Classifying and Predicting Surgical Complications After Laryngectomy: A Novel Approach to Diagnosing and Treating Patients

Objectives: The total laryngectomy is one of the most standardized major surgical procedures in otolaryngology. Several studies have proposed the Clavien-Dindo classification (CDC) as a solution to classifying postoperative complications into 5 grades from less severe to severe. Yet more data on cla...

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Vydáno v:Ear, nose, & throat journal Ročník 103; číslo 1; s. NP53 - NP59
Hlavní autoři: Koenen, Lukas, Arens, Philipp, Olze, Heidi, Dommerich, Steffen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Los Angeles, CA SAGE Publications 01.01.2024
SAGE PUBLICATIONS, INC
Témata:
Machine learning
Medical diagnosis
Patients
Squamous cell carcinoma
Surgery
Surgical outcomes
postoperative complications
Clavien-Dindo classification
machine learning
total laryngectomy
artificial intelligence
ISSN:0145-5613, 1942-7522, 1942-7522
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Shrnutí:Objectives: The total laryngectomy is one of the most standardized major surgical procedures in otolaryngology. Several studies have proposed the Clavien-Dindo classification (CDC) as a solution to classifying postoperative complications into 5 grades from less severe to severe. Yet more data on classifying larger patient populations undergoing major otolaryngologic surgery according to the CDC are needed. Predicting postoperative complications in clinical practice is often subject to generalized clinical scoring systems with uncertain predictive abilities for otolaryngologic surgery. Machine learning offers methods to predict postoperative complications based on data obtained prior to surgery. Methods: We included all patients (N = 148) who underwent a total laryngectomy after diagnosis of squamous cell carcinoma at our institution. A univariate and multivariate logistic regression analysis of multiple complex risk factors was performed, and patients were grouped into severe postoperative complications (CDC ≥ 4) and less severe complications. Four different commonly used machine learning algorithms were trained on the dataset. The best model was selected to predict postoperative complications on the complete dataset. Results: Univariate analysis showed that the most significant predictors for postoperative complications were the Charlson Comorbidity Index (CCI) and whether reconstruction was performed intraoperatively. A multivariate analysis showed that the CCI and reconstruction remained significant. The commonly used AdaBoost algorithm achieved the highest area under the curve with 0.77 with high positive and negative predictive values in subsequent analysis. Conclusions: This study shows that postoperative complications can be classified according to the CDC with the CCI being a useful screening tool to predict patients at risk for postoperative complications. We provide evidence that could help identify single patients at risk for complications and customize treatment accordingly which could finally lead to a custom approach for every patient. We also suggest that there is no increase in complications with patients of higher age.
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ISSN:0145-5613
1942-7522
1942-7522
DOI:10.1177/01455613211029749