The Comparison of Newly Diagnosed Invasive Breast Patient Cohorts in Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE-BPC) and Other Real-World Databases.

BACKGROUND: Oncology databases that integrate genomic and clinical data have become valuable resources for precision medicine. However, the generalizability of these databases has not been comprehensively assessed. OBJECTIVES: To describe the demographics, clinical characteristics, treatments, and overall survival of breast cancer cohorts in GENIE-BPC and three other databases. METHODS: This study utilized GENIE-BPC, SEER, SEER-Medicare, and Merative MarketScan Research Databases. Women with invasive breast cancer were identified through EHR, cancer registries or ICD-9/10-CM codes. The ages were 18+ years or per database requirement. Treatments were based on EHR or HCPCS/NDC codes in claims. Overall survival was estimated as time from diagnosis to death. RESULTS: Of female breast cancer patients in GENIE-BPC (n = 775), SEER (n = 548 336), SEER-Medicare (n = 68 914), and Marketscan (n = 109 499) databases, the median ages at initial diagnosis were 44, 62, 74, and 57 years, respectively. A greater proportion of patients in GENIE-BPC, compared to SEER/SEER-Medicare, had higher nuclear grades (%III-%IV: 57% vs. 26%/24%), advanced disease stage (%IV: 25.3% vs. 5%/3.6%), percent of triple negative breast cancer (19.7% vs. 10.2%/8.5%), and receipt of chemotherapy (85.0% vs. NA/22.3%). The 1-, 3-, and 5-year overall survival rates were lower in GENIE-BPC (78.5%, 60.5%, 55.5%) than in SEER (95.8%, 89.5%, 85.5%) and SEER-Medicare (91.6%, 81.4%, 75.0%). CONCLUSION: Breast cancer patients in GENIE-BPC were younger, had more advanced disease, had a higher proportion of triple negative breast cancer and recipients of chemotherapy, and had poorer overall survival. Researchers must use statistical adjustment when extrapolating results (e.g., biomarker prevalence) from GENIE-BPC to the larger breast cancer population.

Patterns of use of symptomatic treatments for Alzheimer's disease dementia (AD).

BACKGROUND: Symptomatic treatment for Alzheimer's disease (AD) dementia could temporarily slow symptom worsening and improve the quality of life for both AD dementia patients and their caregivers. A comprehensive evaluation of symptomatic treatment patterns using recent data for newly diagnosed AD dementia has not been performed and compared across different countries. METHODS: The drug name, time to the first therapy, duration, discontinuation or switches were described in newly diagnosed AD dementia patients in two databases (a major U.S. health plan [US] and UK-Clinical Practice Research Datalink [CPRD GOLD]). This analysis included patients with newly diagnosed AD dementia in 2018-2019, who initiated symptomatic AD drug therapy, with ≥ 1 year baseline period and ≥ 1 year of follow-up. RESULTS: Over median follow-ups of 698 and 645 days, 63% and 65% of AD dementia patients used symptomatic treatments, with 34% and 77% newly initiating therapy, constituting analytic samples of 7637 patients in the US database and 4470 patients in the CPRD, respectively. The median time to the first therapy was 14 days for US and 49 days for CPRD; donepezil ranked the as most frequently used (69% vs 61%), followed by memantine (19% vs 28%) in the US database and CPRD, respectively. Median time on first therapy was 213 and 334 days, and 30% and 12% of patients proceeded to a second treatment in the US and CPRD databases, respectively. CONCLUSION: Approximately two thirds of newly diagnosed AD dementia patients utilized approved symptomatic treatment. Time on first therapy was relatively short (< 1 year) and the majority did not move to a second therapy, highlighting the need for better adherence and persistence to existing AD symptomatic therapies and the need for additional therapies to alleviate the significant burden of AD dementia.

Contribution of Comorbid Conditions to the Diagnosis of Insomnia: A Database Study in a Commercially Insured Population.

ABSTRACT: Insomnia is a common sleep disorder characterized as dissatisfaction with sleep quantity or quality resulting in distress or impairment of social, occupational, or other daily functioning. It is unknown if there are medical conditions that have strong associations with insomnia but are unrecognized in previous literature. In this cross-sectional study based on IBM Marketscan Research Databases, we measured insomnia and 78 medical conditions in patients with 2-year continuous enrollment during 2018-2019. We selected important comorbidities associated with insomnia for eight age-sex groups and built logistic regression models to measure the associations. The prevalence of diagnosed insomnia increased with age, from <0.4% in the age group 0-17 to 4%-5% in the age group ≥65. Females had a higher prevalence of insomnia than males. Anxiety and depression were two important comorbidities across all age-sex subgroups. Most odds ratios of comorbidities remained significant after adjusting for other comorbidities in regression models. We did not find any new medical conditions that had strong associations with insomnia but were unrecognized in previous literature. The findings can help physicians use comorbidities to identify patients with high risk of insomnia.

Evaluation and Comparison of Real-World Databases for Conducting Research in Patients With Colorectal Cancer.

