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Background <p>The COVID-19 pandemic gave rise to clinical trials focused on systematic, accurate primary data capture, and reduced reliance on source data verification (SDV). Here, we report on a natural experiment that allowed us to assess the quality, cost, and impact of this approach compared to traditional SDV.</p> Methods <p>The I-SPY COVID trial (NCT04488081) was a multicenter, open-label, platform trial that employed a streamlined daily checklist, daily capture of labs and medications, and centralized monitoring to ensure accurate data collection in lieu of SDV. The trial enrolled 1,111 patients in 11 drug arms with severe COVID-19. After the trial arms were closed, extensive retrospective SDV was performed on 333 (30.0%) patients, including 10,101 of 44,486 (23%) electronic case report forms (eCRFs), allowing us to evaluate the impact of our strategy on data integrity, outcomes, and costs.</p> Results <p>We find that retrospective SDV results in changes to 0.36% (1,234 / 340,532) of data fields. It results in no changes to the type of outcome recorded (death, recovery, or censored), but changes in the day of recovery in 9 instances, by a median of 2 days (range 1-7). Two additional AEs are added during SDV that had not previously been captured. Costs associated with retrospective SDV of 23% eCRFs are 61,073 person-hours at a cost of $6.1 M.</p> Conclusions <p>Extensive SDV does not change any results or conclusions of the I-SPY COVID trial, which was designed with a systematic strategy for data capture, monitoring, and safety. This strategy could improve the efficiency of clinical trials and eliminate the need for manual SDV.</p>

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Systematic data capture reduces the need for source data verification: exploratory analysis from a phase 2 multicenter randomized controlled platform trial

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Ali B. Abbasi,
  • Kathleen D. Liu,
  • Derek W. Russell,
  • D. Clark Files,
  • Karl W. Thomas,
  • Fady Youssef,
  • Sheetal Gandotra,
  • Andrea Discacciati,
  • Noha Lim,
  • Adam L. Asare,
  • Martin Eklund,
  • Michael Matthay,
  • Laura J. Esserman,
  • Neil R. Aggarwal,
  • Ellen L. Burnham,
  • Carrie Higgins,
  • Jeff McKeehan,
  • Timothy Albertson,
  • Angela Haczku,
  • Erin Hardy,
  • Richart Harper,
  • Brian Morrissey,
  • Christian Sandrock,
  • Sara Auld,
  • Philip Yang,
  • Joshua Detelich,
  • Gavin Harris,
  • Katherine Nugent,
  • Max Adelman,
  • Jeremy R. Beitler,
  • Anita Darmanian,
  • Amy L. Dzierba,
  • Ivan Garcia,
  • Katarzyna Gosek,
  • Purnema Madahar,
  • Aaron M. Mittel,
  • Justin Muir,
  • Amanda Rosen,
  • John Schicchi,
  • Alexis L. Serra,
  • Romina Wahab,
  • Paul Berger,
  • Carolyn S. Calfee,
  • Melissa Coleman,
  • Alejandra Jauregui,
  • Nathan Cobb,
  • Rajiv Sonti,
  • Amen M. Hamed,
  • Alessio Crippa,
  • Andrea Discacciati,
  • Martin Eklund,
  • Laura Esserman,
  • D. Clark Files,
  • Karl Thomas,
  • Kevin W. Gibbs,
  • Leigha Landreth,
  • Mary LaRose,
  • Lisa Parks,
  • Adina Wynn,
  • Eliot Friedman,
  • Derek W. Russell,
  • Donna Harris,
  • Abhishek Methukupally,
  • Siddharth Patel,
  • Sheetal Gandotra,
  • Kashif Khan,
  • Se Fum Wong,
  • Albert Yen,
  • Jonathan Koff,
  • Lindsie Boerger,
  • John Kazianis,
  • Santhi Kumar,
  • Kathleen D. Liu,
  • Thomas R. Martin,
  • Mark M. Wurfel,
  • Michael A. Matthay,
  • Brian Daniel,
  • Nuala J. Meyer,
  • Caroline A. G. Ittner,
  • Nilam S. Mangalmurti,
  • John P. Reilly,
  • Timothy Obermiller,
  • Philip A. Robinson,
  • Farjad Sarafian,
  • Usman Shah,
  • Richard G. Wunderink,
  • G. R. Scott Budinger,
  • Helen K. Donnelly,
  • Benjamin D. Singer,
  • Fady A. Youssef,
  • Daniel Belvins,
  • Catherine Nguyen,
  • Alexis Suarez,
  • Maged A. Tanios,
  • Scott Fields,
  • James Hurst-Hopf,
  • Lamorna Brown Swigart,
  • Christina Creel-Bulos,
  • Christina Spainhour,
  • Ari Moskowitz,
  • Praveen Vijhani,
  • Adrienne M. Casciato,
  • Vaney Capetillo,
  • Kenenth Wei,
  • Tracie Huynh,
  • Anna Rodriguez-Vasquez,
  • Joseph L. Nates,
  • Jessica Suarez,
  • Siddharth Nair,
  • Sandya Samavedam,
  • Michael Bernstein,
  • Christopher Jordan,
  • Daniel H. Kett,
  • Karla Leon Escalona,
  • Richard A. Lee,
  • Kenneth Remy,
  • Ali Zarrinpar,
  • Robert Hyzy,
  • Kristine Nelson,
  • Caroline Quill,
  • Emilio Mazza,
  • Kristin Broderick,
  • Jermiah Hayanga,
  • Ana Costa Monteiro,
  • Joseph Levitt,
  • Ruixiao Lu,
  • Paul Henderson,
  • Adam Asare,
  • Imogene Dunn,
  • Alejandro Botello Barragan

摘要

Background

The COVID-19 pandemic gave rise to clinical trials focused on systematic, accurate primary data capture, and reduced reliance on source data verification (SDV). Here, we report on a natural experiment that allowed us to assess the quality, cost, and impact of this approach compared to traditional SDV.

Methods

The I-SPY COVID trial (NCT04488081) was a multicenter, open-label, platform trial that employed a streamlined daily checklist, daily capture of labs and medications, and centralized monitoring to ensure accurate data collection in lieu of SDV. The trial enrolled 1,111 patients in 11 drug arms with severe COVID-19. After the trial arms were closed, extensive retrospective SDV was performed on 333 (30.0%) patients, including 10,101 of 44,486 (23%) electronic case report forms (eCRFs), allowing us to evaluate the impact of our strategy on data integrity, outcomes, and costs.

Results

We find that retrospective SDV results in changes to 0.36% (1,234 / 340,532) of data fields. It results in no changes to the type of outcome recorded (death, recovery, or censored), but changes in the day of recovery in 9 instances, by a median of 2 days (range 1-7). Two additional AEs are added during SDV that had not previously been captured. Costs associated with retrospective SDV of 23% eCRFs are 61,073 person-hours at a cost of $6.1 M.

Conclusions

Extensive SDV does not change any results or conclusions of the I-SPY COVID trial, which was designed with a systematic strategy for data capture, monitoring, and safety. This strategy could improve the efficiency of clinical trials and eliminate the need for manual SDV.