A real-world study on characteristics, treatments and outcomes in US patients with advanced stage ovarian cancer.

BACKGROUND: Detailed epidemiologic descriptions of large populations of advanced stage ovarian cancer patients have been lacking to date. This study aimed to describe the patient characteristics, treatment patterns, survival, and incidence rates of health outcomes of interest (HOI) in a large cohort of advanced stage ovarian cancer patients in the United States (US). METHODS: This cohort study identified incident advanced stage (III/IV) ovarian cancer patients in the US diagnosed from 2010 to 2018 in the HealthCore Integrated Research Database (HIRD) using a validated predictive model algorithm. Descriptive characteristics were presented overall and by treatment line. The incidence rates and 95% confidence intervals for pre-specified HOIs were evaluated after advanced stage diagnosis. Overall survival, time to treatment discontinuation or death (TTD), and time to next treatment or death (TTNT) were defined using treatment information in claims and linkage with the National Death Index. RESULTS: We identified 12,659 patients with incident advanced stage ovarian cancer during the study period. Most patients undergoing treatment received platinum agents (75%) and/or taxanes (70%). The most common HOIs (> 24 per 100 person-years) included abdominal pain, nausea and vomiting, anemia, and serious infections. The median overall survival from diagnosis was 4.5 years, while approximately half of the treated cohort had a first-line time to treatment discontinuation or death (TTD) within the first 4 months, and a time to next treatment or death (TTNT) from first to second-line of about 6 months. CONCLUSIONS: This study describes commercially insured US patients with advanced stage ovarian cancer from 2010 to 2018, and observed diverse treatment patterns, incidence of numerous HOIs, and limited survival in this population.

Development of predictive models to identify advanced-stage cancer patients in a US healthcare claims database.

BACKGROUND: Although healthcare databases are a valuable source for real-world oncology data, cancer stage is often lacking. We developed predictive models using claims data to identify metastatic/advanced-stage patients with ovarian cancer, urothelial carcinoma, gastric adenocarcinoma, Merkel cell carcinoma (MCC), and non-small cell lung cancer (NSCLC). METHODS: Patients with ≥1 diagnosis of a cancer of interest were identified in the HealthCore Integrated Research Database (HIRD), a United States (US) healthcare database (2010-2016). Data were linked to three US state cancer registries and the HealthCore Integrated Research Environment Oncology database to identify cancer stage. Predictive models were constructed to estimate the probability of metastatic/advanced stage. Predictors available in the HIRD were identified and coefficients estimated by Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation to control overfitting. Classification error rates and receiver operating characteristic curves were used to select probability thresholds for classifying patients as cases of metastatic/advanced cancer. RESULTS: We used 2723 ovarian cancer, 6522 urothelial carcinoma, 1441 gastric adenocarcinoma, 109 MCC, and 12,373 NSCLC cases of early and metastatic/advanced cancer to develop predictive models. All models had high discrimination (C > 0.85). At thresholds selected for each model, PPVs were all >0.75: ovarian cancer = 0.95 (95% confidence interval [95% CI]: 0.94-0.96), urothelial carcinoma = 0.78 (95% CI: 0.70-0.86), gastric adenocarcinoma = 0.86 (95% CI: 0.83-0.88), MCC = 0.77 (95% CI 0.68-0.89), and NSCLC = 0.91 (95% CI 0.90 - 0.92). CONCLUSION: Predictive modeling was used to identify five types of metastatic/advanced cancer in a healthcare claims database with greater accuracy than previous methods.

Patterns in influenza antiviral medication use before and during the 2009 H1N1 pandemic, Vaccine Safety Datalink Project, 2000-2010.

BACKGROUND: U.S. recommendations for using influenza antiviral medications changed in response to viral resistance (to reduce adamantane use) and during the 2009 H1N1 pandemic (to focus on protecting high-risk patients). Little information is available on clinician adherence to these recommendations. We characterized population-based outpatient antiviral medication usage, including diagnosis and testing practices, before and during the pandemic. METHODS: Eight medical care organizations in the Vaccine Safety Datalink Project provided data on influenza antiviral medication dispensings from January 2000 through June 2010. Dispensing rates were explored in relation to changes in recommendations and influenza diagnosis and laboratory testing frequencies. Factors associated with oseltamivir dispensings in pandemic versus pre-pandemic periods were identified using multivariable logistic regression. RESULTS: Antiviral use changed coincident with recommendations to avoid adamantanes in 2006, to use alternatives to oseltamivir in 2008, and to use oseltamivir during the pandemic. Of 38,019 oseltamivir dispensings during the pandemic, 31% were to patients not assigned an influenza diagnosis, and 97% were to patients not tested for influenza. Oseltamivir was more likely to be dispensed in pandemic versus pre-pandemic periods to patients <25 years old and to those with underlying conditions, including chronic pulmonary disease or pregnancy (P<0.0001 for each factor in multivariable analysis). CONCLUSIONS: Antiviral medication usage patterns suggest that clinicians followed recommendations to change antiviral prescribing based on resistance and to focus on high-risk patients during the pandemic. Medications were commonly dispensed to patients without influenza diagnoses and tests, suggesting that antiviral dispensings may offer useful supplemental data for monitoring influenza incidence.

Real-time vaccine safety surveillance for the early detection of adverse events.

BACKGROUND: Rare but serious adverse events associated with vaccines or drugs are often nearly impossible to detect in prelicensure studies and require monitoring after introduction of the agent in large populations. Sequential testing procedures are needed to detect vaccine or drug safety problems as soon as possible after introduction. OBJECTIVE: To develop and evaluate a new real-time surveillance system that uses dynamic data files and sequential analysis for early detection of adverse events after the introduction of new vaccines. RESEARCH DESIGN: The Centers for Disease Control and Prevention (CDC)-sponsored Vaccine Safety Datalink Project developed a real-time surveillance system and initiated its use in an ongoing study of a new meningococcal vaccine for adolescents. Dynamic data files from 8 health plans were updated and aggregated for analysis every week. The analysis used maximized sequential probability ratio testing (maxSPRT), a new signal detection method that supports continuous or time-period analysis of data as they are collected. RESULTS: Using the new real-time surveillance system, ongoing analyses of meningococcal conjugate vaccine (MCV) safety are being conducted on a weekly basis. Two forms of maxSPRT were implemented: an analysis using concurrent matched controls, and an analysis based on expected counts of the outcomes of interest, which were estimated based on historical data. The analysis highlights both theoretical and operational issues, including how to (1) choose appropriate outcomes and stopping rules, (2) select control groups, and (3) accommodate variation in exposed:unexposed ratios between time periods and study sites. CONCLUSIONS: Real-time surveillance combining dynamic data files, aggregation of data, and sequential analysis methods offers a useful and highly adaptable approach to early detection of adverse events after the introduction of new vaccines.