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The identification of prognostic factors is of prime pursuit in the world of oncology and oncologic research. Although a fairly intuitive concept, one might define a prognostic factor is a patient parameter from which can be gleaned an expected deviation in a given measurable outcome, usually survival or response to some sort of intervention. The “independent” nature of a given prognostic factor is established by multivariate statistical analysis delineating its independence from other prognostic factors [1]. Independent prognostic factors can be particularly useful in clinical medicine since, by their independence, they can be applied to various clinical scenarios and they can be relied upon even in the absence of other clinical information. As a consequence, the identification of independent prognostic factors is a major focus of oncologic study, one that will likely continue ad infinitum. But, as the common aphorism compels us to ask, when is enough enough?

Introduction Epidemiological data for rare malignancies can be difficult to obtain; population-based data sets are often abridged to group disparate rare malignancies into larger more manageable clusters. These problems are acute in Canada, where such epidemiological data are often estimated from “second-hand” US data (e.g. Surveillance, Epidemiology and End-Results (SEER) data). Recently, the Canadian Institute of Health Information (CIHI) has set out to make epidemiological data more readily accessible, permitting University-affiliated researchers to access the anonymized and codified CIHI Discharge Abstract Database (DAD). Methods We undertook to estimate the incidence, demographics, and outcome data relating to the various subtypes of peripheral T-cell lymphomas (PTCLs) in Canada (excluding Quebec and British Columbia, for which data was not collected). The CIHI DAD consists of a two fiscal-year anonymized 10% random sample of all hospital discharge abstracts in Canada. The DAD is indexed by a unique anonymous patient identifiers and includes relative date metrics, by which all dates originally present on the abstract are standardized to a unique but confidential CIHI DAD reference date. From these data we were able to identify all hematolymphoid diagnoses, isolate the PTCLs, separate new from historical diagnoses, and identify patient age range, gender and disposition data. When the disposition data were combined with the relative date metrics, a gross estimate of T-cell lymphoma overall survival (relative to all other hematolymphoid diagnoses) was generated. Population normalization was achieved using inter-censal estimates obtained from Statistics Canada. Results PTCL incidence was estimated at 0.72 cases per 100,000 per annum (comparable to recently published SEER data). We also estimated a prevalence of 21 PTCLs per 100,000 healthcare encounters. Most cases of PTCL originated from males (63%) and the distribution of age ranges was skewed toward older adults (median age by number of cases = 60 years). The most frequent diagnosis was PTCL, NOS (46%). By the Cox-proportional hazards method, there was a statistically significant difference in survival between the T-cell lymphomas and non T-cell hematolymphoid malignancies (regression co-efficient for PTCL vs. non-PTCL diagnosis p = 0.003) in favor of the latter; not surprisingly, age was also predictive of overall survival, regardless of the subtype of malignancy (regression co-efficient p = 0.004). Conclusions To our knowledge, the above is the first attempt to estimate the epidemiology of PTCLs in Canada. In addition, we present a unique approach to obtaining high-quality (albeit geographically incomplete) Canadian epidemiological data via the CIHI DAD database; this dataset may serve as a valuable resource in the context of rare diseases whose epidemiological data may not be widely or publicly available. Citation Format: Etienne R. Mahe, Princess Margaret Cancer Centre Advanced MolecularDiagnostics Laboratory, Trevor Pugh, Tracy Stockley, Suzanne Kamel-Reid. Filling the void of Canadian T-cell lymphoma epidemiology: Data from the canadian institute for health information discharge abstract database. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3708. doi:10.1158/1538-7445.AM2015-3708