Predicting in-hospital mortality in patients undergoing complex gastrointestinal surgery: Determining the optimal risk adjustment method

Archives of Surgery

Volumn 147, Issue 2, 2012, Pages 126-135

  Grendar, Jan ^a   Shaheen, Abdel A ^a   Myers, Robert P ^a   Parker, Robyn ^a   Vollmer, Charles M ^a   Ball, Chad G ^a   Quan, May Lynn ^a   Kaplan, Gilaad G ^b   Al Manasra, Tariq ^a   Dixon, Elijah ^a  

^a UNIVERSITY OF CALGARY   (Canada)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ADULT; AGED; ARTICLE; COMORBIDITY; FEMALE; HEALTH CARE COST; HOSPITALIZATION; HUMAN; LENGTH OF STAY; LIVER SURGERY; MAJOR CLINICAL STUDY; MALE; MORTALITY; PANCREAS SURGERY; PREDICTOR VARIABLE; PRIORITY JOURNAL; RISK ASSESSMENT; STOMACH SURGERY; SURGICAL TECHNIQUE;

EID: 84857394305     PISSN: 00040010     EISSN: 15383644     Source Type: Journal    
DOI: 10.1001/archsurg.2011.296     Document Type: Article

References (40)

1. Myers RP, Quan H, Hubbard JN, Shaheen AA, Kaplan GG. Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods. Hepatology. 2009;49(2):568-577.
2. Davila JA, El-Serag HBGI. GI epidemiology: databases for epidemiological studies. Aliment Pharmacol Ther. 2007;25(2):169-176. (Pubitemid 46046373)
3. Hall BL, Hirbe M, Waterman B, Boslaugh S, Dunagan WC. Comparison of mortality risk adjustment using a clinical data algorithm (American College of Surgeons National Surgical Quality Improvement Program) and an administrative data algorithm (Solucient) at the case level within a single institution. J Am Coll Surg. 2007;205(6):767-777. (Pubitemid 350123598)
4. Gordon HS, Johnson ML, Wray NP, et al. Mortality after noncardiac surgery: prediction from administrative versus clinical data. Med Care. 2005;43(2):159-167. (Pubitemid 40222634)
5. Humphries KH, Rankin JM, Carere RG, Buller CE, Kiely FM, Spinelli JJ. Comorbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review? J Clin Epidemiol. 2000;53(4):343-349. (Pubitemid 30216472)
6. Ma C, Crespin M, Proulx MC, et al. Accuracy of administrative data in identifying ulcerative colitis patients presenting with acute flare and undergoing colectomy [abstract]. Am J Gastroenterol. 2010;105:S467 Abstract 1270.
7. Quan H, Khan N, Hemmelgarn BR, et al Hypertension Outcome and Surveillance Team of the Canadian Hypertension Education Programs. Validation of a case definition to define hypertension using administrative data. Hypertension. 2009;54(6):1423-1428.
8. Quan H, Li B, Saunders LD, et al IMECCHI Investigators. Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database. Health Serv Res. 2008;43(4):1424-1441. (Pubitemid 352019142)
9. Luthi JC, Troillet N, Eisenring MC, et al. Administrative data outperformed single-day chart review for comorbidity measure. Int J Qual Health Care. 2007;19(4):225-231. (Pubitemid 47248945)
10. Quan H, Parsons GA, Ghali WA. Validity of procedure codes in International Classification of Diseases, 9th revision, Clinical Modification administrative data. Med Care. 2004;42(8):801-809.
11. Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived from ICD-9-CCM administrative data. Med Care. 2002;40(8):675-685.
12. Chen G, Khan N, Walker R, Quan H. Validating ICD coding algorithms for diabetes mellitus from administrative data. Diabetes Res Clin Pract. 2010;89(2):189-195.
13. Chen G, Faris P, Hemmelgarn B, Walker RL, Quan H. Measuring agreement of administrative data with chart data using prevalence unadjusted and adjusted kappa. BMC Med Res Methodol. 2009;9:5.
14. Clement FM, James MT, Chin R, et al Alberta Kidney Disease Network. Validation of a case definition to define chronic dialysis using outpatient administrative data. BMC Med Res Methodol. 2011;11:25.
15. So L, Beck CA, Brien S, et al. Chart documentation quality and its relationship to the validity of administrative data discharge records. Health Informatics J. 2010;16(2):101-113.
16. Bozic KJ, Chiu VW, Takemoto SK, et al. The validity of using administrative claims data in total joint arthroplasty outcomes research. J Arthroplasty. 2010;25(6)(suppl):58-61.
17. Southern DA, Roberts B, Edwards A, et al. Validity of administrative data claim-based methods for identifying individuals with diabetes at a population level. Can J Public Health. 2010;101(1):61-64.
