scholarly journals Causal Deep Learning on Real-world Data Reveals the Comparative Effectiveness of Anti-hyperglycemic Treatments

2021 ◽  
Author(s):  
Chinmay Belthangady ◽  
Stefanos Giampanis ◽  
Will Stedden ◽  
Paula Alves ◽  
Stephanie Chong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Comparative Effectiveness ◽  
Chronic Conditions ◽  
Treatment Guidelines ◽  
Treatment Options ◽  
Treatment Selection ◽  
Treatment Optimization ◽  
Real World Data ◽  
World Data

Abstract Type 2 Diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4 of total U.S. healthcare spending. Current treatment guidelines endorse a massive number of potential anti-hyperglycemic treatment options in various permutations and combinations. Personalized strategies for optimizing treatment selection are lacking. Real-world data from a nationwide population of over one million diabetics was analyzed to evaluate the comparative effectiveness of more than 80 different treatment strategies ranging from monotherapy up to combinations of five concomitant classes of drugs across each of 10 clinical subgroups defined by age, insulin dependence, and number of other chronic conditions. A causal deep learning approach developed on such data allows for more personalized recommendations of treatment selection. Significant differences were observed in blood sugar reduction between patients receiving high vs low ranked treatment options and that less than 2% of the population is on a highly ranked treatment. This method can be extended to explore treatment optimization of other chronic conditions.

scholarly journals Causal Deep Learning on Real-world Data Reveals the Comparative Effectiveness of Anti-hyperglycemic Treatments

2021 ◽  
Author(s):  
Chinmay Belthangady ◽  
Stefanos Giampanis ◽  
Will Stedden ◽  
Paula Alves ◽  
Stephanie Chong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Comparative Effectiveness ◽  
Chronic Conditions ◽  
Treatment Guidelines ◽  
Treatment Options ◽  
Treatment Selection ◽  
Treatment Optimization ◽  
Real World Data ◽  
World Data

Abstract Type 2 Diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4 of total U.S. healthcare spending. Current treatment guidelines endorse a massive number of potential anti-hyperglycemic treatment options in various permutations and combinations. Personalized strategies for optimizing treatment selection are lacking. We analyzed real-world data from a nationwide population of over one million diabetics to evaluate the comparative effectiveness of more than 80 different treatment strategies ranging from monotherapy up to combinations of five concomitant classes of drugs across each of 10 clinical subgroups defined by age, insulin dependence, and number of other chronic conditions. Our causal deep learning approach developed on such data allows for more personalized recommendations of treatment selection. We observe significant differences in blood sugar reduction between patients receiving high vs low ranked treatment options and that less than 2% of the population is on a highly ranked treatment. This method can be extended to explore treatment optimization of other chronic conditions.

Examination of Data, Analytical Issues and Proposed Methods for Conducting Comparative Effectiveness Research Using "Real-World Data"

2011 ◽  
Vol 17 (9 Supp A) ◽  
pp. 1-37
Author(s):  
Demissie Alemayehu ◽  
Riaz Ali ◽  
Jose Ma.J. Alvir ◽  
Joseph C. Cappelleri ◽  
Mark J. Cziraky ◽  
...  
Keyword(s):  
Comparative Effectiveness Research ◽  
Real World ◽  
Comparative Effectiveness ◽  
Effectiveness Research ◽  
Real World Data ◽  
World Data

scholarly journals Real-world data from expanded access programmes in health technology assessments: a review of NICE technology appraisals

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e052186
Author(s):  
Tobias B Polak ◽  
David GJ Cucchi ◽  
Joost van Rosmalen ◽  
Carin A Uyl-de Groot
Keyword(s):  
Resource Use ◽  
Real World ◽  
Treatment Options ◽  
Health Technology ◽  
Primary Outcome Measure ◽  
Disease Area ◽  
Real World Data ◽  
Cross Sectional ◽  
World Data ◽  
Expanded Access

ObjectivesTo quantify and characterise the usage of expanded access (EA) data in National Institute for Health and Care Excellence (NICE) technology appraisals (TAs). EA offers patients who are ineligible for clinical trials or registered treatment options, access to investigational therapies. Although EA programmes are increasingly used to collect real-world data, it is unknown if and how these date are used in NICE health technology assessments.DesignCross-sectional study of NICE appraisals (2010–2020). We automatically downloaded and screened all available appraisal documentation on NICE website (over 8500 documents), searching for EA-related terms. Two reviewers independently labelled the EA usage by disease area, and whether it was used to inform safety, efficacy and/or resource use. We qualitatively describe the five appraisals with the most occurrences of EA-related terms.Primary outcome measureNumber of TAs that used EA data to inform safety, efficacy and/or resource use analyses.ResultsIn 54.2% (206/380 appraisals), at least one reference to EA was made. 21.1% (80/380) of the TAs used EA data to inform safety (n=43), efficacy (n=47) and/or resource use (n=52). The number of TAs that use EA data remained stable over time, and the extent of EA data utilisation varied by disease area (p=0.001).ConclusionNICE uses EA data in over one in five appraisals. In synthesis with evidence from well-controlled trials, data collected from EA programmes may meaningfully inform cost-effectiveness modelling.

scholarly journals Merkel Cell Carcinoma Treatment in Finland in 1986–2016—A Real-World Data Study

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1224
Author(s):  
Helka Sahi ◽  
Jenny Their ◽  
Mika Gissler ◽  
Virve Koljonen
Keyword(s):  
Cell Carcinoma ◽  
Merkel Cell Carcinoma ◽  
Real World ◽  
Treatment Options ◽  
Merkel Cell ◽  
Real World Data ◽  
World Data ◽  
Treatment Data ◽  
Late Group ◽  
Merkel Cell Polyoma Virus

Merkel cell carcinoma (MCC) is a rare cutaneous carcinoma that has gained enormous interest since the discovery of Merkel cell polyoma virus, which is a causative oncogenic agent in the majority of MCC tumours. Increased research has focused on effective treatment options with immuno-oncology. In this study, we reviewed the real-world data on different treatments given to MCC patients in Finland in 1986–2016. We used the Finnish Cancer Registry database to find MCC patients and the Hospital Discharge Register and the Cause-of-Death Register to obtain treatment data. We identified 376 MCC patients and 33 different treatment entities and/or combinations of treatment. An increase was noted in the incidence of MCC since 2005. Therefore, the cohort was divided into two groups: the “early“ group with time of diagnosis between years 1986 and 2004 and the “late” group with time of diagnosis between 2005 and 2016. The multitude of different treatment combinations is a relatively new phenomenon; before the year 2005, only 11 treatments or treatment combinations were used for MCC patients. Our data show that combining radiation therapy with simple excision provided a survival advantage, which was, however, lost after adjustment for stage or age. Our registry study serves as a baseline treatment efficacy comparison as we move into the age of immunotherapy in MCC. Standardizing the treatment of MCC patients in Finland requires more work on awareness and multidisciplinary co-operation.

scholarly journals Deep learning from “passive feeding” to “selective eating” of real-world data

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Chong Guo ◽  
Danyao Nie ◽  
Duoru Lin ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Diagnostic System ◽  
Standard Of Care ◽  
Poor Quality ◽  
Diagnostic Systems ◽  
Real World Data ◽  
Clinical Images ◽  
World Data ◽  
Selective Eating

Abstract Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality (“passive feeding”), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system (“selective eating”). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that “selective eating” of real-world data is necessary and needs to be considered in the development of image-based AI systems.

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