The Fontan outcomes network: first steps towards building a lifespan registry for individuals with Fontan circulation in the United States

Alsaied, Tarek; Allen, Kiona Y.; Anderson, Jeffrey B.; Anixt, Julia S.; Brown, David W.; Cetta, Frank; Cordina, Rachael; d’Udekem, Yves; Didier, Meghan; Ginde, Salil; Maria, Michael V. Di; Eversole, Michelle; Goldberg, David J.; Goldstein, Bryan H.; Hoffmann, Erin; Kovacs, Adrienne H.; Lannon, Carole; Lihn, Stacey; Lubert, Adam M.; Marino, Bradley S.; Mullen, Emily; Pickles, Diane; Rathod, Rahul H.; Rychik, Jack; Tweddell, James S.; Wooton, Sharyl; Wright, Gail E.; Younoszai, Adel K.; Glenn, Tom; Wilmoth, Alicia; Schumacher, K.

doi:10.1017/s1047951120001869

Cited by 22 publications

(12 citation statements)

References 27 publications

(31 reference statements)

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“…One way to address homogenous research teams is through the inclusion of experts in race or ethnicity or in those communities being examined. These biases can also be reduced by including members of the study population in the research process as experts and advisers [ 219 ]. Although big data from social media can be collected without ever connecting with the people who contributed the data, it does not eliminate the ethical need for researchers to include representative perspectives in research processes.…”

Section: Discussionmentioning

confidence: 99%

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review

Golder¹,

Stevens²,

O’Connor³

et al. 2022

J Med Internet Res

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Background A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.

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Section: Discussionmentioning

confidence: 99%

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review

Golder¹,

Stevens²,

O’Connor³

et al. 2022

J Med Internet Res

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“…Pediatric cardiologists have made headway into innovative data collection collaboratives and developmental disease networks (115,116), with newer forays into lifespan initiatives. For ACHD patients in North America, the Alliance for Adult Research in Congenital Cardiology (AARCC) has led multicenter international collaborative research assembling (117).…”

Section: From Data Sources To Data Sciencementioning

confidence: 99%

Lifespan Perspective on Congenital Heart Disease Research

Diller

Arvanitaki

Opotowsky

et al. 2021

Journal of the American College of Cardiology

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“…Clearly, a considerable amount of infrastructure is already in place, yet issues related to accessibility, accuracy, completeness, depth, and timeliness of the collected data remain an issue. Since the publication of this report, several data collection networks for CHD have been established, including among others Advanced Cardiac Therapies Improving Outcomes Network (ACTION) ( 36 ), Fontan Outcomes Network (FON) ( 37 ), PartneRships in cOngeniTal hEart disease (PROTEA) ( 38 ), and BELgian COngenital Heart Disease Database combining Administrative and Clinical data (BELCODAC) ( 39 ). Continued efforts to establish multicenter clinical registries with in-depth and up-to-date information collection as well as meaning integration of information from various sources will be instrumental in paving the way for the full potential of MBE to be realized.…”

Section: The Way Forwardmentioning

confidence: 99%

Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

Eynde

Manlhiot

Bruaene

et al. 2021

Front. Cardiovasc. Med.

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Built on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individually, each may differ in important ways from an RCT cohort, and the physician will wonder each time if following EBM will provide best guidance for this unique patient. In an effort to overcome this weakness, and promote higher quality care through a more personalized approach, a new framework has been proposed: Medicine-Based Evidence (MBE). In this approach, big data and deep learning techniques are embraced to interrogate treatment responses among patients in real-world clinical practice. Such statistical models are then integrated with mechanistic disease models to construct a “digital twin,” which serves as the real-time digital counterpart of a patient. MBE is thereby capable of dynamically modeling the effects of various treatment decisions in the context of an individual's specific characteristics. In this article, we discuss how MBE could benefit patients with congenital heart disease, a field where RCTs are difficult to conduct and often fail to provide definitive solutions because of a small number of subjects, their clinical complexity, and heterogeneity. We will also highlight the challenges that must be addressed before MBE can be embraced in clinical practice and its full potential can be realized.

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The Fontan outcomes network: first steps towards building a lifespan registry for individuals with Fontan circulation in the United States

Cited by 22 publications

References 27 publications

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review

Lifespan Perspective on Congenital Heart Disease Research

Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

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