Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery
Andreas Vollmer,
Simon Nagler,
Marius Hörner,
Stefan Hartmann,
Roman C. Brands,
Niko Breitenbücher,
Anton Straub,
Alexander Kübler,
Michael Vollmer,
Sebastian Gubik,
Gernot Lang,
Jakob Wollborn,
Babak Saravi
Affiliations
Andreas Vollmer
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany; Corresponding author.
Simon Nagler
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Marius Hörner
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Stefan Hartmann
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Roman C. Brands
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Niko Breitenbücher
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Anton Straub
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Alexander Kübler
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Michael Vollmer
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany; Department of Orthopedics and Trauma Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Oral and Maxillofacial Surgery, University Hospital of Tübingen, 72076, Tübingen, Germany
Sebastian Gubik
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany
Gernot Lang
Department of Orthopedics and Trauma Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
Jakob Wollborn
Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
Babak Saravi
Department of Orthopedics and Trauma Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
Background: Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods: We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results: The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions: The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.
