Analysis of acute extremity compartment syndrome using a genealogic population database.

INTRODUCTION: Acute extremity compartment syndrome ("CS") is an under-researched, highly morbid condition affecting trauma populations. The purpose of this study was to analyze incidence rates and risk factors for extremity compartment syndrome using a high-quality population database. Additionally, we evaluated heritable risk for CS using available genealogic data. We hypothesized that diagnosis of extremity compartment syndrome would demonstrate heritability. MATERIALS AND METHODS: Adult patients with fractures of the tibia, femur, and upper extremity were retrospectively identified by ICD-9, ICD-10, and CPT codes from 1996 to 2020 in a statewide hospital database. Exposed and unexposed cohorts were created based on a diagnosis of CS. Available demographic data were analyzed to determine risk factors for compartment syndrome using logistic regression. Mortality risk at the final follow-up was evaluated using Cox proportional hazard modeling. Patients with a diagnosis of CS were matched with those without a diagnosis for heritability analysis. RESULTS: Of 158,624 fractures, 931 patients were diagnosed with CS. Incidence of CS was 0.59% (tibia 0.83%, femur 0.31%, upper extremity 0.27%). Male sex (78.1% vs. 46.4%; p < 0.001; RR = 3.24), younger age at fracture (38.8 vs. 48.0 years; p < 0.001; RR = 0.74), Medicaid enrollment (13.2% vs. 9.3%; p < 0.001; RR = 1.58), and smoking (41.1% vs. 31.1%; p < 0.001; RR 1.67) were significant risk factors for CS. CS was associated with mortality (RR 1.61, p < 0.001) at mean follow-up 8.9 years in the CS cohort. No significant heritable risk was found for diagnosis of CS. CONCLUSIONS: Without isolating high-risk fractures, rates of CS are lower than previously reported in the literature. Male sex, younger age, smoking, and Medicaid enrollment were independent risk factors for CS. CS increased mortality risk at long-term follow-up. No heritable risk was found for CS. LEVEL OF EVIDENCE: III.

Familial Analysis of Nonunion in a Genealogic Population Database Demonstrates No Evidence of Heritable Risk.

OBJECTIVE: To determine whether there is evidence of heritable risk for nonunion using a large, state-wide population database. DESIGN: Database. SETTING: Level 1 Trauma Center. POPULATION: All Utah residents from 1996 to 2021 who sustained a long bone fracture and their family members were included. OUTCOMES: The primary outcome was nonunion and the prevalence of nonunion among the patients' first-, second-, and third-degree relatives. The secondary objective was to identify demographic, injury, and socioeconomic risk factors associated with nonunion. RESULTS: In total, 150,263 fractures and 6577 nonunions (4.4%) were identified. This was highly refined to a 1:3 matched cohort of 4667 nonunions of 13,981 fractures for familial clustering analysis. Cox proportional hazards did not demonstrate excessive risk of nonunion among first- ( P = 0.863), second- ( P = 0.509), and third-degree relatives ( P = 0.252). Further analysis of the entire cohort demonstrated that male sex (relative risk [RR] = 1.15; P < 0.001), Medicaid enrollment (RR = 2.64; P < 0.001), open fracture (RR = 2.53; P < 0.001), age group 41-60 years (RR = 1.43; P < 0.001), and a history of obesity (RR = 1.20; P < 0.001) were independent risk factors for nonunion. CONCLUSIONS: Our results demonstrate no evidence of heritable risk for nonunion. Independent risk factors for nonunion were male sex, Medicaid enrollment, open fracture, middle age, and a history of obesity. Although it is important to identify modifiable and nonmodifiable risk factors, these results continue to support that the risk of nonunion is multifactorial, relating to injury characteristics, operative techniques, and patient-specific risk factors. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Cardiovascular disease risk in long-term breast cancer survivors: A population-based cohort study.

BACKGROUND: Breast cancer survival is increasing, making late effects such as cardiovascular disease (CVD) more relevant. The purpose of this study was to evaluate incident CVD following breast cancer diagnosis among long-term survivors and to investigate possible risk factors for CVD. METHODS: A population-based cohort of 6641 breast cancer survivors diagnosed between 1997 and 2009 who survived at least 10 years was identified within the Utah Cancer Registry. In addition, 36,612 cancer-free women from the general population, matched by birth year and state, were identified within the Utah Population Database. Cox proportional hazards models were used to calculate CVD hazard ratios (HRs) for >10 to 15 and >15 years. RESULTS: Long-term breast cancer survivors had an increased risk of newly diagnosed diseases of the circulatory system (HR, 1.32; 99% confidence interval [CI], 1.00-1.75) from 10 to 15 years following cancer diagnosis compared with the general population. No increased CVD risks were observed after 15 years. Breast cancer survivors with Charlson Comorbidity Index score ≥2 had a significantly higher risk of diseases of the circulatory system (HR, 2.64; 95% CI, 1.08-6.45) beyond 10 years following breast cancer diagnosis. Similarly, older age, obesity, lower education, and family history of CVD and breast cancer were risk factors for heart and circulatory system diseases among long-term breast cancer survivors. CONCLUSION: Risk of CVD compared to the general population was moderate among this cohort of long-term breast cancer survivors between 10 to 15 years since cancer diagnosis. Awareness of CVD risks is important for breast cancer survivors.

