Impact of Anti-VEGF Treatment and Patient Characteristics on Vision Outcomes in Neovascular Age-related Macular Degeneration: Up to 6-Year Analysis of the AAO IRIS® Registry.

Purpose: To evaluate anti-VEGF treatment patterns and the influence of patient demographic and clinical characteristics on up to 6-year vision outcomes in neovascular age-related macular degeneration. Design: Retrospective, multicenter, noninterventional registry study with up to 6 years of follow-up. Participants: A cohort of 254 655 eyes (226 767 patients) with first anti-VEGF injection and at least 2 years of follow-up; 160 423 eyes had visual acuity (VA) data. Methods: Anonymized patient data were collected in the United States through the IRIS® Registry (Intelligent Research in Sight). Main Outcome Measures: Changes in VA from baseline; frequency of and gaps between intravitreal anti-VEGF injections; treatment discontinuations; switching anti-VEGF agents; and influence of baseline clinical and demographic characteristics on VA. Results: After a mean VA increase of 3.0 ETDRS letters at year 1, annual decreases led to a net loss from baseline of 4.6 letters after 6 years. Patients with longer follow-ups had better baseline and follow-up VA. From a mean of 7.2 in year 1 and 5.6 in year 2, mean injections plateaued between 4.2 to 4.6 in years 3 through 6. Treatment was discontinued in 38.8% of eyes and switched in 32.3%. When adjusting for differences at baseline, every additional injection resulted in a 0.68 letter improvement from baseline to year 1; thus, multiple injections in a year have the potential to be clinically meaningful. Older age, male gender, Medicaid insurance, and not being treated by a retina specialist were associated with a higher likelihood of vision loss at year 1. Of the patients, 58.5% lost ≥ 10 letters VA at least once during follow-up, with 14.5% of patients experiencing sustained poor vision after a median of 3.4 years. Conclusions: After modest mean VA improvement with intravitreal anti-VEGF injections at year 1, patients netted a loss of VA by year 6. Injection frequency decreased over time, and this was paired with a relatively high rate of discontinuation. Modeling suggested that more frequent injections were associated with better VA. Difficulty with continuous adherence to frequent intravitreal injections may have contributed to undertreatment resulting in less-than-optimal vision outcomes. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Analysis of a Real-World Progression Variable and Related Endpoints for Patients with Five Different Cancer Types.

INTRODUCTION: We previously demonstrated that real-world progression (rwP) can be ascertained from unstructured electronic health record (EHR)-derived documents using a novel abstraction approach for patients with advanced non-small cell lung cancer (base case). The objective of this methodological study was to assess the reliability, clinical relevance, and the need for disease-specific adjustments of this abstraction approach in five additional solid tumor types. METHODS: Patients with metastatic breast cancer (mBC), advanced melanoma (aMel), small cell lung cancer (SCLC), metastatic renal cell carcinoma (mRCC), and advanced gastric/esophageal cancer (aGEC) were selected from a real-world database. Disease-specific additions to the base case were implemented as needed. The resulting abstraction approach was applied to each disease cohort to capture rwP events and dates. To provide comprehensive clinical context, real-world progression-free survival (rwPFS) and time to progression (rwTTP) were compared to real-world overall survival (rwOS), time to next treatment (rwTTNT), and time to treatment discontinuation (rwTTD). Endpoint estimates were assessed using the Kaplan-Meier method. Correlations between real-world endpoints and rwOS were calculated using Spearman's ρ. RESULTS: Additions to the base-case rwP abstraction approach were required for mBC, aMel, and SCLC. Inter-abstractor agreement for rwP occurrence, irrespective of date, ranged from 88% to 97%. Occurrence of clinically relevant downstream events (new antineoplastic systemic therapy start, antineoplastic systemic therapy end, or death relative to the rwP event) ranged from 59% (aMel) to 72% (mBC). Median rwPFS ranged from 3.7 (aMel) to 7.7 (mBC) months, and median rwTTP ranged from 4.6 (aMel) to 8.3 (mRCC) months. Correlations between rwOS and rwPFS ranged from 0.52 (aMel) to 0.82 (SCLC). The correlation between rwOS and rwTTD was often lower relative to other comparisons (range 0.40-0.62). CONCLUSION: Derivation of a rwP variable from EHR documentation is feasible and reliable across the five solid tumors. Endpoint analyses show that rwP produces clinically meaningful information.

Distributed Regression Analysis Application in Large Distributed Data Networks: Analysis of Precision and Operational Performance.

