RSS-Feed abonnieren
DOI: 10.1055/s-0039-1693711
Design and Implementation of a Comprehensive Surveillance System for Venous Thromboembolism in a Defined Region Using Electronic and Manual Approaches
Publikationsverlauf
27. März 2019
16. Juni 2019
Publikationsdatum:
31. Juli 2019 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035026/201903/lookinside/thumbnails/10-1055-s-0039-1693711_190065ra-1.jpg)
Abstract
Background Systematic surveillance for venous thromboembolism (VTE) in the United States has been recommended by several organizations. Despite adoption of electronic medical records (EMRs) by most health care providers and facilities, however, systematic surveillance for VTE is not available.
Objectives This article develops a comprehensive, population-based surveillance strategy for VTE in a defined geographical region.
Methods The primary surveillance strategy combined computerized searches of the EMR with a manual review of imaging data at the Duke University Health System in Durham County, North Carolina, United States. Different strategies of searching the EMR were explored. Consolidation of results with autopsy reports (nonsearchable in the EMR) and with results from the Durham Veterans' Administration Medical Center was performed to provide a comprehensive report of new VTE from the defined region over a 2-year timeframe.
Results Monthly searches of the primary EMR missed a significant number of patients with new VTE who were identified by a separate manual search of radiology records, apparently related to delays in data entry and coding into the EMR. Comprehensive searches incorporating a location-restricted strategy were incomplete due to the assigned residence reflecting the current address and not the address at the time of event. The most comprehensive strategy omitted the geographic restriction step and identified all patients with VTE followed by manual review of individual records to remove incorrect entries (e.g., outside the surveillance time period or geographic location; no evidence for VTE). Consolidation of results from the EMR searches with results from autopsy reports and the separate facility identified additional patients not diagnosed within the Duke system.
Conclusion We identified several challenges with implementing a comprehensive VTE surveillance program that could limit accuracy of the results. Improved electronic strategies are needed to cross-reference patients across multiple health systems and to minimize the need for manual review and confirmation of results.
Keywords
venous thromboembolism - deep vein thrombosis - pulmonary embolism - surveillance - electronic medical recordProtection of Human and Animal Subjects
This population-based surveillance study was deemed exempt from review by the Duke IRB since it met all of the specified criteria covered in 45 CFR 46.101 (b) 5.
-
References
- 1 Heit JA. The epidemiology of venous thromboembolism in the community. Arterioscler Thromb Vasc Biol 2008; 28 (03) 370-372
- 2 Cushman M, Tsai AW, White RH. , et al. Deep vein thrombosis and pulmonary embolism in two cohorts: the longitudinal investigation of thromboembolism etiology. Am J Med 2004; 117 (01) 19-25
- 3 Puurunen MK, Gona PN, Larson MG, Murabito JM, Magnani JW, O'Donnell CJ. Epidemiology of venous thromboembolism in the Framingham Heart Study. Thromb Res 2016; 145: 27-33
- 4 Yusuf HR, Tsai J, Atrash HK. , et al; Centers for Disease Control and Prevention (CDC). Venous thromboembolism in adult hospitalizations - United States, 2007-2009. MMWR Morb Mortal Wkly Rep 2012; 61 (22) 401-404
