Healthc Inform Res.  2019 Apr;25(2):124-130. 10.4258/hir.2019.25.2.124.

Risk Factor Analysis of Extended Opioid Use after Coronary Artery Bypass Grafting: A Clinical Data Warehouse-Based Study

Affiliations
  • 1Department of Education and Training, Seoul National University Hospital, Seoul, Korea.
  • 2Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, Korea. mluemoon@snubh.org

Abstract


OBJECTIVES
A clinical data warehouse (CDW) is part of our hospital information system, and it provides user-friendly "˜data search and extraction' interfaces for query composition. We carried out a risk factor analysis for the extended use of opioids after coronary artery bypass grafting (CABG), taking advantage of the CDW system.
METHODS
From 2015 to 2017, clinical data from 461 patients who had undergone either isolated or concomitant CABG were extracted using the CDW; the extracted data included baseline patient characteristics, various examination results, and opioid prescription information. Supplementary data that could not be extracted with the CDW were collected via manual review of the electronic medical records.
RESULTS
Data from a total of 447 patients were analyzed finally. The mean patient age was 66.8 ± 10.9 years, 332 patients (74%) were male, and 235 patients (53%) had diabetes. Among the 447 patients, 90 patients (20.1%) took some type of opioid at the 15th postoperative day. An oral rapid-acting agent was the most frequently used opioid (83%). In the risk factor analysis for extended opioid use, duration of operation was the only significant risk factor (odds ratio = 1.004; 95% confidence interval, 1.001-1.007; p = 0.008).
CONCLUSIONS
Longer operation time was associated with the risk of extended opioid use after CABG. CDW was a helpful tool for extracting mass clinical data rapidly, but to maximize its utility, the data should be checked carefully as they are entered in the system so that post-processing can be minimized. Further refinement of the clinical data input and output interface is warranted.

Keyword

Coronary Artery Bypass; Opioid; Pain; Database Management Systems; Data Warehousing

MeSH Terms

Analgesics, Opioid
Coronary Artery Bypass*
Coronary Vessels*
Database Management Systems
Electronic Health Records
Hospital Information Systems
Humans
Male
Prescriptions
Risk Factors*
Analgesics, Opioid

Reference

1. Lahtinen P, Kokki H, Hynynen M. Pain after cardiac surgery: a prospective cohort study of 1-year incidence and intensity. Anesthesiology. 2006; 105(4):794–800.
2. Choi HY, Lee EK. Market analysis of narcotic analgesics in Korea using HIRA claims data. J Korean Acad Manag Care Pharm. 2015; 4(1):31–37.
3. Prather JC, Lobach DF, Goodwin LK, Hales JW, Hage ML, Hammond WE. Medical data mining: knowledge discovery in a clinical data warehouse. Proc AMIA Annu Fall Symp. 1997; 1997:101–105.
4. Sahut D'Izarn M, Caumont Prim A, Planquette B, Revel MP, Avillach P, Chatellier G, et al. Risk factors and clinical outcome of unsuspected pulmonary embolism in cancer patients: a case-control study. J Thromb Haemost. 2012; 10(10):2032–2038.
5. Nishida Y, Takahashi Y, Nakayama T, Soma M, Asai S. Comparative effect of olmesartan and candesartan on lipid metabolism and renal function in patients with hypertension: a retrospective observational study. Cardiovasc Diabetol. 2011; 10:74.
Article
6. Kassin MT, Owen RM, Perez SD, Leeds I, Cox JC, Schnier K, et al. Risk factors for 30-day hospital readmission among general surgery patients. J Am Coll Surg. 2012; 215(3):322–330.
Article
7. Edlund MJ, Steffick D, Hudson T, Harris KM, Sullivan M. Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain. Pain. 2007; 129(3):355–362.
Article
8. Grams ME, Sang Y, Coresh J, Ballew S, Matsushita K, Molnar MZ, et al. Acute kidney injury after major surgery: a retrospective analysis of veterans health administration data. Am J Kidney Dis. 2016; 67(6):872–880.
Article
9. Yoo S, Hwang H, Jheon S. Hospital information systems: experience at the fully digitized Seoul National University Bundang Hospital. J Thorac Dis. 2016; 8:Suppl 8. S637–S641.
Article
10. Kehlet H, Jensen TS, Woolf CJ. Persistent postsurgical pain: risk factors and prevention. Lancet. 2006; 367(9522):1618–1625.
Article
11. Wu CL, Raja SN. Treatment of acute postoperative pain. Lancet. 2011; 377(9784):2215–2225.
Article
12. Alston RP, Pechon P. Dysaesthesia associated with sternotomy for heart surgery. Br J Anaesth. 2005; 95(2):153–158.
13. Peters ML, Sommer M, de Rijke JM, Kessels F, Heineman E, Patijn J, et al. Somatic and psychologic predictors of long-term unfavorable outcome after surgical intervention. Ann Surg. 2007; 245:487–494.
Article
14. Ip HY, Abrishami A, Peng PW, Wong J, Chung F. Predictors of postoperative pain and analgesic consumption: a qualitative systematic review. Anesthesiology. 2009; 111(3):657–677.
15. Choiniere M, Watt-Watson J, Victor JC, Baskett RJ, Bussieres JS, Carrier M, et al. Prevalence of and risk factors for persistent postoperative nonanginal pain after cardiac surgery: a 2-year prospective multicentre study. CMAJ. 2014; 186(7):E213–E223.
Article
16. Walther T, Falk V, Metz S, Diegeler A, Battellini R, Autschbach R, et al. Pain and quality of life after minimally invasive versus conventional cardiac surgery. Ann Thorac Surg. 1999; 67(6):1643–1647.
Article
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