scholarly journals Should Target Glucose Values Be Increased to Avoid Severe Hypoglycemia? Real-World Data Say “No.”

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A462-A462
Author(s):  
John Welsh ◽  
Robert Dowd ◽  
David A Price
Keyword(s):  
Real World ◽  
Diabetes Care ◽  
Treatment Guidelines ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Real World Data ◽  
Convenience Sample ◽  
Real World Evidence ◽  
Increased Risk ◽  
The Relationship

Abstract Early studies such as the Diabetes Control and Complications Trial showed a strong inverse relationship between A1C and the risk of severe hypoglycemia in type 1 diabetes. This risk has historically limited insulin therapy intensification efforts, and some treatment guidelines (e.g., Rosenzweig et al., J Clin Endocrinol Metab 105:969, 2020) suggest that A1C values <7% confer an increased risk of hypoglycemia. Nowadays, real-time continuous glucose monitoring (CGM) systems can flatten and attenuate the relationship between overall glucose control and hypoglycemia (Oliver et al., Diabetes Care 43:53, 2020). The glucose management indicator (GMI) is an estimate of A1C derived from the CGM system’s mean estimated glucose value (EGV) (Bergenstal et al., Diabetes Care 41:2275, 2018). We analyzed real-world evidence of the relationship between the GMI and exposure to hypoglycemia. Data were from an anonymized convenience sample of US-based users of the G6 CGM system (Dexcom, Inc., San Diego, CA) who used a mobile device to upload EGVs in the third quarter of 2020. Only data from people who had uploaded ≥80% of possible values were included. Each person’s GMI was calculated as GMI = 3.31 + (0.02392 × mean EGV [mg/dL]). Each person’s exposure to hypoglycemia was estimated as the percentage of EGVs <70 mg/dL or <54 mg/dL (%<70 and %<54, respectively). Patients were grouped into 6 categories according to GMI values <6.5%, 6.5 to 6.9%, 7.0 to 7.4%, 7.5 to 7.9%, 8.0 to 8.4%, and ≥8.5%. Mean %<70 mg/dL and %<54 mg/dL were both inversely correlated with GMI, decreasing monotonically as the GMI category increased. GMI category, %<70, and %<54 are as follows: (<6.5%: 5.27%, 1.13%); (6.5 to 6.9%: 2.84%, 0.59%); (7.0 to 7.4%: 1.95%, 0.41%); (7.5 to 7.9%: 1.46%, 0.31%); (8.0 to 8.4%: 1.14%, 0.25%); (≥8.5%: 0.69%, 0.17%). However, in all GMI categories except for the “<6.5%” category, the extent of hypoglycemic exposure was below the consensus targets proposed by Battelino et al. (Diabetes Care 42:1593, 2019) of <4% for EGVs <70 mg/dL and <1% for EGVs <54 mg/dL. The approach of elevating A1C targets to reduce hypoglycemia risk is not supported by real-world evidence for CGM users who have GMI or A1C values ≥6.5%. CGM users can safely strive for A1C values <7.0%.

Real-world evidence of male breast cancer (BC) patients treated with palbociclib (PAL) in combination with endocrine therapy (ET).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 1055-1055 ◽  
Author(s):  
Cynthia Huang Bartlett ◽  
Jack Mardekian ◽  
Michelle Yu-Kite ◽  
Matthew James Cotter ◽  
Sindy Kim ◽  
...  
Keyword(s):  
Real World ◽  
Randomized Clinical Trials ◽  
Treatment Guidelines ◽  
Healthcare Providers ◽  
First Line ◽  
Duration Of Treatment ◽  
Real World Data ◽  
The Real ◽  
Real World Evidence ◽  
Line Setting

