dc.contributor.author | Luzum, Geske | |
dc.contributor.author | Thrane, Gyrd | |
dc.contributor.author | Aam, Stina | |
dc.contributor.author | Eldholm, Rannveig Sakshaug | |
dc.contributor.author | Grambaite, Ramune | |
dc.contributor.author | Munthe-Kaas, Ragnhild | |
dc.contributor.author | Thingstad, Anne Pernille Mæhle | |
dc.contributor.author | Saltvedt, Ingvild | |
dc.contributor.author | Askim, Torunn | |
dc.date.accessioned | 2024-06-12T12:08:39Z | |
dc.date.available | 2024-06-12T12:08:39Z | |
dc.date.created | 2024-01-18T09:52:32Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Archives of Physical Medicine and Rehabilitation. 2014, 105 (5), 921-939. | en_US |
dc.identifier.issn | 0003-9993 | |
dc.identifier.uri | https://hdl.handle.net/11250/3133747 | |
dc.description.abstract | Objective: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.
Design: A prospective multicenter cohort-study with 18-month follow-up.
Setting: Outpatient clinics at 3 university hospitals and 2 local hospitals.
Participants: 474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474).
Interventions: Not applicable.
Main Outcome Measures: The primary outcome, fatigue at 18 months, was assessed using the Fatigue Severity Scale (FSS-7). FSS-7≥5 was defined as fatigue. In total, 45 prediction variables were collected, at initial hospital-stay and 3-month post-stroke.
Results: The best performing model, random forest, predicted 69% of all subjects with fatigue correctly with a sensitivity of 0.69 (95% CI: 0.50, 0.86), a specificity of 0.74 (95% CI: 0.66, 0.83), and an Area under the Receiver Operator Characteristic curve of 0.79 (95% CI: 0.69, 0.87) in new unseen data. The proportion of subjects predicted to suffer from fatigue, who truly suffered from fatigue at 18-months was estimated to 0.41 (95% CI: 0.26, 0.57). The proportion of subjects predicted to be free from fatigue who truly did not have fatigue at 18-months was estimated to 0.90 (95% CI: 0.83, 0.96).
Conclusions:Our findings indicate that the model has satisfactory ability to predict fatigue in the chronic phase post-stroke and may be applicable in clinical settings. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study | en_US |
dc.title.alternative | A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 921-929 | en_US |
dc.source.volume | 105 | en_US |
dc.source.journal | Archives of Physical Medicine and Rehabilitation | en_US |
dc.source.issue | 5 | en_US |
dc.identifier.doi | 10.1016/j.apmr.2023.12.005 | |
dc.identifier.cristin | 2229206 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |