Morphologic features of prostate cancer-encased native vessels: An image analysis study.

BACKGROUND: Vasculature plays a crucial role in the progression of prostate cancer (PC). Changes to the prostatic native vessels have not been studied since 2000 when Garcia et al. demonstrated marked media hypercellularity and increased artery thickness in prostatic native arteries within PC. We aim to further evaluate and characterize prostatic native vessels with a more accurate method with the use of virtual slides and digital analysis. DESIGN: Pathologist-annotated whole-mount digital slides from 96 entirely submitted prostatectomies were annotated for PC (color-coded by Gleason) using Omero platform. A subset of 44 cases met criteria for further analysis of media thickness, cellularity, and wall thickness to lumen ratio. Cases were included based on containing ≥5 native arteries (≥100 µm diameter) encased on at least 3 sides by PC, with vessels (≥100 µm diameter) designated as controls if they were ≥ 1000 µm away from PC. Annotated vessels were segmented and processed using Matlab 2023b. Mean media thickness (corrected for oblique sections), media: lumen ratio (based on numbers of pixels), and media cellularity (nuclei count) were studied by analysis with SPSS by linear mixed model with nested random effects for subject and slide to account for repeated measures. RESULTS: Vessels encased by PC showed greater media thickness (p=0.02), cellularity (p=0.02) and wall thickness/lumen ratio (p= <0.001) compared to vessels away from PC. These values showed an increasing trend according to stage in cellularity (p=0.14), media thickness (p=0.12) and wall thickness/ lumen ratio (p= 0.33) with higher stage (pT3). A Gleason group comparison showed a borderline-significant gradewise trend when analyzing wall thickness/lumen ratio (p=0.06). Grade 5 emerged as significantly different (p=0.02) from grades 3 or 4 non-cribriform. CONCLUSIONS: Similar to the 2000 study, increased media thickness and hypercellularity of vessels encased by PC were evident compared to controls. Borderline grade-dependent increased vessel cellularity changes were seen, suggesting a possible role in PC progression; the predictive value of these changes for outcome is uncertain. Whether the etiology of changes reflects locally increased intravascular pressure of vessels within tumor should be investigated.

Fever education for caregivers in the emergency room (The FEVER study)-an interventional trial.

BACKGROUND: Despite the vast majority of fevers representing benign self-limiting illnesses, caregiver anxiety regarding fever is high. Empowering caregivers with knowledge to safely and appropriately manage fever at home has the potential to reduce demands upon healthcare services. AIM: To improve caregiver knowledge about fever and its management in children via an educational intervention. METHODS: Caregivers of children over 6 months presenting with fever to a Paediatric Emergency Department were recruited. A pre-intervention survey was completed to ascertain caregiver knowledge about fever and its management. The intervention of (i) an infographic about fever, with (ii) a short video on fever was viewed. A post-intervention survey re-assessed knowledge. The primary outcome was the correct definition of fever as a temperature ≥38 °C. RESULTS: Caregivers (n = 51) who correctly defined fever increased from 41% (n = 21) pre-intervention to 94% (n = 48) post-intervention. There was a reduction in common misconceptions about fever, including a higher fever representing a more serious infection (76% vs. 8%). Caregivers reported they were less likely to seek emergency healthcare due to the height and nature of the fever alone. CONCLUSIONS: A simple brief educational intervention can rapidly increase caregiver knowledge about fever in children. There is a continuing need for clear, easily-accessible information for caregivers on this topic. IMPACT: Parental knowledge about fever and how to manage it in their children is low. A simple brief educational intervention can significantly increase caregiver knowledge about fever. A combined written and audiovisual approach is effective and well-received by parents. Educating caregivers has the potential to improve the management of childhood fever at home and to reduce the burden on healthcare services, as well as reduce unpleasant hospital visits for children and their caregivers.

Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol.

