Evidence map›Paper›PMID 34170370›Full record

ArticleEuropean journal of clinical pharmacology2021

Research on Beers Criteria and STOPP/START Criteria based on the FDA FAERS database.

Qianqian Shao, Yulong Xu, Meng Li, Xishi Chu, Wei Liu

Abstract read
PubMed Publisher
In one paragraph

Article in European journal of clinical pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 13% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Deprescribing: Reducing Harm, Enhancing Care.Rambam Maimonides medical journal · 2026
    Review
  2. Article
  3. Article
  4. 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

5 authors at 2 institutions in 1 country.

Qianqian ShaoCollege of Pharmacy, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan Province, China.
Yulong XuCollege of Pharmacy, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan Province, China.
Meng LiCollege of Pharmacy, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan Province, China.
Xishi ChuCollege of Pharmacy, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan Province, China.
Wei LiuCollege of Pharmacy, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan Province, China. liuweiyxy@zzu.edu.cn.ORCID http://orcid.org/0000-0002-4343-3151
Zhengzhou University · CNZhengzhou University of Light Industry · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeInappropriate medication criteria for the elderly have played an important role in ensuring the safety of medications for the elderly. Too few drugs included in the criteria cannot guarantee the safety of medication for the elderly. Too many drugs included in the criteria will result in less selective medication for the elderly. This paper uses real-world data to evaluate the relationship between antihypertensive drugs and falls, so as to provide references for experts and scholars to revise the criteria of potentially inappropriate medications for the elderly and clinical safe medication.

methodWe use the US Food and Drug Administration Adverse Event Reporting System (FDA FAERS) to evaluate the association between specific antihypertensive drugs in six categories (alpha-1 receptor blockers (α-1 blockers), calcium channel blockers (CCBs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), beta-receptor blockers (β-blockers), and diuretics) and falls by data mining algorithms, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), Medicines and Healthcare Products Regulatory Agency (MHRA), and the empirical Bayes geometric mean (EBGM) and compared with the relevant drugs included in the Beers Criteria and STOPP/START Criteria.

resultThere are a total of 5,157,172 co-occurrences found in 973,447 reports aged 65 years or older from 2016 to 2019 in the FDA FAERS database, and the number of co-occurrences of falls is 5917 for the six categories of 51 antihypertensive drugs. Four kinds of mining methods overlap detection of 12 kinds of positive signal drugs, none of which are not included in the Beers Criteria and 7 drugs are included in the STOPP/START Criteria; 1-3 kinds of mining methods overlap detection of positive signal drugs, a total of 12 kinds, and one drug is included in the Beers Criteria and 5 drugs are included in the STOPP/START Criteria; 22 drugs have fall adverse events, but no positive signal is detected, and 13 drugs are included in STOPP/START Criteria; and 5 drugs have no fall adverse events and 3 drugs are included in the STOPP/START Criteria.

conclusionThe FAERS database was used to confirm the potential connection between some antihypertensive drugs and fall adverse events through data mining algorithms. The Beers Criteria did not clearly indicate the antihypertensive drugs that caused falls, and the antihypertensive drugs included in the STOPP/START Criteria were too extensive and did not include β-blockers and diuretics. It is recommended that experts and scholars use real-world data (such as FAERS, EudraVigilance, WHO VigiBase, and so on) to further explore the relationship between specific antihypertensive drugs and falls in the elderly, so as to revise and improve the criteria for inappropriate medications for the elderly.

Indexed as

Accidental FallsAdverse Drug Reaction Reporting SystemsAgedAged, 80 and overAlgorithmsAntihypertensive AgentsBayes TheoremData MiningFemaleHumansHypertensionMalePotentially Inappropriate Medication ListUnited StatesUnited States Food and Drug AdministrationAntihypertensive AgentsAntihypertensive drugsBeers CriteriaFAERSFallSTOPP/START Criteria

Identifiers

PMID34170370
OpenAlexW3174055829

What OpenQuestion holds

Textmetadata
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.