Sökning: WFRF:(Malmgren B.) > Predicting postoper...
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000 | 04174naa a2200529 4500 | |
001 | oai:gup.ub.gu.se/309050 | |
003 | SwePub | |
008 | 240910s2021 | |||||||||||000 ||eng| | |
024 | 7 | a https://gup.ub.gu.se/publication/3090502 URI |
024 | 7 | a https://doi.org/10.1111/epi.169922 DOI |
040 | a (SwePub)gu | |
041 | a eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Josephson, C. B.4 aut |
245 | 1 0 | a Predicting postoperative epilepsy surgery satisfaction in adults using the 19-item Epilepsy Surgery Satisfaction Questionnaire and machine learning |
264 | c 2021-07-09 | |
264 | 1 | b Wiley,c 2021 |
520 | a Objective: The 19-item Epilepsy Surgery Satisfaction Questionnaire (ESSQ-19) is a validated and reliable post hoc means of assessing patient satisfaction with epilepsy surgery. Prediction models building on these data can be used to counsel patients. Methods: The ESSQ-19 was derived and validated on 229 patients recruited from Canada and Sweden. We isolated 201 (88%) patients with complete clinical data for this analysis. These patients were adults (≥18years old) who underwent epilepsy surgery 1year or more prior to answering the questionnaire. We extracted each patient’s ESSQ-19 score (scale is 0–100; 100 represents complete satisfaction) and relevant clinical variables that were standardized prior to the analysis. We used machine learning (linear kernel support vector regression [SVR]) to predict satisfaction and assessed performance using the R2 calculated following threefold cross-validation. Model parameters were ranked to infer the importance of each clinical variable to overall satisfaction with epilepsy surgery. Results: Median age was 41 years (interquartile range [IQR] = 32–53), and 116 (57%) were female. Median ESSQ-19 global score was 68 (IQR = 59–75), and median time from surgery was 5.4years (IQR = 2.0–8.9). Linear kernel SVR performed well following threefold cross-validation, with an R2 of.44 (95% confidence interval =.36–.52). Increasing satisfaction was associated with postoperative self-perceived quality of life, seizure freedom, and reductions in antiseizure medications. Self-perceived epilepsy disability, age, and increasing frequency of seizures that impair awareness were associated with reduced satisfaction. Significance: Machine learning applied postoperatively to the ESSQ-19 can be used to predict surgical satisfaction. This algorithm, once externally validated, can be used in clinical settings by fixing immutable clinical characteristics and adjusting hypothesized postoperative variables, to counsel patients at an individual level on how satisfied they will be with differing surgical outcomes. © 2021 International League Against Epilepsy | |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Neurologi0 (SwePub)302072 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Neurology0 (SwePub)302072 hsv//eng |
653 | a epilepsy surgery | |
653 | a machine learning | |
653 | a patient satisfaction | |
653 | a patient-reported outcomes | |
653 | a questionnaire | |
700 | 1 | a Engbers, J. D. T.4 aut |
700 | 1 | a Sajobi, T. T.4 aut |
700 | 1 | a Wahby, S.4 aut |
700 | 1 | a Lawal, O. A.4 aut |
700 | 1 | a Keezer, M. R.4 aut |
700 | 1 | a Nguyen, D. K.4 aut |
700 | 1 | a Malmgren, Kristina,d 1952u Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology4 aut0 (Swepub:gu)xmalkr |
700 | 1 | a Atkinson, M. J.4 aut |
700 | 1 | a Hader, W. J.4 aut |
700 | 1 | a Macrodimitris, S.4 aut |
700 | 1 | a Patten, S. B.4 aut |
700 | 1 | a Pillay, N.4 aut |
700 | 1 | a Sharma, R.4 aut |
700 | 1 | a Singh, S.4 aut |
700 | 1 | a Starreveld, Y.4 aut |
700 | 1 | a Wiebe, S.4 aut |
710 | 2 | a Göteborgs universitetb Institutionen för neurovetenskap och fysiologi4 org |
773 | 0 | t Epilepsiad : Wileyg 62:9, s. 2103-2112q 62:9<2103-2112x 0013-9580x 1528-1167 |
856 | 4 8 | u https://gup.ub.gu.se/publication/309050 |
856 | 4 8 | u https://doi.org/10.1111/epi.16992 |
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