Abstract
Abstract
Background
Algorithms that use administrative health and electronic medical record (EMR) data to determine cancer recurrence have the potential to replace chart reviews. This study evaluated algorithms to determine breast and colorectal cancer recurrence in a Canadian province with a universal health care system.
Methods
Individuals diagnosed with stage I-III breast or colorectal cancer diagnosed from 2004 to 2012 in Manitoba, Canada were included. Pre-specified and conditional inference tree algorithms using administrative health and structured EMR data were developed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) correct classification, and scaled Brier scores were measured.
Results
The weighted pre-specified variable algorithm for the breast cancer validation cohort (N = 1181, 167 recurrences) demonstrated 81.1% sensitivity, 93.2% specificity, 61.4% PPV, 97.4% NPV, 91.8% correct classification, and scaled Brier score of 0.21. The weighted conditional inference tree algorithm demonstrated 68.5% sensitivity, 97.0% specificity, 75.4% PPV, 95.8% NPV, 93.6% correct classification, and scaled Brier score of 0.39. The weighted pre-specified variable algorithm for the colorectal validation cohort (N = 693, 136 recurrences) demonstrated 77.7% sensitivity, 92.8% specificity, 70.7% PPV, 94.9% NPV, 90.1% correct classification, and scaled Brier score of 0.33. The conditional inference tree algorithm demonstrated 62.6% sensitivity, 97.8% specificity, 86.4% PPV, 92.2% NPV, 91.4% correct classification, and scaled Brier score of 0.42.
Conclusions
Algorithms developed in this study using administrative health and structured EMR data to determine breast and colorectal cancer recurrence had moderate sensitivity and PPV, high specificity, NPV, and correct classification, but low accuracy. The accuracy is similar to other algorithms developed to classify recurrence only (i.e., distinguished from second primary) and inferior to algorithms that do not make this distinction. The accuracy of algorithms for determining cancer recurrence only must improve before replacing chart reviews.
Funder
CancerCare Manitoba Foundation
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Genetics,Oncology
Reference30 articles.
1. Yu X. In: Feuerstein M, Ganz P, editors. Epidemiology of Cancer recurrence, second primary Cancer, and comorbidity among Cancer survivors. New York: Springer; 2011.
2. North American Association of Central Cancer Registries. APPENDIX C - Data Quality Indicators by Year and Registry. In: Hotes Ellison J, Wu XC, McLaughlin C, Lake A, Firth R, et al., editors. Cancer In North America: 1999–2003 Volume One: Incidence. Springfield: North American Association of Cancer Registries Inc.; 2006. p. II-325.
3. Lamont EB, Hernon JE, Weeks JC, Henderson C, Earle CR, Schilsky RL, et al. Measuring disease-free survival and cancer relapse using medicare claims from CALGB breast cancer trial participants (Companion to 9344). J Natl Cancer Inst. 2006;98(18)1335-8.
4. Livaudais-Toman J, Franco R, Prasad-Hayes M, Howell EA, Wisnivesky J, Bickell NA. A validation of administrative claims data to measure ovarian cancer recurrence and secondary debluking surgery. EGEMS. 2016;4(1):1208.
5. Chubak J, Yu O, Pocobelli G, Lamerato L, Webster J, Prout MN, et al. Administrative data algorithms to identify second breast cancer events following early-stage invasive cancer. J Natl Cancer Inst. 2012;104(12):931–40. https://doi.org/10.1093/jnci/djs233.