Evidence map›Paper›PMID 41940896›Full record

SynthesisJournal of neurology2026

Accuracy of muscle ultrasonography in detecting fasciculations for the diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis.

Zhuojuan Tang, Yanping Lei, Ju Huang, Ruyang He, Yujun Wu, Min Li, Zhi Ye, Xiaoyan He, Hao Heng, Yunhong Zha and 1 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Zhuojuan TangDepartment of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Yichang, 443000, China.
Yanping LeiDepartment of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Yichang, 443000, China.
Ju HuangDepartment of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Yichang, 443000, China.
Ruyang HeDepartment of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Yichang, 443000, China.
Yujun WuDepartment of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Yichang, 443000, China.
Min LiDepartment of Central Lab, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Hubei Province Clinical Medical Research Center for Rare Diseases of Nervous System, Yichang, 443000, China.
Zhi YeDepartment of Central Lab, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Hubei Province Clinical Medical Research Center for Rare Diseases of Nervous System, Yichang, 443000, China.
Xiaoyan HeDepartment of Central Lab, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Hubei Province Clinical Medical Research Center for Rare Diseases of Nervous System, Yichang, 443000, China.
Hao HengCollege of Life Sciences, Hebei University, Baoding, 071000, China.
Yunhong ZhaDepartment of Neurology, The First Affiliated Hospital of Ningbo University, Ningbo, 315020, China.
Jun WeiDepartment of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Yichang, 443000, China. junwei@ctgu.edu.cn.ORCID http://orcid.org/0009-0001-6023-0081

Funding

Hubei Province Leading Medical Talents and Hubei Province Famous Doctor Studio Program EWT201947Local Development Project of Science and Technology guided by the Central Commission 2020ZYYD024
6 · The paper itself

Abstract

purposeThis systematic review and meta-analysis aims to evaluate the diagnostic accuracy of muscle ultrasonography in detecting fasciculations for the diagnosis of amyotrophic lateral sclerosis (ALS).

methodsFollowing PRISMA-DTA guidelines, we systematically searched PubMed, Embase, Cochrane Library, Ovid Medline, Sinomed, Web of Science, CNKI and VIP for studies published up to July 8, 2025 that evaluated muscle ultrasonography to detect fasciculations for ALS diagnosis. The study protocol was registered in PROSPERO (CRD420251057866). Studies were screened using predefined inclusion and exclusion criteria and data were extracted. Risk of bias was assessed with QUADAS-2. Statistical analyses (Stata 16.0 and R 4.5.1 with the "midas," "metandi," and "mada" packages) were used to calculate pooled sensitivity (Sen), specificity (Spe), positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio (DOR). We constructed forest plots, hierarchical summary receiver operating characteristic (HSROC) curves, summary ROC (SROC) curves and calculated the area under the SROC curve (AUC). Univariate meta-regression and subgroup analyses explored sources of heterogeneity. Publication bias was assessed using Deeks' funnel plot asymmetry test. Fagan nomograms were also used to illustrate the changes from pre-test to post-test probability and to enhance clinical interpretability.

resultsThirteen studies involving 1176 participants met the inclusion criteria. Muscle ultrasonography for fasciculation detection in ALS yielded a pooled sensitivity of 0.87 (95% CI 0.83-0.91) and specificity of 0.91 (95% CI 0.86-0.94). The pooled LR+ was 9.81 (95% CI 6.25-15.40) and LR- was 0.14 (95% CI 0.10-0.19), with a DOR of 70.03 (95% CI 41.72-117.56). The area under the SROC curve was 0.94 (95% CI 0.91-0.95). Meta-regression identified scan duration as a primary factor influencing diagnostic accuracy, with scan durations ≥ 30 s associated with higher sensitivity but relatively lower specificity. Deeks' funnel plot showed no significant asymmetry (p = 0.61), indicating no notable publication bias. Fagan nomograms showed that, at a pre-test probability of 30%, the post-test probability increased to 81% after a positive MUS result and decreased to 6% after a negative result.

conclusionMuscle ultrasonography demonstrates good pooled diagnostic accuracy for detecting fasciculations in ALS and may serve as a useful adjunct to electrodiagnostic evaluation. Scan duration appears to significantly affect the diagnostic performance, with longer scanning improving sensitivity at the cost of reduced specificity. We speculate that prolonged scanning may be more useful in clinical scenarios where fasciculations are subtle or atypical, whereas shorter scanning may be sufficient when fasciculations are already readily apparent. Nevertheless, further large-scale prospective studies are needed to validate standardized scanning protocols and to better define the clinical role of MUS in ALS diagnostic pathways.

Indexed as

Amyotrophic Lateral SclerosisFasciculationMuscle, SkeletalHumansSensitivity and SpecificityUltrasonographyAmyotrophic lateral sclerosisFasciculationsMeta-analysisMuscle ultrasonographySystematic review

Identifiers

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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 Socratic graph.