INTRODUCTION: Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, with the objective of summarising the existing research on machine learning clinical decision support system tools in the emergency department, focusing on models that can be used for paediatric patients, where a knowledge gap exists. MATERIALS AND METHODS: The methodology used will follow the scoping study framework of Arksey and O'Malley, along with other guidelines. Machine learning clinical decision support system tools for any outcome and population (paediatric/adult/mixed) for use in the emergency department will be included. Articles such as grey literature, letters, pre-prints, editorials, scoping/literature/narrative reviews, non-English full text papers, protocols, surveys, abstract or full text not available and models based on synthesised data will be excluded. Articles from the last five years will be included. Four databases will be searched: Medline (EBSCO), CINAHL (EBSCO), EMBASE and Cochrane Central. Independent reviewers will perform the screening in two sequential stages (stage 1: clinician expertise and stage 2: computer science expertise), disagreements will be resolved by discussion. Data relevant to the research question will be collected. Quantitative analysis will be performed to generate the results. DISCUSSION: The study results will summarise the existing research on machine learning clinical decision support tools in the emergency department, focusing on models that can be used for paediatric patients. This holds the promise to identify opportunities to both incorporate models in clinical practice and to develop future models by utilising reviewers from diverse backgrounds and relevant expertise.

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence.

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Staff competence in caring for LGBTQ+ patients in the paediatric emergency department.

OBJECTIVE: This study aimed to assess the competency of paediatric emergency department (PED) multidisciplinary staff in caring for LGBTQ+ (lesbian, gay, bisexual, transgender, queer/questioning, + inclusive of all identities) adolescents. DESIGN: This was an observational study within which participants were required to complete the LGBT-Development of Clinical Skills Scale self-assessment tool of clinical competence. SETTING: It was conducted across three PEDs and one urgent care centre pertaining to the Children's Health Ireland healthcare group. PARTICIPANTS: Doctors, nurses and healthcare workers were eligible to participate. EXCLUSION CRITERIA: non-front facing staff; prior completion of an eLearning module intended to serve as a future educational intervention. MAIN OUTCOME MEASURES: Participants were assessed on: (1) attitudinal awareness towards LGBTQ+ individuals; (2) knowledge of LGBTQ+ health issues and (3) clinical preparedness in caring for LGBTQ+ patients. Each domain is scored out of a maximum of 7 points. RESULTS: 71 eligible participants completed the study. 40/71 (56%) were doctors, and 31/71 (44%) were nurses. The mean score for attitudinal awareness was 6.54/7 (SD 0.59), indicating overall positive attitudes. The mean score for knowledge was lower (5.34/7, SD 1.03) and lowest for clinical preparedness (3.39/7, SD 0.94). Participants were less confident in caring for transgender than LGB patients and scored very low when asked if they had received adequate training in caring for transgender young people (2.11/7). CONCLUSIONS: This study demonstrates positive attitudes towards LGBTQ+ patients among PED staff. However, there was a gap in knowledge and clinical preparedness. Increased training in caring for LGBTQ+ young people is necessary.

Autoimmune conditions and pancreatic cancer risk in older American adults.

Pancreatic cancer (PC) is highly fatal, and its incidence is increasing in the United States. Population-based registry studies suggest associations between a few autoimmune conditions and PC risk, albeit based on a relatively small number of cases. We conducted a population-based, nested case-control study to examine the associations between autoimmune conditions and PC risk within the Surveillance, Epidemiology, and End Results Program (SEER)-Medicare population. Incident primary malignant PC cases (n = 80 074) were adults ≥66 years and diagnosed between 1992 and 2015. Controls (n = 320 296) were alive at the time cases were diagnosed and frequency-matched to cases (4:1 ratio) by age, sex, and year of diagnosis. We used multivariable-adjusted, unconditional logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for 45 autoimmune conditions identified from Medicare claims. Eight autoimmune conditions including ankylosing spondylitis (OR = 1.45; 95% CI: 1.14-1.84), Graves' disease (OR = 1.18; 95% CI: 1.03-1.34), localized scleroderma (OR = 1.27; 95% CI: 1.06-1.52), pernicious anemia (OR = 1.08; 95% CI: 1.02-1.14), primary sclerosing cholangitis (OR = 1.37; 95% CI: 1.18-1.59), pure red cell aplasia (OR = 1.31; 95% CI: 1.16-1.47), type 1 diabetes (OR = 1.11; 95% CI: 1.07-1.15), and ulcerative colitis (OR = 1.18; 95% CI: 1.07-1.31) were associated with increased PC risk (false discovery rate-adjusted P values <.10). In subtype analyses, these conditions were associated with pancreatic ductal adenocarcinoma, whereas only ulcerative colitis was associated with pancreatic neuroendocrine tumors. Our results support the hypothesis that autoimmune conditions may play a role in PC development.

