PURPOSE: Accurate comorbidity measurement is critical for cancer research. We evaluated comorbidity assessment in the National Cancer Database (NCDB), which uses a code-based Charlson-Deyo Comorbidity Index (CCI), and compared its prognostic performance with a chart-based CCI and individual comorbidities in a national sample of patients with breast, colorectal, or lung cancer. PATIENTS AND METHODS: Through an NCDB Special Study, cancer registrars re-abstracted perioperative comorbidities for 11,243 patients with stage II to III breast cancer, 10,880 with stage I to III colorectal cancer, and 9,640 with stage I to III lung cancer treated with definitive surgical resection in 2006-2007. For each cancer type, we compared the prognostic performance of the NCDB code-based CCI (categorical: 0 or missing data, 1, 2+), Special Study chart-based CCI (continuous), and 18 individual comorbidities in three separate Cox proportional hazards models for postoperative 5-year overall survival. RESULTS: Comorbidity was highest among patients with lung cancer (13.2% NCDB CCI 2+) and lowest among patients with breast cancer (2.8% NCDB CCI 2+). Agreement between the NCDB and Special Study CCI was highest for breast cancer (rank correlation, 0.50) and lowest for lung cancer (rank correlation, 0.40). The NCDB CCI underestimated comorbidity for 19.1%, 29.3%, and 36.2% of patients with breast, colorectal, and lung cancer, respectively. Within each cancer type, the prognostic performance of the NCDB CCI, Special Study CCI, and individual comorbidities to predict postoperative 5-year overall survival was similar. CONCLUSION: The NCDB underestimated comorbidity in patients with surgically resected breast, colorectal, or lung cancer, partly because the NCDB codes missing data as CCI 0. However, despite underestimation of comorbidity, the NCDB CCI was similar to the more complete measures of comorbidity in the Special Study in predicting overall survival.
PURPOSE: Accurate comorbidity measurement is critical for cancer research. We evaluated comorbidity assessment in the National Cancer Database (NCDB), which uses a code-based Charlson-Deyo Comorbidity Index (CCI), and compared its prognostic performance with a chart-based CCI and individual comorbidities in a national sample of patients with breast, colorectal, or lung cancer. PATIENTS AND METHODS: Through an NCDB Special Study, cancer registrars re-abstracted perioperative comorbidities for 11,243 patients with stage II to III breast cancer, 10,880 with stage I to III colorectal cancer, and 9,640 with stage I to III lung cancer treated with definitive surgical resection in 2006-2007. For each cancer type, we compared the prognostic performance of the NCDB code-based CCI (categorical: 0 or missing data, 1, 2+), Special Study chart-based CCI (continuous), and 18 individual comorbidities in three separate Cox proportional hazards models for postoperative 5-year overall survival. RESULTS: Comorbidity was highest among patients with lung cancer (13.2% NCDB CCI 2+) and lowest among patients with breast cancer (2.8% NCDB CCI 2+). Agreement between the NCDB and Special Study CCI was highest for breast cancer (rank correlation, 0.50) and lowest for lung cancer (rank correlation, 0.40). The NCDB CCI underestimated comorbidity for 19.1%, 29.3%, and 36.2% of patients with breast, colorectal, and lung cancer, respectively. Within each cancer type, the prognostic performance of the NCDB CCI, Special Study CCI, and individual comorbidities to predict postoperative 5-year overall survival was similar. CONCLUSION: The NCDB underestimated comorbidity in patients with surgically resected breast, colorectal, or lung cancer, partly because the NCDB codes missing data as CCI 0. However, despite underestimation of comorbidity, the NCDB CCI was similar to the more complete measures of comorbidity in the Special Study in predicting overall survival.
Authors: Jashodeep Datta; Russell S Lewis; Ronac Mamtani; Diana Stripp; Rachel R Kelz; Jeffrey A Drebin; Douglas L Fraker; Giorgos C Karakousis; Robert E Roses Journal: Cancer Date: 2014-05-22 Impact factor: 6.860
Authors: Helmneh M Sineshaw; Mia Gaudet; Elizabeth M Ward; W Dana Flanders; Carol Desantis; Chun Chieh Lin; Ahmedin Jemal Journal: Breast Cancer Res Treat Date: 2014-05-03 Impact factor: 4.872
Authors: Thomas Seisen; Maxine Sun; Stuart R Lipsitz; Firas Abdollah; Jeffrey J Leow; Mani Menon; Mark A Preston; Lauren C Harshman; Adam S Kibel; Paul L Nguyen; Joaquim Bellmunt; Toni K Choueiri; Quoc-Dien Trinh Journal: Eur Urol Date: 2017-04-12 Impact factor: 20.096
Authors: Varun Puri; Traves D Crabtree; Jennifer M Bell; Stephen R Broderick; Daniel Morgensztern; Graham A Colditz; Daniel Kreisel; A Sasha Krupnick; G Alexander Patterson; Bryan F Meyers; Aalok Patel; Clifford G Robinson Journal: J Thorac Oncol Date: 2015-12 Impact factor: 15.609
Authors: William L Read; Ryan M Tierney; Nathan C Page; Irene Costas; Ramaswamy Govindan; Edward L J Spitznagel; Jay F Piccirillo Journal: J Clin Oncol Date: 2004-08-01 Impact factor: 44.544
Authors: Melisa L Wong; Stuart M Lichtman; Gary R Morrow; John Simmons; Tomma Hargraves; Cary P Gross; Jennifer L Lund; Lisa M Lowenstein; Louise C Walter; Cara L McDermott; Supriya G Mohile; Harvey Jay Cohen Journal: J Geriatr Oncol Date: 2019-07-17 Impact factor: 3.599
Authors: Julie K Jang; Scott M Atay; Li Ding; Elizabeth A David; Sean C Wightman; Anthony W Kim; Jason C Ye Journal: Am J Clin Oncol Date: 2022-04-01 Impact factor: 2.339
Authors: Jeffrey E Johnson; Paula D Strassle; Guilherme C de Oliveira; Chris B Agala; Philip Spanheimer; Kristalyn Gallagher; David Ollila; Hyman Muss; Stephanie Downs-Canner Journal: Breast Cancer Res Treat Date: 2021-06-26 Impact factor: 4.624
Authors: Nicholas G Zaorsky; Menglu Liang; Rutu Patel; Christine Lin; Leila T Tchelebi; Kristina B Newport; Edward J Fox; Ming Wang Journal: Radiother Oncol Date: 2021-02-19 Impact factor: 6.901
Authors: Mohammed Yousufuddin; Ye Zhu; Ruaa Al Ward; Jessica Peters; Taylor Doyle; Kelsey L Jensen; Zhen Wang; Mohammad Hassan Murad Journal: Open Heart Date: 2020-03-17