BACKGROUND: A distributed data network approach combined with distributed regression analysis (DRA) can reduce the risk of disclosing sensitive individual and institutional information in multicenter studies. However, software that facilitates large-scale and efficient implementation of DRA is limited. OBJECTIVE: This study aimed to assess the precision and operational performance of a DRA application comprising a SAS-based DRA package and a file transfer workflow developed within the open-source distributed networking software PopMedNet in a horizontally partitioned distributed data network. METHODS: We executed the SAS-based DRA package to perform distributed linear, logistic, and Cox proportional hazards regression analysis on a real-world test case with 3 data partners. We used PopMedNet to iteratively and automatically transfer highly summarized information between the data partners and the analysis center. We compared the DRA results with the results from standard SAS procedures executed on the pooled individual-level dataset to evaluate the precision of the SAS-based DRA package. We computed the execution time of each step in the workflow to evaluate the operational performance of the PopMedNet-driven file transfer workflow. RESULTS: All DRA results were precise (<10-12), and DRA model fit curves were identical or similar to those obtained from the corresponding pooled individual-level data analyses. All regression models required less than 20 min for full end-to-end execution. CONCLUSIONS: We integrated a SAS-based DRA package with PopMedNet and successfully tested the new capability within an active distributed data network. The study demonstrated the validity and feasibility of using DRA to enable more privacy-protecting analysis in multicenter studies.

Geospatial Monitoring of Body Mass Index: Use of Electronic Health Record Data Across Health Care Systems.

OBJECTIVES: The Colorado BMI Monitoring System was developed to assess geographic (ie, census tract) patterns of obesity prevalence rates among children and adults in the Denver-metropolitan region. This project also sought to assess the feasibility of a surveillance system that integrates data across multiple health care and governmental organizations. MATERIALS AND METHODS: We extracted data on height and weight measures, obtained through routine clinical care, from electronic health records (EHRs) at multiple health care sites. We selected sites from 5 Denver health care systems and collected data from visits that occurred between January 1, 2013, and December 31, 2015. We produced shaded maps showing observed obesity prevalence rates by census tract for various geographic regions across the Denver-metropolitan region. RESULTS: We identified clearly distinguishable areas by higher rates of obesity among children than among adults, with several pockets of lower body mass index. Patterns for adults were similar to patterns for children: the highest obesity prevalence rates were concentrated around the central part of the metropolitan region. Obesity prevalence rates were moderately higher along the western and northern areas than in other parts of the study region. PRACTICE IMPLICATIONS: The Colorado BMI Monitoring System demonstrates the feasibility of combining EHRs across multiple systems for public health and research. Challenges include ensuring de-duplication across organizations and ensuring that geocoding is performed in a consistent way that does not pose a risk for patient privacy.

Estimating Childhood Obesity Prevalence in Communities Through Multi-institutional Data Sharing.

CONTEXT: Although local childhood obesity prevalence estimates would be valuable for planning and evaluating obesity prevention efforts in communities, these data are often unavailable. OBJECTIVE: The primary objective was to create a multi-institutional system for sharing electronic health record (EHR) data to produce childhood obesity prevalence estimates at the census tract level. A secondary objective was to adjust obesity prevalence estimates to population demographic characteristics. DESIGN/SETTING/PARTICIPANTS: The study was set in Denver County, Colorado. Six regional health care organizations shared EHR-derived data from 2014 to 2016 with the state health department for children and adolescents 2 to 17 years of age. The most recent height and weight measured during routine care were used to calculate body mass index (BMI); obesity was defined as BMI of 95th percentile or more for age and sex. Census tract location was determined using residence address. Race/ethnicity was imputed when missing, and obesity prevalence estimates were adjusted by sex, age group, and race/ethnicity. MAIN OUTCOME MEASURE(S): Adjusted obesity prevalence estimates, overall, by demographic characteristics and by census tract. RESULTS: BMI measurements were available for 89 264 children and adolescents in Denver County, representing 73.9% of the population estimate from census data. Race/ethnicity was missing for 4.6%. The county-level adjusted childhood obesity prevalence estimate was 13.9% (95% confidence interval, 13.6-14.1). Adjusted obesity prevalence was higher among males, those 12 to 17 years of age, and those of Hispanic race/ethnicity. Adjusted obesity prevalence varied by census tract (range, 0.4%-24.7%). Twelve census tracts had an adjusted obesity prevalence of 20% or more, with several contiguous census tracts with higher childhood obesity occurring in western areas of the city. CONCLUSIONS: It was feasible to use a system of multi-institutional sharing of EHR data to produce local childhood obesity prevalence estimates. Such a system may provide useful information for communities when implementing obesity prevention programs.

Long-Term Patterns of Oral Anticancer Agent Adoption, Duration, and Switching in Patients With CML.