Diagnostic test accuracy of dipstick urinalysis for diagnosing urinary tract infection in febrile infants attending the emergency department.

OBJECTIVE: To report the diagnostic test accuracy of dipstick urinalysis for the detection of urinary tract infections (UTIs) in febrile infants aged 90 days or less attending the emergency department (ED). DESIGN: Retrospective cohort study. PATIENTS: Febrile infants aged 90 days or less attending between 31 August 2018 and 1 September 2019. MAIN OUTCOME MEASURES: The sensitivity, specificity and predictive values of dipstick urinalysis in detecting UTIs defined as growth of ≥100 000 cfu/mL of a single organism and the presence of pyuria (>5 white blood cells per high-power field). SETTING: Eight paediatric EDs in the UK/Ireland. RESULTS: A total of 275 were included in the final analysis. There were 252 (92%) clean-catch urine samples and 23 (8%) were transurethral bladder catheter samples. The median age was 51 days (IQR 35-68.5, range 1-90), and there were 151/275 male participants (54.9%). In total, 38 (13.8%) participants had a confirmed UTI. The most sensitive individual dipstick test for UTI was the presence of leucocytes. Including 'trace' as positive resulted in a sensitivity of 0.87 (95% CI 0.69 to 0.94) and a specificity of 0.73 (95% CI 0.67 to 0.79). The most specific individual dipstick test for UTI was the presence of nitrites. Including trace as positive resulted in a specificity of 0.91 (95% CI 0.86 to 0.94) and a sensitivity of 0.42 (95% CI 0.26 to 0.59). CONCLUSION: Point-of-care urinalysis is moderately sensitive and highly specific for diagnosing UTI in febrile infants. The optimum cut-point to for excluding UTI was leucocytes (1+), and the optimum cut-point for confirming UTI was nitrites (trace). TRIAL REGISTRATION NUMBER: NCT04196192.

Development of a low-dimensional model to predict admissions from triage at a pediatric emergency department.

Objectives: This study aims to develop and internally validate a low-dimensional model to predict outcomes (admission or discharge) using commonly entered data up to the post-triage process to improve patient flow in the pediatric emergency department (ED). In hospital settings where electronic data are limited, a low-dimensional model with fewer variables may be easier to implement. Methods: This prognostic study included ED attendances in 2017 and 2018. The Cross Industry Standard Process for Data Mining methodology was followed. Eligibility criteria was applied to the data set, splitting into 70% train and 30% test. Sampling techniques were compared. Gradient boosting machine (GBM), logistic regression, and naïve Bayes models were created. Variables of importance were obtained from the model with the highest area under the curve (AUC) and used to create a low-dimensional model. Results: Eligible attendances totaled 72,229 (15% admission rate). The AUC was 0.853 (95% confidence interval [CI], 0.846-0.859) for GBM, 0.845 (95% CI, 0.838-0.852) for logistic regression and 0.813 (95% CI, 0.806-0.821) for naïve Bayes. Important predictors in the GBM model used to create a low-dimensional model were presenting complaint, triage category, referral source, registration month, location type (resuscitation/other), distance traveled, admission history, and weekday (AUC 0.835 [95% CI, 0.829-0.842]). Conclusions: Admission and discharge probability can be predicted early in a pediatric ED using 8 variables. Future work could analyze the false positives and false negatives to gain an understanding of the implementation of these predictions.

Hemochromatosis, Iron Overload-Related Diseases, and Pancreatic Cancer Risk in the Surveillance, Epidemiology, and End Results (SEER)-Medicare.

