title: Informatics can identify systemic sclerosis (SSc) patients at risk for scleroderma renal crisis.
abstract: BACKGROUND
  Electronic medical records (EMR) provide an ideal opportunity for the detection, diagnosis, and management of systemic sclerosis (SSc) patients within the Veterans Health Administration (VHA). The objective of this project was to use informatics to identify potential SSc patients in the VHA that were on prednisone, in order to inform an outreach project to prevent scleroderma renal crisis (SRC).
  
  
  METHODS
  The electronic medical data for this study came from Veterans Informatics and Computing Infrastructure (VINCI). For natural language processing (NLP) analysis, a set of retrieval criteria was developed for documents expected to have a high correlation to SSc. The two annotators reviewed the ratings to assemble a single adjudicated set of ratings, from which a support vector machine (SVM) based document classifier was trained. Any patient having at least one document positively classified for SSc was considered positive for SSc and the use of prednisone≥10mg in the clinical document was reviewed to determine whether it was an active medication on the prescription list.
  
  
  RESULTS
  In the VHA, there were 4272 patients that have a diagnosis of SSc determined by the presence of an ICD-9 code. From these patients, 1118 patients (21%) had the use of prednisone≥10mg. Of these patients, 26 had a concurrent diagnosis of hypertension, thus these patients should not be on prednisone. By the use of natural language processing (NLP) an additional 16,522 patients were identified as possible SSc, highlighting that cases of SSc in the VHA may exist that are unidentified by ICD-9. A 10-fold cross validation of the classifier resulted in a precision (positive predictive value) of 0.814, recall (sensitivity) of 0.973, and f-measure of 0.873.
  
  
  CONCLUSIONS
  Our study demonstrated that current clinical practice in the VHA includes the potentially dangerous use of prednisone for veterans with SSc. This present study also suggests there may be many undetected cases of SSc and NLP can successfully identify these patients.
authors.affiliation: Veterans Affair Medical Center Salt Lake City Health Care System, Salt Lake City, Utah, USA; Department of Internal Medicine, Division of Rheumatology, University of Utah School of Medicine and Veterans Affair Medical Center, Sat Lake City, Utah, USA; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Doug
authors.initials: D
authors.lastname: Redd
authors.affiliation: Veterans Affair Medical Center Salt Lake City Health Care System, Salt Lake City, Utah, USA; Department of Internal Medicine, Division of Rheumatology, University of Utah School of Medicine and Veterans Affair Medical Center, Sat Lake City, Utah, USA. Electronic address: tracy.frech@hsc.utah.edu.
authors.forename: Tracy M
authors.initials: TM
authors.lastname: Frech
authors.affiliation: Veterans Affair Medical Center Salt Lake City Health Care System, Salt Lake City, Utah, USA; Department of Internal Medicine, Division of Rheumatology, University of Utah School of Medicine and Veterans Affair Medical Center, Sat Lake City, Utah, USA.
authors.forename: Maureen A
authors.initials: MA
authors.lastname: Murtaugh
authors.affiliation: Veterans Affair Medical Center Denver Health Care System, Denver, Colorado, USA.
authors.forename: Julia
authors.initials: J
authors.lastname: Rhiannon
authors.affiliation: Veterans Affair Medical Center Salt Lake City Health Care System, Salt Lake City, Utah, USA; Department of Internal Medicine, Division of Rheumatology, University of Utah School of Medicine and Veterans Affair Medical Center, Sat Lake City, Utah, USA; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
authors.forename: Qing T
authors.initials: QT
authors.lastname: Zeng
mesh_terms: D000893:Anti-Inflammatory Agents / Q000009:adverse effects / Q000627:therapeutic use; D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D006973:Hypertension; D008491:Medical Informatics Applications; D009323:Natural Language Processing; D011241:Prednisone / Q000009:adverse effects / Q000627:therapeutic use; D051437:Renal Insufficiency / Q000453:epidemiology / Q000517:prevention & control*; D012307:Risk Factors; D012595:Scleroderma, Systemic / Q000150:complications* / Q000188:drug therapy / Q000453:epidemiology* / Q000503:physiopathology
chemical_list: D000893:Anti-Inflammatory Agents; D011241:Prednisone
keywords: Blood pressure; Hypertension; Informatics; Management; Natural language processing; Prednisone; Renal crisis; Scleroderma; Steroid; Systemic sclerosis