title: Predictive analysis for identifying potentially undiagnosed post-stroke spasticity patients in United Kingdom.
abstract: OBJECTIVE
  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.
authors.affiliation: Evidera, Metro Building, 6th Floor, 1 Butterwick, London W6 8DL, United Kingdom. Electronic address: andrew.cox@evidera.com.
authors.forename: Andrew Paul
authors.initials: AP
authors.lastname: Cox
authors.affiliation: Evidera, Metro Building, 6th Floor, 1 Butterwick, London W6 8DL, United Kingdom.
authors.forename: Mireia
authors.initials: M
authors.lastname: Raluy-Callado
authors.affiliation: Evidera, Metro Building, 6th Floor, 1 Butterwick, London W6 8DL, United Kingdom.
authors.forename: Meng
authors.initials: M
authors.lastname: Wang
authors.affiliation: Moseley Hall Hospital, Alcester Road, Birmingham, West Midlands B13 8JL, United Kingdom.
authors.forename: Abdel Magid
authors.initials: AM
authors.lastname: Bakheit
authors.affiliation: Walton Centre NHS Foundation Trust, Lower Lane, Fazakerley, Liverpool, Merseyside L9 7LJ, United Kingdom.
authors.forename: Austen Peter
authors.initials: AP
authors.lastname: Moore
authors.affiliation: Ipsen Pharma, 65, quai Georges Gorse, 92650 Boulogne Billancourt Cedex, France.
authors.forename: Jerome
authors.initials: J
authors.lastname: Dinet
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D005189:False Positive Reactions; D006801:Humans; D000069550:Machine Learning; D009128:Muscle Spasticity / Q000150:complications / Q000175:diagnosis* / Q000453:epidemiology; D015995:Prevalence; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D020521:Stroke / Q000150:complications / Q000175:diagnosis* / Q000453:epidemiology; D006113:United Kingdom
keywords: Electronic medical records; Machine learning; Random forest; Spasticity; Stroke