Shah, Nilay D.

Objective To identify the relationships between county-level area deprivation and patterns of both opioid prescriptions and drug-poisoning mortality. Design, setting and participants For this retrospective cross-sectional study, we used the IQVIA Xponent data to capture opioid prescriptions and Centres for Disease Control and Prevention National Vital Statistics System to assess drug-poisoning mortality. The Area Deprivation Index (ADI) is a composite measure of social determinants of health comprised of 17 US census indicators, spanning four socioeconomic domains. For all US counties with available opioid prescription (2712 counties) and drug-poisoning mortality (3133 counties) data between 2012 and 2017, we used negative binomial regression to examine the association between quintiles of county-level ADI and the rates of opioid prescriptions and drug-poisoning mortality adjusted for year, age, race and sex. Primary outcome measures County-level opioid prescription fills and drug-poisoning mortality. Results Between 2012 and 2017, overall rates of opioid prescriptions decreased from 96.6 to 72.2 per 100 people, while the rates of drug-poisoning mortality increased from 14.3 to 22.8 per 100 000 people. Opioid prescription and drug-poisoning mortality rates were consistently higher with greater levels of deprivation. The risk of filling an opioid prescription was 72% higher, and the risk of drug-poisoning mortality was 36% higher, for most deprived compared with the least deprived counties (both p<0.001). Discussion Counties with greater area-level deprivation have higher rates of filled opioid prescriptions and drug-poisoning mortality. Although opioid prescription rates declined across all ADI quintiles, the rates of drug-poisoning mortality continued to rise proportionately in each ADI quintile. This underscores the need for individualised and targeted interventions that consider the deprivation of communities where people live.

Background: The National Comprehensive Cancer Network and American Society of Clinical Oncology recommend consideration of the use of echocardiography 6 to 12 months after completion of anthracycline-based chemotherapy in at-risk populations. Assessment of BNP (B-type natriuretic peptide) has also been suggested by the American College of Cardiology/American Heart Association/Heart Failure Society of America for the identification of Stage A (at risk) heart failure patients. The real-world frequency of the use of these tests in patients after receipt of anthracycline therapy, however, has not been studied previously. Methods and Results: In this retrospective study, using administrative claims data from the OptumLabs Data Warehouse, we identified 31 447 breast cancer and lymphoma patients (age ≥18 years) who were treated with an anthracycline in the United States between January 1, 2008 and January 31, 2018. Continuous medical and pharmacy coverage was required for at least 6 months before the initial anthracycline dose and 12 months after the final dose. Only 36.1% of patients had any type of cardiac surveillance (echocardiography, BNP, or cardiac imaging) in the year following completion of anthracycline therapy (29.7% echocardiography). Surveillance rate increased from 37.5% in 2008 to 42.7% in 2018 (25.6% in 2008 to 40.5% echocardiography in 2018). Lymphoma patients had a lower likelihood of any surveillance compared with patients with breast cancer (odds ratio, 0.79 [95% CI, 0.74–0.85]; P <0.001). Patients with preexisting diagnoses of coronary artery disease and arrhythmia had the highest likelihood of cardiac surveillance (odds ratio, 1.54 [95% CI, 1.39–1.69] and odds ratio, 1.42 [95% CI, 1.3–1.53]; P <0.001 for both), although no single comorbidity was associated with a >50% rate of surveillance. Conclusions: The majority of survivors of breast cancer and lymphoma who have received anthracycline-based chemotherapy do not undergo cardiac surveillance after treatment, including those with a history of cardiovascular comorbidities, such as heart failure.

610 Background: Personalized treatment for clinical T1 renal cortical masses (RCMs) should account for competing risks related to tumor and patient characteristics. Using a contemporary multi-institutional cohort, we developed treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-day complication rates for patients managed with surgery, thermal ablation (TA), and active surveillance (AS). Methods: Preoperative clinical and radiological features were collected for eligible patients aged 18-91 years treated at four academic centers from 2000-2016. Prediction models used competing risks regressions for CSM and OCM and logistic regressions for 90-day Clavien >3 complications, adjusting for tumor size as well as patient age, sex, ECOG performance status (PS), and Charlson comorbidity index (CCI). Predictions accounted for missing data using multiple imputation. Results: After excluding 25 patients with no follow-up, the cohort included 4995 patients treated with radical nephrectomy (RN, n=1270), partial nephrectomy (PN, n=2842), thermal ablation (n=479), or active surveillance (n=404). Median follow-up was 5.1 years (IQR 2.5-8.5). Predictions from the fitted model are shown in an online calculator ( https://rgulati.shinyapps.io/rcc-risk-calculator ). To illustrate the use of this calculator for a specific patient, a 70-year-old female with a 5.5 cm RCM, PS of 2, and CCI of 3 has a predicted 5-year CSM of 4-7% across treatments, 5-year OCM of 34-49%, and 90-day risk of Clavien ≥3 complications of 4%, 10%, and 6% for RN, PN, and TA respectively. Conclusions: Personalized treatment selection for cT1 RCM is challenging. We present a competing risk calculator that incorporates pretreatment features to quantify competing causes of mortality and treatment-associated complications. Pending validation, this tool may be used in clinical practice to provide patients with estimated individualized treatment-specific probabilities of competing causes of death and complication risks to facilitate shared decision-making.