- 5 Heit JA, Silverstein MD, Mohr DN, Petterson TM, O'Fallon WM, Melton III LJ. Risk factors for deep vein thrombosis and pulmonary embolism: a population-based case-control study. Arch Intern Med 2000; 160 (06) 809-815
- 6 Tsai J, Grant AM, Beckman MG, Grosse SD, Yusuf HR, Richardson LC. Determinants of venous thromboembolism among hospitalizations of US adults: a multilevel analysis. PLoS One 2015; 10 (04) e0123842
- 7 Beckman MG, Hooper WC, Critchley SE, Ortel TL. Venous thromboembolism: a public health concern. Am J Prev Med 2010; 38 (4, Suppl): S495-S501
- 8 Office of the Surgeon General (US); National Heart, Lung, and Blood Institute (US). The Surgeon General’s Call to Action to Prevent Deep Vein Thrombosis and Pulmonary Embolism. Rockville, MD: Office of the Surgeon General (US); 2008. Available at: https://www.ncbi.nlm.nih.gov/books/NBK44178/ . Accessed July 17, 2019
- 9 Raskob GE, Silverstein R, Bratzler DW, Heit JA, White RH. Surveillance for deep vein thrombosis and pulmonary embolism: recommendations from a national workshop. Am J Prev Med 2010; 38 (4, Suppl): S502-S509
- 10 Steinbrook R. Health care and the American Recovery and Reinvestment Act. N Engl J Med 2009; 360 (11) 1057-1060
- 11 Adler-Milstein J, DesRoches CM, Kralovec P. , et al. Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Aff (Millwood) 2015; 34 (12) 2174-2180
- 12 Adler-Milstein J, Furukawa MF, King J, Jha AK. Early results from the hospital Electronic Health Record Incentive Programs. Am J Manag Care 2013; 19 (07) e273-e284
- 13 Tamariz L, Harkins T, Nair V. A systematic review of validated methods for identifying venous thromboembolism using administrative and claims data. Pharmacoepidemiol Drug Saf 2012; 21 (Suppl. 01) 154-162
- 14 Fang MC, Fan D, Sung SH. , et al. Validity of using inpatient and outpatient administrative codes to identify acute venous thromboembolism: the CVRN VTE study. Med Care 2017; 55 (12) e137-e143
- 15 White RH, Garcia M, Sadeghi B. , et al. Evaluation of the predictive value of ICD-9-CM coded administrative data for venous thromboembolism in the United States. Thromb Res 2010; 126 (01) 61-67
- 16 USC Bureau. Secondary. Available at: https://www.census.gov . Accessed July 5, 2019
- 17 Byrne CM, Mercincavage LM, Pan EC, Vincent AG, Johnston DS, Middleton B. The value from investments in health information technology at the U.S. Department of Veterans Affairs. Health Aff (Millwood) 2010; 29 (04) 629-638
- 18 Horvath MM, Winfield S, Evans S, Slopek S, Shang H, Ferranti J. The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvement. J Biomed Inform 2011; 44 (02) 266-276
- 19 Horvath MM, Rusincovitch SA, Brinson S, Shang HC, Evans S, Ferranti JM. Modular design, application architecture, and usage of a self-service model for enterprise data delivery: the Duke Enterprise Data Unified Content Explorer (DEDUCE). J Biomed Inform 2014; 52: 231-242
- 20 Anderson DJ, Rojas LF, Watson S. , et al; CDC Prevention Epicenters Program. Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses. PLoS One 2017; 12 (05) e0176285
- 21 Patel VA, Romain PS, Sanchez J, Fisher DA, Schulteis RD. Obstructive sleep apnea increases the risk of cardiopulmonary adverse events associated with ambulatory colonoscopy independent of body mass index. Dig Dis Sci 2017; 62 (10) 2834-2839
- 22 Chung MJ, Hansen JD, Schulteis RD. , et al. Understanding operator stent choice in the catheterization laboratory using a pre-procedure survey: opportunities for quality improvement. Cardiovasc Revasc Med 2017; 18 (08) 588-591
- 23 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42 (02) 377-381
- 24 Richards CL, Iademarco MF, Atkinson D. , et al. Advances in public health surveillance and information dissemination at the Centers for Disease Control and Prevention. Public Health Rep 2017; 132 (04) 403-410
