David P. Taylor

Population-based family-history-specific risks for colorectal cancer: a constellation approach

BACKGROUND & AIMS Colorectal cancer (CRC) risk estimates based on family history typically include only close relatives. We report familial relative risk (FRR) in probands with various combinations, or constellations, of affected relatives, extending to third-degree. METHODS A population-based resource that includes a computerized genealogy linked to statewide cancer records was used to identify genetic relationships among CRC cases and their first-, second-, and third-degree relatives (FDRs, SDRs, and TDRs). FRRs were estimated by comparing the observed number of affected persons with a particular family history constellation to the expected number, based on cohort-specific CRC rates. RESULTS A total of 2,327,327 persons included in > or =3 generation family histories were analyzed; 10,556 had a diagnosis of CRC. The FRR for CRC in persons with > or =1 affected FDR = 2.05 (95% CI, 1.96-2.14), consistent with published estimates. In the absence of a positive first-degree family history, considering both affected SDRs and TDRs, only 1 constellation had an FRR estimate that was significantly >1.0 (0 affected FDRs, 1 affected SDR, 2 affected TDRs; FRR = 1.33; 95% CI, 1.13-1.55). The FRR for persons with 1 affected FDR, 1 affected SDR, and 0 affected TDRs was 1.88 (95% CI, 1.59-2.20), increasing to FRR = 3.28 (95% CI, 2.44-4.31) for probands with 1 affected FDR, 1 affected SDR, and > or =3 affected TDRs. CONCLUSIONS Increased numbers of affected FDRs influences risk much more than affected SDRs or TDRs. However, when combined with a positive first-degree family history, a positive second- and third-degree family history can significantly increase risk.

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium

RESEARCH OBJECTIVE To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

Comparison of compliance for colorectal cancer screening and surveillance by colonoscopy based on risk.

Purpose: To compare colonoscopy screening/surveillance rates by level of risk for colorectal cancer based on age, personal history of adenomatous polyps or colorectal cancer, or family history of colorectal cancer.Methods: Participants were aged 30–90 years, were seen within 5 years at Intermountain Healthcare, and had family history in the Utah Population Database. Colonoscopy rates were measured for those with/without risk factors.Results: Among those aged 60–69 years, 48.4% had colonoscopy in the last 10 years, with rates declining after age 70 years. Percentages of those having had a colonoscopy in the last 10 years generally increased by risk level from 38.5% in those with a familial relative risk <1.0 to 47.6% in those with a familial relative risk >3.0. Compared with those with no family history, the odds ratio for being screened according to guidelines was higher for those with one first-degree relative diagnosed with colorectal cancer ≥ 60 years or two affected second-degree relatives (1.54, 95% confidence interval: 1.46–1.61) than those with one affected first-degree relative diagnosed <60 years or ≥2 affected first-degree relatives (1.25, 95% confidence interval: 1.14–1.37).Conclusions: Compliance with colonoscopy guidelines was higher for those with familial risk but did not correspond with the degree of risk.

How well does family history predict who will get colorectal cancer? Implications for cancer screening and counseling

Purpose: Using a large, retrospective cohort from the Utah Population Database, we assess how well family history predicts who will acquire colorectal cancer during a 20-year period.Methods: Individuals were selected between ages 35 and 80 with no prior record of colorectal cancer diagnosis, as of the year 1985. Numbers of colorectal cancer-affected relatives and diagnosis ages were collected. Familial relative risk and absolute risk estimates were calculated. Colorectal cancer diagnoses in the cohort were counted between years 1986 and 2005. Cox regression and Harrells C were used to measure the discriminatory power of resulting models.Results: A total of 431,153 individuals were included with 5,334 colorectal cancer diagnoses. Familial relative risk ranged from 0.83 to 12.39 and 20-year absolute risk from 0.002 to 0.21. With familial relative risk as the only predictor, Harrells C = 0.53 and with age only, Harrells C = 0.66. Familial relative risk combined with age produced a Harrells C = 0.67.Conclusion: Family history by itself is not a strong predictor of exactly who will acquire colorectal cancer within 20 years. However, stratification of risk using absolute risk probabilities may be more helpful in focusing screening on individuals who are more likely to develop the disease.