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Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study

OBJECTIVE: To conduct a systematic review, critical appraisal, and external validation of survival prediction tools for patients undergoing intrahepatic cholangiocarcinoma (iCCA) resection. SUMMARY BACKGROUND DATA: Despite the development of several survival prediction tools in recent years for pati...

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Autores principales: Choi, Woo Jin, Walker, Richard, Rajendran, Luckshi, Jones, Owen, Gravely, Annie, Englesakis, Marina, Gallinger, Steven, Hirschfield, Gideon, Hansen, Bettina, Sapisochin, Gonzalo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health, Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513309/
https://www.ncbi.nlm.nih.gov/pubmed/37746604
http://dx.doi.org/10.1097/AS9.0000000000000328
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author Choi, Woo Jin
Walker, Richard
Rajendran, Luckshi
Jones, Owen
Gravely, Annie
Englesakis, Marina
Gallinger, Steven
Hirschfield, Gideon
Hansen, Bettina
Sapisochin, Gonzalo
author_facet Choi, Woo Jin
Walker, Richard
Rajendran, Luckshi
Jones, Owen
Gravely, Annie
Englesakis, Marina
Gallinger, Steven
Hirschfield, Gideon
Hansen, Bettina
Sapisochin, Gonzalo
author_sort Choi, Woo Jin
collection PubMed
description OBJECTIVE: To conduct a systematic review, critical appraisal, and external validation of survival prediction tools for patients undergoing intrahepatic cholangiocarcinoma (iCCA) resection. SUMMARY BACKGROUND DATA: Despite the development of several survival prediction tools in recent years for patients undergoing iCCA resections, there is a lack of critical appraisal and external validation of these models. METHODS: We conducted a systematic review and critical appraisal of survival and recurrence prediction models for patients undergoing curative-intent iCCA resections. Studies were evaluated based on their model design, risk of bias, reporting, performance, and validation results. We identified the best model and externally validated it using our institution’s data. RESULTS: This review included a total of 31 studies, consisting of 26 studies with original prediction tools and 5 studies that only conducted external validations. Among the 26, 54% of the studies conducted internal validations, 46% conducted external validations, and only 1 study scored a low risk of bias. Harrell’s C-statistics ranged from 0.67 to 0.76 for internal validation and from 0.64 to 0.75 for external validation. Only 81% of the studies reported model calibration. Our external validation of the best model (Intrahepatic Cholangiocarcinoma [ICC]-Metroticket) estimated Harrell’s and Uno’s C-statistics of 0.67 (95% CI: 0.56–0.77) and Uno’s time-dependent area under the receiver operating characteristic curve (AUC) of 0.71 (95% CI: 0.53–0.88), with a Brier score of 0.20 (95% CI: 0.15–0.26) and good calibration plots. CONCLUSIONS: Many prediction models have been published in recent years, but their quality remains poor, and minimal methodological quality improvement has been observed. The ICC-Metroticket was selected as the best model (Uno’s time-dependent AUC of 0.71) for 5-year overall survival prediction in patients undergoing curative-intent iCCA resection.
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spelling pubmed-105133092023-09-22 Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study Choi, Woo Jin Walker, Richard Rajendran, Luckshi Jones, Owen Gravely, Annie Englesakis, Marina Gallinger, Steven Hirschfield, Gideon Hansen, Bettina Sapisochin, Gonzalo Ann Surg Open Meta-Analysis OBJECTIVE: To conduct a systematic review, critical appraisal, and external validation of survival prediction tools for patients undergoing intrahepatic cholangiocarcinoma (iCCA) resection. SUMMARY BACKGROUND DATA: Despite the development of several survival prediction tools in recent years for patients undergoing iCCA resections, there is a lack of critical appraisal and external validation of these models. METHODS: We conducted a systematic review and critical appraisal of survival and recurrence prediction models for patients undergoing curative-intent iCCA resections. Studies were evaluated based on their model design, risk of bias, reporting, performance, and validation results. We identified the best model and externally validated it using our institution’s data. RESULTS: This review included a total of 31 studies, consisting of 26 studies with original prediction tools and 5 studies that only conducted external validations. Among the 26, 54% of the studies conducted internal validations, 46% conducted external validations, and only 1 study scored a low risk of bias. Harrell’s C-statistics ranged from 0.67 to 0.76 for internal validation and from 0.64 to 0.75 for external validation. Only 81% of the studies reported model calibration. Our external validation of the best model (Intrahepatic Cholangiocarcinoma [ICC]-Metroticket) estimated Harrell’s and Uno’s C-statistics of 0.67 (95% CI: 0.56–0.77) and Uno’s time-dependent area under the receiver operating characteristic curve (AUC) of 0.71 (95% CI: 0.53–0.88), with a Brier score of 0.20 (95% CI: 0.15–0.26) and good calibration plots. CONCLUSIONS: Many prediction models have been published in recent years, but their quality remains poor, and minimal methodological quality improvement has been observed. The ICC-Metroticket was selected as the best model (Uno’s time-dependent AUC of 0.71) for 5-year overall survival prediction in patients undergoing curative-intent iCCA resection. Wolters Kluwer Health, Inc. 2023-09-01 /pmc/articles/PMC10513309/ /pubmed/37746604 http://dx.doi.org/10.1097/AS9.0000000000000328 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Meta-Analysis
Choi, Woo Jin
Walker, Richard
Rajendran, Luckshi
Jones, Owen
Gravely, Annie
Englesakis, Marina
Gallinger, Steven
Hirschfield, Gideon
Hansen, Bettina
Sapisochin, Gonzalo
Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study
title Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study
title_full Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study
title_fullStr Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study
title_full_unstemmed Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study
title_short Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study
title_sort call to improve the quality of prediction tools for intrahepatic cholangiocarcinoma resection: a critical appraisal, systematic review, and external validation study
topic Meta-Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513309/
https://www.ncbi.nlm.nih.gov/pubmed/37746604
http://dx.doi.org/10.1097/AS9.0000000000000328
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