Predicting Postoperative Length of Stay in Patients Undergoing Laparoscopic Right Hemicolectomy for Colon Cancer: A Machine Learning Approach Using SICE (Società Italiana di Chirurgia Endoscopica) CoDIG Data

Author:

Anania Gabriele1ORCID,Chiozza Matteo1,Pedarzani Emma23,Resta Giuseppe1,Campagnaro Alberto1ORCID,Pedon Sabrina1ORCID,Valpiani Giorgia2ORCID,Silecchia Gianfranco4,Mascagni Pietro56,Cuccurullo Diego7ORCID,Reddavid Rossella8ORCID,Azzolina Danila29ORCID,

Affiliation:

1. Department of Medical Science, University of Ferrara, 44121 Ferrara, Italy

2. Clinical Trial and Biostatistics, Research and Development Unit, University Hospital of Ferrara, 44121 Ferrara, Italy

3. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic, Vascular Sciences, University of Padua, 35122 Padua, Italy

4. Department of Scienze Medico Chirurgiche e Medicina Traslazionale, University of Roma, S. Andrea University Hospital, 00189 Rome, Italy

5. Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00136 Rome, Italy

6. Institute of Image-Guided Surgery, IHU-Strasbourg, 67000 Strasbourg, France

7. Division of Laparoscopic and Robotic Surgery Unit, A.O.R.N. dei Colli Monaldi Hospital, 80131 Naples, Italy

8. Division of Surgical Oncology and Digestive Surgery, Department of Oncology, San Luigi University Hospital, University of Turin, Orbassano, 10043 Turin, Italy

9. Department of Preventive and Environmental Science, University of Ferrara, 44121 Ferrara, Italy

Abstract

The evolution of laparoscopic right hemicolectomy, particularly with complete mesocolic excision (CME) and central vascular ligation (CVL), represents a significant advancement in colon cancer surgery. The CoDIG 1 and CoDIG 2 studies highlighted Italy’s progressive approach, providing useful findings for optimizing patient outcomes and procedural efficiency. Within this context, accurately predicting postoperative length of stay (LoS) is crucial for improving resource allocation and patient care, yet its determination through machine learning techniques (MLTs) remains underexplored. This study aimed to harness MLTs to forecast the LoS for patients undergoing right hemicolectomy for colon cancer, using data from the CoDIG 1 (1224 patients) and CoDIG 2 (788 patients) studies. Multiple MLT algorithms, including random forest (RF) and support vector machine (SVM), were trained to predict LoS, with CoDIG 1 data used for internal validation and CoDIG 2 data for external validation. The RF algorithm showed a strong internal validation performance, achieving the best performances and a 0.92 ROC in predicting long-term stays (more than 5 days). External validation using the SVM model demonstrated 75% ROC values. Factors such as fast-track protocols, anastomosis, and drainage emerged as key predictors of LoS. Integrating MLTs into predicting postoperative LOS in colon cancer surgery offers a promising avenue for personalized patient care and improved surgical management. Using intraoperative features in the algorithm enables the profiling of a patient’s stay based on the planned intervention. This issue is important for tailoring postoperative care to individual patients and for hospitals to effectively plan and manage long-term stays for more critical procedures.

Publisher

MDPI AG

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