Prediction of the disease course in Friedreich ataxia

Hohenfeld, Christian; Terstiege, Ulrich; Dogan, Imis; Giunti, Paola; Parkinson, Michael H.; Mariotti, Caterina; Nanetti, Lorenzo; Fichera, Mario; Durr, Alexandra; Ewenczyk, Claire; Boesch, Sylvia; Nachbauer, Wolfgang; Klopstock, Thomas; Stendel, Claudia; de Rivera Garrido, Francisco Javier Rodriguez; Schoels, Ludger; Hayer, Stefanie N.; Klockgether, Thomas; Giordano, Ilaria; Didszun, Claire; Rai, Myriam; Pandolfo, Massimo; Rauhut, Holger; Schulz, Joerg B. und Reetz, Kathrin (2022): Prediction of the disease course in Friedreich ataxia. In: Scientific Reports, Bd. 12, Nr. 1, 19173

Abstract

We explored whether disease severity of Friedreich ataxia can be predicted using data from clinical examinations. From the database of the European Friedreich Ataxia Consortium for Translational Studies (EFACTS) data from up to five examinations of 602 patients with genetically confirmed FRDA was included. Clinical instruments and important symptoms of FRDA were identified as targets for prediction, while variables such as genetics, age of disease onset and first symptom of the disease were used as predictors. We used modelling techniques including generalised linear models, support-vector-machines and decision trees. The scale for rating and assessment of ataxia (SARA) and the activities of daily living (ADL) could be predicted with predictive errors quantified by root-mean-squared-errors (RMSE) of 6.49 and 5.83, respectively. Also, we were able to achieve reasonable performance for loss of ambulation (ROC-AUC score of 0.83). However, predictions for the SCA functional assessment (SCAFI) and presence of cardiological symptoms were difficult. In conclusion, we demonstrate that some clinical features of FRDA can be predicted with reasonable error;being a first step towards future clinical applications of predictive modelling. In contrast, targets where predictions were difficult raise the question whether there are yet unknown variables driving the clinical phenotype of FRDA.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin ; Medizin > Munich Cluster for Systems Neurology (SyNergy)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 2045-2322
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198