Evidence map›Paper›PMID 38958810›Full record

ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2024

Abbott realtime MTB assay for detecting Mycobacterium tuberculosis complex in respiratory specimens: a cost-benefit analysis.

Josep Ferrer, Estela Giménez, Diego Carretero, Mª Ángeles Clari, Nieves Orta, David Navarro, Eliseo Albert

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Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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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

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Josep FerrerMicrobiology Service, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Estela GiménezMicrobiology Service, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Diego CarreteroMicrobiology Service, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Mª Ángeles ClariMicrobiology Service, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Nieves OrtaMicrobiology Unit, Hospital Francisco de Borja, Gandía, Spain.
David NavarroMicrobiology Service, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Eliseo AlbertMicrobiology Service, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain. eliseo.al.vi@gmail.com.ORCID http://orcid.org/0000-0002-5037-6095

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeMolecular screening for Mycobacterium tuberculosis (MTB) can lead to rapid empirical treatment inception and reduce hospitalization time and complementary diagnostic tests. However, in low-prevalence settings, the cost-benefit balance remains controversial due to the high cost.

methodsWe used a Markov model to perform an economic analysis to evaluate the profit after implementing molecular MTB screening (Period B) compared with conventional culture testing (Period A) in respiratory samples from 7,452 consecutive subjects with presumed tuberculosis (TB).

resultsThe proportion of positivity was comparable between both periods (P > 0.05), with a total of 2.16 and 1.78 samples/patient requested in periods A and B, respectively (P < 0.001). The mean length of hospital stay was 8.66 days (95%CI: 7.63-9.70) in Period B and 11.51 days (95%CI: 10.15-12.87) in Period A (P = 0.001). The healthcare costs associated with diagnosing patients with presumed TB were reduced by €717.95 per patient with PCR screening. The probability of remaining hospitalized and the need for a greater number of outpatient specialty care visits were the variables with the most weight in the model.

conclusionEmploying PCR as an MTB screening method in a low-prevalence setting may increase the profits to the system.

Indexed as

Cost-Benefit AnalysisMolecular Diagnostic TechniquesMycobacterium tuberculosisAdultAgedFemaleHumansMaleMarkov ChainsMiddle AgedTuberculosisTuberculosis, PulmonaryYoung AdultCost-benefitEconomic evaluationMarkov modelMolecular screeningMycobacterium tuberculosis

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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.