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Wang J, Tozzi F, Ashraf Ganjouei A, Romero-Hernandez F, Feng J, Calthorpe L, Castro M, Davis G, Withers J, Zhou C, Chaudhary Z, Adam M, Berrevoet F, Alseidi A, Rashidian N. Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis. J Gastrointest Surg 2024; 28:956-965. [PMID: 38556418 DOI: 10.1016/j.gassur.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/04/2024] [Accepted: 03/08/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND Machine learning (ML) approaches have become increasingly popular in predicting surgical outcomes. However, it is unknown whether they are superior to traditional statistical methods such as logistic regression (LR). This study aimed to perform a systematic review and meta-analysis to compare the performance of ML vs LR models in predicting postoperative outcomes for patients undergoing gastrointestinal (GI) surgery. METHODS A systematic search of Embase, MEDLINE, Cochrane, Web of Science, and Google Scholar was performed through December 2022. The primary outcome was the discriminatory performance of ML vs LR models as measured by the area under the receiver operating characteristic curve (AUC). A meta-analysis was then performed using a random effects model. RESULTS A total of 62 LR models and 143 ML models were included across 38 studies. On average, the best-performing ML models had a significantly higher AUC than the LR models (ΔAUC, 0.07; 95% CI, 0.04-0.09; P < .001). Similarly, on average, the best-performing ML models had a significantly higher logit (AUC) than the LR models (Δlogit [AUC], 0.41; 95% CI, 0.23-0.58; P < .001). Approximately half of studies (44%) were found to have a low risk of bias. Upon a subset analysis of only low-risk studies, the difference in logit (AUC) remained significant (ML vs LR, Δlogit [AUC], 0.40; 95% CI, 0.14-0.66; P = .009). CONCLUSION We found a significant improvement in discriminatory ability when using ML over LR algorithms in predicting postoperative outcomes for patients undergoing GI surgery. Subsequent efforts should establish standardized protocols for both developing and reporting studies using ML models and explore the practical implementation of these models.
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Affiliation(s)
- Jane Wang
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Francesca Tozzi
- Department of General, HPB Surgery and Liver Transplantation, Ghent University Hospital, Ghent, Belgium
| | - Amir Ashraf Ganjouei
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Fernanda Romero-Hernandez
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Jean Feng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States
| | - Lucia Calthorpe
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Maria Castro
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Greta Davis
- Department of Surgery, Division of Plastic and Reconstructive Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Jacquelyn Withers
- Department of Surgery, Division of Plastic and Reconstructive Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Connie Zhou
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Zaim Chaudhary
- University of California, Berkeley, Berkeley, California, United States
| | - Mohamed Adam
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Frederik Berrevoet
- Department of General, HPB Surgery and Liver Transplantation, Ghent University Hospital, Ghent, Belgium
| | - Adnan Alseidi
- Department of Surgery, University of California, San Francisco, San Francisco, California, United States
| | - Nikdokht Rashidian
- Department of General, HPB Surgery and Liver Transplantation, Ghent University Hospital, Ghent, Belgium.
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Adeoye J, Akinshipo A, Thomson P, Su YX. Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements. J Glob Health 2022; 12:03017. [PMID: 35493779 PMCID: PMC9022723 DOI: 10.7189/jogh.12.03017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- John Adeoye
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Oral Cancer Research Theme, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Abdulwarith Akinshipo
- Department of Oral and Maxillofacial Pathology and Biology, Faculty of Dentistry, University of Lagos, Lagos, Nigeria
| | - Peter Thomson
- College of Medicine and Dentistry, James Cook University, Cairns, Queensland, Australia
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Oral Cancer Research Theme, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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