scholarly journals Localized peripheral neuropathic pain: topical treatment with lidocaine 700 mg medicated plaster in routine clinical practice

2022 ◽  
Author(s):  
Michael A Überall ◽  
Irmgard Bösl ◽  
Els Hollanders ◽  
Ingo Sabatschus ◽  
Mariëlle Eerdekens

Aim: To provide real-world evidence for the effectiveness and tolerability of lidocaine 700 mg medicated plaster (LMP) in localized peripheral neuropathic pain (l-PNP) treatment compared with first-line oral medications (OM). Patients & methods: This was a noninterventional, retrospective 6-month cohort study in patients refractory to at least one recommended OM, using anonymized medical care data from the German Pain eRegistry. Treatment groups were matched by propensity scoring, considering seven predefined confounding factors. The primary effectiveness end point was the absolute change in average pain intensity index from baseline at weeks 4, 12 and 24 of treatment and over the treatment period. Results: A total of 3081 datasets were retained per treatment group. LMP provided superior pain reductions and significantly greater improvements in pain-related impairments of daily living and quality of life with significantly better tolerability (p < 0.001 for all parameters) than OM. Conclusion: These real-world data confirm the effectiveness and good tolerability of LMP for l-PNP treatment under routine medical care.

Author(s):  
Hannah Sievers ◽  
Angelika Joos ◽  
Mickaël Hiligsmann

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.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yiqing Zhao ◽  
Saravut J. Weroha ◽  
Ellen L. Goode ◽  
Hongfang Liu ◽  
Chen Wang

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.


2020 ◽  
Author(s):  
Zefang Tang ◽  
Yiqin Yu ◽  
Kenney Ng ◽  
Daby Sow ◽  
Jianying Hu ◽  
...  

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.


10.2196/16933 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e16933 ◽  
Author(s):  
Michelle Helena van Velthoven ◽  
Ching Lam ◽  
Caroline de Cock ◽  
Terese Stenfors ◽  
Hassan Chaudhury ◽  
...  

Background Infection with the herpes simplex virus (HSV) is common but not well understood. Furthermore, there remains a social stigma surrounding HSV that can have psychosocial implications for those infected. Despite many patients infected with HSV experiencing mild-to-severe physical symptoms, only one subeffective treatment is available. A registry collecting real-world data reported by individuals potentially infected with HSV could help patients to better understand and manage their condition. Objective This study aimed to report on the development of a registry to collect real-world data reported by people who might be infected with HSV. Methods A case study design was selected as it provides a systematic and in-depth approach to investigating the planning phase of the registry. The case study followed seven stages: plan, design, prepare, collect, analyze, create, and share. We carried out semistructured interviews with experts, which were thematically analyzed and used to build use cases for the proposed registry. These use cases will be used to generate detailed models of how a real-world evidence registry might be perceived and used by different users. Results The following key themes were identified in the interviews: (1) stigma and anonymity, (2) selection bias, (3) understanding treatment and outcome gaps, (4) lifestyle factors, (5) individualized versus population-level data, and (6) severe complications of HSV. We developed use cases for different types of users of the registry, including individuals with HSV, members of the public, researchers, and clinicians. Conclusions This case study revealed key considerations and insights for the development of an appropriate registry to collect real-world data reported by people who might be infected with HSV. Further development and testing of the registry with different users is required. The registry must also be evaluated for the feasibility and effectiveness of collecting data to support symptom management. This registry has the potential to contribute to the development of vaccines and treatments and provide insights into the impact of HSV on other conditions.


2020 ◽  
Author(s):  
Yiqing ZHAO ◽  
Saravut J Weroha ◽  
Ellen Goode ◽  
Hongfang Liu ◽  
Chen Wang

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 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 on a Foundation-tested women cancer cohort (N=196). Upon retrieval of patients’ genetic information using NLP system, we assessed completeness of genetic data captured in unstructured clinical notes according 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 clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance (VUS). 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, 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 issue 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 real-world utility of genetic information to initiate prescription of targeted therapy.


2021 ◽  
Author(s):  
OV Zhukova ◽  
AL Khokholov

The features of evaluating data from real clinical practice are discussed. Approaches to material processing for decision support in medicine and health care are also given. The development of standardized methods of analysis with the possibility of obtaining a unified indicator for assessing data from routine clinical practice, combined with the development of information technology is the direction of development of the concept of result-oriented health care. The classification of information technologies used in medicine and public health is presented. The main characteristics and functioning features of the developed software modules for automated data evaluation of real clinical practice are presented: a program for the distribution of drugs on the levels of clinical efficacy, a program to assess the effectiveness of therapy for the specified period; a program to determine the interval of clinical efficacy of drugs.


2019 ◽  
Vol 14 (1) ◽  
pp. 174-179 ◽  
Author(s):  
David C. Klonoff

Real-world evidence (RWE) is the clinical evidence about benefits or risks of medical products derived from analyzing real world data (RWD), which are data collected through routine clinical practice. This article discusses the advantages and disadvantages of RWE studies, how these studies differ from randomized controlled trials (RCTs), how to overcome barriers to current skepticism about RWE, how FDA is using RWE, how to improve the quality of RWE, and finally the future of RWE trials.


2012 ◽  
Vol 2 (3) ◽  
pp. 239-250 ◽  
Author(s):  
Till Wagner ◽  
Andrea Roth-Daniek ◽  
Angela Sell ◽  
Janice England ◽  
Kai-Uwe Kern

Yakhak Hoeji ◽  
2020 ◽  
Vol 64 (2) ◽  
pp. 136-155
Author(s):  
Hi Gin Sung ◽  
Han-Heui Park ◽  
Gyu-Won Jung ◽  
Ju-Young Shin

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