Evidence map›Paper›PMID 37580839›Full record

ReviewClinical hypertension2023

Short-term blood pressure variability as a potential therapeutic target for kidney disease.

Ye Eun Ko, Jong Hyun Jhee

Abstract readReview
In one paragraph

Review in Clinical hypertension, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. 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

2 authors.

Ye Eun KoDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Jong Hyun JheeDivision of Nephrology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. jjhlove77@yuhs.ac.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Short-term blood pressure variability (BPV) measured with ambulatory blood pressure (BP) monitoring has been demonstrated to be significant in predicting various clinical outcomes. Short-term BPV is distinguished from long-term BPV based on the time interval in which BP fluctuations are measured. Increased short-term BPV has been linked to detrimental effects on the microvascular structure and contributes to subclinical organ damage in the heart, blood vessels, and kidneys, regardless of the average 24-h BP levels. Short-term BPV can be defined by various measures, including calculated metrics (standard deviation, coefficient of variation, average real variability, weighted standard deviation, variability independent of the mean) or dipping patterns. Nevertheless, the additional role of short-term BPV beyond the predictive value of average 24-h BPs or established risk factors for cardiovascular disease and kidney disease remains unclear. In particular, longitudinal studies that evaluate the association between short-term BPV and kidney function impairment are limited and no conclusive data exist regarding which short-term BPV indicators most accurately reflect the prognosis of kidney disease. The issue of how to treat BPV in clinical practice is another concern that is frequently raised. This paper presents a review of the evidence for the prognostic role of short-term BPV in kidney outcomes. Additionally, this review discusses the remaining concerns about short-term BPV that need to be further investigated as an independent risk modifier.

Indexed as

Ambulatory blood pressure monitoringBlood pressureChronic renal insufficiencyHypertension

Identifiers

PMID37580839
PMCPMC10426225

What OpenQuestion holds

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Registered trials

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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.