Computer-Assisted Categorizing of Head Computed Tomography Reports for Clinical Decision Rule Research - 17/08/11
, Oliver Mayorga, MD a, Christine E. Banfield, BS a, Mark E. Wall, MS c, Ilan Aisic, MS c, Carl Auerbach, PhD d, Paul Gennis, MD a, b
Address for correspondence: Stephen P. Wall, MD, MS, Jacobi Medical Center, Albert Einstein College of Medicine, 1400 Pelham Parkway South, Building 6, Room 1B25, Bronx, NY 10461; 718-918-5822, fax 718-918-7459Résumé |
Study objective |
To develop software that categorizes electronic head computed tomography (CT) reports into groups useful for clinical decision rule research.
Methods |
Data were obtained from the Second National Emergency X-Radiography Utilization Study, a cohort of head injury patients having received head CT. CT reports were reviewed manually for presence or absence of clinically important subdural or epidural hematoma, defined as greater than 1.0 cm in width or causing mass effect. Manual categorization was done by 2 independent researchers blinded to each other’s results. A third researcher adjudicated discrepancies. A random sample of 300 reports with radiologic abnormalities was selected for software development. After excluding reports categorized manually or by software as indeterminate (neither positive nor negative), we calculated sensitivity and specificity by using manual categorization as the standard. System efficiency was defined as the percentage of reports categorized as positive or negative, regardless of accuracy. Software was refined until analysis of the training data yielded sensitivity and specificity approximating 95% and efficiency exceeding 75%. To test the system, we calculated sensitivity, specificity, and efficiency, using the remaining 1,911 reports.
Results |
Of the 1,911 reports, 160 had clinically important subdural or epidural hematoma. The software exhibited good agreement with manual categorization of all reports, including indeterminate ones (weighted κ 0.62; 95% confidence interval [CI] 0.58 to 0.65). Sensitivity, specificity, and efficiency of the computerized system for identifying manual positives and negatives were 96% (95% CI 91% to 98%), 98% (95% CI 98% to 99%), and 79% (95% CI 77% to 80%), respectively.
Conclusion |
Categorizing head CT reports by computer for clinical decision rule research is feasible.
Le texte complet de cet article est disponible en PDF.Plan
| Supervising editor: Jonathan M. Teich, MD, PhD Author contributions: SPW and PG conceived the study and designed the trial. SPW, OM, and CEB supervised and conducted human data collection. SPW and CA supervised the qualitative analysis of the head CT reports. MEW and IA developed, trained, and piloted the head CT interpretation software. SPW and PG supervised software performance testing. SPW provided statistical expertise and analyzed the data. SPW drafted the article, and all authors contributed substantially to its revision. SPW takes responsibility for the paper as a whole. Funding and support: Stephen Wall, MD, MS, is currently in the NIH Loan Repayment Program (contract funded by National Library of Medicine, Fiscal Year 2004). Pointer Software Systems Inc. developed the prototype software at no cost to study investigators. Reprints not available from the authors. |
Vol 48 - N° 5
P. 551 - novembre 2006 Retour au numéro
Article précédent
- Ultrasonographic Guidance for Internal Jugular Vein Cannulation: An Educational Imperative, A Desirable Practice Alternative
- Alfredo Sabbaj, Jerris R. Hedges
- Feasibility of Forearm Ultrasonography-Guided Nerve Blocks of the Radial, Ulnar, and Median Nerves for Hand Procedures in the Emergency Department
- Otto Liebmann, Daniel Price, Chris Mills, Rebekah Gardner, Ralph Wang, Sharon Wilson, Andrew Gray
Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?