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Developing a novel predictive model for identifying risk factors associated with being lost to follow-up among high-risk patients for recurrence following radical resection of hepatocellular carcinoma: the first report

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Title: Developing a novel predictive model for identifying risk factors associated with being lost to follow-up among high-risk patients for recurrence following radical resection of hepatocellular carcinoma: the first report
Authors: Zichen Yu, Wenli Cao, Chengfei Du, Jie Liu, Liping Peng, Fangqiang Wei
Source: BMC Cancer, Vol 25, Iss 1, Pp 1-14 (2025)
Publisher Information: BMC, 2025.
Publication Year: 2025
Collection: LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Subject Terms: Hepatocellular carcinoma, Radical resection, Lost to follow-up, Recurrence, Model, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Description: Abstract Background Follow-up is essential especially for patients who are at a high risk of recurrence after radical resection of hepatocellular carcinoma (HCC). The aim of this study was to develop a predictive model aimed at identifying the risk factors associated with being lost to follow-up (LTFU) in high-risk patients for recurrence following radical resection of HCC. Methods The retrospective study was conducted at our institution between October 2018 to May 2023. The patients who underwent radical liver resection for HCC and had high-risk factors for recurrence were categorized into an LTFU group and a control group. Multivariate logistic regression analysis was utilized to determine risk factors and construct a nomogram predictive model. Results A total of 352 patients were included and subsequently classified into two distinct groups: the LTFU group (n = 123, 34.94%) and the control group (n = 229, 65.06%). Logistic regression analysis was then conducted to explore the potential associations between various factors and the occurrence of LTFU. The findings identified several independent risk factors for LTFU, including smoking (odds ratio, OR = 1.823, 95% confidence interval, CI 1.086–3.060, p = 0.023); residing more than 200 km away from the hospital (OR = 1.857, 95% CI 1.105–3.121, p = 0.019); having an unstable profession (OR = 1.918, 95% CI 1.112–3.311, p = 0.019); and lacking medical insurance (OR = 5.921, 95% CI 1.747–20.071, p = 0.004); the presence of liver cirrhosis (OR = 2.161, 95% CI 1.153–4.048, p = 0.016); an operation time less than 240 min (OR = 2.138, 95% CI 1.240–3.688, p = 0.006); and the absence of postoperative adjuvant therapy (OR = 2.641, 95% CI 1.504–4.637, p = 0.001). Based on these seven significant factors, a main effects model was established, designated as the Wei-LTFU model, which achieved an area under the curve value of 0.744 (95% CI 0.691–0.798) in predicting the likelihood of LTFU. Conclusion A main effects model, namely the Wei-LTFU model, incorporating the seven significant factors was formulated to predict the likelihood of LTFU occurrence, ultimately aiming to assist healthcare workers in developing effective strategies to improve follow-up outcomes for patients.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1471-2407
Relation: https://doaj.org/toc/1471-2407
DOI: 10.1186/s12885-025-14030-1
Access URL: https://doaj.org/article/ce6c07eec8d94e0aa1beae4dbfec14d5
Accession Number: edsdoj.6c07eec8d94e0aa1beae4dbfec14d5
Database: Directory of Open Access Journals
Description
Abstract:Abstract Background Follow-up is essential especially for patients who are at a high risk of recurrence after radical resection of hepatocellular carcinoma (HCC). The aim of this study was to develop a predictive model aimed at identifying the risk factors associated with being lost to follow-up (LTFU) in high-risk patients for recurrence following radical resection of HCC. Methods The retrospective study was conducted at our institution between October 2018 to May 2023. The patients who underwent radical liver resection for HCC and had high-risk factors for recurrence were categorized into an LTFU group and a control group. Multivariate logistic regression analysis was utilized to determine risk factors and construct a nomogram predictive model. Results A total of 352 patients were included and subsequently classified into two distinct groups: the LTFU group (n = 123, 34.94%) and the control group (n = 229, 65.06%). Logistic regression analysis was then conducted to explore the potential associations between various factors and the occurrence of LTFU. The findings identified several independent risk factors for LTFU, including smoking (odds ratio, OR = 1.823, 95% confidence interval, CI 1.086–3.060, p = 0.023); residing more than 200 km away from the hospital (OR = 1.857, 95% CI 1.105–3.121, p = 0.019); having an unstable profession (OR = 1.918, 95% CI 1.112–3.311, p = 0.019); and lacking medical insurance (OR = 5.921, 95% CI 1.747–20.071, p = 0.004); the presence of liver cirrhosis (OR = 2.161, 95% CI 1.153–4.048, p = 0.016); an operation time less than 240 min (OR = 2.138, 95% CI 1.240–3.688, p = 0.006); and the absence of postoperative adjuvant therapy (OR = 2.641, 95% CI 1.504–4.637, p = 0.001). Based on these seven significant factors, a main effects model was established, designated as the Wei-LTFU model, which achieved an area under the curve value of 0.744 (95% CI 0.691–0.798) in predicting the likelihood of LTFU. Conclusion A main effects model, namely the Wei-LTFU model, incorporating the seven significant factors was formulated to predict the likelihood of LTFU occurrence, ultimately aiming to assist healthcare workers in developing effective strategies to improve follow-up outcomes for patients.
ISSN:14712407
DOI:10.1186/s12885-025-14030-1