How much “real world” data is needed for clinical decision-making?

UNSTRUCTURED Real-world data (RWD) collected in routine healthcare processes and transformed to real-world evidence (RWE) has become increasingly interesting within research and medical communities to enhance medical research and support regulatory decision making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which quality RWD must meet in order to be acceptable for decision making within regulatory or routine clinical decision support. An Austrian expert group led by GPMed (Gesellschaft für Pharmazeutische Medizin, Austrian Society for Pharmaceutical Medicine) reviewed drafted guidelines, published recommendations or viewpoints to derive a consensus statement on quality criteria for RWD to be used more effectively for medical research purposes beyond registry-based studies discussed in the European Medicines Agency (EMA) guideline for registry-based studies

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Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis

10.1101/2021.10.05.21264578 ◽

2021 ◽

Author(s):

Gregory M Miller ◽

Austin J Ellis ◽

Rangaprasad Sarangarajan ◽

Amay Parikh ◽

Leonardo O Rodrigues ◽

...

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Clinical Decision Making ◽

Clinical Decision ◽

Long Term Effects ◽

Real World Data ◽

World Data ◽

Antiemetic Agent ◽

Future Loss ◽

Selection Operator

Objective: The COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19 morbidity, mortality, long term effects, and therapeutic solutions. The objective of this study was to generate insights on COVID-19 mortality-associated factors and identify potential new therapeutic options for COVID-19 patients by employing artificial intelligence analytics on real-world data. Materials and Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® platform was used for network learning, inference causality and hypothesis generation to analyze 16,277 PCR positive patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of mortality for specific COVID-19 patient populations. These findings were validated by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. Results: We found that in the SARS-CoV-2 PCR positive patient cohort, early use of the antiemetic agent ondansetron was associated with increased survival in mechanically ventilated patients. Conclusions: The results demonstrate how real world COVID-19 focused data analysis using artificial intelligence can generate valid insights that could possibly support clinical decision-making and minimize the future loss of lives and resources.

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Squaring the Circle? Using Real World Data from Early Access Programs to Inform Pricing and Reimbursement Decision Making

Value in Health ◽

10.1016/j.jval.2018.04.434 ◽

2018 ◽

Vol 21 ◽

pp. S52

Author(s):

R Macaulay ◽

B Anell

Keyword(s):

Decision Making ◽

Real World ◽

Reimbursement Decision ◽

Real World Data ◽

World Data ◽

Squaring The Circle ◽

Early Access ◽

Pricing And Reimbursement ◽

Access Programs

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Awareness, Knowledge, and Utility of RCT Data vs RWE: Results From a Survey of US Cardiologists: Real-world Evidence in Clinical Decision Making

Clinical Medicine Insights Cardiology ◽

10.1177/1179546820953410 ◽

2020 ◽

Vol 14 ◽

pp. 117954682095341 ◽

Cited By ~ 1

Author(s):

Todd C Villines ◽

Mark J Cziraky ◽

Alpesh N Amin

Keyword(s):

Decision Making ◽

Clinical Practice ◽

Real World ◽

Randomized Clinical Trials ◽

Clinical Decision Making ◽

Clinical Decision ◽

The Body ◽

Representative Sampling ◽

Real World Evidence ◽

Wide Range

Real-world evidence (RWE) provides a potential rich source of additional information to the body of data available from randomized clinical trials (RCTs), but there is a need to understand the strengths and limitations of RWE before it can be applied to clinical practice. To gain insight into current thinking in clinical decision making and utility of different data sources, a representative sampling of US cardiologists selected from the current, active Fellows of the American College of Cardiology (ACC) were surveyed to evaluate their perceptions of findings from RCTs and RWE studies and their application in clinical practice. The survey was conducted online via the ACC web portal between 12 July and 11 August 2017. Of the 548 active ACC Fellows invited as panel members, 173 completed the survey (32% response), most of whom were board certified in general cardiology (n = 119, 69%) or interventional cardiology (n = 40, 23%). The survey results indicated a wide range of familiarity with and utilization of RWE amongst cardiologists. Most cardiologists were familiar with RWE and considered RWE in clinical practice at least some of the time. However, a significant minority of survey respondents had rarely or never applied RWE learnings in their clinical practice, and many did not feel confident in the results of RWE other than registry data. These survey findings suggest that additional education on how to assess and interpret RWE could help physicians to integrate data and learnings from RCTs and RWE to best guide clinical decision making.

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In-depth phenotyping for clinical stratification of Gaucher disease

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-021-02034-6 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Simona D’Amore ◽

Kathleen Page ◽

Aimée Donald ◽

Khadijeh Taiyari ◽

Brian Tom ◽

...

