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Comparison of Clinical Outcomes and Safety Associated With Chlorthalidone vs Hydrochlorothiazide in Older Adults With Varying Levels of Kidney Function

2021· article· en· W3199074424 on OpenAlexaffabout
Cedric Edwards, Gregory L. Hundemer, William Petrcich, Mark Canney, Greg Knoll, Kevin D. Burns, Ann Bugeja, Manish M. Sood

Bibliographic record

VenueJAMA Network Open · 2021
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsInstitute for Clinical Evaluative SciencesOttawa HospitalUniversity of Ottawa
FundersAstraZeneca
KeywordsChlorthalidoneHydrochlorothiazideMedicineRenal functionKidney diseaseInternal medicineThiazideAtrial fibrillationCohortCardiologyDiureticBlood pressure

Abstract

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Importance: Thiazide diuretics are commonly prescribed for the treatment of hypertension, a disease highly prevalent among older individuals and in those with chronic kidney disease. How specific thiazide diuretics compare in regard to safety and clinical outcomes in these populations remains unknown. Objective: To compare safety and clinical outcomes associated with chlorthalidone or hydrochlorothiazide use among older adults with varying levels of kidney function. Design, Setting, and Participants: This population-based retrospective cohort study was conducted in Ontario, Canada, from 2007 to 2015. Participants included adults aged 66 years or older who initiated chlorthalidone or hydrochlorothiazide during this period. Data were analyzed from December 2019 through September 2020. Exposures: New chlorthalidone users were matched 1:4 with new hydrochlorothiazide users by a high-dimensional propensity score. Time-to-event models accounting for competing risks examined the associations between chlorthalidone vs hydrochlorothiazide use and the outcomes of interest overall and within estimated glomerular filtration rate (eGFR) categories (≥60, 45-59, and <45 mL/min/1.73 m2). Main Outcomes and Measures: The outcomes of interest were adverse kidney events (ie, eGFR decline ≥30%, dialysis, or kidney transplantation), cardiovascular events (composite of myocardial infarction, coronary revascularization, heart failure, or atrial fibrillation), all-cause mortality, and electrolyte anomalies (ie, sodium or potassium levels outside reference ranges). Results: After propensity score matching, the study cohort included 12 722 adults (mean [SD] age, 74 [7] years; 7063 [56%] women; 5659 [44%] men; mean [SD] eGFR, 69 [19] mL/min/1.73 m2), including 2936 who received chlorthalidone and 9786 who received hydrochlorothiazide. Chlorthalidone use was associated with a higher risk of eGFR decline of 30% or greater (hazard ratio [HR], 1.24 [95% CI, 1.13-1.36]) and cardiovascular events (HR, 1.12 [95% CI, 1.04-1.22]) across all eGFR categories compared with hydrochlorothiazide use. Chlorthalidone use was also associated with a higher risk of hypokalemia compared with hydrochlorothiazide use, which was more pronounced among those with higher eGFR (eGFR ≥60 mL/min/1.73 m2: HR, 1.86 [95% CI, 1.67-2.08]; eGFR 45-59 mL/min/1.73 m2: HR, 1.57 [95% CI, 1.25-1.96]; eGFR <45 mL/min/1.73 m2: HR, 1.10 [95% CI, 0.84-1.45]; P for interaction = .001). No significant differences were observed between chlorthalidone and hydrochlorothiazide for dialysis or kidney transplantation (HR, 1.44 [95% CI, 0.88-2.36]), all-cause mortality (HR, 1.10 [95% CI, 0.93-1.29]), hyperkalemia (HR, 1.05 [95% CI, 0.79-1.39]), or hyponatremia (HR, 1.14 [95% CI, CI 0.98-1.32]). Conclusions and Relevance: This cohort study found that among older adults, chlorthalidone use was associated with a higher risk of eGFR decline, cardiovascular events, and hypokalemia compared with hydrochlorothiazide use. The excess risk of hypokalemia with chlorthalidone was attenuated in participants with reduced kidney function. Placed in context with prior observational studies comparing the safety and clinical outcomes associated with thiazide diuretics, these results suggest that there is no evidence to prefer chlorthalidone over hydrochlorothiazide.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.351
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations30
Published2021
Admission routes2
Has abstractyes

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