ArticleAnnals of medicine2024
A novel hematological score (HS) and its related nomogram model to predict nontuberculous mycobacterial pulmonary disease in patients with suspected multidrug-resistant pulmonary tuberculosis.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Differential analysis of the lower respiratory tract microbiota between patients with non-tuberculous mycobacterial pulmonary disease and pulmonary tuberculosis.BMC microbiology · 2026Article
- Development and internal validation of a LASSO-based clinical prediction model for nontuberculous mycobacterial pulmonary disease versus pulmonary tuberculosis.Frontiers in medicine · 2026Article
- Rapid discrimination ofBMJ health & care informatics · 2025Article
- LASSO regression-based nomogram for distinguishing nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis: a clinical risk prediction model.Scientific reports · 2025Article
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Abstract
backgroundNontuberculous mycobacteria pulmonary disease (NTM-PD) exhibits clinical and radiological characteristics similar to those of pulmonary tuberculosis (PTB). This study aimed to develop a novel hematological score (HS) and its related nomogram model to identify NTM-PD in patients with suspected multidrug-resistant pulmonary tuberculosis (SMDR-PTB) due to lack of response to first-line anti-TB treatment (ATT).
methodsWe retrospectively recruited patients with SMDR-PTB from Wuhan Jinyintan Hospital between January 2014 and January 2022. These patients were divided into NTM-PD and MDR-PTB groups based on pathogen test results. Participants were randomly allocated to training and validation set in a 7:3 ratio. The ROC and LASSO regression were employed to select variables. Multivariate logistic analysis was conducted on the training set to develop the HS and its related nomogram models, followed by internal validation on the validation set.
resultsThe HS was constructed and developed on CKMB, ADA, GGT, LDL, and UHR, demonstrating good predictive value with AUCs of 0.900 and 0.867 in the training and validation sets, respectively. The HS-based nomogram model consists of Age, Gender, DM, HIV infection, ILD and HS, and exhibited strong discriminative ability, accuracy, and clinical utility in two sets. The AUCs were 0.930 and 0.948 in the training set and validation set, respectively.
conclusionHS may be a useful biomarker for identifying NTM-PD in patients with SMDR-PTB. The HS-based nomogram model serves as a convenient and efficient tool for guiding the treatment of SMDR-PTB patients.
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