1055 Background: The rarity of BC in men limits the feasibility of randomized clinical studies in this population. Treatment guidelines recommend that men with BC be treated similarly to postmenopausal women. PAL, a cyclin-dependent kinase 4/6 inhibitor, is used in men with metastatic BC (mBC) in real-world clinical practice, presenting an opportunity to utilize real-world evidence to enable healthcare providers to assess novel agents in this space. Methods: Two parallel approaches were taken. In the first approach, pharmacy and medical claims data from IQVIA Inc were retrospectively analyzed to describe the treatment patterns and duration of PAL + ET (aromatase inhibitor or fulvestrant) compared to ET in men with mBC. The second approach was a retrospective analysis of data derived from electronic health records in the Flatiron Health database to understand real-world clinical response to PAL + ET vs ET alone. Median duration of treatment (mDOT) was estimated by the Kaplan-Meier method. Results: Between Feb 2015 and Apr 2017, 12.9% (147/1139 [IQVIA dataset]) of men receiving treatment for mBC were prescribed PAL + ET for any line of therapy. The mDOT in the first-line setting was numerically longer in the PAL cohort (n=37) compared with the non-PAL cohort (n=214; 8.5 vs 4.3 mo, respectively). In particular, mDOT in the first-line setting was longer with PAL + letrozole (LET; n=26) than with LET alone (n=63; 9.4 vs 3.0 mo, respectively). In the Flatiron Health dataset between Feb 2015 and July 2017, the real-world maximum response rate in the PAL + ET cohort across all lines of therapy in the mBC setting (n=12) was 33.3% (2 complete responses [CR], 2 partial responses [PR]) vs 12.5% (0 CR, 1 PR) for the ET alone cohort (n=8). Conclusions: The real-world data sources used in this study support that men with mBC derive clinical benefit from the addition of PAL to ET. Given the challenges of conducting randomized clinical trials in men with mBC, noninterventional, real-world evidence data appear to be useful to delineate the benefit of such therapies in this setting. Funding: Pfizer.

scholarly journals Second primary malignancies in ruxolitinib-treated myelofibrosis: real-world evidence from 219 consecutive patients

2019 ◽  
Vol 3 (21) ◽  
pp. 3196-3200 ◽  
Author(s):  
Margherita Maffioli ◽  
Toni Giorgino ◽  
Barbara Mora ◽  
Alessandra Iurlo ◽  
Elena Elli ◽  
...  
Keyword(s):  
Real World ◽  
Second Primary ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence ◽  
Increased Risk ◽  
Key Points ◽  
Second Primary Malignancies

Key Points We present real-world data on all ruxolitinib-treated myelofibrosis patients in a 10-million-resident region, with a follow-up of 2 years. We found no evidence of an increased risk of developing lymphomas.

381-P: Real-World Evidence that Impaired Awareness of Hypoglycemia Increases Severe Hypoglycemia Rates in T2DM (InHypo-DM Study)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 381-P
Author(s):  
ALEXANDRIA RATZKI-LEEWING ◽  
STEWART B. HARRIS ◽  
NATALIE H. AU ◽  
SUSAN WEBSTER-BOGAERT ◽  
JUDITH B. BROWN ◽  
...  
Keyword(s):  
Real World ◽  
Severe Hypoglycemia ◽  
Real World Evidence ◽  
Impaired Awareness

878-P: Real-World Data from U.S. Patients Using a Long-Term Implantable Continuous Glucose Monitoring (CGM) System: Age Effect on Glycemic Control

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 878-P
Author(s):  
KATHERINE TWEDEN ◽  
SAMANWOY GHOSH-DASTIDAR ◽  
ANDREW D. DEHENNIS ◽  
FRANCINE KAUFMAN
Keyword(s):  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Real World ◽  
Glucose Monitoring ◽  
Age Effect ◽  
Real World Data ◽  
World Data

scholarly journals Real-world evidence: perspectives on challenges, value, and alignment of regulatory and national health technology assessment data collection requirements

2021 ◽  
Vol 37 ◽  
Author(s):  
Hannah Sievers ◽  
Angelika Joos ◽  
Mickaël Hiligsmann
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Real World ◽  
Health Technology ◽  
Practical Experience ◽  
Added Value ◽  
Real World Data ◽  
Conflicting Demands ◽  
Real World Evidence ◽  
Semistructured Interviews

