Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease
Abstract Objective Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong pat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-06-01
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| Series: | Annals of Clinical and Translational Neurology |
| Online Access: | https://doi.org/10.1002/acn3.51771 |
| _version_ | 1797803332737171456 |
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| author | Baoyi Huang Xiang Geng Zhiyin Yu Chengfeng Zhang Zheng Chen |
| author_facet | Baoyi Huang Xiang Geng Zhiyin Yu Chengfeng Zhang Zheng Chen |
| author_sort | Baoyi Huang |
| collection | DOAJ |
| description | Abstract Objective Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time. Methods A total of 1260 ALS patients from the PRO‐ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score. Results Three baseline covariates and seven time‐dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6‐month survival probability according to the longitudinal information of individual patients. Interpretation We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions. |
| first_indexed | 2024-03-13T05:19:17Z |
| format | Article |
| id | doaj.art-82a28b7065aa4278b368f11f8f8cd46a |
| institution | Directory Open Access Journal |
| issn | 2328-9503 |
| language | English |
| last_indexed | 2024-03-13T05:19:17Z |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Annals of Clinical and Translational Neurology |
| spelling | doaj.art-82a28b7065aa4278b368f11f8f8cd46a2023-06-15T16:11:55ZengWileyAnnals of Clinical and Translational Neurology2328-95032023-06-0110689290310.1002/acn3.51771Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis diseaseBaoyi Huang0Xiang Geng1Zhiyin Yu2Chengfeng Zhang3Zheng Chen4Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research) Southern Medical University Guangzhou ChinaDepartment of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research) Southern Medical University Guangzhou ChinaDepartment of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research) Southern Medical University Guangzhou ChinaDepartment of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research) Southern Medical University Guangzhou ChinaDepartment of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research) Southern Medical University Guangzhou ChinaAbstract Objective Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time. Methods A total of 1260 ALS patients from the PRO‐ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score. Results Three baseline covariates and seven time‐dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6‐month survival probability according to the longitudinal information of individual patients. Interpretation We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions.https://doi.org/10.1002/acn3.51771 |
| spellingShingle | Baoyi Huang Xiang Geng Zhiyin Yu Chengfeng Zhang Zheng Chen Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease Annals of Clinical and Translational Neurology |
| title | Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease |
| title_full | Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease |
| title_fullStr | Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease |
| title_full_unstemmed | Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease |
| title_short | Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease |
| title_sort | dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease |
| url | https://doi.org/10.1002/acn3.51771 |
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