History of Cholecystectomy and Risk of Clostridium Difficile Infection.

BACKGROUND: Cholecystectomy affects bile acid physiology. There is growing evidence that both primary and secondary bile acids play a role in the pathogenesis of Clostridium difficile infections (CDIs). AIMS: The aim of this study is to elucidate the relationship and risk of CDI in patients with cholecystectomy. METHODS: We performed a matched cohort study of patients in an integrated healthcare system in Northern California from January 2000 to December 2018. Patients with cholecystectomy (cases, n = 12,617) identified based on Current Procedure Terminology codes were age- and sex-matched to patients without cholecystectomy (controls, n = 37,851). We excluded those with history of CDI at baseline and calculated the hazard ratio (HR) for development of CDI after adjusting for confounders. RESULTS: We found total of 351 incident CDI during average of 4.66 years of follow-up among cases and controls. In multivariate analysis, cholecystectomy was associated with elevated risk of CDI (HR 1.53, 95% confidence interval 1.14-2.04) compared with controls. Stratified analysis shows this effect does not differ according use of proton pump inhibitors (Pinteraction = 0.142), antibiotics (Pinteraction = 0.387), and hospitalization (Pinteraction = 0.252). CONCLUSIONS: Cholecystectomy is associated with mild increased risk of incident CDI, but this effect is not influenced by use of proton pump inhibitors, antibiotics, or hospitalization. Future prospective studies should be conducted to validate these findings and evaluate bile acid changes after a cholecystectomy.

Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research.

BACKGROUND: Understanding of cancer outcomes is limited by data fragmentation. In the current study, the authors analyzed the information yielded by integrating breast cancer data from 3 sources: electronic medical records (EMRs) from 2 health care systems and the state registry. METHODS: Diagnostic test and treatment data were extracted from the EMRs of all patients with breast cancer treated between 2000 and 2010 in 2 independent California institutions: a community-based practice (Palo Alto Medical Foundation; "Community") and an academic medical center (Stanford University; "University"). The authors incorporated records from the population-based California Cancer Registry and then linked EMR-California Cancer Registry data sets of Community and University patients. RESULTS: The authors initially identified 8210 University patients and 5770 Community patients; linked data sets revealed a 16% patient overlap, yielding 12,109 unique patients. The percentage of all Community patients, but not University patients, treated at both institutions increased with worsening cancer prognostic factors. Before linking the data sets, Community patients appeared to receive less intervention than University patients (mastectomy: 37.6% vs 43.2%; chemotherapy: 35% vs 41.7%; magnetic resonance imaging: 10% vs 29.3%; and genetic testing: 2.5% vs 9.2%). Linked Community and University data sets revealed that patients treated at both institutions received substantially more interventions (mastectomy: 55.8%; chemotherapy: 47.2%; magnetic resonance imaging: 38.9%; and genetic testing: 10.9% [P < .001 for each 3-way institutional comparison]). CONCLUSIONS: Data linkage identified 16% of patients who were treated in 2 health care systems and who, despite comparable prognostic factors, received far more intensive treatment than others. By integrating complementary data from EMRs and population-based registries, a more comprehensive understanding of breast cancer care and factors that drive treatment use was obtained.

Trends in Detection of Adenoma and Sessile Serrated Lesions Over a Decade in a Community-Based Healthcare System.