- 25 Noone AM, Cronin KA, Altekruse SF. , et al. Cancer incidence and survival trends by subtype using data from the surveillance epidemiology and end results program, 1992-2013. Cancer Epidemiol Biomarkers Prev 2017; 26 (04) 632-641
- 26 Moorman JE, Akinbami LJ, Bailey CM. , et al. National surveillance of asthma: United States, 2001-2010. Vital Health Stat 3 2012; (35) 1-58
- 27 Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance--United States, 1999-2011. Chest 2013; 144 (01) 284-305
- 28 Dugan VG, Blanton L, Elal AIA. , et al. Update: influenza activity - United States, October 1-November 25, 2017. MMWR Morb Mortal Wkly Rep 2017; 66 (48) 1318-1326
- 29 Childs JE, Krebs JW, Real LA, Gordon ER. Animal-based national surveillance for zoonotic disease: quality, limitations, and implications of a model system for monitoring rabies. Prev Vet Med 2007; 78 (3-4): 246-261
- 30 Lamb E, Satre J, Hurd-Kundeti G. , et al; Centers for Disease Control and Prevention (CDC). Update on progress in electronic reporting of laboratory results to public health agencies - United States, 2014. Morb Mortal Wkly Rep 2015; 64 (12) 328-330
- 31 Mac Kenzie WR, Davidson AJ, Wiesenthal A. , et al. The promise of electronic case reporting. Public Health Rep 2016; 131 (06) 742-746
- 32 Yusuf HR, Reyes N, Zhang QC, Okoroh EM, Siddiqi AE, Tsai J. Hospitalizations of adults ≥60 years of age with venous thromboembolism. Clin Appl Thromb Hemost 2014; 20 (02) 136-142
- 33 Bilimoria KY, Chung J, Ju MH. , et al. Evaluation of surveillance bias and the validity of the venous thromboembolism quality measure. JAMA 2013; 310 (14) 1482-1489
- 34 Lau BD, Haut ER, Hobson DB. , et al. ICD-9 code-based venous thromboembolism performance targets fail to measure up. Am J Med Qual 2016; 31 (05) 448-453
- 35 Farrow NE, Lau BD, JohnBull EA. , et al. Is the Meaningful Use Venous Thromboembolism VTE-6 Measure Meaningful? A retrospective analysis of one hospital's VTE-6 cases. Jt Comm J Qual Patient Saf 2016; 42 (09) 410-416
- 36 Heit JA, Silverstein MD, Mohr DN, Petterson TM, O'Fallon WM, Melton III LJ. Predictors of survival after deep vein thrombosis and pulmonary embolism: a population-based, cohort study. Arch Intern Med 1999; 159 (05) 445-453
- 37 Goldhaber SZ, Hennekens CH, Evans DA, Newton EC, Godleski JJ. Factors associated with correct antemortem diagnosis of major pulmonary embolism. Am J Med 1982; 73 (06) 822-826
- 38 Stein PD, Henry JW. Prevalence of acute pulmonary embolism among patients in a general hospital and at autopsy. Chest 1995; 108 (04) 978-981
- 39 Wendelboe AM, Campbell J, McCumber M. , et al. The design and implementation of a new surveillance system for venous thromboembolism using combined active and passive methods. Am Heart J 2015; 170 (03) 447-54.e18
- 40 Tian Z, Sun S, Eguale T, Rochefort CM. Automated extraction of VTE events from narrative radiology reports in electronic health records: a validation study. Med Care 2017; 55 (10) e73-e80
- 41 McPeek Hinz ER, Bastarache L, Denny JC. A natural language processing algorithm to define a venous thromboembolism phenotype. AMIA Annu Symp Proc 2013; 2013: 975-983
- 42 Gálvez JA, Pappas JM, Ahumada L. , et al. The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children. J Thromb Thrombolysis 2017; 44 (03) 281-290
- 43 Nelson RE, Grosse SD, Waitzman NJ. , et al. Using multiple sources of data for surveillance of postoperative venous thromboembolism among surgical patients treated in Department of Veterans Affairs hospitals, 2005-2010. Thromb Res 2015; 135 (04) 636-642