A nomogram for estimating baseline respiratory insufficiency in patients with amyotrophic lateral sclerosis
Year: 2026
Type: article
Study design: Observational Study
Abstract: OBJECTIVES: To develop and validate a nomogram for estimating the risk of baseline respiratory insufficiency in patients with amyotrophic lateral sclerosis (ALS). This also aims to analyze the association between readily available clinical predictors and pulmonary function. MATERIALS AND METHODS: This retrospective study assessed 142 ALS patients treated at the First Hospital of Shanxi Medical University from August 2020 to June 2023. ALS was diagnosed based on revised El Escorial criteria with Awaji modifications. Clinical data and pulmonary function tests (PFTs) were performed during a single hospital stay. Respiratory insufficiency was marked as forced vital capacity (FVC) < 80% of predicted. Muscle strength of neck and limbs was measured with the Medical Research Council (MRC) scale. Multivariable logistic regression evaluated independent predictors of respiratory insufficiency. A nomogram was created and internally validated using bootstrap resampling (1,000 iterations). Model performance was assessed with ROC curve analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS: Of the 142 patients, 30 (21.1%) presented with baseline respiratory insufficiency. In the multivariable analysis, neck flexor muscle strength (OR = 0.497, 95% CI: 0.321-0.769; p = 0.002) and bulbar onset (OR = 4.392, 95% CI: 1.674-11.521; p = 0.003) were independent predictors in the multivariable analysis. The nomogram showed good discrimination and calibration (AUC = 0.823, 95% CI: 0.739-0.907). CONCLUSIONS: Weakness of neck flexors and bulbar onset are independently associated with baseline respiratory insufficiency in ALS patients. The proposed nomogram may serve as a useful tool for baseline screening and risk stratification. External validation in larger multicenter cohorts is warranted before clinical application.
Source: Annals of Medicine
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,557
OpenAlex ID: W7155173773