Abstract Objective This study aims to assess stakeholder perceptions on the challenges and value of real-world evidence (RWE) post approval, the differences in regulatory and health technology assessment (HTA) real-world data (RWD) collection requirements under the German regulation for more safety in drug supply (GSAV), and future alignment opportunities to create a complementary framework for postapproval RWE requirements. Methods Eleven semistructured interviews were conducted purposively with pharmaceutical industry experts, regulatory authorities, health technology assessment bodies (HTAbs), and academia. The interview questions focused on the role of RWE post approval, the added value and challenges of RWE, the most important requirements for RWD collection, experience with registries as a source of RWD, perceptions on the GSAV law, RWE requirements in other countries, and the differences between regulatory and HTA requirements and alignment opportunities. The interviews were recorded, transcribed, and translated for coding in Nvivo to summarize the findings. Results All experts agree that RWE could close evidence gaps by showing the actual value of medicines in patients under real-world conditions. However, experts acknowledged certain challenges such as: (i) heterogeneous perspectives and differences in outcome measures for RWE generation and (ii) missing practical experience with RWD collected through mandatory registries within the German benefit assessment due to an unclear implementation of the GSAV. Conclusions This study revealed that all stakeholder groups recognize the added value of RWE but experience conflicting demands for RWD collection. Harmonizing requirements can be achieved through common postlicensing evidence generation (PLEG) plans and joint scientific advice to address uncertainties regarding evidence needs and to optimize drug development.

scholarly journals Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yiqing Zhao ◽  
Saravut J. Weroha ◽  
Ellen L. Goode ◽  
Hongfang Liu ◽  
Chen Wang
Keyword(s):  
Targeted Therapy ◽  
Data Quality ◽  
Real World ◽  
Genetic Information ◽  
Genetic Data ◽  
Real World Data ◽  
Rule Based ◽  
Clinical Notes ◽  
Real World Evidence ◽  
F Measure

Abstract Background Next-generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in the clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients’ genetic information and further associate treatment decisions with genetic information. Methods We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N = 196). Upon retrieval of patients’ genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients’ treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy. Results We identified seven topics in the clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance. Our rule-based system achieved a precision of 0.87, recall of 0.93 and F-measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F-measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F-measure of 0.82 for seven-topic classification. We found in result-containing sentences, the capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies. Conclusions In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes. Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate the real-world utility of genetic information to initiate a prescription of targeted therapy.

Patient Demographics and Clinical Outcomes Among Type 1 Diabetes Patients Using Continuous Glucose Monitors: Data From T1D Exchange Real-World Observational Study

2021 ◽  
pp. 193229682110497
Author(s):  
Daniel J. DeSalvo ◽  
Nudrat Noor ◽  
Cicilyn Xie ◽  
Sarah D. Corathers ◽  
Shideh Majidi ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Quality Improvement ◽  
Clinical Outcomes ◽  
Real World ◽  
Private Insurance ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Electronic Health Record Data ◽  
Diabetes Patients

Background: The benefits of Continuous Glucose Monitoring (CGM) on glycemic management have been demonstrated in numerous studies; however, widespread uptake remians limited. The aim of this study was to provide real-world evidence of patient attributes and clinical outcomes associated with CGM use across clinics in the U.S. based T1D Exchange Quality Improvement (T1DX-QI) Collaborative. Method: We examined electronic Health Record data from eight endocrinology clinics participating in the T1DX-QI Collaborative during the years 2017-2019. Results: Among 11,469 type 1 diabetes patients, 48% were CGM users. CGM use varied by race/ethnicity with Non-Hispanic Whites having higher rates of CGM use (50%) compared to Non-Hispanic Blacks (18%) or Hispanics (38%). Patients with private insurance were more likely to use CGM (57.2%) than those with public insurance (33.3%) including Medicaid or Medicare. CGM users had lower median HbA1c (7.7%) compared to nonusers (8.4%). Rates of diabetic ketoacidosis (DKA) and severe hypoglycemia were significantly higher in nonusers compared to CGM users. Conclusion: In this real-world study of patients in the T1DX-QI Collaborative, CGM users had better glycemic control and lower rates of DKA and severe hypoglycemia (SH) events, compared to nonusers; however, there were significant sociodemographic disparities in CGM use. Quality improvement and advocacy measures to promote widespread and equitable CGM uptake have the potential to improve clinical outcomes.

