Evidence map›Paper›PMID 28260692›Full record

Trial reportAging2017

Individualizing treatment targets for elderly patients with type 2 diabetes: factors influencing clinical decision making in the 24-week, randomized INTERVAL study.

W David Strain, Abhijit S Agarwal, Päivi M Paldánius

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Aging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Trends in HbAPharmacoepidemiology and drug safety · 2021
    Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. 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

3 authors.

W David StrainDiabetes and Vascular Research Centre, University of Exeter Medical School, Exeter EX2 5AX, UK.
Abhijit S AgarwalNovartis Pharmaceuticals Corporation, East Hanover, NJ 07936-1080, USA.
Päivi M PaldániusNovartis Pharma AG, Postfach, CH-4002 Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We tested the feasibility of setting individualized glycemic goals and factors influencing targets set in a clinical trial in elderly patients with type 2 diabetes.A 24-week, randomized, double-blind, placebo-controlled study was conducted in 45 outpatient centers in seven European countries. 278 drug-naïve or inadequately controlled (mean HbA1c 7.9%) patients with type 2 diabetes aged ≥70 years with HbA1c levels ≥7.0% and ≤10.0% were enrolled. Investigator-defined individualized HbA1c targets and the impact of baseline characteristics on individualized treatment targets was evaluated.The average individualized HbA1c target was set at 7.0%. HbA1c at baseline predicted a target setting such that higher the HbA1c, more aggressive was the target (P<0.001). Men were more likely to be set aggressive targets than women (P=0.026). Frailty status of patients showed a trend towards significance (P=0.068), whereas diabetes duration, age, or polypharmacy did not. There was heterogeneity between countries regarding how baseline factors were viewed.Despite training and guidance to individualize HbA1c goals, targets were still set in line with conventional values. A strong influence of country-specific guidelines on target setting was observed; confirming the importance of further education to implement new international guidelines in older adults.

Indexed as

Clinical Decision-MakingPrecision MedicineAgedDiabetes Mellitus, Type 2Double-Blind MethodFemaleHumansHypoglycemic AgentsMaleSex FactorsHypoglycemic Agentselderlyindividualizationpredictorstype 2 diabetes

Identifiers

PMID28260692
PMCPMC5391230

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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.