Risk of Prostate Cancer Associated With Familial and Hereditary Cancer Syndromes.

PURPOSE: Recently developed clinical guidelines suggest that men in families with specific cancer syndromes, such as hereditary breast and ovarian cancer (HBOC), consider genetic testing, especially in the setting of aggressive disease. However, although a family history (FH) of the same disease among close relatives is an established risk factor for prostate cancer (PC), a direct comparison of PC risk for men with each syndrome in a single population is needed. METHODS: The Utah Population Database was used to identify 619,630 men, age ≥ 40 years, who were members of a pedigree that included at least 3 consecutive generations. Each man was evaluated for FH of hereditary PC (HPC), HBOC, and Lynch syndrome (LS) and for his own PC status. PC occurrences (N = 36,360) were classified into one or more subtypes: early onset (EO), lethal, and/or clinically significant. Relative risks (RRs) associated with each subtype, adjusted for important covariables, were calculated in STATA using a modified Poisson regression with robust error variances to obtain corresponding RR CIs for each FH definition. RESULTS: An FH of HPC conveyed the greatest relative risk for all PC subtypes combined (RR, 2.30; 95% CI, 2.22 to 2.40), followed by HBOC and LS (both with 1 < RR < 2 and statistically significant). The strongest risks associated with FH were observed for EO disease in all pedigree types, consistent with the contribution of genetic factors to disease occurrence. CONCLUSION: In this large, population-based, family database, the risk of PC varied by cancer FH and was most strongly associated with EO disease. These results are critically valuable in understanding and targeting high-risk populations that would benefit from genetic screening and enhanced surveillance.

Mental Health Disorders are More Common in Colorectal Cancer Survivors and Associated With Decreased Overall Survival.

OBJECTIVES: To determine the risk and risk factors for mental illness among colorectal cancer (CRC) survivors across short-term and long-term follow-up periods. METHODS: We used the Utah Cancer Registry to identify CRC survivors diagnosed between 1997 and 2013. Mental health diagnoses were available in electronic medical records and statewide facilities data that were linked by the Utah Population Database. CRC survivors were matched to individuals from a general population cohort. The risk of developing a mental illness was compared between cohorts. The association between mental illness and mortality was also analyzed. RESULTS: In total, 8961 CRC survivors and 35,897 individuals in a general population cohort were identified. CRC survivors were at increased risk for any mental health diagnosis at 0 to 2 years (hazard ratio [HR], 3.70; 95% confidence interval [CI], 3.47-3.95), >2 to 5 years (HR, 1.23; 95% CI, 1.09-1.38), and >5 years (HR, 1.20; 95% CI, 1.07-1.36) after cancer diagnosis. CRC survivors were also at increased risk of depressive disorders specifically during the same time periods. At >5 years, CRC survivors still had an increased risk of developing many mental health diagnoses. Factors associated with increased risk of any mental health disorder among CRC survivors included colostomy and Charlson Comorbidity Index of 1+. There was an increased risk of death for CRC survivors diagnosed with any mental health disorder (HR, 2.18; 95% CI, 2.02-2.35) and depression (HR, 2.10; 95% CI, 1.92-2.28). CONCLUSIONS: CRC survivors are at increased risk for mental health disorders in the short-term and long-term. Survivors who develop mental health disorders also experience decreased survival.

Identifying clinical/translational research cohorts: ascertainment via querying an integrated multi-source database.

BACKGROUND: Ascertainment of potential subjects has been a longstanding problem in clinical research. Various methods have been proposed, including using data in electronic health records. However, these methods typically suffer from scaling effects-some methods work well for large cohorts; others work for small cohorts only. OBJECTIVE: We propose a method that provides a simple identification of pre-research cohorts and relies on data available in most states in the USA: merged public health data sources. MATERIALS AND METHODS: The Utah Population Database Limited query tool allows users to build complex queries that may span several types of health records, such as cancer registries, inpatient hospital discharges, and death certificates; in addition, these can be combined with family history information. The architectural approach incorporates several coding systems for medical information. It provides a front-end graphical user interface and enables researchers to build and run queries and view aggregate results. Multiple strategies have been incorporated to maintain confidentiality. RESULTS: This tool was rapidly adopted; since its release, 241 users representing a wide range of disciplines from 17 institutions have signed the user agreement and used the query tool. Three examples are discussed: pregnancy complications co-occurring with cardiovascular disease; spondyloarthritis; and breast cancer. DISCUSSION AND CONCLUSIONS: This query tool was designed to provide results as pre-research so that institutional review board approval would not be required. This architecture uses well-described technologies that should be within the reach of most institutions.

