate feedback from 25 clinician-users to develop web-based and app-based tools that execute C-SuRC. Next Steps/Implementation: We will solicit support from all important VA stakeholders, many of whom have already endorsed this proposal, and disseminate our findings and the web-based and app-based C-SuRC tools in the VA nationally as a routine instrument in the pre-operative assessment of patients with cirrhosis.

背景：有肝硬变患者的围手术期死亡率是无肝硬变患者的2-4倍。 由肝硬变相关因素引起的肝硬变，如门脉高压和肝脏综合功能受损。 目前还没有模型可以准确地估计卵巢癌患者围手术期的死亡率和发病率。 肝硬变。我们的首要目标是开发和验证一个特定于肝硬变的手术风险计算器(C- Surc)，准确地估计了肝硬变患者的围手术期死亡率和并发症。 意义/影响：C-SURC将改善肝硬变患者手术方式的选择，改善 获得低死亡率患者的择期手术，预防高死亡率患者的手术和 确定可修改的风险因素，这些因素可以在手术前优化以改善结果。 创新： ·C-Surc将是第一个专门为肝硬变患者设计的手术风险计算器 合并了所有三种主要的预测因素，这些因素对慢性前列腺癌患者的手术死亡率有贡献 肝硬变，即与肝硬变相关的、与手术相关的和与共病相关的预测因素。 ·C-SURC将使用我们通过合并VASQIP和CDW开发的唯一数据集进行开发 数据。这是一个具有全国代表性的肝硬变患者外科手术数据集。 前瞻性收集基线特征和手术结果。 ·我们将开发和比较传统的Logistic回归模型以及最新的Logistic回归模型， 梯度增强(XGBoost)机器学习算法。 ·我们将使用一种新的方法来解释机器学习算法(Shap)的预测，它 分配每个风险因素对模型预测的死亡率的贡献。这具有深远的意义 医学预测分析中“可解释的人工智能”的含义。Shap值可以用来“解释” 预测并确定可在手术前改善的潜在可修改因素。 ·我们将应用以用户为中心的设计，开发基于Web和基于APP的执行C-Surc的工具。 具体目标： SA1.建立和外部验证模型(C-SURC)，准确估计术后30天 使用常规可获得的肝硬变相关药物的患者的死亡率和并发症。 共病相关和手术相关的预测因素。 SA2.使用一种新的方法(Shapley加法解释或Shap)来计算 由我们的C-Surc梯度增强的机器学习模型预测的死亡风险的每个风险因素 在个别病人身上。 SA3.吸收用户的反馈，并应用以用户为中心的设计最佳实践来开发Web- 基于和基于APP的工具，执行C-Surc并显示对手术结果的预测 使用Shap值分析单个患者以及每个关键风险因素对预测风险的贡献。 方法：我们将使用传统的Logistic回归模型和最先进的梯度增强机 C-Surc开发的学习模型。我们将测试C-SURC的分辨率、校准和准确性， 对其进行外部验证，并与现有的手术风险计算器进行比较。我们将使用Shap值来计算 各危险因素对机器学习模型预测死亡率的贡献。我们将把 来自25名临床医生用户的反馈，以开发基于网络和基于应用程序的工具来执行C-Surc。 下一步/实施：我们将寻求所有重要退伍军人管理局利益相关者的支持，其中许多人已经 已经批准了这项建议，并发布了我们的研究结果以及基于网络和基于应用程序的C-Surc 国家退伍军人管理局作为肝硬变患者术前评估的常规工具。