BACKGROUND: Experimental studies suggest that iron overload might increase pancreatic cancer risk. We evaluated whether prediagnostic hemochromatosis and iron-overload diseases, including sideroblastic and congenital dyserythropoietic anemias, and non-alcoholic-related chronic liver disease (NACLD) were associated with pancreatic cancer risk in older adults. METHODS: We conducted a population-based, case-control study within the U.S. Surveillance, Epidemiology, and End Results Program (SEER)-Medicare linked data. Incident primary pancreatic cancer cases were adults > 66 years. Controls were alive at the time cases were diagnosed and matched to cases (4:1 ratio) by age, sex, and calendar year. Hemochromatosis, iron-overload anemias, and NACLD were reported 12 or more months before pancreatic cancer diagnosis or control selection using Medicare claims data. Adjusted unconditional logistic regression models were used to calculate ORs and 95% confidence intervals (CI) between hemochromatosis, sideroblastic and congenital dyserythropoietic anemias, NACLD, and pancreatic cancer. RESULTS: Between 1992 and 2015, 80,074 pancreatic cancer cases and 320,296 controls were identified. Overall, we did not observe statistically significant associations between hemochromatosis, sideroblastic anemia, or congenital dyserythropoietic anemia and pancreatic cancer; however, sideroblastic anemia was associated with later primary pancreatic cancer (OR, 1.30; 95% CI, 1.03-1.64). NACLD was associated with first (OR, 1.10; 95% CI, 1.01-1.19), later (OR, 1.17; 95% CI, 1.02-1.35), and all (OR, 1.12; 95% CI, 1.04-1.20) pancreatic cancer. CONCLUSIONS: Overall hemochromatosis and iron-overload anemias were not associated with pancreatic cancer, whereas NACLD was associated with increased risk in this large study of older adults. IMPACT: These results partly support the hypothesis that iron-overload diseases increase pancreatic cancer risk.

Structures of paediatric pain management: a PERUKI service evaluation study.

Background: Pain is very common in childhood emergency department (ED) attendances, but is under-recognised and undertreated. Sequential national paediatric analgesia audits demonstrate suboptimal outcomes in several domains. The Donabedian framework examines the structures, processes and outcomes to evaluate quality of care. To date there has been no network-level exploration of structures supporting analgesic practices or attempts to address failure to attain national standards. Objective: To benchmark current variation in assessment and management of childhood pain at network level. Methods: Online survey distributed between December 2016 and January 2017 exploring health system structures including pain score tools, pain assessment/protocols, training, practice guidelines and analgesic agent usage. We explored structures, processes and outcomes to identify interventions, and their potential effectiveness and feasibility. Results: In total 95% (38/40 sites) responded, including 25 tertiary (66%) and 13 secondary hospitals (34%), with a total annual paediatric ED census of 1 225 000 (range 11 500-65 000). Availability of analgesics varied included topical wound anaesthesia in 29/38 sites (76%), oral diclofenac sodium in 22/38 sites (58%) and tramadol in 16/38 sites (42%). Pain assessment was mandatory in initial assessment in 34/38 sites (89%), and 18/38 sites had a policy on frequency of pain assessment (47%). Local guidance aligned with national guidance in 21/38 sites (55%). There was no staff training at induction/orientation in 14/38 sites (37%) and no mandatory competencies in pain management in 23/38 sites (61%). Play specialist services were available in 21/38 sites (55%). Conclusion: Despite national guidance and recommendations from multiple audits, there are substantial variations in structures relating to pain assessment and management across sites. The lack of uniformity is a likely root cause for the persistent suboptimal practices identified by serial national audits. A whole system and person-centred approach to improving pain outcomes by utilising effective interventions seeks to improve paediatric pain outcomes.

Predicting Admissions From a Paediatric Emergency Department - Protocol for Developing and Validating a Low-Dimensional Machine Learning Prediction Model.