BACKGROUND: Oral tyrosine kinase inhibitors (TKIs) have been the standard of care for chronic myeloid leukemia (CML) since 2001. However, few studies have evaluated changes in the treatment landscape of CML over time. This study assessed the long-term treatment patterns of oral anticancer therapies among patients with CML. METHODS: This retrospective cohort study included patients newly diagnosed with CML between January 1, 2000, and December 31, 2016, from 10 integrated healthcare systems. The proportion of patients treated with 5 FDA-approved oral TKI agents-bosutinib, dasatinib, imatinib, nilotinib, and ponatinib-in the 12 months after diagnosis were measured, overall and by year, between 2000 and 2017. We assessed the use of each oral agent through the fourth-line setting. Multivariable logistic regression estimated the odds of receiving any oral agent, adjusting for sociodemographic and clinical characteristics. RESULTS: Among 853 patients with CML, 81% received an oral agent between 2000 and 2017. Use of non-oral therapies decreased from 100% in 2000 to 5% in 2005, coinciding with imatinib uptake from 65% in 2001 to 98% in 2005. Approximately 28% of patients switched to a second-line agent, 9% switched to a third-line agent, and 2% switched to a fourth-line agent. Adjusted analysis showed that age at diagnosis, year of diagnosis, and comorbidity burden were statistically significantly associated with odds of receiving an oral agent. CONCLUSIONS: A dramatic shift was seen in CML treatments away from traditional, nonoral chemotherapy toward use of novel oral TKIs between 2000 and 2017. As the costs of oral anticancer agents reach new highs, studies assessing the long-term health and financial outcomes among patients with CML are warranted.

Clinical Molecular Marker Testing Data Capture to Promote Precision Medicine Research Within the Cancer Research Network.

PURPOSE: To evaluate health care systems for the availability of population-level data on the frequency of use and results of clinical molecular marker tests to inform precision cancer care. METHODS: We assessed cancer-related molecular marker test data availability across 12 US health care systems in the Cancer Research Network. Overall, these systems provide care to a diverse population of more than 12 million people in the United States. We performed qualitative analyses of test data availability for five blood-based protein, nine germline, and 14 tissue-based tumor marker tests in each health care system's electronic health record and tumor registry using key informants, test code lists, and manual review of data types and output. We then performed quantitative analyses to estimate the proportion of patients with cancer with test utilization data and results for specific molecular marker tests. RESULTS: Health systems were able to systematically capture population-level data on all five blood protein markers, six of 14 tissue-based tumor markers, and none of the nine germline markers. Successful, systematic data capture was achievable for tests with electronic data feeds for test results (blood protein markers) or through prior manual abstraction by tumor registrars (select tumor-based markers). For test results stored in scanned image files (particularly germline and tumor marker tests), information on which test was performed and test results was not readily accessible in an electronic format. CONCLUSION: Even in health care systems with sophisticated electronic health records, there were few codified data elements available for evaluating precision cancer medicine test use and results at the population level. Health care organizations should establish standards for electronic reporting of precision medicine tests to expedite cancer research and facilitate the implementation of precision medicine approaches.

Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data Service.

Electronic health records (EHRs) provide an alternative to traditional public health surveillance surveys and administrative data for measuring the prevalence and impact of chronic health conditions in populations. As the infrastructure for secondary use of EHR data improves, many stakeholders are poised to benefit from data partnerships for regional access to information. Electronic health records can be transformed into a common data model that facilitates data sharing across multiple organizations and allows data to be used for surveillance. The Colorado Health Observation Regional Data Service, a regional distributed data network, has assembled diverse data partnerships, flexible infrastructure, and transparent governance practices to better understand the health of communities through EHR-based, public health surveillance. This article describes attributes of regional distributed data networks using EHR data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. Colorado Health Observation Regional Data Service and our experience may serve as a model for other regions interested in similar surveillance efforts. While benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.

Patient-Reported Outcome-Based Performance Measures for Older Adults with Multiple Chronic Conditions.

