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@ARTICLE{Weinreich:282301,
      author       = {Weinreich, Marcel and McDonough, Harry and Heverin, Mark
                      and Domhnaill, Éanna Mac and Yacovzada, Nancy and Magen,
                      Iddo and Cohen, Yahel and Harvey, Calum and Elazzab, Ahmed
                      and Gornall, Sarah and Boddy, Sarah and Alix, James J P and
                      Kurz, Julian M and Kenna, Kevin P and Zhang, Sai and
                      Iacoangeli, Alfredo and Al-Khleifat, Ahmad and Snyder,
                      Michael P and Hobson, Esther and Chio, Adriano and
                      Malaspina, Andrea and Hermann, Andreas and Ingre, Caroline
                      and Costa, Juan Vazquez and van den Berg, Leonard and
                      Panadés, Monica Povedano and van Damme, Philip and Corcia,
                      Phillipe and de Carvalho, Mamede and Al-Chalabi, Ammar and
                      Hornstein, Eran and Elhaik, Eran and Shaw, Pamela J and
                      Hardiman, Orla and McDermott, Christopher and Cooper-Knock,
                      Johnathan},
      title        = {{O}ptimised 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.},
      journal      = {EBioMedicine},
      volume       = {121},
      issn         = {2352-3964},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DZNE-2025-01271},
      pages        = {105962},
      year         = {2025},
      abstract     = {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.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.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.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.Research Ireland (RI) and Biogen have
                      supported the PRECISION ALS programme.},
      keywords     = {Humans / Gastrostomy: methods / Amyotrophic Lateral
                      Sclerosis: diagnosis / Amyotrophic Lateral Sclerosis:
                      surgery / Amyotrophic Lateral Sclerosis: therapy / Machine
                      Learning / Male / Female / Retrospective Studies / Middle
                      Aged / Aged / Europe / Bayes Theorem / Amyotrophic lateral
                      sclerosis (ALS) (Other) / Gastrostomy (Other) / Machine
                      learning (Other) / Personalised medicine (Other) /
                      Time-to-event prediction (Other)},
      cin          = {AG Hermann},
      ddc          = {610},
      cid          = {I:(DE-2719)1511100},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41075354},
      pmc          = {pmc:PMC12547709},
      doi          = {10.1016/j.ebiom.2025.105962},
      url          = {https://pub.dzne.de/record/282301},
}