1
|
Namavarian A, Gabinet-Equihua A, Deng Y, Khalid S, Ziai H, Deutsch K, Huang J, Gilbert RW, Goldstein DP, Yao CMKL, Irish JC, Enepekides DJ, Higgins KM, Rudzicz F, Eskander A, Xu W, de Almeida JR. Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP. Laryngoscope 2024. [PMID: 38651539 DOI: 10.1002/lary.31443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/17/2024] [Accepted: 03/27/2024] [Indexed: 04/25/2024]
Abstract
OBJECTIVE Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC. MATERIALS AND METHODS A retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy. RESULTS Totally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4-day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS-NSQIP calculator's performance (0.23, 59%). CONCLUSION We developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS-NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice. LEVEL OF EVIDENCE Level 3 Laryngoscope, 2024.
Collapse
Affiliation(s)
- Amirpouyan Namavarian
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | | | - Yangqing Deng
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Shuja Khalid
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Hedyeh Ziai
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Konrado Deutsch
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jingyue Huang
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Ralph W Gilbert
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - David P Goldstein
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Christopher M K L Yao
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Jonathan C Irish
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Danny J Enepekides
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Kevin M Higgins
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Frank Rudzicz
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Antoine Eskander
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - John R de Almeida
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sinai Health System, Toronto, Ontario, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|