As measures of health care quality have become more sophisticated, the goals of patient care have expanded into helping patients optimize their functional status and well-being. Patient-reported outcome (PRO) based performance measures (PMs) can measure how well these aspects of care are being delivered and compare the performance of health care systems and different provider groups. Most PMs focus on technical quality of care or such outcomes as survival. For older adults, especially those over age 80 with multiple chronic conditions (MCC), it might be equally important or even more important to have a good quality of life. Therefore, policymakers and researchers have been particularly interested in designing PMs that reflect these patients' goals. To date, no PRO-based PMs have been formally developed or validated specifically for use in older adults with MCC. RAND analysts tested PMs that were based on two prominent instruments for assessing health-related quality of life: the Veterans RAND 36 Item Health Survey (VR-36) and the Patient-Reported Outcomes Measurement Information System 29-item (PROMIS-29) profile instrument. The PROMIS-29 is in widespread use but has undergone limited validation in a geriatric population with MCC. The study had two main aims: first, to validate the PROMIS-29 in this population, and second, to develop a better understanding of the practical use of PRO-based PMs in a geriatric population. To this end, the analysts assessed PM performance based on serial administration of the VR-36 or PROMIS-29, specifically in the MCC population studied.

Estimating the Impact of Diabetes Mellitus on Worker Productivity Using Self-Report, Electronic Health Record and Human Resource Data.

OBJECTIVE: We assessed the relationship between diabetes mellitus (DM) and measures of worker productivity, direct health care costs, and costs associated with lost productivity (LP) among health care industry workers across two integrated health care systems. METHODS: We used data from the Value Based Benefit Design Health and Wellness Study Phase II (VBD), a prospective study of employees surveyed across health systems. Survey and health care utilization data were linked to estimate LP and health care utilization costs. RESULTS: Mean marginal lost productive time per week was 0.56 hours higher for respondents with DM. Mean adjusted monthly total health care utilization costs were $467 higher for respondents with DM. CONCLUSION: The impact of DM is reflected in higher rates of LP and higher indirect costs for employers related to LP and higher health care resource use.

Distributed Data Networks That Support Public Health Information Needs.

INTRODUCTION: Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems. METHODS: We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability. RESULTS: Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability. CONCLUSION: Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

Curing Chronic Hepatitis C: A Cost Comparison of the Combination Simeprevir Plus Sofosbuvir vs. Protease-Inhibitor-Based Triple Therapy

ABSTRACT Introduction. Interferon-free, multi-direct acting antiviral (DAA) therapy for chronic hepatitis C virus (HCV) infection is highly effective and well tolerated, but costly. To gain perspective on the evolving economics of HCV therapy, we compared the cost per cure of a multi-DAA regimen with the prior standard of triple therapy. Material and methods. Patients infected with HCV genotype 1 who were treated through the University of Colorado Hepatology Clinic between May 2011 and December 2014 comprised the study population. The multi-DAA regimen of simeprevir plus sofosbuvir (SMV/SOF) was compared to the triple therapy regimen consisting of peginterferon and ribavirin, with either boceprevir or telaprevir (TT). Sustained-virologic response (SVR) rates, total costs per treatment and adverse events were recorded. Total cost per SVR were compared for the two treatments, controlling for patient demographics and clinical characteristics. Results. One hundred eighty-three patients received SMV/SOF (n = 70) or TT (n = 113). Patients receiving SMV/SOF were older, more treatment experienced, and had a higher stage of fibrosis. SVRs were 86% and 59%, average total costs per patient were $152,775 and $95,943, and average total costs per SVR were $178,237 vs. $161,813.49 for SMV/SOF and TT groups, respectively. Medication costs accounted for 98% of SMV/SOF and 85% of TT treatment costs. Conclusion. The high cure rate of multi-DAA treatment of HCV is offset by the high costs of the DAAs, such that the cost per cure from TT to multi-DAA therapy has been relatively constant. In order to cure more patients, either additional financial resources will need to be allocated to the treatment of HCV or drug costs will need to be reduced.

Curing Chronic Hepatitis C: A Cost Comparison of the Combination Simeprevir Plus Sofosbuvir vs. Protease-Inhibitor-Based Triple Therapy.

INTRODUCTION: Interferon-free, multi-direct acting antiviral (DAA) therapy for chronic hepatitis C virus (HCV) infection is highly effective and well tolerated, but costly. To gain perspective on the evolving economics of HCV therapy, we compared the cost per cure of a multi-DAA regimen with the prior standard of triple therapy. MATERIAL AND METHODS: Patients infected with HCV genotype 1 who were treated through the University of Colorado Hepatology Clinic between May 2011 and December 2014 comprised the study population. The multi-DAA regimen of simeprevir plus sofosbuvir (SMV/SOF) was compared to the triple therapy regimen consisting of peginterferon and ribavirin, with either boceprevir or telaprevir (TT). Sustained-virologic response (SVR) rates, total costs per treatment and adverse events were recorded. Total cost per SVR were compared for the two treatments, controlling for patient demographics and clinical characteristics. RESULTS: One hundred eighty-three patients received SMV/SOF (n = 70) or TT (n = 113). Patients receiving SMV/SOF were older, more treatment experienced, and had a higher stage of fibrosis. SVRs were 86% and 59%, average total costs per patient were $152,775 and $95,943, and average total costs per SVR were $178,237 vs. $161,813.49 for SMV/SOF and TT groups, respectively. Medication costs accounted for 98% of SMV/SOF and 85% of TT treatment costs. CONCLUSION: The high cure rate of multi-DAA treatment of HCV is offset by the high costs of the DAAs, such that the cost per cure from TT to multi-DAA therapy has been relatively constant. In order to cure more patients, either additional financial resources will need to be allocated to the treatment of HCV or drug costs will need to be reduced.

