Backpack health reduces data-sharing barriers between the medical community and individuals with rare diseases.

Technology has changed the way we approach medical care: health data is constantly being generated, medical discoveries are progressing more rapidly, and individuals are more connected across the world than ever before. Backpack Health is a global personal health record platform that harnesses the power of technology to connect users to their primary health data sources, the medical community, and researchers. By syncing with existing patient portals, health data can be stored on the Backpack Health platform and easily accessed and controlled by users in one connected interface. Individuals manage and collate their current and past conditions, genetic test results, symptoms, medications, procedures, labs, and other health data. Users are empowered to disseminate their information to clinicians, researchers, foundations, and pharmaceutical and biotechnology companies they connect with through the Backpack Health application. Here, we describe how two rare disease advocacy groups, The Marfan Foundation and Project Alive, utilize Backpack Health to connect with their target populations. Through secure transfer of pseudonymized data, groups can query their members to improve understanding of clinical features and to facilitate meaningful research. Responses to the groups' surveys show strong member engagement with high completion rates and increases in new Backpack Health users when surveys are deployed. Data from these surveys have been published and used to better inform clinical outcomes for treatment trials. By connecting users directly to the foundations, clinicians, researchers, and industry partners working on their condition, Backpack Health is instrumental in fast-tracking medical discoveries and treatment for rare diseases.

Health care resource utilization and cost of severe hypoglycemia treatment in insulin-treated patients with diabetes in the United States.

BACKGROUND: Hypoglycemia is a major limiting factor in achieving glycemic control in persons with diabetes. In some instances, recovery from a severe hypoglycemia event may require health care resource utilization (HCRU), including the use of emergency medical services (EMS), visits to the emergency department (ED), and inpatient hospitalization. OBJECTIVES: To (a) describe the profiles of patients who experience severe hypoglycemic events and (b) characterize HCRU and the associated cost related to severe hypoglycemia treatment. METHODS: This retrospective, observational cohort study used administrative claims data from IBM MarketScan Research Databases. The study examined a cohort of subjects who experienced severe hypoglycemic events that involved HCRU during the 1-year index period. Baseline patient demographic data were collected according to patient profiles, such as payer type, type of diabetes, age, and type of insulin. HCRU and the associated cost data categorized by the patient profiles and care progression scenarios were described. RESULTS: 9,563 patients from the IBM MarketScan Research Databases experienced a severe hypoglycemic event during the index period and were included in the study; approximately 75% of those patients did not experience a severe hypoglycemic event in the previous year. Of the 9,563 patients in the cohort, the largest patient profile (n = 1,767, 18.5%) consisted of those who were on Medicaid, had type 2 diabetes, and used basal/bolus or premixed-only insulins. Overall, more than 90% of the index severe hypoglycemic events involved visits to the ED. EMS claims in the 24 hours before the ED visit were found for half of the severe hypoglycemic events (51.5%). CONCLUSIONS: Differences in HCRU and the associated costs for the treatment of severe hypoglycemia were observed among patients based on insurance, diabetes, and insulin types. Clinicians need to be aware of these differences. Optimizing treatment of severe hypoglycemia, specifically EMS care, and examining patient profiles to develop targeted interventions could potentially provide benefits to patients and reduce cost and resource utilization. DISCLOSURES: This study was funded by Eli Lilly and Company. All authors are employees and shareholders of Eli Lilly and Company. The data presented here have been presented in poster form at AMCP Nexus 2020 Virtual, October 19-23, 2020; ADCES Virtual Conference 2020, August 13-16, 2020; and Virtual ISPOR 2020, May 18-20, 2020.

Reasons for discontinuing insulin and factors associated with insulin discontinuation in patients with type 2 diabetes mellitus: a real-world evidence study.

