Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.
Обработка статьи · этапы, ошибки и управление
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.
Arnaldi Dario, Mattioli Pietro, Raffa Stefano, Pardini Matteo, Massa Federico, Iranzo Alex, Perissinotti Andres, Niñerola-Baizán Aida, Gaig Carles, Serradell Monica, Muñoz-Lopetegi Amaia, Mayà Gerard, Liguori Claudio, Fernandes Mariana, Placidi Fabio, Chiaravalloti Agostino, Šonka Karel, Dušek Petr, Zogala David, Trnka Jiri, Boeve Bradley F, Miyagawa Toji, Lowe Val J, Miyamoto Tomoyuki, Miyamoto Masayuki, Puligheddu Monica, Figorilli Michela, Serra Alessandra, Hu Michele T, Klein Johannes C, Bes Frederik, Kunz Dieter, Cochen De Cock Valérie, de Verbizier Delphine, Plazzi Giuseppe, Antelmi Elena, Terzaghi Michele, Bossert Irene, Kulcsárová Kristína, Martino Alessio, Giuliani Alessandro, Pagani Marco, Nobili Flavio, Morbelli Silvia, International REM Sleep Behavior Disorder Study Group
Аннотация
METHODS: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion.
RESULTS: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters.
INTERPRETATION: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. ANN NEUROL 2024;95:1178-1192.