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Concordance between clinical outcomes in the Systolic Blood Pressure Intervention Trial and in the electronic health record.
Chu, Chi D; Lenoir, Kristin M; Rai, Nayanjot Kaur; Soman, Sandeep; Dwyer, Jamie P; Rocco, Michael V; Agarwal, Anil K; Beddhu, Srinivasan; Powell, James R; Suarez, Maritza M; Lash, James P; McWilliams, Andrew; Whelton, Paul K; Drawz, Paul E; Pajewski, Nicholas M; Ishani, Areef; Tuot, Delphine S.
Afiliação
  • Chu CD; Department of Medicine, University of California, San Francisco, San Francisco, CA, United States of America. Electronic address: Chi.Chu@ucsf.edu.
  • Lenoir KM; Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
  • Rai NK; Division of Renal Diseases and Hypertension, University of Minnesota, Minneapolis, United States of America.
  • Soman S; Division of Nephrology and Hypertension, Henry Ford Hospital, Detroit, MI, United States of America.
  • Dwyer JP; Division of Nephrology & Hypertension, University of Utah Health, Salt Lake City, UT, United States of America.
  • Rocco MV; Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States of America.
  • Agarwal AK; Department of Medicine, Veterans Affairs Central California Health Care System, Fresno, CA, United States of America.
  • Beddhu S; Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States of America.
  • Powell JR; Division of General Internal Medicine, Brody School of Medicine, East Carolina University, Greenville, NC, United States of America.
  • Suarez MM; Department of Medicine, University of Miami Miller School of Medicine, Miami, FL, United States of America.
  • Lash JP; Division of Nephrology, University of Illinois at Chicago, Chicago, IL, United States of America.
  • McWilliams A; Department of Internal Medicine, Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, NC, United States of America.
  • Whelton PK; Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States of America.
  • Drawz PE; Division of Renal Diseases and Hypertension, University of Minnesota, Minneapolis, United States of America.
  • Pajewski NM; Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
  • Ishani A; Division of Renal Diseases and Hypertension, University of Minnesota, Minneapolis, United States of America.
  • Tuot DS; Department of Medicine, University of California, San Francisco, San Francisco, CA, United States of America.
Contemp Clin Trials ; 128: 107172, 2023 05.
Article em En | MEDLINE | ID: mdl-37004812
ABSTRACT

BACKGROUND:

Randomized trials are the gold standard for generating clinical practice evidence, but follow-up and outcome ascertainment are resource-intensive. Electronic health record (EHR) data from routine care can be a cost-effective means of follow-up, but concordance with trial-ascertained outcomes is less well-studied.

METHODS:

We linked EHR and trial data for participants of the Systolic Blood Pressure Intervention Trial (SPRINT), a randomized trial comparing intensive and standard blood pressure targets. Among participants with available EHR data concurrent to trial-ascertained outcomes, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for EHR-recorded cardiovascular disease (CVD) events, using the gold standard of SPRINT-adjudicated outcomes (myocardial infarction (MI)/acute coronary syndrome (ACS), heart failure, stroke, and composite CVD events). We additionally compared the incidence of non-CVD adverse events (hyponatremia, hypernatremia, hypokalemia, hyperkalemia, bradycardia, and hypotension) in trial versus EHR data.

RESULTS:

2468 SPRINT participants were included (mean age 68 (SD 9) years; 26% female). EHR data demonstrated ≥80% sensitivity and specificity, and ≥ 99% negative predictive value for MI/ACS, heart failure, stroke, and composite CVD events. Positive predictive value ranged from 26% (95% CI; 16%, 38%) for heart failure to 52% (95% CI; 37%, 67%) for MI/ACS. EHR data uniformly identified more non-CVD adverse events and higher incidence rates compared with trial ascertainment.

CONCLUSIONS:

These results support a role for EHR data collection in clinical trials, particularly for capturing laboratory-based adverse events. EHR data may be an efficient source for CVD outcome ascertainment, though there is clear benefit from adjudication to avoid false positives.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Cardiovasculares / Acidente Vascular Cerebral / Síndrome Coronariana Aguda / Insuficiência Cardíaca / Hipertensão / Infarto do Miocárdio Tipo de estudo: Clinical_trials / Diagnostic_studies / Prognostic_studies Limite: Aged / Female / Humans / Male Idioma: En Revista: Contemp Clin Trials Assunto da revista: MEDICINA / TERAPEUTICA Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Cardiovasculares / Acidente Vascular Cerebral / Síndrome Coronariana Aguda / Insuficiência Cardíaca / Hipertensão / Infarto do Miocárdio Tipo de estudo: Clinical_trials / Diagnostic_studies / Prognostic_studies Limite: Aged / Female / Humans / Male Idioma: En Revista: Contemp Clin Trials Assunto da revista: MEDICINA / TERAPEUTICA Ano de publicação: 2023 Tipo de documento: Article