Amyotrophic lateral sclerosis & frontotemporal degeneration · 2026
Development of a machine learning-based survival prediction model for ALS inclusive of the advanced-stage population.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Beaulieu Danielle, Smith Kelly, Ross Campbell, Yip Sylvia, Felizardo Tania C, Fournier Christina, Glass Jonathan D, Berry James D, Fowler Daniel H, Ennist David L, Pooled Resource Open-Access ALS Clinical Trials Consortium
Аннотация
OBJECTIVE: Develop a machine learning-based model for survival prediction in ALS, including advanced-stage patients (≤50% predicted normal vital capacity [VC50]).
METHODS: Training data from the PRO-ACT Database (n = 6896) was supplemented with advanced-stage ALS patients (n = 678), with model validation on distinct advanced-stage ALS patients (n = 403). Baseline patient characteristics, including slopes from symptom onset, were used to train a random forest model to identify parameters with the greatest relative importance (RI) for predicting survival outcomes. These parameters were used to train a gradient-boosting machine (GBM) model that generated patient-level survival predictions (log-hazard). Model discrimination and calibration were quantified by C-index and calibration-in-the-large plus calibration slope, respectively. Kaplan-Meier curves were generated, with patient stratification into tertiles based on the predicted survival risk score.
RESULTS: Baseline characteristics with the highest RI for driving survival predictions included: VC% slope (20.2%); age (12.4%); VC% (9.9%); VC(L) (7.5%); ALSFRS-R (6.6%); and ALSFRS-R slope (5.1%). Model performance upon external validation was satisfactory for both discrimination (C-index, 0.709 [95% CI, 0.671-0.746]) and calibration (calibration-in-the-large, 0.083 [95% CI, -0.073-0.232]; calibration slope, 0.992 [95% CI, 0.789-1.198]). At 8-months from baseline, the model successfully stratified patients by survival prognosis, with low-, average-, and high-risk population tertiles having observed median survival probabilities of 85, 69, and 43%, respectively.
CONCLUSIONS: This model accurately predicts survival prognosis in ALS, including patients with severely impaired respiratory function. This new understanding of patient-specific factors that drive survival prognostication will be invaluable for reducing patient heterogeneity in clinical trials evaluating novel therapeutic modalities in early- and advanced-stage ALS.
METHODS: Training data from the PRO-ACT Database (n = 6896) was supplemented with advanced-stage ALS patients (n = 678), with model validation on distinct advanced-stage ALS patients (n = 403). Baseline patient characteristics, including slopes from symptom onset, were used to train a random forest model to identify parameters with the greatest relative importance (RI) for predicting survival outcomes. These parameters were used to train a gradient-boosting machine (GBM) model that generated patient-level survival predictions (log-hazard). Model discrimination and calibration were quantified by C-index and calibration-in-the-large plus calibration slope, respectively. Kaplan-Meier curves were generated, with patient stratification into tertiles based on the predicted survival risk score.
RESULTS: Baseline characteristics with the highest RI for driving survival predictions included: VC% slope (20.2%); age (12.4%); VC% (9.9%); VC(L) (7.5%); ALSFRS-R (6.6%); and ALSFRS-R slope (5.1%). Model performance upon external validation was satisfactory for both discrimination (C-index, 0.709 [95% CI, 0.671-0.746]) and calibration (calibration-in-the-large, 0.083 [95% CI, -0.073-0.232]; calibration slope, 0.992 [95% CI, 0.789-1.198]). At 8-months from baseline, the model successfully stratified patients by survival prognosis, with low-, average-, and high-risk population tertiles having observed median survival probabilities of 85, 69, and 43%, respectively.
CONCLUSIONS: This model accurately predicts survival prognosis in ALS, including patients with severely impaired respiratory function. This new understanding of patient-specific factors that drive survival prognostication will be invaluable for reducing patient heterogeneity in clinical trials evaluating novel therapeutic modalities in early- and advanced-stage ALS.