Evaluation of record linkage between a large healthcare provider and the Utah Population Database.

OBJECTIVE: Electronically linked datasets have become an important part of clinical research. Information from multiple sources can be used to identify comorbid conditions and patient outcomes, measure use of healthcare services, and enrich demographic and clinical variables of interest. Innovative approaches for creating research infrastructure beyond a traditional data system are necessary. MATERIALS AND METHODS: Records from a large healthcare system's enterprise data warehouse (EDW) were linked to a statewide population database, and a master subject index was created. The authors evaluate the linkage, along with the impact of missing information in EDW records and the coverage of the population database. The makeup of the EDW and population database provides a subset of cancer records that exist in both resources, which allows a cancer-specific evaluation of the linkage. RESULTS: About 3.4 million records (60.8%) in the EDW were linked to the population database with a minimum accuracy of 96.3%. It was estimated that approximately 24.8% of target records were absent from the population database, which enabled the effect of the amount and type of information missing from a record on the linkage to be estimated. However, 99% of the records from the oncology data mart linked; they had fewer missing fields and this correlated positively with the number of patient visits. DISCUSSION AND CONCLUSION: A general-purpose research infrastructure was created which allows disease-specific cohorts to be identified. The usefulness of creating an index between institutions is that it allows each institution to maintain control and confidentiality of their own information.

The impact of a growing minority population on identification of duplicate records in an enterprise data warehouse.

Patient medical records are often fragmented across disparate healthcare databases, potentially resulting in duplicate records that may be detrimental to health care services. These duplicate records can be found through a process called record linkage. This paper describes a set of duplicate records in a medical data warehouse found by linking to an external resource containing family history and vital records. Our objective was to investigate the impact database characteristics and linkage methods have on identifying duplicate records using an external resource. Frequency counts were made for demographic field values and compared between the set of duplicate records, the data warehouse, and the external resource. Considerations for understanding the relationship that records labeled as duplicates have with dataset characteristics and linkage methods were identified. Several noticeable patterns were identified where frequency counts between sets deviated from what was expected including how the growth of a minority population affected which records were identified as duplicates. Record linkage is a complex process where results can be affected by subtleties in data characteristics, changes in data trends, and reliance on external data sources. These changes should be taken into account to ensure any anomalies in results describe real effects and are not artifacts caused by datasets or linkage methods. This paper describes how frequency count analysis can be an effective way to detect and resolve anomalies in linkage results and how external resources that provide additional contextual information can prove useful in discovering duplicate records.

Familial aggregation of juvenile idiopathic arthritis.

OBJECTIVE: To estimate the degree of familial aggregation of juvenile idiopathic arthritis (JIA), determine whether the aggregation of JIA and the aggregation of type 1 diabetes mellitus (type 1 DM) overlap, and identify multiplex JIA pedigrees. METHODS: Records of individuals with JIA or type 1 DM were probabilistically linked with records in the Utah Population Database (UPDB), a large computerized family history database. For each case of JIA or type 1 DM, 10 matched controls or 5 matched controls, respectively, were selected. All familial relationships among cases of JIA or type 1 DM were established. A familial risk score was calculated for each subject. For various levels of familial exposure to JIA or type 1 DM, one's risk (odds ratio [OR]) of developing JIA or type 1 DM was established (cases compared with controls). Recurrence risks for JIA were computed for relatives of JIA cases compared with relatives of controls. Extended JIA families were identified from a list of common ancestors. RESULTS: Records of a total of 443 patients were linked with the UPDB. Of these, 381 (86.0%) met criteria for JIA. An increased risk for JIA was observed among relatives of probands with JIA. The prevalence of type 1 DM among JIA cases was higher than the US prevalence of type 1 DM (P < 0.003). The recurrence risk for JIA was significantly elevated among first-degree relatives of cases with JIA (OR 30.4). The overall prevalence of JIA was 28/100,000. Four extended JIA pedigrees were identified. CONCLUSION: There is familial aggregation of JIA in the Intermountain West region of the US. We have demonstrated that multiplex JIA pedigrees can be identified using a genealogic database.