scholarly journals Real-world evidence on sodium-glucose cotransporter-2 inhibitor use and risk of Fournier’s gangrene

2020 ◽  
Vol 8 (1) ◽  
pp. e000985 ◽  
Author(s):  
Jeff Yufeng Yang ◽  
Tiansheng Wang ◽  
Virginia Pate ◽  
John B Buse ◽  
Til Stürmer
Keyword(s):  
Real World ◽  
Fournier’S Gangrene ◽  
Sensitivity Analyses ◽  
Standardized Mortality Ratio ◽  
Fournier's Gangrene ◽  
Diagnosis Codes ◽  
Sodium Glucose Cotransporter ◽  
Real World Evidence ◽  
Increased Risk ◽  
The Impact

BackgroundSodium-glucose cotransporter-2 inhibitors (SGLT2i) have been associated with increased occurrence of Fournier’s gangrene (FG), a rare but serious form of necrotizing fasciitis, leading to a warning from the Food and Drug Administration. Real-world evidence on FG is needed to validate this warning.MethodsWe used data from IBM MarketScan (2013–2017) to compare the incidence of FG among adult patients who initiated either SGLT2i, a dipeptidyl peptidase-4 inhibitor (DPP4i), or any non-SGLT2i antihyperglycemic medication. FG was defined using inpatient International Classification of Diseases, Ninth Edition and Tenth Edition diagnosis codes 608.83 and N49.3, respectively, combined with procedure codes for debridement, surgery, or systemic antibiotics. We estimated crude incidence rates (IRs) using Poisson regression, and crude and adjusted HRs (aHR) and 95% CIs using standardized mortality ratio-weighted Cox proportional hazards models. Sensitivity analyses examined the impact of alternative outcome definitions.ResultsWe identified 211 671 initiators of SGLT2i (n=93 197) and DPP4i (n=118 474), and 305 329 initiators of SGLT2i (n=32 868) and non-SGLT2i (n=272 461). Crude FG IR ranged from 3.2 to 3.8 cases per 100 000 person-years during a median follow-up of 0.51–0.58 years. Compared with DPP4i, SGLT2i initiation was not associated with increased risk of FG for any outcome definition, with aHR estimates ranging from 0.25 (0.04–1.74) to 1.14 (0.86–1.51). In the non-SGLT2i comparison, we observed an increased risk of FG for SGLT2i initiators when using FG diagnosis codes alone, using all diagnosis settings (aHR 1.80; 0.53–6.11) and inpatient diagnoses only (aHR 4.58; 0.99–21.21).ConclusionsNo evidence of increased risk of FG associated with SGLT2i was observed compared with DPP4i, arguably the most relevant clinical comparison. However, uncertainty remains based on potentially higher risk in the broader comparison with all non-SGLT2i antihyperglycemic agents and the rarity of FG.Trial registration numberEUPAS Register Number 30018.

scholarly journals Disease Network Delineates the Disease Progression Profile of Cardiovascular Diseases

2020 ◽  
Author(s):  
Zefang Tang ◽  
Yiqin Yu ◽  
Kenney Ng ◽  
Daby Sow ◽  
Jianying Hu ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Disease Progression ◽  
Real World ◽  
Disease Risk ◽  
Disease Network ◽  
Real World Data ◽  
Risk Models ◽  
Age Bias ◽  
Real World Evidence ◽  
Tremendous Amount

AbstractAs Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built based on EHR data from 14.3 million patients 1 to delineate the progression profiles of cardiovascular diseases (CVD). The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, we extracted a list of salient features to build a series of disease risk models based on the progression pairs in the disease network. The progCDN network can be further used to validate or explore novel disease relationships in real world data. Features with sufficient abundance and high correlation can be widely applied to train disease risk models when using EHR data.

Sign in / Sign up

Export Citation Format

Share Document