ASH Research Collaborative: a real-world data infrastructure to support real-world evidence development and learning healthcare systems in hematology

Wood, William A.; Marks, Peter; Plovnick, Robert M.; Hewitt, Kathleen; Neuberg, Donna; Walters, Samuel R.; Dolan, Brendan K.; Tucker, Emily; Abrams, Charles S.; Thompson, Alexis A.; Anderson, Kenneth C.; Kluetz, Paul G.; Farrell, Ann T.; Rivera, Donna R.; Gertzog, Matthew; Pappas, Gregory

doi:10.1182/bloodadvances.2021005902

Cited by 14 publications

(11 citation statements)

References 15 publications

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“…Afterwards, the full text screening step analyzed in total 29 articles, out of which 15 were either focused on AI and cancer but without using the OMOP or focused solely on cancer or AI. The remaining nine articles either contained cancer studies on OMOP-based data not using predictive AI models [ 11 , 33 , 34 , 35 , 36 ] or performed predictive analysis on OMOP-based data of a non-cancerous disease [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. An example for the first group are preliminary studies that are focused on harmonizing data in the OMOP using extract, load, and transform (ETL) processes.…”

Section: Resultsmentioning

confidence: 99%

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

Ahmadi

Peng

Wolfien

et al. 2022

IJMS

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The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a high-throughput manner. Such data in conjunction with clinical routine data are proven to be highly useful in deriving population-level and patient-level predictions, especially in the field of cancer precision medicine. However, data harmonization across multiple national and international clinical sites is an essential step for the assessment of events and outcomes associated with patients, which is currently not adequately addressed. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an internationally established research data repository introduced by the Observational Health Data Science and Informatics (OHDSI) community to overcome this issue. To address the needs of cancer research, the genomic vocabulary extension was introduced in 2020 to support the standardization of subsequent data analysis. In this review, we evaluate the current potential of the OMOP CDM to be applicable in cancer prediction and how comprehensively the genomic vocabulary extension of the OMOP can serve current needs of AI-based predictions. For this, we systematically screened the literature for articles that use the OMOP CDM in predictive analyses in cancer and investigated the underlying predictive models/tools. Interestingly, we found 248 articles, of which most use the OMOP for harmonizing their data, but only 5 make use of predictive algorithms on OMOP-based data and fulfill our criteria. The studies present multicentric investigations, in which the OMOP played an essential role in discovering and optimizing machine learning (ML)-based models. Ultimately, the use of the OMOP CDM leads to standardized data-driven studies for multiple clinical sites and enables a more solid basis utilizing, e.g., ML models that can be reused and combined in early prediction, diagnosis, and improvement of personalized cancer care and biomarker discovery.

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Section: Resultsmentioning

confidence: 99%

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

Ahmadi

Peng

Wolfien

et al. 2022

IJMS

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“…This raises the issue of sharing individual patient data so that the secondary use of available health data should be promoted, which begins by encouraging secure and facilitated access to those data by researchers worldwide, as proposed by the American Society of Hematology’s Research Collaborative. 45 Last, the method of analysis should be justified based on the type of available data and on the underlying target population and the therapeutic question of interest (eg, to treat all patients or not?). The use of external controls finally entails merging different sources of data, which may complicate the verification of causal assumptions and not adequately control for confounding factors, which is a necessary but not sufficient framework for valid estimation of treatment effect.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

“…External controls could be directly selected from pertinent and efficacious active arms from previously completed RCTs 20 or reconstituted from real-world data (RWD). 45 …”

Section: Introductionmentioning

confidence: 99%

Enriching single-arm clinical trials with external controls: possibilities and pitfalls

et al. 2023

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For the past decade, it has become commonplace to provide rapid answers and early patient access to innovative treatments in the absence of randomized clinical trials (RCT), with benefits estimated from single-arm trials. This trend is important in oncology, notably when assessing new targeted therapies. Some of those uncontrolled trials further include an external/synthetic control group as an innovative way to provide an indirect comparison to a pertinent control group. We aimed to provide some guidelines as a comprehensive tool for critical appraisal of those comparisons or for performing one. We used the example of ciltacabtagene autoleucel for the treatment of adult patients with relapsed or refractory multiple myeloma after three or more treatment lines as an illustrative example. A 3-step guidance is proposed. The first step includes the definition of an estimand, which encompasses the treatment effect and targeted population (whole population or restricted to single-arm trial or external controls), reflecting a clinical question. The second step relies on the adequate selection of external controls from previous RCTs or real-world data from patient cohorts, registries, or electronic patient files. The third step consists of choosing the statistical approach targeting the treatment effect defined above, and depends on the available data (individual-level data or aggregated external data). The validity of the treatment effect derived from indirect comparisons heavily depends on careful methodological considerations included in the proposed 3-step procedure. Because the level of evidence of a well-conducted RCT cannot be guaranteed, the evaluation is more important than in standard settings.

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“…The ASH Research Collaborative will be the coordinating entity for data collection and analysis. 31 CONSA is a registry trial based on standard screening and diagnostic procedures and early intervention therapies, specifically penicillin prophylaxis and childhood immunizations (supplemental Appendix 1). 16 Clinical SCD standards for the consortium were established by the National Heart, Lung, and Blood Institute 2014 report and adapted for low-resource settings by public health care networks and pediatric guidelines (eg, the World Health Organization's Expanded Programme on Immunization), consortium's members, and other global SCD experts drawing on SCD care guidelines and the region's newborn screening experience.…”

Section: Consortium Hypothesis Goals and Objectivesmentioning

confidence: 99%

The Consortium on Newborn Screening in Africa for sickle cell disease: study rationale and methodology

et al. 2022

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Sickle cell disease (SCD) is a common condition within sub-Saharan Africa and associated with high under-5 (U5) mortality. The American Society of Hematology instituted the Consortium on Newborn Screening in Africa (CONSA) for SCD, a seven-country network of sites to implement standardized newborn hemoglobinopathy screening and early intervention for children with SCD in sub-Saharan Africa. CONSA's overall hypothesis is that early infant SCD screening and entry into standardized, continuous care will reduce U5 mortality compared to historical estimates in the region. Primary trial objectives are to determine the population-based birth incidence of SCD and effectiveness of early standardized care for preventing early mortality consortium-wide at each country's site(s). Secondary objectives are to establish universal screening and early interventions for SCD within clinical networks of CONSA partners and assess trial implementation. Outcomes will be evaluated from data collected using a shared patient registry. Standardized trial procedures will be implemented among designated birth populations in seven African countries whose programs met eligibility criteria. Treatment protocol includes administering antibacterial and antimalarial prophylaxis and standard childhood vaccinations against infections commonly affecting children with SCD. Infants with a positive screen and confirmation of SCD within the catchment areas defined by each consortium partner will be enrolled in the clinical intervention protocol and followed regularly until age five years. Effectiveness of these early interventions, along with culturally appropriate family education and counseling, will be evaluated by comparing U5 mortality in the enrolled cohort to estimated pre-program data. Here we describe the methodology planned for this trial.

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ASH Research Collaborative: a real-world data infrastructure to support real-world evidence development and learning healthcare systems in hematology

Cited by 14 publications

References 15 publications

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

Enriching single-arm clinical trials with external controls: possibilities and pitfalls

The Consortium on Newborn Screening in Africa for sickle cell disease: study rationale and methodology

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