Retrospective derivation and validation of an automated electronic search algorithm to identify post operative cardiovascular and thromboembolic complications

Applied Clinical Informatics

Volumn 6, Issue 3, 2015, Pages 565-576

  Tien, M ^a   Kashyap, R ^a   Wilson, G A ^a   Hernandez Torres, V ^a   Jacob, A K ^a   Schroeder, D R ^a   Mantilla, Carlos B ^a  

^a MAYO CLINIC   (United States)

Author keywords

Clinical research informatics; Myocardial infarction; Search algorithm; Venous thromboembolism

Indexed keywords

ADVERSE EFFECTS; ALGORITHM; AUTOMATION; DATA MINING; ELECTRONIC HEALTH RECORD; HIP REPLACEMENT; HUMAN; KNEE REPLACEMENT; MEDICAL INFORMATICS; MYOCARDIAL INFARCTION; POSTOPERATIVE COMPLICATIONS; PROCEDURES; PULMONARY EMBOLISM; RETROSPECTIVE STUDY; VALIDATION STUDY; VENOUS THROMBOSIS;

ALGORITHMS; ARTHROPLASTY, REPLACEMENT, HIP; ARTHROPLASTY, REPLACEMENT, KNEE; AUTOMATION; DATA MINING; ELECTRONIC HEALTH RECORDS; HUMANS; MEDICAL INFORMATICS; MYOCARDIAL INFARCTION; POSTOPERATIVE COMPLICATIONS; PULMONARY EMBOLISM; RETROSPECTIVE STUDIES; VENOUS THROMBOSIS;

EID: 84982733719     PISSN: None     EISSN: 18690327     Source Type: Journal    
DOI: 10.4338/ACI-2015-03-RA-0026     Document Type: Review

References (17)

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(9): 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(1): 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(3): 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(5): 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(4): 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(5): 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(3): 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(1): 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(2): 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(8): 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(5): 454-462.