Lu Xu1, Bingjie He1, Yunjing Zhang1, Lu Chen2, Dongsheng Fan2, Siyan Zhan3,4,5, Shengfeng Wang6. 1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China. 2. Department of Neurology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, China. 3. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China. siyan-zhan@bjmu.edu.cn. 4. Research Center of Clinical Epidemiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China. siyan-zhan@bjmu.edu.cn. 5. Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China. siyan-zhan@bjmu.edu.cn. 6. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China. shengfeng1984@126.com.
Abstract
BACKGROUND: Increasing prognostic models for amyotrophic lateral sclerosis (ALS) have been developed. However, no comprehensive evaluation of these models has been done. The purpose of this study was to map the prognostic models for ALS to assess their potential contribution and suggest future improvements on modeling strategy. METHODS: Databases including Medline, Embase, Web of Science, and Cochrane library were searched from inception to 20 February 2021. All studies developing and/or validating prognostic models for ALS were selected. Information regarding modelling method and methodological quality was extracted. RESULTS: A total of 28 studies describing the development of 34 models and the external validation of 19 models were included. The outcomes concerned were ALS progression (n = 12; 35%), change in weight (n = 1; 3%), respiratory insufficiency (n = 2; 6%), and survival (n = 19; 56%). Among the models predicting ALS progression or survival, the most frequently used predictors were age, ALS Functional Rating Scale/ALS Functional Rating Scale-Revised, site of onset, and disease duration. The modelling method adopted most was machine learning (n = 16; 47%). Most of the models (n = 25; 74%) were not presented. Discrimination and calibration were assessed in 12 (35%) and 2 (6%) models, respectively. Only one model by Westeneng et al. (Lancet Neurol 17:423-433, 2018) was assessed with overall low risk of bias and it performed well in both discrimination and calibration, suggesting a relatively reliable model for practice. CONCLUSIONS: This study systematically reviewed the prognostic models for ALS. Their usefulness is questionable due to several methodological pitfalls and the lack of external validation done by fully independent researchers. Future research should pay more attention to the addition of novel promising predictors, external validation, and head-to-head comparisons of existing models.
BACKGROUND: Increasing prognostic models for amyotrophic lateral sclerosis (ALS) have been developed. However, no comprehensive evaluation of these models has been done. The purpose of this study was to map the prognostic models for ALS to assess their potential contribution and suggest future improvements on modeling strategy. METHODS: Databases including Medline, Embase, Web of Science, and Cochrane library were searched from inception to 20 February 2021. All studies developing and/or validating prognostic models for ALS were selected. Information regarding modelling method and methodological quality was extracted. RESULTS: A total of 28 studies describing the development of 34 models and the external validation of 19 models were included. The outcomes concerned were ALS progression (n = 12; 35%), change in weight (n = 1; 3%), respiratory insufficiency (n = 2; 6%), and survival (n = 19; 56%). Among the models predicting ALS progression or survival, the most frequently used predictors were age, ALS Functional Rating Scale/ALS Functional Rating Scale-Revised, site of onset, and disease duration. The modelling method adopted most was machine learning (n = 16; 47%). Most of the models (n = 25; 74%) were not presented. Discrimination and calibration were assessed in 12 (35%) and 2 (6%) models, respectively. Only one model by Westeneng et al. (Lancet Neurol 17:423-433, 2018) was assessed with overall low risk of bias and it performed well in both discrimination and calibration, suggesting a relatively reliable model for practice. CONCLUSIONS: This study systematically reviewed the prognostic models for ALS. Their usefulness is questionable due to several methodological pitfalls and the lack of external validation done by fully independent researchers. Future research should pay more attention to the addition of novel promising predictors, external validation, and head-to-head comparisons of existing models.
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