Predicting 5-Year Risk of RRT in Stage 3 or 4 CKD: Development and External Validation.

BACKGROUND AND OBJECTIVES: Only a minority of patients with CKD progress to renal failure. Despite the potential benefits of risk stratification in the CKD population, risk prediction models are not routinely used. Our objective was to develop and externally validate a clinically useful and pragmatic prediction model for the 5-year risk of progression to RRT in stage 3 or 4 CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used a retrospective cohort design. The development cohort consisted of 22,460 Kaiser Permanente Northwest members with stage 3 or 4 CKD (baseline 2002-2008). The validation cohort consisted of 16,553 Kaiser Permanente Colorado members with stage 3-4 CKD (baseline 2006-2008). The final model included eight predictors: age, sex, eGFR, hemoglobin, proteinuria/albuminuria, systolic BP, antihypertensive medication use, and diabetes and its complications. RESULTS: In the Northwest and Colorado cohorts, there were 737 and 360 events, and observed 5-year Kaplan-Meier risks of 4.72% (95% confidence interval [95% CI], 4.38 to 5.06) and 2.57% (95% CI, 2.30 to 2.83), respectively. Our prediction model performed extremely well in the development cohort, with a c-statistic of 0.96, an R2 of 79.7%, and good calibration. We had similarly good performance in the external validation cohort, with a c-statistic of 0.95, R2 of 81.2%, and good calibration. In the external validation cohort, the observed risk was slightly lower than the predicted risk in the highest-risk quintile. Using the top quintile of predicted risk as a cutpoint gave a sensitivity of 92.2%. CONCLUSIONS: We developed a pragmatic prediction model and risk score for predicting the 5-year RRT risk in stage 3 and 4 CKD. This model uses variables that are typically available in routine primary care settings, and can be used to help guide important decisions such as timing of referral to nephrology and fistula placement.

The Spatial Distribution of Adult Obesity Prevalence in Denver County, Colorado: An Empirical Bayes Approach to Adjust EHR-Derived Small Area Estimates.

OBJECTIVES: Measuring obesity prevalence across geographic areas should account for environmental and socioeconomic factors that contribute to spatial autocorrelation, the dependency of values in estimates across neighboring areas, to mitigate the bias in measures and risk of type I errors in hypothesis testing. Dependency among observations across geographic areas violates statistical independence assumptions and may result in biased estimates. Empirical Bayes (EB) estimators reduce the variability of estimates with spatial autocorrelation, which limits the overall mean square-error and controls for sample bias. METHODS: Using the Colorado Body Mass Index (BMI) Monitoring System, we modeled the spatial autocorrelation of adult (≥ 18 years old) obesity (BMI ≥ 30 kg m2) measurements using patient-level electronic health record data from encounters between January 1, 2009, and December 31, 2011. Obesity prevalence was estimated among census tracts with >=10 observations in Denver County census tracts during the study period. We calculated the Moran's I statistic to test for spatial autocorrelation across census tracts, and mapped crude and EB obesity prevalence across geographic areas. RESULTS: In Denver County, there were 143 census tracts with 10 or more observations, representing a total of 97,710 adults with a valid BMI. The crude obesity prevalence for adults in Denver County was 29.8 percent (95% CI 28.4-31.1%) and ranged from 12.8 to 45.2 percent across individual census tracts. EB obesity prevalence was 30.2 percent (95% CI 28.9-31.5%) and ranged from 15.3 to 44.3 percent across census tracts. Statistical tests using the Moran's I statistic suggest adult obesity prevalence in Denver County was distributed in a non-random pattern. Clusters of EB obesity estimates were highly significant (alpha=0.05) in neighboring census tracts. Concentrations of obesity estimates were primarily in the west and north in Denver County. CONCLUSIONS: Statistical tests reveal adult obesity prevalence exhibit spatial autocorrelation in Denver County at the census tract level. EB estimates for obesity prevalence can be used to control for spatial autocorrelation between neighboring census tracts and may produce less biased estimates of obesity prevalence.