Introduction Glycemic targets and glucose-lowering regimens should be individualized based on multiple factors, including the presence of comorbidities. We examined contemporary patterns of glycemic control and use of medications known to cause hypoglycemia among adults with diabetes across age and multimorbidity. Research design and methods We retrospectively examined glycosylated hemoglobin (HbA1c) levels and rates of insulin/sulfonylurea use as a function of age and multimorbidity using administrative claims and laboratory data for adults with type 2 diabetes included in OptumLabs Data Warehouse, 1 January 2014 to 31 December 2016. Comorbidity burden was assessed by counts of any of 16 comorbidities specified by guidelines as warranting relaxation of HbA1c targets, classified as being diabetes concordant (diabetes complications or risk factors), discordant (unrelated to diabetes), or advanced (life limiting). Results Among 194 157 patients with type 2 diabetes included in the study, 45.2% had only concordant comorbidities, 30.6% concordant and discordant, 2.7% only discordant, and 13.0% had ≥1 advanced comorbidity. Mean HbA1c was 7.7% among 18–44 year-olds versus 6.9% among ≥75 year-olds, and was higher among patients with comorbidities: 7.3% with concordant only, 7.1% with discordant only, 7.1% with concordant and discordant, and 7.0% with advanced comorbidities compared with 7.4% among patients without comorbidities. The odds of insulin use decreased with age (OR 0.51 (95% CI 0.48 to 0.54) for age ≥75 vs 18–44 years) but increased with accumulation of concordant (OR 5.50 (95% CI 5.22 to 5.79) for ≥3 vs none), discordant (OR 1.72 (95% CI 1.60 to 1.86) for ≥3 vs none), and advanced (OR 1.45 (95% CI 1.25 to 1.68) for ≥2 vs none) comorbidities. Conversely, sulfonylurea use increased with age (OR 1.36 (95% CI 1.29 to 1.44) for age ≥75 vs 18–44 years) but decreased with accumulation of concordant (OR 0.76 (95% CI 0.73 to 0.79) for ≥3 vs none), discordant (OR 0.70 (95% CI 0.64 to 0.76) for ≥3 vs none), but not advanced (OR 0.86 (95% CI 0.74 to 1.01) for ≥2 vs none) comorbidities. Conclusions The proportion of patients achieving low HbA1c levels was highest among older and multimorbid patients. Older patients and patients with higher comorbidity burden were more likely to be treated with insulin to achieve these HbA1c levels despite potential for hypoglycemia and uncertain long-term benefit.

AbstractThere is increasing interest in health care organizations functioning as learning health systems (LHSs) to improve the quality and efficiency of health care delivery while generating new knowledge. Individuals must be trained in associated concepts and competencies and subsequently positioned (or embedded) within the delivery system for maximum effect as they perform their scholarship. Potential researchers within LHSs come from many different training backgrounds; therefore, each LHS scholar requires a goal‐directed plan tailored to his or her needs. There are few tools available to guide development, training, or evaluation of individuals interested in becoming leaders of research in LHSs. In this paper, we present a newly developed tool for guiding the training of such researchers, the Learning Health Systems Competency Appraisal Inventory (LHS‐CAI). The LHS‐CAI is modeled after the Clinical Research Appraisal Index (CRAI) used within Clinical and Translational Science Award sites across the United States. The LHS‐CAI is a tool for trainees at all levels to use with their mentors in an interactive manner. The tool can then identify areas in which more training is needed and at what level to ensure success as a researcher within LHSs. We further modified the CRAI format to better leverage the LHS‐CAI as a key part of an LHS scholar's individual development plan. To implement the LHS‐CAI, we have identified key points within the Minnesota Learning Health System Mentored Career Development Program (MN‐LHS) at which assessment of expertise for each competency would be useful to LHS scholars, mentors, and program leaders. Scholars in this program come from various clinical and academic backgrounds but are all targeting their career trajectories toward leading embedded LHS research. They will reevaluate their expertise upon completion of the program, with comparison to baseline serving as a key program evaluation tool. The LHS‐CAI is currently being implemented with the first cohort of scholars in the MN‐LHS program.