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Optimised machine learning for time-to-event prediction in healthcare applied to timing of gastrostomy in ALS: a multi-centre, retrospective model development and validation study

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Název: Optimised machine learning for time-to-event prediction in healthcare applied to timing of gastrostomy in ALS: a multi-centre, retrospective model development and validation study
Autoři: Weinreich, Marcel, McDonough, Harry, Heverin, Mark, Domhnaill, Éanna Mac, Yacovzada, Nancy, Magen, Iddo, Cohen, Yahel, Harvey, Calum, Elazzab, Ahmed, Gornall, Sarah, Boddy, Sarah, Alix, James J.P., Kurz, Julian M., Kenna, Kevin P., Zhang, Sai, Iacoangeli, Alfredo, Al-Khleifat, Ahmad, Snyder, Michael P., Hobson, Esther, Chio, Adriano, Malaspina, Andrea, Hermann, Andreas, Ingre, Caroline, Costa, Juan Vazquez, van den Berg, Leonard, Panadés, Monica Povedano, van Damme, Philip, Corcia, Phillipe, de Carvalho, Mamede, Al-Chalabi, Ammar, Hornstein, Eran, Elhaik, Eran, Shaw, Pamela J., Hardiman, Orla, McDermott, Christopher, Cooper-Knock, Johnathan
Přispěvatelé: Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Engineering Health, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Teknik för hälsa, Originator, Lund University, Faculty of Science, Department of Biology, Sections at the Department of Biology, Evolutionary Ecology and Infection Biology, Lunds universitet, Naturvetenskapliga fakulteten, Biologiska institutionen, Avdelningar vid Biologiska institutionen, Evolutionär ekologi och infektionsbiologi, Originator
Zdroj: EBioMedicine. 121
Témata: Natural Sciences, Computer and Information Sciences, Other Computer and Information Science, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Annan data- och informationsvetenskap, Medical and Health Sciences, Clinical Medicine, Neurology, Medicin och hälsovetenskap, Klinisk medicin, Neurologi, Gastroenterology and Hepatology, Gastroenterologi och hepatologi, Artificial Intelligence, Artificiell intelligens
Popis: Background: Amyotrophic lateral sclerosis (ALS) is invariably fatal but there are large variations in the rate of progression. The lack of predictability can make it difficult to plan clinical interventions. This includes the requirement for gastrostomy where early or late placement can adversely impact quality of life and survival. Methods: We designed a model to predict the timing of gastrostomy requirement in ALS as indicated by 5% weight loss from diagnosis. We considered >5000 different prediction model configurations including spline models and a set of deep learning (DL) models designed for time-to-event prediction. The optimal prediction model was chosen via a Bayesian framework to avoid overfitting. Model covariates were measurements routinely collected at diagnosis; a separate longitudinal model also incorporated weight at six months. We employed a training dataset of 3000 patients from Europe, and two external validation cohorts spanning distinct populations and clinical contexts (United States, n = 299; and Sweden, n = 215). Missing data was imputed using a random forest model. Findings: The optimal model configuration was a logistic hazard DL model. The optimal model achieved a median absolute error (MAE) between predicted and measured time of 3.7 months, with AUROC 0.75 for gastrostomy requirement at 12 months. To increase accuracy we updated predictions for those who had not received gastrostomy at six months after diagnosis: here MAE was 2.6 months (AUROC 0.86). Combining both models achieved MAE of 1.2 months for the modal group of patients. Prediction performance is stable across both validation cohorts. Missing data was imputed without degrading model performance. Interpretation: To enter routine clinical practice a prospective study will be required, but we have demonstrated stable performance across multiple populations and clinical contexts suggesting that our prediction model can be used to guide individualised gastrostomy decision making for patients with ALS. Funding: Research Ireland (RI) and Biogen have supported the PRECISION ALS programme.
Přístupová URL adresa: https://doi.org/10.1016/j.ebiom.2025.105962
Databáze: SwePub
Popis
Abstrakt:Background: Amyotrophic lateral sclerosis (ALS) is invariably fatal but there are large variations in the rate of progression. The lack of predictability can make it difficult to plan clinical interventions. This includes the requirement for gastrostomy where early or late placement can adversely impact quality of life and survival. Methods: We designed a model to predict the timing of gastrostomy requirement in ALS as indicated by 5% weight loss from diagnosis. We considered >5000 different prediction model configurations including spline models and a set of deep learning (DL) models designed for time-to-event prediction. The optimal prediction model was chosen via a Bayesian framework to avoid overfitting. Model covariates were measurements routinely collected at diagnosis; a separate longitudinal model also incorporated weight at six months. We employed a training dataset of 3000 patients from Europe, and two external validation cohorts spanning distinct populations and clinical contexts (United States, n = 299; and Sweden, n = 215). Missing data was imputed using a random forest model. Findings: The optimal model configuration was a logistic hazard DL model. The optimal model achieved a median absolute error (MAE) between predicted and measured time of 3.7 months, with AUROC 0.75 for gastrostomy requirement at 12 months. To increase accuracy we updated predictions for those who had not received gastrostomy at six months after diagnosis: here MAE was 2.6 months (AUROC 0.86). Combining both models achieved MAE of 1.2 months for the modal group of patients. Prediction performance is stable across both validation cohorts. Missing data was imputed without degrading model performance. Interpretation: To enter routine clinical practice a prospective study will be required, but we have demonstrated stable performance across multiple populations and clinical contexts suggesting that our prediction model can be used to guide individualised gastrostomy decision making for patients with ALS. Funding: Research Ireland (RI) and Biogen have supported the PRECISION ALS programme.
ISSN:23523964
DOI:10.1016/j.ebiom.2025.105962