Introduction: Patients boarding in the Emergency Department can contribute to overcrowding, leading to longer waiting times and patients leaving without being seen or completing their treatment. The early identification of potential admissions could act as an additional decision support tool to alert clinicians that a patient needs to be reviewed for admission and would also be of benefit to bed managers in advance bed planning for the patient. We aim to create a low-dimensional model predicting admissions early from the paediatric Emergency Department. Methods and Analysis: The methodology Cross Industry Standard Process for Data Mining (CRISP-DM) will be followed. The dataset will comprise of 2 years of data, ~76,000 records. Potential predictors were identified from previous research, comprising of demographics, registration details, triage assessment, hospital usage and past medical history. Fifteen models will be developed comprised of 3 machine learning algorithms (Logistic regression, naïve Bayes and gradient boosting machine) and 5 sampling methods, 4 of which are aimed at addressing class imbalance (undersampling, oversampling, and synthetic oversampling techniques). The variables of importance will then be identified from the optimal model (selected based on the highest Area under the curve) and used to develop an additional low-dimensional model for deployment. Discussion: A low-dimensional model comprised of routinely collected data, captured up to post triage assessment would benefit many hospitals without data rich platforms for the development of models with a high number of predictors. Novel to the planned study is the use of data from the Republic of Ireland and the application of sampling techniques aimed at improving model performance impacted by an imbalance between admissions and discharges in the outcome variable.

Population-based utilization and costs associated with tyrosine kinase inhibitors for first-line treatment of chronic myelogenous leukemia among elderly patients.

BACKGROUND: Following approval of imatinib, a breakthrough tyrosine kinase inhibitor (TKI), survival significantly improved by more than 20% since 2001 among treated chronic myelogenous leukemia (CML) patients. Subsequently, more expensive second-generation TKIs with varying selectivity profiles have been approved. Population-based studies are needed to evaluate the real-world utilization of TKI therapies, particularly given their escalating costs and recommendations for maintenance therapy. OBJECTIVE: To assess the utilization patterns of first-line TKIs, overall and by specific agent, among elderly CML patients in the United States, and the cost implications. METHODS: CML patients aged 65 years and older at diagnosis between 2007 and 2015 were identified from population-based cancer registries in the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database. The percentage of CML patients receiving imatinib, dasatinib, or nilotinib within the first year of diagnosis was calculated along with time to first-line treatment initiation. Bivariate comparisons and Cox proportional hazards models were used to identify factors associated with TKI initiation. Average monthly patient responsibility, including patient out-of-pocket (OOP) costs, stratified by Part D low-income subsidy (LIS) status were also calculated. RESULTS: Among the 1,589 CML patients included, receipt of any TKI within 1 year of diagnosis increased from 66.2% to 78.9%. In 2015, the distribution of first-line TKI therapies was 41.3% imatinib, 28.3% dasatinib, and 9.3% nilotinib. Almost 60% of patients initiated TKI treatment within 3 months of diagnosis. Multivariable analysis indicated that TKI use in the first year was lower among the very elderly (aged > 75 years vs. 65-69 years: HR = 0.72; 95% CI = 0.63-0.83), patients with more comorbidities (Hierarchical Condition Category risk score > 2 vs. HR = 0.74, 95% CI = 0.62-0.88), and patients ineligible for LIS (HR = 0.75; 95% CI = 0.65-0.87). Average monthly patient OOP cost was significantly lower for LIS-eligible versus LIS-ineligible patients: imatinib (2016: $12 vs. $487), dasatinib (2016: $34 vs. $557), and nilotinib (2016: $1 vs. $526). CONCLUSIONS: TKI use has increased significantly since 2007. While imatinib remained the most frequently prescribed first-line agent, by 2015 newer TKIs represented one third of the market share. Utilization patterns indicated persistent age, comorbidity, and financial barriers. TKI use is indicated for long-term therapy, and increased adoption of newer, more expensive agents raises concerns about the sustained affordability of CML treatment, particularly among unsubsidized patients. DISCLOSURES: No outside funding supported this study. There are no reported conflicts of interest.

The utility of ketones at triage: a prospective cohort study.