Keyword(s):

Real World ◽

Gaucher Disease ◽

Orthopaedic Surgery ◽

Clinical Decision Making ◽

Burden Of Illness ◽

Fragility Fractures ◽

Neurological Manifestations ◽

Real World Data ◽

World Data ◽

Advanced Therapies

Abstract Background The Gaucher Investigative Therapy Evaluation is a national clinical cohort of 250 patients aged 5–87 years with Gaucher disease in the United Kingdom—an ultra-rare genetic disorder. To inform clinical decision-making and improve pathophysiological understanding, we characterized the course of Gaucher disease and explored the influence of costly innovative medication and other interventions. Retrospective and prospective clinical, laboratory and radiological information including molecular analysis of the GBA1 gene and comprising > 2500 variables were collected systematically into a relational database with banking of collated biological samples in a central bioresource. Data for deep phenotyping and life-quality evaluation, including skeletal, visceral, haematological and neurological manifestations were recorded for a median of 17.3 years; the skeletal and neurological manifestations are the main focus of this study. Results At baseline, 223 of the 250 patients were classified as type 1 Gaucher disease. Skeletal manifestations occurred in most patients in the cohort (131 of 201 specifically reported bone pain). Symptomatic osteonecrosis and fragility fractures occurred respectively in 76 and 37 of all 250 patients and the first osseous events occurred significantly earlier in those with neuronopathic disease. Intensive phenotyping in a subgroup of 40 patients originally considered to have only systemic features, revealed neurological involvement in 18: two had Parkinson disease and 16 had clinical signs compatible with neuronopathic Gaucher disease—indicating a greater than expected prevalence of neurological features. Analysis of longitudinal real-world data enabled Gaucher disease to be stratified with respect to advanced therapies and splenectomy. Splenectomy was associated with an increased hazard of fragility fractures, in addition to osteonecrosis and orthopaedic surgery; there were marked gender differences in fracture risk over time since splenectomy. Skeletal disease was a heavy burden of illness, especially where access to specific therapy was delayed and in patients requiring orthopaedic surgery. Conclusion Gaucher disease has been explored using real-world data obtained in an era of therapeutic transformation. Introduction of advanced therapies and repeated longitudinal measures enabled this heterogeneous condition to be stratified into obvious clinical endotypes. The study reveals diverse and changing phenotypic manifestations with systemic, skeletal and neurological disease as inter-related sources of disability.

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Analyzing treatment patterns and time to the next treatment in chronic lymphocytic leukemia real-world data using automated temporal phenotyping.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e19512 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e19512-e19512

Author(s):

Kyeryoung Lee ◽

Zongzhi Liu ◽

Meng Ma ◽

Yun Mai ◽

Christopher Gilman ◽

...

Keyword(s):

Chronic Lymphocytic Leukemia ◽

Real World ◽

Clinical Decision Making ◽

Single Agent ◽

Treatment Patterns ◽

Lymphocytic Leukemia ◽

Time Interval ◽

Real World Data ◽

World Data ◽

Therapeutic Class

e19512 Background: Targeted therapy is an important treatment for chronic lymphocytic leukemia (CLL). However, optimal strategies for deploying small molecule inhibitors or antibody therapies in the real world are not well understood, largely due to a lack of outcomes data. We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. Here, the CLL treatment pattern and time to the next treatment (TTNT) were analyzed in real-world data (RWD) using patient electronic health records. Methods: We identified a CLL cohort with LOT from the Mount Sinai Data Warehouse (2003-2020). Each LOT consisted of either a single agent or combinations defined by NCCN CLL guidelines. We developed a natural language processing (NLP)-based temporal phenotyping approach to automatically identify the number of lines and therapeutic regimens. The sequence of treatment and time interval for each patient were derived from the systematic treatment data. Time to event analysis and multivariate (i.e., age, gender, race, other treatment patterns) Cox proportional hazard (CoxPH) models were used to analyze the patterns and predictors of TTNT. Results: Four hundred eleven CLL patients received 1 to 7 LOTs. Ibrutinib was the predominant 1st LOT (40.8% of patients) followed by anti-CD20-based antibody therapies and chemotherapy in 30.6 and 19.2% of patients, respectively, followed by Acalabrutinib, Venetoclax, and Idelalisib in 3.4, 2.7, and 0.7% of patients, respectively (Table 1). The 2nd to 5th LOT showed the same or similar trends. We next analyzed the TTNT in the 1st line of each therapeutic class. Acalabrutinib resulted in a longer median TTNT than Ibrutinib. Both Acalabrutinib and Ibrutinib showed longer TTNT compared to Venetoclax (median TTNTs were 742 and 598 vs. 373 days: HR = 0.23, p=0.015 and HR = 0.48, p=0.03, respectively). In addition, patients with age equal to or older than 65 showed longer TNNT (HR=0.16, p=0.016). Conclusions: Our result shows the potential of RWD usage in clinical decision making as real-world evidence reported here is consistent with results derived from clinical trial data. Linking this study to genetic data and other covariates affecting treatment outcomes may provide additional insights into the optimal sequences of the targeted therapies in CLL. Table 1: Therapeutic class and patient numbers (%) in each line.[Table: see text]

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Automated Diagnosis through Ontologies and Logical Descriptions

Engineering Effective Decision Support Technologies ◽

10.4018/978-1-4666-4002-3.ch007 ◽

2013 ◽

pp. 117-134

Author(s):

Alejandro Rodríguez-González ◽

Ángel García-Crespo ◽

Ricardo Colomo-Palacios ◽

José Emilio Labra Gayo ◽

Juan Miguel Gómez-Berbís ◽

...

Keyword(s):

Decision Making ◽

Health Systems ◽

Real World ◽

Information Age ◽

Semantic Technologies ◽

Automated Diagnosis ◽

Real World Data ◽

World Data ◽

Degree Of Maturity ◽

Automate Diagnosis

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

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