Above and Beyond Age: Prediction of Major Postoperative Adverse Events in Head and Neck Surgery

Marco A. Mascarella, Nikesh Muthukrishnan, Farhad Maleki, Marie Jeanne Kergoat, Keith Richardson, Alex Mlynarek, Veronique Isabelle Forest, Caroline Reinhold, Diego R. Martin, Michael Hier, Nader Sadeghi, Reza Forghani

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Objective: Major postoperative adverse events (MPAEs) following head and neck surgery are not infrequent and lead to significant morbidity. The objective of this study was to ascertain which factors are most predictive of MPAEs in patients undergoing head and neck surgery. Methods: A cohort study was carried out based on data from patients registered in the National Surgical Quality Improvement Program (NSQIP) from 2006 to 2018. All patients undergoing non-ambulatory head and neck surgery based on Current Procedural Terminology codes were included. Perioperative factors were evaluated to predict MPAEs within 30-days of surgery. Age was classified as both a continuous and categorical variable. Retained factors were classified by attributable fraction and C-statistic. Multivariate regression and supervised machine learning models were used to quantify the contribution of age as a predictor of MPAEs. Results: A total of 43 701 operations were analyzed with 5106 (11.7%) MPAEs. The results of supervised machine learning indicated that prolonged surgeries, anemia, free tissue transfer, weight loss, wound classification, hypoalbuminemia, wound infection, tracheotomy (concurrent with index head and neck surgery), American Society of Anesthesia (ASA) class, and sex as most predictive of MPAEs. On multivariate regression, ASA class (21.3%), hypertension on medication (15.8%), prolonged operative time (15.3%), sex (13.1%), preoperative anemia (12.8%), and free tissue transfer (9%) had the largest attributable fractions associated with MPAEs. Age was independently associated with MPAEs with an attributable fraction ranging from 0.6% to 4.3% with poor predictive ability (C-statistic 0.60). Conclusion: Surgical, comorbid, and frailty-related factors were most predictive of short-term MPAEs following head and neck surgery. Age alone contributed a small attributable fraction and poor prediction of MPAEs. Level of evidence: 3.

Original languageEnglish (US)
Pages (from-to)697-703
Number of pages7
JournalAnnals of Otology, Rhinology and Laryngology
Issue number7
StatePublished - Jul 2022


  • adverse events
  • frailty
  • head and neck cancer
  • machine learning
  • surgery

ASJC Scopus subject areas

  • Otorhinolaryngology


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