Methodology for Using Real-World Data From Electronic Health Records to Assess Chemotherapy Administration in Women With Breast Cancer.

PURPOSE: Identification of patients' intended chemotherapy regimens is critical to most research questions conducted in the real-world setting of cancer care. Yet, these data are not routinely available in electronic health records (EHRs) at the specificity required to address these questions. We developed a methodology to identify patients' intended regimens from EHR data in the Optimal Breast Cancer Chemotherapy Dosing (OBCD) study. METHODS: In women older than 18 years, diagnosed with primary stage I-IIIA breast cancer at Kaiser Permanente Northern California (2006-2019), we categorized participants into 24 drug combinations described in National Comprehensive Cancer Network guidelines for breast cancer treatment. Participants were categorized into 50 guideline chemotherapy administration schedules within these combinations using an iterative algorithm process, followed by chart abstraction where necessary. We also identified patients intended to receive nonguideline administration schedules within guideline drug combinations and nonguideline drug combinations. This process was adapted at Kaiser Permanente Washington using abstracted data (2004-2015). RESULTS: In the OBCD cohort, 13,231 women received adjuvant or neoadjuvant chemotherapy, of whom 10,213 (77%) had their intended regimen identified via the algorithm, 2,416 (18%) had their intended regimen identified via abstraction, and 602 (4.5%) could not be identified. Across guideline drug combinations, 111 nonguideline dosing schedules were used, alongside 61 nonguideline drug combinations. A number of factors were associated with requiring abstraction for regimen determination, including: decreasing neighborhood household income, earlier diagnosis year, later stage, nodal status, and human epidermal growth factor receptor 2 (HER2)+ status. CONCLUSION: We describe the challenges and approaches to operationalize complex, real-world data to identify intended chemotherapy regimens in large, observational studies. This methodology can improve efficiency of use of large-scale clinical data in real-world populations, helping answer critical questions to improve care delivery and patient outcomes.

Assessment of breast cancer chemotherapy dose reduction in an integrated healthcare delivery system.

PURPOSE: Most cytotoxic drugs are dosed using body surface area (BSA), yet not all cancer patients receive the full BSA-determined dose. Prior work suggests that breast cancer patients who are obese are more likely to experience dose reduction than normal weight patients. However, the factors driving dose reduction remain unclear. METHODS: In 452 women diagnosed with stage I-IIIA primary breast cancer at Kaiser Permanente Northern California, we evaluated the association between obesity and dose reduction, and further explored other factors in relation to dose reduction, including various sociodemographic characteristics, tumor characteristics, and comorbidities. Study participants were a part of the Pathways Study, diagnosed between 2006 and 2013 and treated with cyclophosphamide + doxorubicin, followed by paclitaxel (ACT). Dose reduction was assessed using first cycle dose proportion (FCDP) and average relative dose intensity (ARDI), a metric of dose intensity over the course of chemotherapy. RESULTS: Overall, 8% of participants received a FCDP < 90% and 21.2% had an ARDI < 90%, with dose reduction increasing with body mass index. In adjusted logistic regression models, obese women had 4.1-fold higher odds of receiving an ARDI < 90% than normal weight women (95% CI: 1.9-8.9; p-trend = 0.0006). Increasing age was positively associated with an ADRI < 90%, as was the presence of comorbidity. Dose reduction was less common in later calendar years. CONCLUSION: Results offer insight on factors associated with chemotherapy dosing for a common breast cancer regimen. Larger studies are required to evaluate relevance to other regimens, and further work will be needed to determine whether dose reductions impact outcomes in obese women.

Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences.

BACKGROUND: We updated algorithms to identify breast cancer recurrences from administrative data, extending previously developed methods. METHODS: In this validation study, we evaluated pairs of breast cancer recurrence algorithms (vs. individual algorithms) to identify recurrences. We generated algorithm combinations that categorized discordant algorithm results as no recurrence [High Specificity and PPV (positive predictive value) Combination] or recurrence (High Sensitivity Combination). We compared individual and combined algorithm results to manually abstracted recurrence outcomes from a sample of 600 people with incident stage I-IIIA breast cancer diagnosed between 2004 and 2015. We used Cox regression to evaluate risk factors associated with age- and stage-adjusted recurrence rates using different recurrence definitions, weighted by inverse sampling probabilities. RESULTS: Among 600 people, we identified 117 recurrences using the High Specificity and PPV Combination, 505 using the High Sensitivity Combination, and 118 using manual abstraction. The High Specificity and PPV Combination had good specificity [98%, 95% confidence interval (CI): 97-99] and PPV (72%, 95% CI: 63-80) but modest sensitivity (64%, 95% CI: 44-80). The High Sensitivity Combination had good sensitivity (80%, 95% CI: 49-94) and specificity (83%, 95% CI: 80-86) but low PPV (29%, 95% CI: 25-34). Recurrence rates using combined algorithms were similar in magnitude for most risk factors. CONCLUSIONS: By combining algorithms, we identified breast cancer recurrences with greater PPV than individual algorithms, without additional review of discordant records. IMPACT: Researchers should consider tradeoffs between accuracy and manual chart abstraction resources when using previously developed algorithms. We provided guidance for future studies that use breast cancer recurrence algorithms with or without supplemental manual chart abstraction.

