Novel screening tool may predict complication risk after Laryngectomy: Study

The larynx is anatomically subdivided into 3 different structures: supraglottic, glottic, and infraglottic. Total laryngectomy is the treatment of choice for locally advanced laryngeal cancer in which a surgical resection seems possible. Organ preservation approaches are available, yet they seem to be less effective in achieving disease-free survival. Although laryngeal cancers are ranked 18th of the most prevalent cancers worldwide, the morbidity of treatment is significant and surgical decision-making challenging, inherent to the often invasive nature and the involvement of surrounding anatomic structures.

The workload for head and neck surgeons is substantial and will increase due to the aging population of the western civilization. Therefore, with increasing cost pressure in the health care system, the reduction in postoperative complications is also essential importance. Common complications are pharyngocutaneous fistulas, postoperative bleeding, and insufficiency of the voice prosthesis.

The Clavien-Dindo classification (CDC) is a method to uniformly classify surgical complications in a way that is internationally recognized. It has been established as leading method for classifying postoperative complications in recent years.

Predicting postoperative complications is often based on established clinical tools and expert opinion. The most commonly used scoring systems generalize and neglect the variety of surgical disciplines, as well as the variety in surgeries within a discipline. Machine learning is a subfield of artificial intelligence and has been reintroduced in recent years as a method to make predictions based on the characteristics of individual patient groups in various clinical settings.

Koenen et al performed a study aimed at analyzing known comorbidities for complications after total laryngectomy by grading them according to the CDC.

Study included all patients (N = 148) who underwent a total laryngectomy after diagnosis of squamous cell carcinoma at institution. A 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.

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.

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.

After years of using the CDC for postoperative complications in other surgical fields, it has emerged as a method to uniformly classify postoperative complications in an internationally accepted way. Of the different risk factors analyzed, the CCI and whether reconstruction was performed were significant. Every point of increase in the CCI led to a 1.9-fold increase in the risk of having a CDC IV or higher complication. As the CCI is already in routine use, it is an easy-to-implement scoring system. Authors find the CCI best suited because of an easy scoring system that is readily available on many online sources.

Adverse events in modern hospital services occur on a daily basis. The CDC is an opportunity to assess postoperative complications uniformly among surgical specialties worldwide. A uniform approach will lead to higher comparability, which can ultimately contribute to an improvement in the quality of care. The study showed that a reasonable estimate of postoperative complications can be made using machine learning algorithms. Bigger databases covering various surgeries are needed in order to train more elaborate models and integrate them into the clinical workflow.

Source: Koenen et al; Ear, Nose & Throat Journal;