Evidence map›Paper›PMID 39609890›Full record

ArticleOrphanet journal of rare diseases2024

A micro-costing study of mass-spectrometry based quantitative proteomics testing applied to the diagnostic pipeline of mitochondrial and other rare disorders.

Francisco Santos Gonzalez, Daniella H Hock, David R Thorburn, Dylan Mordaunt, Nicholas A Williamson, Ching-Seng Ang, David A Stroud, John Christodoulou, Ilias Goranitis

Abstract read
In one paragraph

Article in Orphanet journal of rare diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

9 authors.

Francisco Santos GonzalezEconomics of Genomics and Precision Medicine Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, 207-221 Bouverie St., Parkville, Melbourne, VIC, 3010, Australia.
Daniella H HockDepartment of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, 3010, Australia.
David R ThorburnMurdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC, 3052, Australia.
Dylan MordauntEconomics of Genomics and Precision Medicine Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, 207-221 Bouverie St., Parkville, Melbourne, VIC, 3010, Australia.
Nicholas A WilliamsonMelbourne Mass Spectrometry and Proteomics Facility, Bio21 Molecular Science & Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia.
Ching-Seng AngMelbourne Mass Spectrometry and Proteomics Facility, Bio21 Molecular Science & Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia.
David A StroudMurdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC, 3052, Australia. david.stroud@unimelb.edu.au.
John ChristodoulouMurdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC, 3052, Australia. john.christodoulou@mcri.edu.au.
Ilias GoranitisEconomics of Genomics and Precision Medicine Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, 207-221 Bouverie St., Parkville, Melbourne, VIC, 3010, Australia. ilias.goranitis@unimelb.edu.au.

Funding

Department of Health and Aged Care, Australian Government MRFF Grants: 2007567 and 2016030Melbourne Research, University of Melbourne Application reference: 774837
6 · The paper itself

Abstract

backgroundMass spectrometry-based quantitative proteomics has a demonstrated utility in increasing the diagnostic yield of mitochondrial disorders (MDs) and other rare diseases. However, for this technology to be widely adopted in routine clinical practice, it is crucial to accurately estimate delivery costs. Resource use and unit costs required to undertake a proteomics test were measured and categorized into consumables, equipment, and labor. Unit costs were aggregated to obtain a total cost per patient, reported in 2023 Australian dollars (AUD). Probabilistic and deterministic sensitivity analysis were conducted to evaluate parameter uncertainty and identify key cost drivers.

resultsThe mean cost of a proteomics test was $897 (US$ 607) per patient (95% CI: $734-$1,111). Labor comprised 53% of the total costs. At $342 (US$ 228) per patient, liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) was the most expensive non-salary component. An integrated analysis pipeline where all the standard analysis are performed automatically, as well as discounts or subsidized LC-MS/MS equipment or consumables can lower the cost per test.

conclusionsProteomics testing provide a lower-cost option and wider application compared to respiratory chain enzymology for mitochondrial disorders and potentially other functional assays in Australia. Our analysis suggests that streamlining and automating workflows can reduce labor costs. Using PBMC samples may be a cheaper and more efficient alternative to generating fibroblasts, although their use has not been extensively tested yet. Use of fibroblasts could potentially lower costs when fibroblasts are already available by avoiding the expense of isolating PBMCs. A joint evaluation of the health and economic implications of proteomics is now needed to support its introduction to routine clinical care.

Indexed as

Mitochondrial DiseasesProteomicsRare DiseasesChromatography, LiquidHumansMass SpectrometryTandem Mass SpectrometryFunctional genomicsHealth economicsMicro-costingMitochondrial disordersProteomics

Identifiers

PMID39609890
PMCPMC11605922

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LicenceCC BY
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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.