Towards Prevention of Acute Syndromes

A. Ahmed; C. Thongprayoon; B.W. Pickering; A. Akhoundi; G. Wilson; D. Pieczkiewicz; V. Herasevich

doi:10.4338/ACI-2013-07-RA-0045

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2014; 05(01): 58-72
DOI: 10.4338/ACI-2013-07-RA-0045

Research Article

Schattauer GmbH

Towards Prevention of Acute Syndromes

Electronic Identification of At-Risk Patients During Hospital Admission

A. Ahmed

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

²Institute for Health Informatics, University of Minnesota, Minneapolis

⁴Department of Medicine, Division of Critical Care Medicine, Mayo Clinic

,

C. Thongprayoon

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

⁴Department of Medicine, Division of Critical Care Medicine, Mayo Clinic

,

B.W. Pickering

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

³Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic

,

A. Akhoundi

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

,

G. Wilson

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

³Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic

,

D. Pieczkiewicz

²Institute for Health Informatics, University of Minnesota, Minneapolis

,

V. Herasevich

¹Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic, Rochester

³Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic

› Institutsangaben

Abstract

Summary

Background: Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predis-posing conditions at the time of hospital admission.

Methods: This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS pre-disposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction.

Results: Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%.

Conclusion: A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.

Citation: 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 Inf 2014; 5: 58–72

