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: 10652247
• 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 2026-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652247
• 关键词: Alcoholic Liver Diseases; Algorithms; Calibration; Cardiovascular system; Caring; Characteristics; Cirrhosis; Clinic; Clinical; Compensation; Complex; Data; Data Set; Development; Diabetes Mellitus; Discrimination; Epidemic; Feedback; Functional disorder; 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; Population; Portal Hypertension; Postoperative Period; Predictive Analytics; Prevalence; Process; Reporting; Resources; Risk; Risk Assessment; Risk Factors; Series; Structure; Testing; Trees; United States Department of Veterans Affairs; Validation; Veterans; Veterans Health Administration; 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 mortality; predictive modeling; prevent; programs; prospective; risk prediction; 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：肝硬化患者的围手术期死亡率比没有的患者高2-4倍 与肝硬化有关的因子（例如门静脉高血压和肝合成功能受损）引起的肝硬化。 目前尚无模型，可以准确估计患有围手术期死亡率和发病率 肝硬化。我们的总体目的是开发和验证肝硬化特异性手术风险计算器（C- Surc）准确估计肝硬化患者的周期性死亡率和并发症。 显着性/影响：C-SURC将改善肝硬化患者进行外科手术的选择，改善 患有低死亡率的患者接受选修手术，预防高死亡率患者的手术 确定可修改的危险因素，可以在手术前进行优化以改善预后。 创新： •C-SURC将是专为肝硬化患者设计的第一个外科手术风险计算器 结合所有三个主要类别的预测因素，这些预测因素有助于患者 肝硬化，与肝硬化有关，与手术相关且合并症相关的预测因子。 •C-SURC将使用我们通过合并vasqip和cdw开发的唯一数据集开发 数据。这是一个全国代表性的VA数据集，该数据集接受了手术程序 具有前瞻性收集的基线特征和手术结果。 •我们将开发和比较传统的逻辑回归模型以及最先进的模型 梯度增强（XGBoost）机器学习算法。 •我们将使用一种新颖的方法来解释机器学习算法的预测（SHAP），该算法的预测 分配每个风险因素对模型预测的死亡率的贡献。这有很大的 在医学预测分析中对“可解释的AI”的影响。形状值可用于“解释” 预测并确定可以在手术前可以改善的潜在可修改因素。 •我们将以以用户为中心的设计来开发执行C-SURC的基于Web的和基于应用程序的工具。 具体目的： SA1。开发和外部验证模型（C-SURC），该模型可准确估计30天术后 使用常规可用肝硬化有关的肝硬化患者的死亡率和并发症， 合并症与手术相关的预测因子。 SA2。使用一种新颖的方法（Shapley添加性解释或“ Shap”​​）来计算 我们的C-SURC梯度提高了机器学习模型所预测的死亡率风险的每个风险因素 在个别患者中。 SA3。结合用户的反馈并将最佳实践应用于以用户为中心的设计来开发网络 基于应用程序和基于应用 单个患者以及每个关键风险因素使用Shap值对预测风险的贡献。 方法：我们将使用常规的逻辑回归模型和最先进的梯度增强机器 C-SURC开发的学习模型。我们将测试C-SURC的歧视，校准和准确性， 外部验证它并将其与现有的手术风险计算器进行比较。我们将使用形状值计算 贡献了机器学习模型预测的死亡率的每个风险因素。我们将合并 来自25种临床用户的反馈，以开发执行C-SURC的基于Web的和基于应用程序的工具。 下一步/实施：我们将征求所有重要的VA利益相关者的支持，其中许多 已经认可了这项建议，并传播了我们的发现以及基于网络和基于应用的C-SURC 在全国范围内，VA中的工具是肝硬化患者术前评估的常规工具。