PURPOSE: Evaluating whether patient populations in clinico-genomic oncology databases are comparable with whom in other databases without genomic component is important. METHODS: Four databases were compared for colorectal cancer (CRC) cases and stage IV CRC cases: American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE-BPC), The Cancer Genome Atlas (TCGA), SEER-Medicare, and MarketScan Commercial and Medicare Supplemental claims databases. These databases were also compared with the SEER registry database which serves as national benchmarks. Demographics, clinical characteristics, and overall survival were compared in patients with newly diagnosed CRC and patients with stage IV CRC across databases. Treatment patterns were further compared in patients with stage IV CRC. RESULTS: A total of 65,976 patients with CRC and 13,985 patients with stage IV CRC were identified. GENIE-BPC had the youngest patient population (mean age [years]: CRC, 54.1; stage IV CRC, 52.7). SEER-Medicare had the oldest patient population (CRC, 77.7; stage IV CRC, 77.3). Most patients were male and of White race across databases. GENIE-BPC had the highest proportion of patients with stage IV CRC (48.4% v other databases 13.8%-25.4%) and patients receiving treatments (95.7% v 37.6%-59.1%). Infusional fluorouracil, leucovorin, and oxaliplatin with or without bevacizumab was the most common regimen across databases accounting for 47.3%-78.5% of patients receiving first line of therapy. The median survival from diagnosis was 36, 94, 44 months (CRC) and 23, 36, 15 months (stage IV CRC) for patients in GENIE-BPC after left truncation, TCGA, and SEER-Medicare databases, respectively. CONCLUSION: Compared with other databases, GENIE-BPC had the youngest patients with CRC with the most advanced disease and the largest proportion of patients receiving treatment. Investigators should consider adjustments when extrapolating results from clinico-genomic databases to the general CRC population.

An algorithm to identify medical practices common to both the General Practice Research Database and The Health Improvement Network database.

PURPOSE: To identify practices common to both the General Practice Research Database and The Health Improvement Network database for purposes of combining the databases for analysis without duplicate records. METHODS: We developed two independent algorithms to identify practices common to the two databases. The first used the total number of patients in the therapy and clinical data sets and the total number of etoricoxib and celecoxib users each year during the study period. The second used the total number of patients stratified by gender and four different categories of birth year. Further checking of potential matched practice pairs identified by the two algorithms was performed by comparing the patient-level medical records by birth year, dates of clinical visits, and diagnosis codes. RESULTS: Three hundred twelve potential matched pairs of practices were found by both algorithms. Fifteen additional potential pairs were matched by only one algorithm: 13 by algorithm 1 (A1) only and 2 by algorithm 2 (A2) only. The examination of the patient-level visit dates and diagnosis codes for the matches revealed that all of the 327 potential pairs of duplicate practices were in fact the same practice in the two databases. CONCLUSIONS: The two algorithms successfully found the practices common to the two different databases without de-identifying the practices. The identification of the common practices allows for combining the two databases without duplicate records to create a larger data set for analysis, with 168 more practices than when using the General Practice Research Database alone, or with 268 more practices than when using The Health Improvement Network alone. Copyright © 2012 John Wiley & Sons, Ltd.

Comparison of ICD-9-CM to ICD-10-CM Crosswalks Derived by Physician and Clinical Coder vs. Automated Methods.

Purpose: To evaluate whether automated methods are sufficient for deriving ICD-10-CM algorithms by comparing ICD-9-CM to ICD-10-CM crosswalks from general equivalence mappings (GEMs) with physician/clinical coder-derived crosswalks. Patients and methods: Forward mapping was used to derive ICD-10-CM crosswalks for 10 conditions. As a sensitivity analysis, forward-backward mapping (FBM) was also conducted for three clinical conditions. The physician/coder independently developed crosswalks for the same conditions. Differences between the crosswalks were summarized using the Jaccard similarity coefficient (JSC). Results: Physician/coder crosswalks were typically far more inclusive than GEMs crosswalks. Crosswalks for peripheral artery disease were most dissimilar (JSC: 0.06), while crosswalks for mild cognitive impairment (JSC: 1) and congestive heart failure (0.85) were most similar. FBM added ICD-10-CM codes for all three conditions but did not consistently increase similarity between crosswalks. Conclusion: The GEMs and physician/coder algorithms rarely aligned fully; human review is still required for ICD-9-CM to ICD-10-CM crosswalk development.

Epidemiology of Treatment-Resistant Depression in the United States.

