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

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.

Quality criteria for Real-World Data in pharmaceutical research and healthcare decision making. An Austrian Expert Consensus (Preprint)

2021 ◽  
Author(s):  
Peter Klimek ◽  
Dejan Baltic ◽  
Martin Brunner ◽  
Alexander Degelsegger-Marquez ◽  
Gerhard Garhöfer ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Research ◽  
Real World ◽  
Pharmaceutical Medicine ◽  
Pharmaceutical Research ◽  
Quality Criteria ◽  
Clinical Decision ◽  
European Medicines Agency ◽  
Real World Data ◽  
World Data

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

scholarly journals In-depth phenotyping for clinical stratification of Gaucher disease

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.

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

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]

The future of research in hematology: Integration of conventional studies with real-world data and artificial intelligence

Blood Reviews ◽  
2021 ◽  
pp. 100914
Author(s):  
Francesco Passamonti ◽  
Giovanni Corrao ◽  
Gastone Castellani ◽  
Barbara Mora ◽  
Giulia Maggioni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
The Future

Answering real-world questions using real-world data: Understanding dynamic treatment decisions and outcomes in metastatic colorectal cancer (mCRC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18061-e18061
Author(s):  
Hui-Li Wong ◽  
Koen Degeling ◽  
Azim Jalali ◽  
Jeremy David Shapiro ◽  
Suzanne Kosmider ◽  
...  
Keyword(s):  
Simulation Model ◽  
Data Visualization ◽  
Real World ◽  
Clinical Decision Making ◽  
Discrete Event ◽  
Routine Practice ◽  
Real World Data ◽  
Specific Patient ◽  
World Data ◽  
Wide Range

e18061 Background: The wide range of possible combinations and sequences available for mCRC treatment presents a major challenge to clinicians, who need to determine the optimal approach for an individual patient or patient subset. In the absence of clinical trial evidence, real world data are an increasingly valuable resource that can be utilized not only to understand treatment patterns and outcomes in routine practice, but also to define an optimal treatment strategy for individual patients across multiple lines of therapy. Methods: Real world data from an Australian mCRC registry were used to develop an interactive data visualization tool that displays treatment variation, customizable to different levels of detail and specific patient subsets, based on patient and disease characteristics. Next, a discrete event simulation model was developed to predict progression-free (PFS) and overall survival (OS) for first line palliative treatment with doublet chemotherapy alone or with bevacizumab, based on data of 867 patients that were treated accordingly. Results: Of 2694 Australian patients enrolled, 2057 (76%) started 1st line treatment with chemotherapy and/or a biologic agent, 1087 (40%) and 428 (16%) received 2nd and 3rd line therapy, respectively. Combined, these 3 lines of treatment accounted for 733 unique sequences. After recoding treatment to the most intensive chemotherapy and the first exposed biologic, 472 unique sequences remained. In exploratory analyses, the simulation model estimated that median 1st line PFS (95% CI) of 219 (25%) patients could be improved from 175 (156, 199) to 269 days (247, 293) by targeting a different treatment. Conclusions: This was an initial exploration of the potential for data visualization and simulation modeling to inform understanding of practice in mCRC and to guide clinical decision making. Such tools allow clinicians and health system providers to define variation in practice patterns and to identify opportunities to improve care and outcomes. Ultimately, the aim is to improve the delivery of personalized cancer care, where other applications such as conditional survival and cost-effectiveness analyses may be useful.

Stratification of patients by tumor type using molecular profiling in real-world data.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19262-e19262
Author(s):  
Alyssa Antonopoulos ◽  
Elizabeth Eldridge ◽  
George Managadze ◽  
Elia Stupka ◽  
Hakim Lakhani ◽  
...  
Keyword(s):  
Real World ◽  
Clinical Decision Making ◽  
Single Gene ◽  
Data Access ◽  
Clinical Decision ◽  
Molecular Data ◽  
Tumor Type ◽  
Real World Data ◽  
Number Of Patients ◽  
Biomarker Data

e19262 Background: While Next-Generation Sequencing (NGS) tests become increasingly more common for diagnosis, molecular characterization, and treatment, a significant amount of molecular data derives from single-gene or analyte tests. Single gene test information is stored in disparate sources including electronic medical record (EMR) and data access for clinical use remains a challenge. A solution that harmonizes biomarker data beyond standard NGS-centric data and linked to rich clinical data is required for the complete patient picture. Methods: Health Catalyst’s extended real-world database, Touchstone includes a molecular data mart that integrates data from provider and life sciences proprietary NGS panels, Laboratory Information Systems, and other repositories. A portion of the data is derived from single-gene tests documented in the EMR. Biomarker data from EMRs was extracted from six health systems via a proprietary pipeline for extracting biomarker data. The algorithm relies on a curated ontology for molecular terms and publicly available terminologies for human genetics. Minor transformations extract pertinent variant information where available to harmonize with NGS-level data. Results: Over 44 thousand molecular labs from over 24 thousand patients were identified with this method. The oncology classes for which molecular data was identified in the greatest number of patients include skin, hematological, breast, digestive, and lung cancers (Table). PRTN3, EGFR, BRAF, JAK2, ERBB2, and KRAS are among the most commonly tested genes. Conclusions: Integrated real-world clinical and biomarker data from single gene tests can inform clinical decision-making and support clinical trial recruitment across a broader set of patient population. [Table: see text]

Verification of statistical oncological endpoints on encrypted data: Confirming the feasibility of real-world data sharing without the need to reveal protected patient information.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18725-e18725
Author(s):  
Ravit Geva ◽  
Barliz Waissengrin ◽  
Dan Mirelman ◽  
Felix Bokstein ◽  
Deborah T. Blumenthal ◽  
...  
Keyword(s):  
Data Sharing ◽  
Real World ◽  
Clinical Decision Making ◽  
Homomorphic Encryption ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Raw Data ◽  
Encrypted Data ◽  
World Data

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

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