Evidence map›Paper›PMID 29199442›Full record

ArticleAnnals of clinical biochemistry2018

Assessing the cost-effectiveness of a routine versus an extensive laboratory work-up in the diagnosis of anaemia in Dutch general practice.

Michelle Ma Kip, Annemarie Schop, Karlijn Stouten, Soraya Dekker, Geert-Jan Dinant, Hendrik Koffijberg, Patrick Je Bindels, Maarten J IJzerman, Mark-David Levin, Ron Kusters

Open access · hybridAbstract read
In one paragraph

Article in Annals of clinical biochemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

3 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

10 authors at 5 institutions in 1 country.

Michelle Ma Kip1 Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands.ORCID https://orcid.org/0000-0002-6405-6789
Annemarie Schop2 Department of Internal Medicine, Albert Schweitzer Hospital, Dordrecht, the Netherlands.
Karlijn Stouten3 Department of Clinical Chemistry, Albert Schweitzer Hospital, Dordrecht, the Netherlands.
Soraya Dekker1 Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands.
Geert-Jan Dinant4 Department of Family Medicine, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, the Netherlands.
Hendrik Koffijberg1 Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands.
Patrick Je Bindels5 Department of General Practice, Erasmus Medical Center, Rotterdam, the Netherlands.
Maarten J IJzerman1 Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands.
Mark-David Levin2 Department of Internal Medicine, Albert Schweitzer Hospital, Dordrecht, the Netherlands.
Ron Kusters1 Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands.
University of Twente · NLAlbert Schweitzer Ziekenhuis · NLErasmus MC · NLJeroen Bosch Ziekenhuis · NLMaastricht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Establishing the underlying cause of anaemia in general practice is a diagnostic challenge. Currently, general practitioners individually determine which laboratory tests to request (routine work-up) in order to diagnose the underlying cause. However, an extensive work-up (consisting of 14 tests) increases the proportion of patients correctly diagnosed. This study investigates the cost-effectiveness of this extensive work-up. Methods A decision-analytic model was developed, incorporating all societal costs from the moment a patient presents to a general practitioner with symptoms suggestive of anaemia (aged ≥ 50 years), until the patient was (correctly) diagnosed and treated in primary care, or referred to (and diagnosed in) secondary care. Model inputs were derived from an online survey among general practitioners, expert estimates and published data. The primary outcome measure was expressed as incremental cost per additional patient diagnosed with the correct underlying cause of anaemia in either work-up. Results The probability of general practitioners diagnosing the correct underlying cause increased from 49.6% (95% CI: 44.8% to 54.5%) in the routine work-up to 56.0% (95% CI: 51.2% to 60.8%) in the extensive work-up (i.e. +6.4% [95% CI: -0.6% to 13.1%]). Costs are expected to increase slightly from €842/patient (95% CI: €704 to €994) to €845/patient (95% CI: €711 to €994), i.e. +€3/patient (95% CI: €-35 to €40) in the extensive work-up, indicating incremental costs of €43 per additional patient correctly diagnosed. Conclusions The extensive laboratory work-up is more effective for diagnosing the underlying cause of anaemia by general practitioners, at a minimal increase in costs. As accompanying benefits in terms of quality of life and reduced productivity losses could not be captured in this analysis, the extensive work-up is likely cost-effective.

Indexed as

AnemiaClinical Laboratory TechniquesCost-Benefit AnalysisGeneral PracticeHumansMiddle AgedRoot Cause AnalysisanaemiaCost-effectivenessdiagnostic testinggeneral practice

Identifiers

PMID29199442
PMCPMC6196589
OpenAlexW2786094972

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.