ArticleBMC neurology2025
A nurse-led nomogram for predicting the risk of myasthenic crisis in patients with myasthenia gravis and bulbar weakness.
Article in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundThe present study aimed to develop and validate a nurse-led clinical prediction model for assessing the risk of myasthenic crisis (MC) in myasthenia gravis (MG) patients with bulbar weakness.
methodsA retrospective analysis was conducted on MG patients meeting the inclusion criteria from January 2022 to June 2024. The training group included 308 patients (January 2022-January 2024), and the validation group included 77 patients (February 2024-June 2024). The primary outcome was MC occurrence. A binary logistic regression model was constructed and presented as a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) values, calibration curves, and decision curve analysis (DCA). Internal validation was performed by bootstrap resampling, while external validation was conducted using a validation cohort.
resultsThe study included 385 MG patients. Logistic regression analysis identified Myasthenia Gravis Foundation of America classification, presence of thymoma, body position, partial pressure of carbon dioxide, oxygenation index, and oropharyngeal secretions as the independent predictors of MC. The nomogram showed good discrimination, with AUC values of 0.806 (sensitivity: 76.0%, specificity: 71.7%) in the training group and 0.832 (sensitivity: 60.0%, specificity: 95.2%) in the validation group. The model also exhibited good calibration, and the results of DCA confirmed the clinical utility of the model across a range of risk thresholds.
conclusionThis nomogram can serve as an effective tool for nurses to identify MG patients at a high risk of developing MC. Because this study was a single-center retrospective study, future multicenter validation studies are required to further verify and expand the clinical applicability of this model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.