Evidence map›Paper›PMID 31668726›Full record

SynthesisLancet (London, England)2019

Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis.

Marc A Suchard, Martijn J Schuemie, Harlan M Krumholz, Seng Chan You, RuiJun Chen, Nicole Pratt, Christian G Reich, Jon Duke, David Madigan, George Hripcsak and 1 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in Lancet (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 205 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
205citing papers in PubMed, 11 pooled it
–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

205 citing papers in PubMed, 11 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Guideline
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  8. Systematically exploring repurposing effects of antihypertensives.Pharmacoepidemiology and drug safety · 2022
    Pooled it
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  11. Pooled it
  12. Trial
  13. Effect of Initiation and Continuous Adherence to ARBs Versus ACEIs on Risk of Adjudicated Mild Cognitive Impairment or Dementia.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Trial
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  17. Evaluating large-scale propensity score adjustment when sample size is small.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
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  19. Observational
  20. Article

145 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Marc A SuchardDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA; Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA. Electronic address: msuchard@ucla.edu.
Martijn J SchuemieDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA; Epidemiology Analytics, Janssen Research & Development, Titusville, NJ, USA.
Harlan M KrumholzDepartment of Medicine, Yale University School of Medicine, New Haven, CA, USA.
Seng Chan YouDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.
RuiJun ChenDepartment of Medicine, Weill Cornell Medical College, New York, NY, USA; Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA.
Nicole PrattQuality Use of Medicines and Pharmacy Research Centre, Sansom Institute, University of South Australia, Adelaide, SA, Australia.
Christian G ReichReal World Analytics Solutions, IQVIA, Durham, NC, USA.
Jon DukeGeorgia Tech Research Institute, Georgia Tech College of Computing, Atlanta, GA, USA.
David MadiganDepartment of Statistics, Columbia University, New York, NY, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA; Medical Informatics Services, New York-Presbyterian Hospital, New York, NY, USA.
Patrick B RyanEpidemiology Analytics, Janssen Research & Development, Titusville, NJ, USA; Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Technology CoreU19AI135995 · NIAID · SCRIPPS RESEARCH INSTITUTE, THE · PI Kristian Graugaard Andersen · 2018 to 2026
$32.0M
DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
NCATS NIH HHS UL1 TR001863NIAID NIH HHS U19 AI135995NLM NIH HHS R01 LM006910
6 · The paper itself

Abstract

backgroundUncertainty remains about the optimal monotherapy for hypertension, with current guidelines recommending any primary agent among the first-line drug classes thiazide or thiazide-like diuretics, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, dihydropyridine calcium channel blockers, and non-dihydropyridine calcium channel blockers, in the absence of comorbid indications. Randomised trials have not further refined this choice.

methodsWe developed a comprehensive framework for real-world evidence that enables comparative effectiveness and safety evaluation across many drugs and outcomes from observational data encompassing millions of patients, while minimising inherent bias. Using this framework, we did a systematic, large-scale study under a new-user cohort design to estimate the relative risks of three primary (acute myocardial infarction, hospitalisation for heart failure, and stroke) and six secondary effectiveness and 46 safety outcomes comparing all first-line classes across a global network of six administrative claims and three electronic health record databases. The framework addressed residual confounding, publication bias, and p-hacking using large-scale propensity adjustment, a large set of control outcomes, and full disclosure of hypotheses tested.

findingsUsing 4·9 million patients, we generated 22 000 calibrated, propensity-score-adjusted hazard ratios (HRs) comparing all classes and outcomes across databases. Most estimates revealed no effectiveness differences between classes; however, thiazide or thiazide-like diuretics showed better primary effectiveness than angiotensin-converting enzyme inhibitors: acute myocardial infarction (HR 0·84, 95% CI 0·75-0·95), hospitalisation for heart failure (0·83, 0·74-0·95), and stroke (0·83, 0·74-0·95) risk while on initial treatment. Safety profiles also favoured thiazide or thiazide-like diuretics over angiotensin-converting enzyme inhibitors. The non-dihydropyridine calcium channel blockers were significantly inferior to the other four classes.

interpretationThis comprehensive framework introduces a new way of doing observational health-care science at scale. The approach supports equivalence between drug classes for initiating monotherapy for hypertension-in keeping with current guidelines, with the exception of thiazide or thiazide-like diuretics superiority to angiotensin-converting enzyme inhibitors and the inferiority of non-dihydropyridine calcium channel blockers.

fundingUS National Science Foundation, US National Institutes of Health, Janssen Research & Development, IQVIA, South Korean Ministry of Health & Welfare, Australian National Health and Medical Research Council.

Indexed as

Antihypertensive AgentsHypertensionAdolescentAdultAgedAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsCalcium Channel BlockersChildCohort StudiesComparative Effectiveness ResearchDatabases, FactualDiureticsEvidence-Based MedicineFemaleHeart FailureAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsAntihypertensive AgentsCalcium Channel BlockersDiuretics

Identifiers

PMID31668726
PMCPMC6924620

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.