Evidence map›Paper›PMID 37004812›Full record

Trial reportContemporary clinical trials2023

Concordance between clinical outcomes in the Systolic Blood Pressure Intervention Trial and in the electronic health record.

Chi D Chu, Kristin M Lenoir, Nayanjot Kaur Rai, Sandeep Soman, Jamie P Dwyer, Michael V Rocco, Anil K Agarwal, Srinivasan Beddhu, James R Powell, Maritza M Suarez and 7 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Contemporary clinical trials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Observational
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Chi D ChuDepartment of Medicine, University of California, San Francisco, San Francisco, CA, United States of America. Electronic address: Chi.Chu@ucsf.edu.
Kristin M LenoirDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
Nayanjot Kaur RaiDivision of Renal Diseases and Hypertension, University of Minnesota, Minneapolis, United States of America.
Sandeep SomanDivision of Nephrology and Hypertension, Henry Ford Hospital, Detroit, MI, United States of America.
Jamie P DwyerDivision of Nephrology & Hypertension, University of Utah Health, Salt Lake City, UT, United States of America.
Michael V RoccoDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States of America.
Anil K AgarwalDepartment of Medicine, Veterans Affairs Central California Health Care System, Fresno, CA, United States of America.
Srinivasan BeddhuDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States of America.
James R PowellDivision of General Internal Medicine, Brody School of Medicine, East Carolina University, Greenville, NC, United States of America.
Maritza M SuarezDepartment of Medicine, University of Miami Miller School of Medicine, Miami, FL, United States of America.
James P LashDivision of Nephrology, University of Illinois at Chicago, Chicago, IL, United States of America.
Andrew McWilliamsDepartment of Internal Medicine, Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, NC, United States of America.
Paul K WheltonDepartment of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States of America.
Paul E DrawzDivision of Renal Diseases and Hypertension, University of Minnesota, Minneapolis, United States of America.
Nicholas M PajewskiDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
Areef IshaniDivision of Renal Diseases and Hypertension, University of Minnesota, Minneapolis, United States of America.
Delphine S TuotDepartment of Medicine, University of California, San Francisco, San Francisco, CA, United States of America.

Funding

Institutional Career Development Core SupplementKL2TR001870 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KANAYA, ALKA M., SARKAR, URMIMALA · 2016 to 2025
$19.7M
Blood Pressure and Kidney Function - SPRINT vs Electronic Health Record DataR01HL136679 · NHLBI · UNIVERSITY OF MINNESOTA · PI DRAWZ, PAUL ENGLUND · 2017 to 2021
$3.8M
UCSF Learning Health System K12 Career Development ProgramK12HS026383 · AHRQ · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GONZALES, RALPH, SARKAR, URMIMALA · 2018 to 2022
$3.6M
Targeted Automated Nephrology e-Consultation for Diabetic Kidney DiseaseK23DK131316 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHU, CHI · 2023 to 2024
$277k
AHRQ HHS K12 HS026383NCATS NIH HHS KL2 TR001870NHLBI NIH HHS R01 HL136679NIDDK NIH HHS K23 DK131316
6 · The paper itself

Abstract

backgroundRandomized 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.

methodsWe 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.

results2468 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.

conclusionsThese 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.

Indexed as

Acute Coronary SyndromeCardiovascular DiseasesHeart FailureHypertensionMyocardial InfarctionStrokeAgedAntihypertensive AgentsBlood PressureElectronic Health RecordsFemaleHumansMaleTreatment OutcomeAntihypertensive AgentsCardiovascular outcomesElectronic health recordOutcome ascertainmentPragmatic trial

Identifiers

PMID37004812
PMCPMC10547257

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.