TY - JOUR
T1 - From Algorithms to Clinical Utility
T2 - A Systematic Review of Individualized Risk Prediction Models for Colorectal Cancer
AU - Herrera, Deborah Jael
AU - van de Veerdonk, Wessel
AU - Seibert, Daiane Maria
AU - Boke, Moges Muluneh
AU - Gutiérrez-Ortiz, Claudia
AU - Yimer, Nigus Bililign
AU - Feyen, Karen
AU - Ferrari, Allegra
AU - Van Hal, Guido
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/12
Y1 - 2023/12
N2 - Individualized risk prediction models for colorectal cancer (CRC) play a pivotal role in shaping risk-based screening approaches, garnering attention for use in informed decision making by patients and clinicians. While the incorporation of new predictors and the development of advanced yet complex prediction models can enhance model performance, their practical implementation in clinical settings remains challenging. This systematic review assessed individualized CRC risk prediction models for their validity and potential clinical utility. Utilizing the Cochrane Collaboration methods and PROBAST tool, we conducted comprehensive searches across key databases and risk of bias assessment, respectively. Out of 41 studies included evaluating 44 risk prediction models, 12 conventional and 3 composite models underwent external validation. All risk models exhibited varying discriminatory accuracy, with the area under the curve (AUCs) ranging from 0.57 to 0.90. However, most studies showed an unclear or high risk of bias, with concerns about applicability. Of the five models with promising clinical utility, only two underwent external validation and one employed a decision curve analysis. These models demonstrated a discriminating and well-calibrated performance. While high-performing CRC risk prediction models exist, a need for transparent reporting of performance metrics and their clinical utility persists. Further research on this area is needed to facilitate the integration of these models into clinical practice, particularly in CRC screening.
AB - Individualized risk prediction models for colorectal cancer (CRC) play a pivotal role in shaping risk-based screening approaches, garnering attention for use in informed decision making by patients and clinicians. While the incorporation of new predictors and the development of advanced yet complex prediction models can enhance model performance, their practical implementation in clinical settings remains challenging. This systematic review assessed individualized CRC risk prediction models for their validity and potential clinical utility. Utilizing the Cochrane Collaboration methods and PROBAST tool, we conducted comprehensive searches across key databases and risk of bias assessment, respectively. Out of 41 studies included evaluating 44 risk prediction models, 12 conventional and 3 composite models underwent external validation. All risk models exhibited varying discriminatory accuracy, with the area under the curve (AUCs) ranging from 0.57 to 0.90. However, most studies showed an unclear or high risk of bias, with concerns about applicability. Of the five models with promising clinical utility, only two underwent external validation and one employed a decision curve analysis. These models demonstrated a discriminating and well-calibrated performance. While high-performing CRC risk prediction models exist, a need for transparent reporting of performance metrics and their clinical utility persists. Further research on this area is needed to facilitate the integration of these models into clinical practice, particularly in CRC screening.
KW - advanced neoplasia
KW - clinical utility
KW - colorectal cancer
KW - model performance
KW - risk factors
KW - risk prediction
KW - risk scores
UR - http://www.scopus.com/inward/record.url?scp=85180731975&partnerID=8YFLogxK
U2 - 10.3390/gidisord5040045
DO - 10.3390/gidisord5040045
M3 - Review article
AN - SCOPUS:85180731975
SN - 2624-5647
VL - 5
SP - 549
EP - 579
JO - Gastrointestinal Disorders
JF - Gastrointestinal Disorders
IS - 4
ER -