A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly
Frailty in the elderly population is associated with increased vulnerability to stressors, including surgical interventions. This study compared machine learning (ML) models with a clinical bedside tool, the Gastrointestinal Surgery Frailty Index (GiS-FI), for predicting mortality and morbidity in e...
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2025-05-01
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| author | Barbara Frezza Mario Cesare Nurchis Gabriella Teresa Capolupo Filippo Carannante Marco De Prizio Fabio Rondelli Danilo Alunni Fegatelli Alessio Gili Luca Lepre Gianluca Costa |
| author_facet | Barbara Frezza Mario Cesare Nurchis Gabriella Teresa Capolupo Filippo Carannante Marco De Prizio Fabio Rondelli Danilo Alunni Fegatelli Alessio Gili Luca Lepre Gianluca Costa |
| author_sort | Barbara Frezza |
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| description | Frailty in the elderly population is associated with increased vulnerability to stressors, including surgical interventions. This study compared machine learning (ML) models with a clinical bedside tool, the Gastrointestinal Surgery Frailty Index (GiS-FI), for predicting mortality and morbidity in elderly patients undergoing gastrointestinal cancer surgery. In a multicenter analysis of 937 patients aged ≥65 years, the performance of various predictive models including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Stepwise Regression, K-Nearest Neighbors, Neural Network, and Support Vector Machine algorithms were evaluated. The overall 30-day mortality and morbidity rates were 6.1% and 35.7%, respectively. For mortality prediction, the RF model demonstrated superior performance with an AUC of 0.822 (95% CI 0.714–0.931), outperforming the GiS-FI score (AUC = 0.772, 95% CI 0.675–0.868). For morbidity prediction, all models showed more modest discrimination, with stepwise regression and LASSO regression achieving the highest performance (AUCs of 0.652 and 0.647, respectively). Our findings suggest that ML approaches, particularly RF algorithm, offer enhanced predictive accuracy compared to traditional clinical scores for mortality risk assessment in elderly cancer patients undergoing gastrointestinal surgery. These advanced analytical tools could provide valuable decision support for surgical risk stratification in this vulnerable population. |
| format | Article |
| id | doaj-art-9799077e057144eebd74dc773cc2f438 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-9799077e057144eebd74dc773cc2f4382025-08-20T01:56:25ZengMDPI AGBioengineering2306-53542025-05-0112554410.3390/bioengineering12050544A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the ElderlyBarbara Frezza0Mario Cesare Nurchis1Gabriella Teresa Capolupo2Filippo Carannante3Marco De Prizio4Fabio Rondelli5Danilo Alunni Fegatelli6Alessio Gili7Luca Lepre8Gianluca Costa9General Surgery Unit, San Donato Hospital, Azienda USL Toscana Sud-Est, 52100 Arezzo, ItalyDepartment of Life Sciences, Health and Health Professions, Link Campus University, 00165 Roma, ItalyOperative Research Unit of Colorectal Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Roma, ItalyOperative Research Unit of Colorectal Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Roma, ItalyGeneral Surgery Unit, San Donato Hospital, Azienda USL Toscana Sud-Est, 52100 Arezzo, ItalyGeneral Surgery and Surgical Specialties Unit, Santa Maria Hospital Terni, Teaching Hospital of Perugia University, 05100 Perugia, ItalyDepartment of Life Sciences, Health and Health Professions, Link Campus University, 00165 Roma, ItalyDepartment of Life Sciences, Health and Health Professions, Link Campus University, 00165 Roma, ItalyGeneral and Emergency Surgery Unit, Santo Spirito in Sassia Hospital, ASL RM1, 00193 Roma, ItalyDepartment of Life Sciences, Health and Health Professions, Link Campus University, 00165 Roma, ItalyFrailty in the elderly population is associated with increased vulnerability to stressors, including surgical interventions. This study compared machine learning (ML) models with a clinical bedside tool, the Gastrointestinal Surgery Frailty Index (GiS-FI), for predicting mortality and morbidity in elderly patients undergoing gastrointestinal cancer surgery. In a multicenter analysis of 937 patients aged ≥65 years, the performance of various predictive models including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Stepwise Regression, K-Nearest Neighbors, Neural Network, and Support Vector Machine algorithms were evaluated. The overall 30-day mortality and morbidity rates were 6.1% and 35.7%, respectively. For mortality prediction, the RF model demonstrated superior performance with an AUC of 0.822 (95% CI 0.714–0.931), outperforming the GiS-FI score (AUC = 0.772, 95% CI 0.675–0.868). For morbidity prediction, all models showed more modest discrimination, with stepwise regression and LASSO regression achieving the highest performance (AUCs of 0.652 and 0.647, respectively). Our findings suggest that ML approaches, particularly RF algorithm, offer enhanced predictive accuracy compared to traditional clinical scores for mortality risk assessment in elderly cancer patients undergoing gastrointestinal surgery. These advanced analytical tools could provide valuable decision support for surgical risk stratification in this vulnerable population.https://www.mdpi.com/2306-5354/12/5/544cancersurgeryelderlyfrailtyscoremachine learning |
| spellingShingle | Barbara Frezza Mario Cesare Nurchis Gabriella Teresa Capolupo Filippo Carannante Marco De Prizio Fabio Rondelli Danilo Alunni Fegatelli Alessio Gili Luca Lepre Gianluca Costa A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly Bioengineering cancer surgery elderly frailty score machine learning |
| title | A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly |
| title_full | A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly |
| title_fullStr | A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly |
| title_full_unstemmed | A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly |
| title_short | A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly |
| title_sort | comparison of machine learning based models and a simple clinical bedside tool to predict morbidity and mortality after gastrointestinal cancer surgery in the elderly |
| topic | cancer surgery elderly frailty score machine learning |
| url | https://www.mdpi.com/2306-5354/12/5/544 |
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