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

> **NIH VA I01** · VA PUGET SOUND HEALTHCARE SYSTEM · 2022 · —

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10237196
- **Project number:** 5I01HX003062-02
- **Recipient organization:** VA PUGET SOUND HEALTHCARE SYSTEM
- **Principal Investigator:** George Ioannou
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2020-08-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10237196

## Citation

> US National Institutes of Health, RePORTER application 10237196, Development and Validation of a Cirrhosis-specific Surgical Risk Calculator (C-SuRC) (5I01HX003062-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10237196. Licensed CC0.

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