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.
Cirrhosis is a major and rising clinical problem in VA due to the high prevalence of risk factors for cirrhosis such viral hepatitis B and C, alcoholic liver disease, obesity, diabetes and fatty liver disease, which has reached epidemic proportions. There are 73,331 patients with cirrhosis currently in VA care, who have high mortality, morbidity, hospitalization and resource utilization rates. Veterans with cirrhosis frequently require surgery, which carries very high risks, especially in those patients with more advanced cirrhosis and liver dysfunction. The proposed C-SuRC will address a major gap in cirrhosis care by providing accurate preoperative risk assessment for Veterans with cirrhosis. C-SuRC estimates will apply directly to Veterans, since C-SuRC will be developed using data from a representative Veteran population. We will develop web-based and app-based tools to execute C-SuRC, which will be used by clinicians for the preoperative assessment of VA patients with cirrhosis who require surgery.
