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Short-term outcome prediction for myasthenia gravis: an explainable machine learning model

BACKGROUND: Myasthenia gravis (MG) is an autoimmune disease characterized by muscle weakness and fatigability. The fluctuating nature of the disease course impedes the clinical management. OBJECTIVE: The purpose of the study was to establish and validate a machine learning (ML)–based model for predi...

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Detalles Bibliográficos
Autores principales: Zhong, Huahua, Ruan, Zhe, Yan, Chong, Lv, Zhiguo, Zheng, Xueying, Goh, Li-Ying, Xi, Jianying, Song, Jie, Luo, Lijun, Chu, Lan, Tan, Song, Zhang, Chao, Bu, Bitao, Da, Yuwei, Duan, Ruisheng, Yang, Huan, Luo, Sushan, Chang, Ting, Zhao, Chongbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969443/
https://www.ncbi.nlm.nih.gov/pubmed/36860354
http://dx.doi.org/10.1177/17562864231154976
Descripción
Sumario:BACKGROUND: Myasthenia gravis (MG) is an autoimmune disease characterized by muscle weakness and fatigability. The fluctuating nature of the disease course impedes the clinical management. OBJECTIVE: The purpose of the study was to establish and validate a machine learning (ML)–based model for predicting the short-term clinical outcome in MG patients with different antibody types. METHODS: We studied 890 MG patients who had regular follow-ups at 11 tertiary centers in China from 1 January 2015 to 31 July 2021 (653 patients for derivation and 237 for validation). The short-term outcome was the modified post-intervention status (PIS) at a 6-month visit. A two-step variable screening was used to determine the factors for model construction and 14 ML algorithms were used for model optimisation. RESULTS: The derivation cohort included 653 patients from Huashan hospital [age 44.24 (17.22) years, female 57.6%, generalized MG 73.5%], and the validation cohort included 237 patients from 10 independent centers [age 44.24 (17.22) years, female 55.0%, generalized MG 81.2%]. The ML model identified patients who were improved with an area under the receiver operating characteristic curve (AUC) of 0.91 [0.89–0.93], ‘Unchanged’ 0.89 [0.87–0.91], and ‘Worse’ 0.89 [0.85–0.92] in the derivation cohort, whereas identified patients who were improved with an AUC of 0.84 [0.79–0.89], ‘Unchanged’ 0.74 [0.67–0.82], and ‘Worse’ 0.79 [0.70–0.88] in the validation cohort. Both datasets presented a good calibration ability by fitting the expectation slopes. The model is finally explained by 25 simple predictors and transferred to a feasible web tool for an initial assessment. CONCLUSION: The explainable, ML-based predictive model can aid in forecasting the short-term outcome for MG with good accuracy in clinical practice.