| アブストラクト | PURPOSE OF THE RESEARCH: Spasticity is one of the well-recognized complications of stroke which may give rise to pain and limit patients' ability to perform daily activities. The predisposing factors and direct effects of post-stroke spasticity also involve high management costs in terms of healthcare resources, and case-control designs are required for establishing such differences. Using 'The Health Improvement Network' (THIN) database, such a study would not provide reliable estimates since the prevalence of post-stroke spasticity was found to be 2%, substantially below the most conservative previously reported estimates. The objective of this study was to use predictive analysis techniques to determine if there are a substantial number of potentially under-recorded patients with post-stroke spasticity. METHODS: This study used retrospective data from adult patients with a diagnostic code for stroke between 2007 and 2011 registered in THIN. Two algorithm approaches were developed and compared, a statistically validated data-trained algorithm and a clinician-trained algorithm. RESULTS: A data-trained algorithm using Random Forest showed better prediction performance than clinician-trained algorithm, with higher sensitivity and only marginally lower specificity. Overall accuracy was 75% and 72%, respectively. The data-trained algorithm predicted an additional 3912 records consistent with patients developing spasticity in the 12months following a stroke. CONCLUSIONS: Using machine learning techniques, additional unrecorded post-stroke spasticity patients were identified, increasing the condition's prevalence in THIN from 2% to 13%. This work shows the potential for under-reporting of PSS in primary care data, and provides a method for improved identification of cases and control records for future studies. |
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| ジャーナル名 | Journal of biomedical informatics |
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| Pubmed追加日 | 2016/3/2 |
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| 投稿者 | Cox, Andrew Paul; Raluy-Callado, Mireia; Wang, Meng; Bakheit, Abdel Magid; Moore, Austen Peter; Dinet, Jerome |
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| 組織名 | Evidera, Metro Building, 6th Floor, 1 Butterwick, London W6 8DL, United Kingdom.;Electronic address: andrew.cox@evidera.com.;Moseley Hall Hospital, Alcester Road, Birmingham, West Midlands B13 8JL, United;Kingdom.;Walton Centre NHS Foundation Trust, Lower Lane, Fazakerley, Liverpool, Merseyside;L9 7LJ, United Kingdom.;Ipsen Pharma, 65, quai Georges Gorse, 92650 Boulogne Billancourt Cedex, France. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/26925518/ |
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