BACKGROUND: Evidence suggests that insulin therapy of patients with type 2 diabetes mellitus (T2DM) is frequently discontinued. However, the reasons for discontinuing insulin and factors associated with insulin discontinuation in this patient population are not well understood. METHODS: We conducted a retrospective cohort study of adults with T2DM prescribed insulin between 2010 and 2017 at Partners HealthCare. Reasons for discontinuing insulin and factors associated with insulin discontinuation were studied using electronic medical records (EMR) data. Natural language processing (NLP) was applied to identify reasons from unstructured clinical notes. Factors associated with insulin discontinuation were extracted from structured EMR data and evaluated using multivariable logistic regression. RESULTS: Among 7009 study patients, 2957 (42.2%) discontinued insulin within 12 months after study entry. Most patients who discontinued insulin (2121 / 71.7%) had reasons for discontinuation documented. The most common reasons were improving blood glucose control (33.2%), achieved weight loss (18.5%) and initiation of non-insulin diabetes medications (16.7%). In multivariable analysis adjusted for demographics and comorbidities, patients were more likely to discontinue either basal or bolus insulin if they were on a basal-bolus regimen (OR 1.6, 95% CI 1.3 to 1.8; p <  0.001) or were being seen by an endocrinologist (OR 2.6; 95% CI 2.2 to 3.0; p <  0.001). CONCLUSIONS: In this large real-world evidence study conducted in an area with a high penetration of health insurance, insulin discontinuation countenanced by healthcare providers was common. In most cases it was linked to achievement of glycemic control, achieved weight loss and initiation of other diabetes medications. Factors associated with and stated reasons for insulin discontinuation were different from those previously described for non-adherence to insulin therapy, identifying it as a distinct clinical phenomenon.

Splicing profile by capture RNA-seq identifies pathogenic germline variants in tumor suppressor genes.

Germline variants in tumor suppressor genes (TSGs) can result in RNA mis-splicing and predisposition to cancer. However, identification of variants that impact splicing remains a challenge, contributing to a substantial proportion of patients with suspected hereditary cancer syndromes remaining without a molecular diagnosis. To address this, we used capture RNA-sequencing (RNA-seq) to generate a splicing profile of 18 TSGs (APC, ATM, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, MLH1, MSH2, MSH6, MUTYH, NF1, PALB2, PMS2, PTEN, RAD51C, RAD51D, and TP53) in 345 whole-blood samples from healthy donors. We subsequently demonstrated that this approach can detect mis-splicing by comparing splicing profiles from the control dataset to profiles generated from whole blood of individuals previously identified with pathogenic germline splicing variants in these genes. To assess the utility of our TSG splicing profile to prospectively identify pathogenic splicing variants, we performed concurrent capture DNA and RNA-seq in a cohort of 1000 patients with suspected hereditary cancer syndromes. This approach improved the diagnostic yield in this cohort, resulting in a 9.1% relative increase in the detection of pathogenic variants, demonstrating the utility of performing simultaneous DNA and RNA genetic testing in a clinical context.

Assessment of Diagnostic Outcomes of RNA Genetic Testing for Hereditary Cancer.

Importance: Performing DNA genetic testing (DGT) for hereditary cancer genes is now a well-accepted clinical practice; however, the interpretation of DNA variation remains a challenge for laboratories and clinicians. Adding RNA genetic testing (RGT) enhances DGT by clarifying the clinical actionability of hereditary cancer gene variants, thus improving clinicians' ability to accurately apply strategies for cancer risk reduction and treatment. Objective: To evaluate whether RGT is associated with improvement in the diagnostic outcome of DGT and in the delivery of personalized cancer risk management for patients with hereditary cancer predisposition. Design, Setting, and Participants: Diagnostic study in which patients and/or families with inconclusive variants detected by DGT in genes associated with hereditary breast and ovarian cancer, Lynch syndrome, and hereditary diffuse gastric cancer sent blood samples for RGT from March 2016 to April 2018. Clinicians who ordered genetic testing and received a reclassification report for these variants were surveyed to assess whether RGT-related variant reclassifications changed clinical management of these patients. To quantify the potential number of tested individuals who could benefit from RGT, a cohort of 307 812 patients who underwent DGT for hereditary cancer were separately queried to identify variants predicted to affect splicing. Data analysis was conducted from March 2016 and September 2018. Main Outcomes and Measures: Variant reclassification outcomes following RGT, clinical management changes associated with RGT-related variant reclassifications, and the proportion of patients who would likely be affected by a concurrent DGT and RGT multigene panel testing approach. Results: In total, 93 if 909 eligible families (10.2%) submitted samples for RGT. Evidence from RGT clarified the interpretation of 49 of 56 inconclusive cases (88%) studied; 26 (47%) were reclassified as clinically actionable and 23 (41%) were clarified as benign. Variant reclassifications based on RGT results changed clinical management recommendations for 8 of 18 patients (44%) and 14 of 18 families (78%), based on responses from 18 of 45 clinicians (40%) surveyed. A total of 7265 of 307 812 patients who underwent DGT had likely pathogenic variants or variants of uncertain significance potentially affecting splicing, indicating that approximately 1 in 43 individuals could benefit from RGT. Conclusions and Relevance: In this diagnostic study, conducting RNA testing resolved a substantial proportion of variants of uncertain significance in a cohort of individuals previously tested for cancer predisposition by DGT. Performing RGT might change the diagnostic outcome of at least 1 in 43 patients if performed in all individuals undergoing genetic evaluation for hereditary cancer.