Glucosamine and Chondroitin Use and Mortality Among Adults in the United States from 1999 to 2014.

Introduction: Glucosamine and chondroitin are supplements that are often, but not always, used in combination for arthritis and joint pain. Multiple studies have suggested that glucosamine and chondroitin may be associated with reduced risk of several diseases, as well as all-cause, cancer- and respiratory disease-specific mortality. Methods: Nationally representative data from the National Health and Nutrition Examination Survey (NHANES) were used to further evaluate the association between glucosamine and chondroitin with mortality. Participants include 38,021 adults, ages 20+ years and older, who completed the detailed NHANES between 1999 and 2014. Participants were followed for death through linkage with the National Death Index through the end of 2015, over which time 4905 deaths occurred. Adjusted hazard ratios (HRs) for overall and cause-specific mortality were estimated using Cox regression models. Results: Despite glucosamine and chondroitin use appearing to be inversely associated with mortality in the minimally adjusted models, no association was observed in multivariable models (glucosamine: HR = 1.02; 95% confidence interval [CI]: 0.86-1.21, chondroitin: HR = 1.04, 95% CI: 0.87-1.25). No association with cancer mortality or other mortality rate was observed after multivariable adjustment. There was a suggestive, nonsignificant inverse association for cardiovascular-specific mortality (glucosamine HR = 0.72; 95% CI: 0.46-1.15, chondroitin: HR = 0.76; 95% CI: 0.47-1.21). Conclusion: The lack of significant relationship between glucosamine and chondroitin use and all-cause or cause-specific mortality after adjusting extensively for multiple covariates in this nationally representative adult population was in contrast to prior literature. Given the limited power to explore the cause-specific mortality, future well-powered studies will be needed to better understand the potential association with cardiovascular-specific mortality.

Association between fish oil supplements use and serum per- and polyfluoroalkyl substances (PFAS): Results from the National Health and Nutrition Examination Survey.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are widespread pollutants and classified as potentially carcinogenic to humans. Although consumption of fish, seafood, and their byproducts is a known source of dietary PFAS exposure, little is known about the association between use of fish oil supplements and PFAS. Here, we examine associations between fish oil supplement use and serum PFAS concentrations. METHODS: This analysis includes adults, ages 25 years of age and older, surveyed as part of the National Health and Nutrition Examinations Survey (NHANES). Outcomes include five serum PFAS compounds: perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorohexane sulphonic acid (PFHxS) and perfluorodecanoic acid (PFDA). To determine the association between fish oil use and log-transformed PFAS concentrations, survey-weighted linear regression was used to estimate multivariate-adjusted ratios between supplement-users' and non-users' geometric mean serum PFAS concentrations. RESULTS: No association was observed between fish oil use and PFAS. While results did not vary substantially by age, gender, study cycle, there was some indication of a potential inverse association in subgroups of interest. Specifically, an inverse association was observed between fish oil supplement use and PFOS levels in older adults, females, and in early calendar years; an inverse association was also observed between fish oil and PFNA in females and early calendar years. CONCLUSIONS: While fish oil users did not experience increased serum PFAS, there was an unexpected inverse association in some population subgroups. Further research will be needed to better understand whether this pattern reflects true differences, chance, or bias.

Patient-derived xenograft models-the future of personalised cancer treatment.

For many tumours there is a lack of randomised data from which we can guide systemic treatments. Although gene expression profiling along with proteomics has led to advances in diagnosis, classification and prognosis, our ability to target many cancers has been further limited due to a lack of therapeutic options. The use of patient-derived xenograft (PDX) models in the setting of a rare malignancy is discussed here by Kamili et al, with the successful establishment of new model systems.

The impact of new cancer drug therapies on site specialised cancer treatment activity in a UK cancer network 2014-2018.

INTRODUCTION: Drug treatment for cancer has changed dramatically over the past decade with many new drugs often with multiple applications. More recently, the detailed pathway for approval from the National Institute for Health and Care Excellence (NICE) in the UK has been simplified. To explore how these changes have impacted on systemic anti-cancer therapy tumour site-specific prescribing and workload activities, we have reviewed the prescribing records for 2014-2018 in a UK cancer network. METHODS: Information about the numbers of new systemic anti-cancer therapy drugs and NICE approvals were obtained from print editions of the British National Formulary (BNF) and the NICE website. Data on the numbers of new chemotherapy courses and individual treatment-related attendances were obtained from the cancer network Chemocare electronic prescribing system. RESULTS: During the five-year study period, there were 49 new systemic anti-cancer therapy drugs for all tumour types, and a total of 65 NICE technology approvals for solid tumour indications. Overall numbers of treatment courses increased by 40.7% and total treatment-related visits by 80.6%. There was a wide variation across tumour types with the highest number of increased visits seen for melanoma (349.3%) and prostate cancer (242.3%), but in contrast, no appreciable increases were seen for lower gastrointestinal cancers or small cell lung cancer. CONCLUSION: The study confirms the major impact of the arrival of new drug technology and positive NICE appraisals on increasing systemic anti-cancer therapy prescribing and chemotherapy unit activity. The data in this study may be of help in planning for future service delivery planning and workforce configurations.