Evidence map›Paper›PMID 40576568›Full record

ArticleEmerging microbes & infections2025

Development and validation of a hierarchical machine learning method using MALDI-TOF mass spectrometry for rapid SCCmec typing and PVL detection in MRSA: a multi-centre study.

Tai-Han Lin, Ming-Jr Jian, Fujiko Mitsumoto-Kaseida, Norihito Kaku, Hsing-Yi Chung, Chih-Kai Chang, Cherng-Lih Perng, Yung-Chih Wang, Chih-Chien Wang, Yuan-Hao Chen and 2 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Emerging microbes & infections, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

12 authors.

Tai-Han LinDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Ming-Jr JianDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Fujiko Mitsumoto-KaseidaDepartment of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
Norihito KakuDepartment of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
Hsing-Yi ChungDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Chih-Kai ChangDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Cherng-Lih PerngDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.ORCID 0000-0001-7182-1031
Yung-Chih WangDivision of Infectious Diseases and Tropical Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Chih-Chien WangDepartment of Pediatrics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Yuan-Hao ChenDepartment of Neurological Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Katsunori YanagiharaDepartment of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
Hung-Sheng ShangDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMethicillin-resistant

methodsWe retrospectively analysed 345,748 bacterial specimens collected from five healthcare institutions between 2010 and 2024. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry data were analysed using a hierarchical classification framework enhanced by machine learning models to identify the MRSA status, staphylococcal cassette chromosome mec subtypes, and PVL presence. Area under the curve (AUC), sensitivity, and specificity were used for model evaluation.

resultsAI-CDSS was highly accurate for MRSA genotyping (AUCs > 0.9) and PVL detection (AUC = 0.85). Automating hierarchical classifications effectively replaced labour-intensive PCR processes, reducing diagnostic complexity and resource use.

conclusionsAI-CDSS is a scalable and efficient method for MRSA genotyping and PVL detection. By streamlining diagnostics and supporting timely clinical interventions, this system can improve infection management and patient care, which will reduce mortality associated with MRSA infections.

Indexed as

Bacterial ToxinsBacterial Typing TechniquesLeukocidinsMachine LearningMethicillin-Resistant Staphylococcus aureusSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationStaphylococcal InfectionsExotoxinsGenotypeHumansRetrospective StudiesSensitivity and SpecificityBacterial ToxinsExotoxinsLeukocidinsPanton-Valentine leukocidinAI-CDSSartificial intelligenceartificial intelligence-clinical decision support systemsMRSAPVL

Identifiers

PMID40576568
PMCPMC12272661

What OpenQuestion holds

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LicenceCC BY-NC
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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.