http://dx.doi.org/10.4338/ACI-2013-07-RA-0045

Keywords

Electronic search - EMR - risk factor - ARDS

Volltext

Referenzen

References
1 Kohn L. To err is human: an interview with the Institute of Medicine’s Linda Kohn. Jt Comm J Qual Improv 2000; 26 (Suppl. 04) 227-234.
2 Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003; 348 (25) 2526-2534.
3 Escobar GJ, LaGuardia JC, Turk BJ, Ragins A, Kipnis P, Draper D. Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med 2012; 7 (05) 388-395.
4 Haug PJ, Gardner RM, Tate KE, Evans RS, East TD, Kuperman G, Pryor TA, Huff SM, Warner HR. Decision support in medicine: examples from the HELP system. Comput Biomed Res 1994; 27 (05) 396-418.
5 Herasevich V, Yilmaz M, Khan H, Hubmayr RD, Gajic O. Validation of an electronic surveillance system for acute lung injury. Intensive Care Med 2009; 35 (06) 1018-1023.
6 Ahmed A, Kojicic M, Herasevich V, Gajic O. Early identification of patients with or at risk of acute lung injury. Neth J Med 2009; 67 (09) 268-271.
7 Kor DJ, Erlich J, Gong MN, Malinchoc M, Carter RE, Gajic O, Talmor DS. Association of prehospitalization aspirin therapy and acute lung injury: results of a multicenter international observational study of at-risk patients. Crit Care Med 2011; 39 (11) 2393-2400.
8 Gajic O, Dabbagh O, Park PK, Adesanya A, Chang SY, Hou P, Anderson H, 3rd, Hoth JJ, Mikkelsen ME, Gentile NT, Gong MN, Talmor D, Bajwa E, Watkins TR, Festic E, Yilmaz M, Iscimen R, Kaufman DA, Esper AM, Sadikot R, Douglas I, Sevransky J, Malinchoc M. Early identification of patients at risk of acute lung injury: evaluation of lung injury prediction score in a multicenter cohort study. Am J Respir Crit Care Med 2011; 183 (04) 462-470.
9 Trillo-Alvarez C, Cartin-Ceba R, Kor DJ, Kojicic M, Kashyap R, Thakur S, Thakur L, Herasevich V, Malinchoc M, Gajic O. Acute lung injury prediction score: derivation and validation in a population-based sample. Eur Respir J 2011; 37 (03) 604-609.
10 Overhage JM, Suico J, McDonald CJ. Electronic laboratory reporting: barriers, solutions and findings. J Public Health Manag Pract 2001; 7 (06) 60-66.
11 Wurtz R, Cameron BJ. Electronic laboratory reporting for the infectious diseases physician and clinical microbiologist. Clin Infect Dis 2005; 40 (11) 1638-1643.
12 Erickstad L, Reed G, Bhat D, Roehrborn CG, Lotan Y. Use of electronic medical records to identify patients at risk for prostate cancer in an academic institution. Prostate Cancer Prostatic Dis 2011; 14 (01) 85-89.
13 Brownstein JS, Murphy SN, Goldfine AB, Grant RW, Sordo M, Gainer V, Colecchi JA, Dubey A, Nathan DM, Glaser JP, Kohane IS. Rapid identification of myocardial infarction risk associated with diabetes medications using electronic medical records. Diabetes Care 2010; 33 (03) 526-531.
14 Keyhani S, Hebert PL, Ross JS, Federman A, Zhu CW, Siu AL. Electronic health record components and the quality of care. Med Care 2008; 46 (12) 1267-1272.
15 Baron RJ. Quality improvement with an electronic health record: achievable, but not automatic. Ann Intern Med 2007; 147 (08) 549-552.
16 Tse J, You W. How accurate is the electronic health record? –a pilot study evaluating information accuracy in a primary care setting. Stud Health Technol Inform 2011; 168: 158-164.
17 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.
18 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 4-5.
19 Julian DG. Treatment of cardiac arrest in acute myocardial ischaemia and infarction. Lancet 1961; 2 7207 840-844.
20 Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, Peterson E, Tomlanovich M. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med 2001; 345 (19) 1368-1377.
21 Song JU, Suh GY, Park HY, Lim SY, Han SG, Kang YR, Kwon OJ, Woo S, Jeon K. Early intervention on the outcomes in critically ill cancer patients admitted to intensive care units. Intensive Care Med 2012; 38 (09) 1505-1513.
22 Stanfill MH, Williams M, Fenton SH, Jenders RA, Hersh WR. A systematic literature review of automated clinical coding and classification systems. J Am Med Inform Assoc 2010; 17 (06) 646-651.
23 Glickman SW, Anstrom KJ, Lin L, Chandra A, Laskowitz DT, Woods CW, Freeman DH, Kraft M, Beskow LM, Weinfurt KP, Schulman KA, Cairns CB. Challenges in enrollment of minority, pediatric, and geriatric patients in emergency and acute care clinical research. Ann Emerg Med 2008; 51 (06) 775-780 e3.
24 Hanauer DA, Englesbe MJ, Cowan Jr., JA, Campbell DA. Informatics and the American College of Surgeons National Surgical Quality Improvement Program: automated processes could replace manual record review. J Am Coll Surg 2009; 208 (01) 37-41.
25 Kor DJ, Warner DO, Alsara A, Fernandez-Perez ER, Malinchoc M, Kashyap R, Li G, Gajic O. Derivation and diagnostic accuracy of the surgical lung injury prediction model. Anesthesiology 2011; 115 (01) 117-128.
26 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.
27 Patrick SW, Davis MM, Sedman AB, Meddings JA, Hieber S, Lee GM, Stillwell TL, Chenoweth CE, Espinosa C, Schumacher RE. Accuracy of hospital administrative data in reporting central line-associated bloodstream infections in newborns. Pediatrics 2013; 131 (Suppl. 01) S75-S80.
28 Stickler DE, Royer JA, Hardin JW. Accuracy and usefulness of ICD-10 death certificate coding for the identification of patients with ALS: results from the South Carolina ALS Surveillance Pilot Project. Amyotroph Lateral Scler 2012; 13 (01) 69-73.
29 Bensley RP, Yoshida S, Lo RC, Fokkema M, Hamdan AD, Wyers MC, Chaikof EL, Schermerhorn ML. Accuracy of administrative data versus clinical data to evaluate carotid endarterectomy and carotid stenting. J Vasc Surg. 2013 ; Mar 9.
30 Kottke TE, Baechler CJ. An algorithm that identifies coronary and heart failure events in the electronic health record. Prev Chronic Dis. [Research Support, N. I. H., Extramural Research Support, Non-U. S. Gov’t Research Support, U. S. Gov’t, P. H. S.]. 2013 Feb; 10: E29.
31 van der Kooij J, Goossen WT, Goossen-Baremans AT, de Jong-Fintelman M, van Beek L. Using SNOMED CT codes for coding information in electronic health records for stroke patients. Stud Health Technol Inform 2006; 124: 815-823.
32 Chiang MF, Hwang JC, Yu AC, Casper DS, Cimino JJ, Starren JB. Reliability of SNOMED-CT coding by three physicians using two terminology browsers. AMIA Annu Symp Proc 2006: 131-135.
33 Fitzhenry F, Murff HJ, Matheny ME, Gentry N, Fielstein EM, Brown SH, Reeves RM, Aronsky D, Elkin PL, Messina VP, Speroff T. Exploring the frontier of electronic health record surveillance: the case of postoperative complications. Med Care 2013; 51 (06) 509-516.
34 Yang H, Spasic I, Keane JA, Nenadic G. A text mining approach to the prediction of disease status from clinical discharge summaries. J Am Med Inform Assoc 2009; 16 (04) 596-600.
35 Meystre S, Haug PJ. Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. J Biomed Inform 2006; 39 (06) 589-99.