OBJECTIVE: To establish the relationship between serum point-of-care (POC) ketones at triage and moderate-to-severe dehydration based on the validated Gorelick Scales. DESIGN, SETTING AND PATIENTS: Prospective unblinded study from April 2016 to February 2017 in a paediatric emergency department. Patients aged 1 month to 5 years, with vomiting and/or diarrhoea and/or decreased intake with signs of moderate or severe dehydration or clinical concern for hypoglycaemia were eligible. MAIN OUTCOME MEASURES: The primary outcome was to describe the relationship between triage POC ketones to the two Gorelick Scales. Secondary outcomes were to examine the response of ketone levels to fluid/glucose administration and patient disposition. RESULTS: One-hundred and ninety-eight patients were included; median age 1.8 years. The median triage ketones were 4.6 (IQR 2.8-5.6) mmol/L. A weak correlation was identified between triage ketones and the 10-point Gorelick Scale (Spearman's ρ=0.217, p=0.002), however no correlation between triage ketones and the 4-point Gorelick Scale was identified. Those admitted had median triage ketones of 5.2 (IQR 4-6) mmol/L and repeat ketones of 4.6 (IQR 3.3-5.7) mmol/L compared with 4.2 (IQR 2.4-5.3) mmol/L and 2.9 (IQR 1.6-4.2) mmol/L in those discharged home. CONCLUSION: No correlation between triage POC ketones and the 4-point Gorelick Scale was established. POC ketones at triage have poor accuracy for predicting hospital admission. The elevated profile of POC ketones in non-diabetic children with acute illness suggests a potential target of tailored treatments for further research.

Cancer-Attributable Medical Costs for Colorectal Cancer Patients by Phases of Care: What Is the Effect of a Prior Cancer History?

Medical care costing studies have excluded patients with a prior cancer history. This study aims to update methods for estimating medical care costs attributable to cancer and to evaluate the effect of a prior history of cancer on costs for colorectal cancer (CRC) patients. We used Surveillance, Epidemiology, and End Results (SEER)-Medicare data and matched cancer patients to controls without cancer to estimate cancer-attributable costs by phases of care using Medicare 2007-2013 claims. CRC annualized average cancer-attributable costs were $56.0 K, $5.3 K, $92.5 K, and $24.3 K in the initial, continuing, and end-of-life cancer and noncancer death phases, respectively, in 2014 dollars. Costs were higher for patients diagnosed with more advanced stage, younger ages, and nonwhite races. Costs for patients with prior cancers were consistently higher than patients without prior cancers, especially in the continuing (4.9 K vs 7.2 K) and end-of-life noncancer death (22.7 K vs 30.0 K). Our CRC costs improve previous estimates by using more recent data and updated methods.

Home Health Use Following a Cancer Diagnosis Among Patients Enrolled in Medicare Advantage and Traditional Medicare: Findings From the Newly Linked SEER-Medicare and Home Health OASIS Data.

BACKGROUND: This article describes characteristics of patients receiving home health following an initial cancer diagnosis, comparing those enrolled in Medicare Advantage (MA) and Traditional Medicare (TM), using the newly linked 2010-2014 National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER)-Medicare and home health Outcome and Assessment Information Set (OASIS) data. METHODS: We identified SEER-Medicare beneficiaries with at least one OASIS assessment within 3 months of cancer diagnosis in 2010-2014, and summarized their demographic and clinical characteristics. Demographic and diagnostic data were obtained from the SEER-Medicare data, while further details about cognitive status, mood, function, and medical history were obtained from OASIS. We assessed differences between MA and TM beneficiaries using chi-square tests for independence, t-tests, and Kruskal-Wallis tests. RESUTLS: We identified 104 023 patients who received home health within 3 months of cancer diagnosis: 81 587 enrolled in TM and 22 436 enrolled in MA. TM cancer patients had higher unadjusted rates of home health use than MA patients (16.3% vs 10.3%, P < .001). TM cancer patients receiving home health had more limitations in their cognitive function than their MA counterparts and longer lengths of service (mean = 42.2 days vs 39.4 days, P < .001; median = 27 vs 26 days, interquartile range = 42). CONCLUSION: This study demonstrates the large number of cancer patients in the SEER-Medicare-OASIS data and describes characteristics for TM and MA patients. These newly linked data can be used to assess home health care among older patients with cancer.

