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Abstract Details
Novel Application of Predictive Modeling: A Tailored Approach to Promoting HCC Surveillance in Patients With Cirrhosis
Clin Gastroenterol Hepatol. 2022 Aug;20(8):1795-1802.e2.doi: 10.1016/j.cgh.2021.02.038. Epub 2021 Mar 2.
1Department of Internal Medicine, University of Texas Southwestern Medical Center and Parkland Health & Hospital, Dallas, Texas; Department of Population Sciences, University of Texas Southwestern Medical Center and Parkland Health & Hospital, Dallas, Texas; Harold C. Simmons Cancer Center, University of Texas Southwestern Medical Center and Parkland Health & Hospital, Dallas, Texas. Electronic address: amit.singal@utsouthwestern.edu.
2Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana.
3Mays Business School, Texas A&M University, College Station, Texas.
4Jones Graduate School of Business, Rice University, Houston, Texas.
5Department of Population Sciences, University of Texas Southwestern Medical Center and Parkland Health & Hospital, Dallas, Texas.
6Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan; VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan; Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), Ann Arbor, Michigan.
7Department of Population Sciences, University of Texas Southwestern Medical Center and Parkland Health & Hospital, Dallas, Texas; Harold C. Simmons Cancer Center, University of Texas Southwestern Medical Center and Parkland Health & Hospital, Dallas, Texas.
Abstract
Objective: There has been increased interest in interventions to promote hepatocellular carcinoma (HCC) surveillance given low utilization and high proportions of late stage detection. Accurate prediction of patients likely versus unlikely to respond to interventions could allow a cost-effective approach to outreach and facilitate targeting more intensive interventions to likely non-responders.
Design: We conducted a secondary analysis of a randomized clinical trial evaluating a mailed outreach strategy to promote HCC surveillance among 1200 cirrhosis patients at a safety-net health system between December 2014 and March 2017. We developed regularized logistic regression (RLR) and gradient boosting machine (GBM) algorithm models to predict surveillance completion during each of the 3 screening rounds in a training set (n = 960). Model performance was assessed using multiple performance metrics in an independent test set (n = 240).
Results: Among 1200 patients, surveillance was completed in 41-47% of patients over the three rounds. The RLR and GBM models demonstrated good discriminatory accuracy, with area under receiver operating characteristic (AUROC) curves of 0.67 and 0.66 respectively in the first surveillance round and improved to 0.77 by the third surveillance round after incorporating prior screening behavior as a feature. Additional performance characteristics including the Brier score, Hosmer-Lemeshow test and reliability diagrams were also evaluated. The most important variables for the predictive model were prior screening completion status and past primary care contact.
Conclusions: Predictive models can help stratify patients' likelihood to respond to surveillance outreach invitations, facilitating tailored strategies to maximize effectiveness and cost-effectiveness of HCC surveillance population health programs.