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

背景：肝硬化患者的围手术期死亡率是非肝硬化患者的 2-4 倍 由于门静脉高压、肝合成功能受损等肝硬化相关因素引起的肝硬化。 目前尚无模型可以准确估计患有以下疾病的患者的围手术期死亡率和发病率： 肝硬化。我们的首要目标是开发和验证肝硬化特定的手术风险计算器（C- SuRC），准确估计肝硬化患者的围手术期死亡率和并发症。 意义/影响：C-SuRC 将改善肝硬化患者手术的选择，改善 为低死亡率患者提供择期手术机会，为高死亡率患者提供手术机会 确定可以在手术前优化的可改变的风险因素，以改善结果。 创新： • C-SuRC 将是第一个专为肝硬化患者设计的手术风险计算器， 纳入了导致患者手术死亡率的所有三类主要预测因素 肝硬化，即肝硬化相关、手术相关和合并症相关的预测因子。 • C-SuRC 将使用我们通过合并 VASQIP 和 CDW 开发的独特数据集进行开发 数据。这是接受外科手术的肝硬化患者的全国代表性 VA 数据集 前瞻性收集基线特征和手术结果。 • 我们将开发并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（SHAP）的预测，该算法 将每个风险因素对模型预测的死亡率的贡献分配。这有着深刻的 对医学预测分析中“可解释的人工智能”的影响。 SHAP值可以用来“解释” 预测并确定可以在手术前改善的潜在可改变因素。 • 我们将应用以用户为中心的设计来开发执行C-SuRC 的基于网络和应用程序的工具。 具体目标： SA1。开发并在外部验证模型 (C-SuRC)，该模型可准确估计术后 30 天 使用常规的肝硬化相关药物治疗肝硬化患者的死亡率和并发症， 合并症相关和手术相关的预测因子。 SA2。使用新颖的方法（SHApley Additive exPlanations 或“SHAP”）来计算 我们的 C-SuRC 梯度增强机器学习模型预测的死亡风险的每个风险因素 在个别患者中。 SA3。纳入用户的反馈并应用以用户为中心的设计中的最佳实践来开发网络 基于和基于应用程序的工具，执行 C-SuRC 并显示手术结果的预测 个体患者以及每个关键风险因素对使用 SHAP 值预测风险的贡献。 方法：我们将使用传统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的辨别力、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SHAP值来计算 机器学习模型预测的每个风险因素对死亡率的贡献。我们将纳入 根据 25 名临床医生用户的反馈，开发执行 C-SuRC 的基于网络和应用程序的工具。 后续步骤/实施：我们将寻求所有重要的 VA 利益相关者的支持，其中许多人已经 已批准该提案，并传播我们的调查结果以及基于网络和应用程序的 C-SuRC VA 工具在全国范围内作为肝硬化患者术前评估的常规工具。

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.