Background: The prevalence of treatment-resistant depression (TRD) among patients with pharmaceutically treated depression (PTD) varies greatly in publications. The aim of this study is to estimate the prevalence of TRD using 2 large claims databases in the US.Methods: This cross-sectional study used data from the Humana and Optum databases. Patients aged ≥ 18 years who had at least 1 diagnosis of major depressive disorder (ICD-10-CM codes: F32.xx, F33.xx) and 1 antidepressant prescription filled in 2018 were identified as having PTD. Among patients with PTD, TRD was defined as experiencing failure of treatment with at least 2 antidepressants with ≥ 4 weeks of adequate treatment. We estimated the age- and gender-standardized prevalence of TRD and then used logistic regression to investigate if TRD risk varies by age, sex, race, and geographic region. Finally, we described the timeline of TRD development in incident PTD patients.Results: We identified 296,055 and 277,941 patients with PTD in the Humana and Optum databases, among whom 17,640 (6.0%) and 16,131 (5.8%) had TRD. After age and sex standardization, TRD prevalence among PTD patients was 6.8% in Humana vs 5.8% in Optum. Females, middle-aged adults, and White patients had higher risk of TRD. The median time from index antidepressant use to TRD was about 6 months in incident PTD patients.Conclusions: The prevalence of TRD among patients with PTD was similar in the 2 databases. TRD prevalence varies by sex, race, and age, with a higher prevalence in females, White patients, and those in the age group of 45-64 years. However, the absolute differences were small.

Medications in Patients with Dementia and Behavioral Disturbance.

BACKGROUND: Behavioral disturbance (BD) is common in dementia patients, with no FDA approved medications for this condition. Little data exists on the real-world medication use in this population. OBJECTIVE: To describe real-world medications use in this population. METHODS: A cross-sectional study was conducted using the MarketScan database for outpatient medications and the Cerner database for inpatient medications. The study period was Oct 2015-Jun 2018. Patients with dementia and BD were identified through ICD-10-CM. We examined outpatient medications prescribed during 6-month before or after BD event date, and inpatient medications during inpatient visits, especially on central nervous systems (CNS) drugs including antidementia drugs, antidepressants, antipsychotics, anxiolytics, and anticonvulsants. RESULTS: A total of 56,544 outpatients and 34,245 patient hospitalizations were assessed separately. Among outpatients, patients filled more medications after a BD event. The use of the five CNS drug classes generally increased after a BD event, and the largest increase was seen in antipsychotics (23%to 33%). Among inpatients, the median number of medications used in each hospitalization was 14. The use of antipsychotics was particularly high (64%), followed by anxiolytics (51%). A list of 60 unique medications were suggested to be the commonly used drugs in dementia patients with BD. CONCLUSION: In dementia patients with BD, anti-dementia medications, antidepressants, anticonvulsants, hypnotics and antipsychotics were the most used drug classes. Antidepressants and antipsychotics use were more frequent after a BD event, which suggests a need for safe drugs targeting BD in dementia patients.

A Systematic Review of Coding Systems Used in Pharmacoepidemiology and Database Research.

BACKGROUND: Clinical coding systems have been developed to translate real-world healthcare information such as prescriptions, diagnoses and procedures into standardized codes appropriate for use in large healthcare datasets. Due to the lack of information on coding system characteristics and insufficient uniformity in coding practices, there is a growing need for better understanding of coding systems and their use in pharmacoepidemiology and observational real world data research. OBJECTIVES: To determine: 1) the number of available coding systems and their characteristics, 2) which pharmacoepidemiology databases are they adopted in, 3) what outcomes and exposures can be identified from each coding system, and 4) how robust they are with respect to consistency and validity in pharmacoepidemiology and observational database studies. METHODS: Electronic literature database and unpublished literature searches, as well as hand searching of relevant journals were conducted to identify eligible articles discussing characteristics and applications of coding systems in use and published in the English language between 1986 and 2016. Characteristics considered included type of information captured by codes, clinical setting(s) of use, adoption by a pharmacoepidemiology database, region, and available mappings. Applications articles describing the use and validity of specific codes, code lists, or algorithms were also included. Data extraction was performed independently by two reviewers and a narrative synthesis was performed. RESULTS: A total of 897 unique articles and 57 coding systems were identified, 17% of which included country-specific modifications or multiple versions. Procedures (55%), diagnoses (36%), drugs (38%), and site of disease (39%) were most commonly and directly captured by these coding systems. The systems were used to capture information from the following clinical settings: inpatient (63%), ambulatory (55%), emergency department (ED, 34%), and pharmacy (13%). More than half of all coding systems were used in Europe (59%) and North America (57%). 34% of the reviewed coding systems were utilized in at least 1 of the 16 pharmacoepidemiology databases of interest evaluated. 21% of coding systems had studies evaluating the validity and consistency of their use in research within pharmacoepidemiology databases of interest. The most prevalent validation method was comparison with a review of patient charts, case notes or medical records (64% of reviewed validation studies). The reported performance measures in the reviewed studies varied across a large range of values (PPV 0-100%, NPV 6-100%, sensitivity 0-100%, specificity 23-100% and accuracy 16-100%) and were dependent on many factors including coding system(s), therapeutic area, pharmacoepidemiology database, and outcome. CONCLUSIONS: Coding systems vary by type of information captured, clinical setting, and pharmacoepidemiology database and region of use. Of the 57 reviewed coding systems, few are routinely and widely applied in pharmacoepidemiology database research. Indication and outcome dependent heterogeneity in coding system performance suggest that accurate definitions and algorithms for capturing specific exposures and outcomes within large healthcare datasets should be developed on a case-by-case basis and in consultation with clinical experts.