Sensitivity of Medicare Data to Identify Oncologists.

BACKGROUND: Health services researchers have studied how care from oncologists impacts treatment and outcomes for cancer patients. These studies frequently identify physician specialty using files from the Center for Medicare and Medicaid Services (CMS) or the American Medical Association (AMA). The completeness of the CMS data resources, individually or combined, to identify oncologists is unknown. This study assessed the sensitivity of CMS data to capture oncologists included in the AMA Physician Masterfile. METHODS: Oncologists were identified from three CMS data resources: physician claims, the National Plan and Provider Enumeration System Registry, and the Medicare Data on Provider Practice and Specialty file. CMS files and AMA data were linked using a unique physician identifier. Sensitivity to identify any oncologists, radiation oncologists (ROs), surgical oncologists (SOs), and medical oncologists (MOs) was calculated for individual and combined CMS files. For oncologists in the AMA data not identified as oncologists in the CMS data, their CMS specialty was assessed. RESULTS: Individual CMS files each captured approximately 83% of the 17 934 oncologists in the AMA Masterfile; combined CMS files captured 90.4%. By specialty, combined CMS data captured 98.2% of ROs, 89.3% of MOs, and 70.1% of SOs. For ROs and SOs in the AMA data not identified as oncologists in the CMS data, their CMS specialty was usually similar to the AMA subspecialty; ROs were radiologists and SOs were surgeons. CONCLUSION: Using combined files from CMS identified most ROs and MOs found in the AMA, but not most SOs. Determining whether to use the AMA data or CMS files for a particular research project will depend on the specific research question and the type of oncologist included in the study.

An Evaluation of the Utility of Big Data to Supplement Cancer Treatment Information: Linkage Between IQVIA Pharmacy Database and the Surveillance, Epidemiology, and End Results Program.

Oral anticancer medications (OAMs) are increasingly utilized. We evaluated the representativeness and completeness of IQVIA, a large aggregator of pharmacy data, for breast cancer, colon cancer, chronic myeloid leukemia, and myeloma cases diagnosed in six Surveillance, Epidemiology, and End Results Program (SEER) registries between 2007 and 2011. Patient's SEER and SEER-Medicare data were linked and compared with IQVIA pharmacy data from 2006 to 2012 for specific OAMs. Overall, 67.6% of SEER cases had a pharmacy claim in IQVIA during the treatment assessment window. This varied by location, race and ethnicity, and insurance status. IQVIA consistently identified fewer cases who received an OAM of interest than SEER-Medicare. The difference was least pronounced for breast cancer agents and most pronounced for myeloma agents. The IQVIA pharmacy database included a large portion of persons in the SEER areas. Future studies should assess receipt of OAMs for other cancer sites and in different SEER registries.

Using the SEER-Medicare Data to Assess Incident Chronic Myeloid Leukemia and Bladder Cancer Cases Missed by Cancer Registries.

The growing use of oral systemic therapies and transition of some cancer treatments to the outpatient setting makes capturing all cancer case patients more difficult. We aim to develop algorithms to identify potentially missed incident case patients and estimate impact on incidence rates. We reviewed claims from SEER-Medicare 5% noncancer control patient sample to identify potentially missed chronic myeloid leukemia (CML) and bladder case patients based on diagnosis codes, cancer-related treatments, and oncology consultations. Observed rates of definite missed CML and definite and probable missed bladder case patients were calculated and the impact of missed case patients of these two cancers on SEER 65+ incidence rates were estimated. From 2008 to 2015, the algorithm estimated 781 definite CML case patients missed, increasing the number by 10.7%. From 2007 to 2015, the algorithm estimated 4629 definite and 5772 probable bladder case patients missed, increasing the number by 3.8% to 8.1%. Our algorithms offer potential methods for identifying missed case patients and validating the completeness of cancer registries.