Quantitative Analysis of BRCA1 and BRCA2 Germline Splicing Variants Using a Novel RNA-Massively Parallel Sequencing Assay.

Clinical genetic testing for hereditary breast and ovarian cancer (HBOC) is becoming widespread. However, the interpretation of variants of unknown significance (VUS) in HBOC genes, such as the clinically actionable genes BRCA1 and BRCA2, remain a challenge. Among the variants that are frequently classified as VUS are those with unclear effects on splicing. In order to address this issue we developed a high-throughput RNA-massively parallel sequencing assay-CloneSeq-capable to perform quantitative and qualitative analysis of transcripts in cell lines and HBOC patients. This assay is based on cloning of RT-PCR products followed by massive parallel sequencing of the cloned transcripts. To validate this assay we compared it to the RNA splicing assays recommended by members of the ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles) consortium. This comparison was performed using well-characterized lymphoblastoid cell lines (LCLs) generated from carriers of the BRCA1 or BRCA2 germline variants that have been previously described to be associated with splicing defects. CloneSeq was able to replicate the ENIGMA results, in addition to providing quantitative characterization of BRCA1 and BRCA2 germline splicing alterations in a high-throughput fashion. Furthermore, CloneSeq was used to analyze blood samples obtained from carriers of BRCA1 or BRCA2 germline sequence variants, including the novel uncharacterized alteration BRCA1 c.5152+5G>T, which was identified in a HBOC family. CloneSeq provided a high-resolution picture of all the transcripts induced by BRCA1 c.5152+5G>T, indicating it results in significant levels of exon skipping. This analysis proved to be important for the classification of BRCA1 c.5152+5G>T as a clinically actionable likely pathogenic variant. Reclassifications such as these are fundamental in order to offer preventive measures, targeted treatment, and pre-symptomatic screening to the correct individuals.

Defining a clinically meaningful effect for the design and interpretation of randomized controlled trials.

OBJECTIVE: This article captures the proceedings of a meeting aimed at defining clinically meaningful effects for use in randomized controlled trials for psychopharmacological agents. DESIGN: Experts from a variety of disciplines defined clinically meaningful effects from their perspectives along with viewpoints about how to design and interpret randomized controlled trials. SETTING: The article offers relevant, practical, and sometimes anecdotal information about clinically meaningful effects and how to interpret them. PARTICIPANTS: The concept for this session was the work of co-chairs Richard Keefe and the late Andy Leon. Faculty included Richard Keefe, PhD; James McNulty, AbScB; Robert S. Epstein, MD, MS; Shelby D. Reed, PhD; Juan Sanchez, MD; Ginger Haynes, PhD; Andrew C. Leon, PhD; Helena Chmura Kraemer, PhD; Ellen Frank, PhD, and Kenneth L. Davis, MD. RESULTS: The term clinically meaningful effect is an important aspect of designing and interpreting randomized controlled trials but can be particularly difficult in the setting of psychopharmacology where effect size may be modest, particularly over the short term, because of a strong response to placebo. Payers, regulators, patients, and clinicians have different concerns about clinically meaningful effects and may describe these terms differently. The use of moderators in success rate differences may help better delineate clinically meaningful effects. CONCLUSION: There is no clear consensus on a single definition for clinically meaningful differences in randomized controlled trials, and investigators must be sensitive to specific concerns of stakeholders in psychopharmacology in order to design and execute appropriate clinical trials.