Individualized risk estimation for postoperative complications after surgery for oral cavity cancer Journal Article


Authors: Awad, M. I.; Palmer, F. L.; Kou, L.; Yu, C.; Montero, P. H.; Shuman, A. G.; Ganly, I.; Shah, J. P.; Kattan, M. W.; Patel, S. G.
Article Title: Individualized risk estimation for postoperative complications after surgery for oral cavity cancer
Abstract: Importance Postoperative complications after head and neck surgery carry the potential for significant morbidity. Estimating the risk of complications in an individual patient is challenging. Objective To develop a statistical tool capable of predicting an individual patient's risk of developing a major complication after surgery for oral cavity squamous cell carcinoma. Design, Setting, and Participants Retrospective case series derived from an institutional clinical oncologic database, augmented by medical record abstraction, at an academic tertiary care cancer center. Participants were 506 previously untreated adult patients with biopsy-proven oral cavity squamous cell carcinoma who underwent surgery between January 1, 2007, and December 31, 2012. Main Outcomes and Measures The primary end pointwas a major postoperative complication requiring invasive intervention (Clavien-Dindo classification grades III-V). Patients treated between January 1, 2007, and December 31, 2008 (354 of 506 [70.0%]) comprised the modeling cohort and were used to develop a nomogram to predict the risk of developing the primary end point. Univariable analysis and correlation analysis were used to prescreen 36 potential predictors for incorporation in the subsequent multivariable logistic regression analysis. The variables with the highest predictive value were identified with the step-down model reduction method and included in the nomogram. Patients treated between January 1, 2007, and December 31, 2008 (152 of 506 [30.0%]) were used to validate the nomogram. Results Clinical characteristics were similar between the 2 cohorts for most comparisons. Thirty-six patients in the modeling cohort (10.2%) and 16 patients in the validation cohort (10.5%) developed a major postoperative complication. The 6 preoperative variables with the highest individual predictive value were incorporated within the nomogram, including body mass index, comorbidity status, preoperative white blood cell count, preoperative hematocrit, planned neck dissection, and planned tracheotomy. The nomogram predicted a major complication with a validated concordance index of 0.79. Inclusion of surgical operative variables in the nomogram maintained predictive accuracy (concordance index, 0.77). Conclusions and Relevance A statistical tool was developed that accurately estimates an individual patient's risk of developing a major complication after surgery for oral cavity squamous cell carcinoma. © 2015 American Medical Association. All rights reserved.
Keywords: adult; controlled study; treatment outcome; cancer surgery; major clinical study; neck dissection; conference paper; cancer patient; cancer staging; cohort analysis; creatinine; creatinine blood level; retrospective study; aspartate aminotransferase blood level; postoperative complication; alkaline phosphatase; aspartate aminotransferase; bilirubin; cancer center; body mass; albumin; disease severity; karnofsky performance status; surgical infection; comorbidity; alkaline phosphatase blood level; leukocyte count; sodium; sodium blood level; bilirubin blood level; nomogram; oral surgery; predictive value; potassium; hematocrit; mouth squamous cell carcinoma; albumin blood level; potassium blood level; tracheotomy; human; male; female
Journal Title: JAMA Otolaryngology - Head and Neck Surgery
Volume: 141
Issue: 11
ISSN: 2168-6181
Publisher: American Medical Association  
Date Published: 2015-11-01
Start Page: 960
End Page: 968
Language: English
DOI: 10.1001/jamaoto.2015.2200
PROVIDER: scopus
PUBMED: 26469394
PMCID: PMC4976497
DOI/URL:
Notes: Export Date: 15 January 2016 -- Conference Paper -- Source: Scopus
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MSK Authors
  1. Snehal G Patel
    336 Patel
  2. Ian Ganly
    309 Ganly
  3. Jatin P Shah
    636 Shah
  4. Andrew Gregg Shuman
    24 Shuman
  5. Frank Palmer
    82 Palmer
  6. Mahmoud Issam Awad
    9 Awad