18. Jetté N, Reid AY, Quan H, Hill MD, Wiebe S. How accurate is ICD coding for epilepsy? Epilepsia. 2010;51(1):62-69.
19. Welke KF, Diggs BS, Karamlou T, Ungerleider RM. Comparison of pediatric cardiac surgical mortality rates from national administrative data to contemporary clinical standards. Ann Thorac Surg. 2009;87(1):216-223.
20. Shahian DM, Silverstein T, Lovett AF, Wolf RE, Normand SL. Comparison of clinical and administrative data sources for hospital coronary artery bypass graft surgery report cards. Circulation. 2007;115(12):1518-1527. (Pubitemid 46648636)
21. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. (Pubitemid 17055592)
22. Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42(4):355-360.
23. Farley JF, Harley CR, Devine JW. A comparison of comorbidity measurements to predict healthcare expenditures. Am J Manag Care. 2006;12(2):110-119.
24. Schneeweiss S, Seeger JD, Maclure M, Wang PS, Avorn J, Glynn RJ. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol. 2001;154(9):854-864. (Pubitemid 33000813)
25. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619.
26. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27. (Pubitemid 128456396)
27. HCUP Databases. Healthcare Cost and Utilization Project (HCUP) Web site. www.hcup-us.ahrq.gov/databases.jsp. Accessed February 2010.
28. Whalen D, Houchens R, Elixhauser A. 2004 HCUP Nationwide Inpatient Sample (NIS) comparison report. HCUP Method Series Report 2007-03. Healthcare Cost and Utilization Project (HCUP) Web site. http://www.hcup-us.ahrq.gov/reports/ methods/methods.jsp. Published December 2, 2006. Accessed February 2010.
29. Consumer Price Index: all urban consumers (US medical care 1982-1984=100). US Dept of Labor Bureau of Labor Statistics Web site. http://www.bls.gov/cpi/home.htm. Accessed February 2010.
30. Thomson Medstat Inc. Medstat Disease Staging Software version 5.24: reference guide. http://www.hcup-us.ahrq.gov/db/nation/nis/ Disease%20Staging%20V5.24%20Reference%20Guide.pdf. Published November 2006. Accessed February 2010.
31. Averill RF, Goldfield N, Hughes JS, et al. All Patient Refined Diagnosis-Related Groups (APR-DRGs) version 20.0: methodology overview. http://www.hcup-us.ahrq.gov/db/nation/nis/APR- RGsV20MethodologyOverviewandBibliography.pdf. Published July 2003. Accessed February 2010.
32. Thombs BD, Singh VA, Halonen J, Diallo A, Milner SM. The effects of preexisting medical comorbidities on mortality and length of hospital stay in acute burn injury: evidence from a national sample of 31,338 adult patients. Ann Surg. 2007;245(4):629-634. (Pubitemid 46555668)
33. Shwartz M, Ash AS. Evaluating risk-adjustment models empirically. In: Iezzoni LI, ed. Risk Adjustment for Measuring Health Outcomes. 3rd ed. Chicago, IL: Health Administration Press; 2003:231-273.
34. Iezzoni LI, Ash AS, Shwartz M, Daley J, Hughes JS, Mackiernan YD. Predicting who dies depends on how severity is measured: implications for evaluating patient outcomes. Ann Intern Med. 1995;123(10):763-770.
35. Iezzoni LI, Ash AS, Shwartz M, Landon BE, Mackiernan YD. Predicting inhospital deaths from coronary artery bypass graft surgery: do different severity measures give different predictions? Med Care. 1998;36(1):28-39. (Pubitemid 128456397)
36. Iezzoni LI, Shwartz M, Ash AS, Mackiernan YD. Predicting in-hospital mortality for stroke patients: results differ across severity-measurement methods. Med Decis Making. 1996;16(4):348-356. (Pubitemid 26337637)
37. Shwartz M, Iezzoni LI, Ash AS, Mackiernan YD. Do severity measures explain differences in length of hospital stay? the case of hip fracture. Health Serv Res. 1996;31(4):365-385. (Pubitemid 26359155)
38. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837-845.
39. Hughes JS, Iezzoni LI, Daley J, Greenberg L. How severity measures rate hospitalized patients. J Gen Intern Med. 1996;11(5):303-311. (Pubitemid 26174601)
40. Jencks SF, Williams DK, Kay TL. Assessing hospital-associated deaths from discharge data: the role of length of stay and comorbidities. JAMA. 1988;260(15):2240-2246.