# Improving Surgical Risk Prediction and Decision Making among Patients with Cirrhosis

> **NIH NIH K08** · UNIVERSITY OF PENNSYLVANIA · 2021 · $167,856

## Abstract

PROJECT SUMMARY
Patients with cirrhosis have increased surgical risk relative to the general population Several risk factors have
been established to predict cirrhosis surgical risk. These are reflected in the primary clinical tools used for risk
prediction—the Model for End-stage Liver Disease-sodium (MELD-Na), Child-Turcotte-Pugh (CTP) score, and
the Mayo surgical risk score—which rely on age, cirrhosis severity, ASA physical status score, and etiology of
liver disease. However, significant heterogeneity in post-operative mortality by surgery type (e.g., cardiac
versus orthopedic) suggests that these tools are inadequate. The literature on cirrhosis surgical risk prediction
is further limited by: 1) single-center designs with small sample sizes, 2) lack of granular data for risk
prediction, 3) evidence of poor prediction score calibration, 4) lack of key stakeholder involvement to inform
real-world implementation of prediction tools, and 5) no incorporation of decision analysis methods to compare
surgery to non-operative management. The impact of these shortcomings is that many patients with cirrhosis
are denied necessary surgery due to overestimates of risk, and others receive surgery with inaccurate
prognostic counseling or inadequate consideration of non-operative options. Granular, population-level data
are needed to address the above gaps. By using national Veterans Health Administration (VHA) and University
of Pennsylvania Hospital System (UPHS) data, we hypothesize that we will be able to create and implement
an accurate, well-calibrated cirrhosis surgical risk calculator with broad clinical utility. The primary aims of this
proposal are as follows: Aim 1 – derive, internally validate, and externally validate cirrhosis surgical risk
models for short- and intermediate-term post-operative mortality among diverse patients with cirrhosis.; Aim 2
– create a web application for surgical risk prediction informed by key stakeholder input.; Aim 3 – use Markov
modeling to compare operative to non-operative management pathways and determine optimal clinical
decisions for a common clinical scenario: acute cholecystitis. This proposal will foster Dr. Nadim Mahmud's
development as an independent, NIH-funded clinical researcher with a focus on improving risk prediction for
patients with chronic liver diseases, as well as specific expertise in advanced prediction modeling, qualitative
methods, and decision analysis. This will be facilitated through a comprehensive mentorship plan consisting of:
1) biweekly to monthly meetings with his mentorship team, 2) formal coursework in advanced prediction
modeling, qualitative research methods, and decision analysis through the Center for Clinical Epidemiology
and Biostatistics (CCEB), Wharton School, Department of Health Policy Research (HPR), Operations,
Information, and Decisions Department (OIDD), and Department of Statistics (STAT) at the University of
Pennsylvania, 3) structured research workshops and nation...

## Key facts

- **NIH application ID:** 10127415
- **Project number:** 1K08DK124577-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Nadim Mahmud
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $167,856
- **Award type:** 1
- **Project period:** 2021-07-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10127415

## Citation

> US National Institutes of Health, RePORTER application 10127415, Improving Surgical Risk Prediction and Decision Making among Patients with Cirrhosis (1K08DK124577-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10127415. Licensed CC0.

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