Retrospective Derivation and Validation of an Automated Electronic Search Algorithm to Identify Post operative Cardiovascular and Thromboembolic Complications

Summary

Background: With increasing numbers of hospitals adopting electronic medical records, electronic search algorithms for identifying postoperative complications can be invaluable tools to expedite data abstraction and clinical research to improve patient outcomes.

Objectives: To derive and validate an electronic search algorithm to identify postoperative thromboembolic and cardiovascular complications such as deep venous thrombosis, pulmonary embolism, or myocardial infarction within 30 days of total hip or knee arthroplasty.

Methods: A total of 34 517 patients undergoing total hip or knee arthroplasty between January 1, 1996 and December 31, 2013 were identified. Using a derivation cohort of 418 patients, several iterations of a free-text electronic search were developed and refined for each complication. Subsequently, the automated search algorithm was validated on an independent cohort of 2 857 patients, and the sensitivity and specificities were compared to the results of manual chart review.

Results: In the final derivation subset, the automated search algorithm achieved a sensitivity of 91% and specificity of 85% for deep vein thrombosis, a sensitivity of 96% and specificity of 100% for pulmonary embolism, and a sensitivity of 100% and specificity of 95% for myocardial infarction. When applied to the validation cohort, the search algorithm achieved a sensitivity of 97% and specificity of 99% for deep vein thrombosis, a sensitivity of 97% and specificity of 100% for pulmonary embolism, and a sensitivity of 100% and specificity of 99% for myocardial infarction.

Conclusions: The derivation and validation of an electronic search strategy can accelerate the data abstraction process for research, quality improvement, and enhancement of patient care, while maintaining superb reliability compared to manual review.

Citation: Tien M, Kashyap R, Wilson GA, Hernandez-Torres V, Jacob AK, Schroeder DR, Mantilla CB. Retrospective Derivation and Validation of an Automated Electronic Search Algorithm to Identify Postoperative Thromboembolic and Cardiovascular Complications. Appl Clin Inform 2015; 6: 565–576

http://dx.doi.org/10.4338/ACI-2015-03-RA-0026

Keywords

Clinical research informatics - search algorithm - myocardial infarction - venous thromboembolism

 

Conflicts of Interest

The authors have no conflicts of interest.

• References

• 1 Hsiao CJ, Hing E, Socey TC, Cai B. Electronic health record systems and intent to apply for meaningful use incentives among office-based physician practices: United States, 2001–2011. NCHS data brief. 2011: 1-8
• 2 Singh B, Singh A, Ahmed A, Wilson GA, Pickering BW, Herasevich V, Gajic O, Li G. Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records. Mayo Clin Proc 2012; 87 (09) 817-824.
• 3 Ahmed A, Thongprayoon C, Pickering BW, Akhoundi A, Wilson G, Pieczkiewicz D, Herasevich V. Towards prevention of acute syndromes: electronic identification of at-risk patients during hospital admission. Appl Clin Inform 2014; 5 (01) 58-72.
• 4 Smischney NJ, Velagapudi VM, Onigkeit JA, Pickering BW, Herasevich V, Kashyap R. Retrospective derivation and validation of a search algorithm to identify emergent endotracheal intubations in the intensive care unit. Appl Clin Inform 2013; 4 (03) 419-427.
• 5 Rishi MA, Kashyap R, Wilson G, Hocker S. Retrospective derivation and validation of a search algorithm to identify extubation failure in the intensive care unit. BMC Anesthesiol 2014; 14: 41.
• 6 Alsara A, Warner DO, Li G, Herasevich V, Gajic O, Kor DJ. Derivation and validation of automated electronic search strategies to identify pertinent risk factors for postoperative acute lung injury. Mayo Clin Proc 2011; 86 (05) 382-388.
• 7 Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V, Basford M, Chute CG, Kullo IJ, Li R, Pacheco JA, Rasmussen LV, Spangler L, Denny JC. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 2013; 20 e1 e147-e154.
• 8 Carroll RJ, Thompson WK, Eyler AE, Mandelin AM, Cai T, Zink RM, Pacheco JA, Boomershine CS, Lasko TA, Xu H, Karlson EW, Perez RG, Gainer VS, Murphy SN, Ruderman EM, Pope RM, Plenge RM, Kho AN, Liao KP, Denny JC. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc 2012; 19 e1 e162-e169.
• 9 Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am 2007; 89 (04) 780-785.
• 10 Mantilla CB, Horlocker TT, Schroeder DR, Berry DJ, Brown DL. Frequency of myocardial infarction, pulmonary embolism, deep venous thrombosis, and death following primary hip or knee arthroplasty. Anesthesiology 2002; 96 (05) 1140-1146.
• 11 Mantilla CB, Horlocker TT, Schroeder DR, Berry DJ, Brown DL. Risk factors for clinically relevant pulmonary embolism and deep venous thrombosis in patients undergoing primary hip or knee arthroplasty. Anesthesiology 2003; 99 (03) 552-560.
• 12 Memtsoudis SG, Della Valle AG, Besculides MC, Esposito M, Koulouvaris P, Salvati EA. Risk factors for perioperative mortality after lower extremity arthroplasty: a population-based study of 6,901,324 patient discharges. J Arthroplasty 2010; 25 (01) 19-26.
• 13 Berry DJ, Kessler M, Morrey BF. Maintaining a hip registry for 25 years Mayo Clinic experience. Clin Orthop Relat Res 1997; 344: 61-68
• 14 Chute CG, Beck SA, Fisk TB, Mohr DN. The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data. J Am Med Inform Assoc 2010; 17 (02) 131-135.
• 15 Herasevich V, Kor DJ, Li M, Pickering BW. ICU data mart: a non-iT approach. A team of clinicians, researchers and informatics personnel at the Mayo Clinic have taken a homegrown approach to building an ICU data mart. Healthc Inform 2011; 28 (11) 42 44-45.
• 16 Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. Jama 2011; 306 (08) 848-855.
• 17 Wisniewski MF, Kieszkowski P, Zagorski BM, Trick WE, Sommers M, Weinstein RA. Development of a clinical data warehouse for hospital infection control. J Am Med Inform Assoc 2003; 10 (05) 454-462.