INTRODUCTION: Adenoma detection rate (ADR) is an accepted benchmark for screening colonoscopy. Factors driving ADR and its relationship with sessile serrated lesions detection rate (SSLDR) over time remain unclear. We aim to explore patient, physician, and procedural influences on ADR and SSLDR trends. METHODS: Using a large healthcare system in northern California from January 2010 to December 2020, a total of 146,818 screening colonoscopies performed by 33 endoscopists were included. ADR and SSLDR were calculated over time using natural language processing. Logistic regression was used to calculate the odd ratios of patient demographics, physician attributes, and procedural details over time. RESULTS: Between 2010 and 2020, ADR rose from 19.4% to 44.4%, whereas SSLDR increased from 1.6% to 11.6%. ADR increased by 2.7% per year (95% confidence interval 1.9%-3.4%), and SSLDR increased by 1.0% per year (95% confidence interval 0.8%-1.2%). Higher ADR was associated with older age, male sex, higher body mass index, current smoker, higher comorbidities, and high-risk colonoscopy. By contrast, SSLDR was associated with younger age, female sex, white race, and fewer comorbidities. Patient and procedure characteristics did not significantly change over time ( P -interaction >0.05). Longer years in practice and male physician were associated with lower ADR and SSLDR in 2010, but significantly attenuated over time ( P -interaction <0.05). DISCUSSION: Both ADR and SSLDR have increased over time. Patient and procedure factors did not significantly change over time. Male endoscopist and longer years in practice had lower initial ADR and SSLDR, but significantly lessened over time.

Synergistic drug combinations from electronic health records and gene expression.

OBJECTIVE: Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding. METHOD: We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis. RESULTS: From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence. CONCLUSIONS: This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.

Use of Gene Expression Profiling and Chemotherapy in Early-Stage Breast Cancer: A Study of Linked Electronic Medical Records, Cancer Registry Data, and Genomic Data Across Two Health Care Systems.

PURPOSE: The 21-gene recurrence score (RS) identifies patients with breast cancer who derive little benefit from chemotherapy; it may reduce unwarranted variability in the use of chemotherapy. We tested whether the use of RS seems to guide chemotherapy receipt across different cancer care settings. METHODS: We developed a retrospective cohort of patients with breast cancer by using electronic medical record data from Stanford University (hereafter University) and Palo Alto Medical Foundation (hereafter Community) linked with demographic and staging data from the California Cancer Registry and RS results from the testing laboratory (Genomic Health Inc., Redwood City, CA). Multivariable analysis was performed to identify predictors of RS and chemotherapy use. RESULTS: In all, 10,125 patients with breast cancer were diagnosed in the University or Community systems from 2005 to 2011; 2,418 (23.9%) met RS guidelines criteria, of whom 15.6% received RS. RS was less often used for patients with involved lymph nodes, higher tumor grade, and age < 40 or ≥ 65 years. Among RS recipients, chemotherapy receipt was associated with a higher score (intermediate v low: odds ratio, 3.66; 95% CI, 1.94 to 6.91). A total of 293 patients (10.6%) received care in both health care systems (hereafter dual use); although receipt of RS was associated with dual use (v University: odds ratio, 1.73; 95% CI, 1.18 to 2.55), there was no difference in use of chemotherapy after RS by health care setting. CONCLUSION: Although there was greater use of RS for patients who sought care in more than one health care setting, use of chemotherapy followed RS guidance in University and Community health care systems. These results suggest that precision medicine may help optimize cancer treatment across health care settings.

Oncoshare: lessons learned from building an integrated multi-institutional database for comparative effectiveness research.

Comparative effectiveness research (CER) using observational data requires informatics methods for the extraction, standardization, sharing, and integration of data derived from a variety of electronic sources. In the Oncoshare project, we have developed such methods as part of a collaborative multi-institutional CER study of patterns, predictors, and outcome of breast cancer care. In this paper, we present an evaluation of the approaches we undertook and the lessons we learned in building and validating the Oncoshare data resource. Specifically, we determined that 1) the state or regional cancer registry makes the most efficient starting point for determining inclusion of subjects; 2) the data dictionary should be based on existing registry standards, such as Surveillance, Epidemiology and End Results (SEER), when applicable; 3) the Social Security Administration Death Master File (SSA DMF), rather than clinical resources, provides standardized ascertainment of mortality outcomes; and 4) CER database development efforts, despite the immediate availability of electronic data, may take as long as two years to produce validated, reliable data for research. Through our efforts using these methods, Oncoshare integrates complex, longitudinal data from multiple electronic medical records and registries and provides a rich, validated resource for research on oncology care.