BACKGROUND: Comorbidities are diseases or conditions that coexist with a disease of interest. The importance of comorbidities is that they can alter treatment decisions, change resource utilization, and confound the results of survival analysis. OBJECTIVE: The objective of this study was to determine the best comorbidity index to use in survival analysis of patients with squamous cell carcinoma of the head and neck. METHOD: Four validated indexes, with very different methodologies (i.e., the Charlson Index, the Cumulative Illness Rating Scale, the Kaplan-Feinstein Classification, the Index of Co-existent Disease), were tested using data from 379 unselected consecutive patients with complete 3-year follow-up from the Kingston Regional Cancer Center. Kaplan-Meier analysis and Cox Proportional Hazards Regression were used to stratify patients into three levels of increasing severity of comorbidity for each index. The Proportion of Variance Explained and Receiver Operating Characteristics curves were used to compare the performance of the indexes. CONCLUSION: The Kaplan-Feinstein Classification was the most successful in stratifying patients in this population.
BACKGROUND: Comorbidities are diseases or conditions that coexist with a disease of interest. The importance of comorbidities is that they can alter treatment decisions, change resource utilization, and confound the results of survival analysis. OBJECTIVE: The objective of this study was to determine the best comorbidity index to use in survival analysis of patients with squamous cell carcinoma of the head and neck. METHOD: Four validated indexes, with very different methodologies (i.e., the Charlson Index, the Cumulative Illness Rating Scale, the Kaplan-Feinstein Classification, the Index of Co-existent Disease), were tested using data from 379 unselected consecutive patients with complete 3-year follow-up from the Kingston Regional Cancer Center. Kaplan-Meier analysis and Cox Proportional Hazards Regression were used to stratify patients into three levels of increasing severity of comorbidity for each index. The Proportion of Variance Explained and Receiver Operating Characteristics curves were used to compare the performance of the indexes. CONCLUSION: The Kaplan-Feinstein Classification was the most successful in stratifying patients in this population.
Authors: Patrick Kierkegaard; Mira D Vale; Spencer Garrison; Brent K Hollenbeck; John M Hollingsworth; Jason Owen-Smith Journal: J Surg Oncol Date: 2019-12-23 Impact factor: 3.454
Authors: Jacob Rinkinen; Shailesh Agarwal; Jeff Beauregard; Oluseyi Aliu; Matthew Benedict; Steven R Buchman; Stewart C Wang; Benjamin Levi Journal: J Surg Res Date: 2014-10-07 Impact factor: 2.192
Authors: Ying Huang; Wei Chen; Waqar Haque; Vivek Verma; Yan Xing; Bin S Teh; Edward Brian Butler Journal: Cancer Med Date: 2018-03-01 Impact factor: 4.452
Authors: Margherita Napolitani; Giovanni Guarducci; Gulnara Abinova; Gabriele Messina; Nicola Nante Journal: Int J Environ Res Public Health Date: 2022-03-15 Impact factor: 3.390