Literature DB >> 19634805

Clinical validation of the AHRQ postoperative venous thromboembolism patient safety indicator.

Katherine E Henderson1, Angela j Recktenwald, Richard M Reichley, Thomas C Bailey, Brian M Waterman, Rebecca L Diekemper, Patricia E Storey, Belinda K Ireland, Wm Claiborne Dunagan.   

Abstract

BACKGROUND: The Agency for Healthcare Research and Quality (AHRQ) patient safety indicators (PSIs) screen for potentially preventable complications in hospitalized patients using hospital administrative data. The PSI for postoperative venous thromboembolism (VTE) relies on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for deep vein thrombosis (DVT) or pulmonary embolism (PE) in secondary diagnoses fields. In a clinical validation study of the PSI for postoperative VTE, natural language processing (NLP), supplemented by pharmacy and billing data, was used to identify VTE events missed by medical records coders.
METHODS: In a retrospective review of postsurgical discharges, charts were processed using the AHRQ PSI software. Cases were identified as possible false negatives by flagging charts for possible VTEs using pharmacy and billing data to identify all patients who were therapeutically anticoagulated or had placement of an inferior vena caval filter. All charts were reviewed by a physician blinded to screening results. Physician interpretation was considered the gold standard for VTE classification.
RESULTS: The AHRQ PSI had a positive predictive value (PPV) of .545 (95% confidence interval [CI], .453-.634) and a negative predictive value (NPV) of .997 (95% CI, .995-.999). Sensitivity was .87 and specificity was .98. Secondary coding review suggested that all 9 false-negative results were miscoded; if they had been properly coded, the sensitivity would increase to 1.00. Most false-positive cases resulted from superficial venous clots identified by the PSI due to coding ambiguity. DISCUSSION: The VTE PSI performed well as a screening tool but generated a significant number of false-positive cases, a problem that could be substantially reduced with improved coding methods.

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Year:  2009        PMID: 19634805     DOI: 10.1016/s1553-7250(09)35052-7

Source DB:  PubMed          Journal:  Jt Comm J Qual Patient Saf        ISSN: 1553-7250


  18 in total

1.  Detecting adverse events in surgery: comparing events detected by the Veterans Health Administration Surgical Quality Improvement Program and the Patient Safety Indicators.

Authors:  Hillary J Mull; Ann M Borzecki; Susan Loveland; Kathleen Hickson; Qi Chen; Sally MacDonald; Marlena H Shin; Marisa Cevasco; Kamal M F Itani; Amy K Rosen
Journal:  Am J Surg       Date:  2013-11-07       Impact factor: 2.565

2.  Determinants of adverse events in vascular surgery.

Authors:  Tina Hernandez-Boussard; Kathryn M McDonald; John M Morton; Ronald L Dalman; Fritz R Bech
Journal:  J Am Coll Surg       Date:  2012-03-15       Impact factor: 6.113

3.  Association between hospital imaging use and venous thromboembolism events rates based on clinical data.

Authors:  Mila H Ju; Jeanette W Chung; Christine V Kinnier; David J Bentrem; David M Mahvi; Clifford Y Ko; Karl Y Bilimoria
Journal:  Ann Surg       Date:  2014-09       Impact factor: 12.969

4.  Relationship between patient safety and hospital surgical volume.

Authors:  Tina Hernandez-Boussard; John R Downey; Kathryn McDonald; John M Morton
Journal:  Health Serv Res       Date:  2011-08-30       Impact factor: 3.402

5.  Improving accuracy of International Classification of Diseases codes for venous thromboembolism in administrative data.

Authors:  Kristen M Sanfilippo; Tzu-Fei Wang; Brian F Gage; Weijian Liu; Kenneth R Carson
Journal:  Thromb Res       Date:  2015-01-14       Impact factor: 3.944

6.  A natural language processing algorithm to define a venous thromboembolism phenotype.

Authors:  Eugenia R McPeek Hinz; Lisa Bastarache; Joshua C Denny
Journal:  AMIA Annu Symp Proc       Date:  2013-11-16

7.  Impact of including readmissions for qualifying events in the patient safety indicators.

Authors:  Sheryl M Davies; Olga Saynina; Laurence C Baker; Kathryn M McDonald
Journal:  Am J Med Qual       Date:  2014-01-24       Impact factor: 1.852

8.  Patient safety in plastic surgery: identifying areas for quality improvement efforts.

Authors:  Tina Hernandez-Boussard; Kathryn M McDonald; Kim F Rhoads; Catherine M Curtin
Journal:  Ann Plast Surg       Date:  2015-05       Impact factor: 1.539

9.  Impact of alternative coding schemes on incidence rates of key complications after total hip arthroplasty: a risk-adjusted analysis of a national data set.

Authors:  Peter Cram; Said A Ibrahim; Xin Lu; Brian R Wolf
Journal:  Geriatr Orthop Surg Rehabil       Date:  2012-03

10.  Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes.

Authors:  Feichen Shen; David W Larson; James M Naessens; Elizabeth B Habermann; Hongfang Liu; Sunghwan Sohn
Journal:  J Healthc Inform Res       Date:  2018-11-06
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