Download PDF
Methods Inf Med 2014; 53(02): 115-120
DOI: 10.3414/ME13-01-0095
Original Articles
Schattauer GmbH

Does Co-morbidity Provide Significant Improvement on Age Adjustment when Predicting Medical Outcomes?[*]

G. Mnatzaganian
1   Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
2   Discipline of Public Health, School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia
,
P. Ryan
2   Discipline of Public Health, School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia
,
J. E. Hiller
1   Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
2   Discipline of Public Health, School of Population Health, The University of Adelaide, Adelaide, South Australia, Australia
› Author Affiliations
Further Information

Publication History

received: 23 August 2013

accepted: 09 January 2014

Publication Date:
20 January 2018 (online)

    Permissions and Reprints

Summary

Objective: Using three risk-adjustment methods we evaluated whether co-morbidity derived from electronic hospital patient data provided significant improvement on age adjustment when predicting major outcomes following an elective total joint replacement (TJR) due to osteoarthritis.

Methods: Longitudinal data from 819 elderly men who had had a TJR were integrated with hospital morbidity data (HMD) and mortality records. For each participant, any mor bidity or health-related outcome was retrieved from the linked data in the period 1970 through to 2007 and this enabled us to better account for patient co-morbidities. Co-mor bidities recorded in the HMD in all admissions preceding the index TJR admission were used to construct three risk-adjustment methods, namely Charlson co-morbidity index (CCI), Elixhauser’s adjustment method, and number of co-morbidities. Postoperative outcomes evaluated included length of hospital stay, 90-day readmission, and 1-year and 2-year mortality. These were modelled using Cox proportional hazards regression as a function of age for the baseline models, and as a function of age and each of the risk-adjustment methods. The difference in the statistical performance between the models that included age alone and those that also included the co-morbidity adjustment method was as sessed by measuring the difference in the Harrell’s C estimates between pairs of mod els applied to the same patient data using Bootstrap analysis with 1000 replications.

Results: Number of co-morbidities did not provide any significant improvement in model discrimination when added to baseline models observed in all outcomes. CCI significantly improved model discrimination when predicting post-operative mortality but not when length of stay or readmission was modelled. For every one point increase in CCI, postoperative 1- and 2-year mortality increased by 37% and 30%, respectively. Elixhauser’s method outperformed the other two providing significant improvement on age adjustment in all outcomes.

Conclusion: The predictive performance of co-morbidity derived from electronic hospital data is outcome and risk-adjustment method specific.

Keywords

Hospital morbidity data - model discrimination - age - co-morbidity adjustment method - length of stay - readmission - mortality

* Supplementary material published on our web-site www.methods-online.com


 

  • References

  • 1 Greenfield S, Apolone G, McNeil BJ, Cleary PD. The importance of co-existent disease in the occurrence of postoperative complications and one-year recovery in patients undergoing total hip replacement. Comorbidity and outcomes after hip replacement. Med Care 1993; 31 (02) 141-154.
    CrossrefPubMedSearch in Google Scholar
  • 2 Higgins TL, McGee WT, Steingrub JS, Rapoport J, Lemeshow S, Teres D. Early indicators of prolonged intensive care unit stay: impact of illness severity, physician staffing, and pre-intensive care unit length of stay. Crit Care Med 2003; 31 (01) 45-51.
    CrossrefPubMedSearch in Google Scholar
  • 3 Mnatzaganian G, Ryan P, Norman PE, Davidson DC, Hiller JE. Use of routine hospital morbidity data together with weight and height of patients to predict in-hospital complications following total joint replacement. BMC Health Serv Res 2012; 12: 380
    CrossrefPubMedSearch in Google Scholar
  • 4 Frey C, Zhou H, Harvey D, White RH. Co-morbidity is a strong predictor of early death and multi-organ system failure among patients with acute pancreatitis. J Gastrointest Surg 2007; 11 (06) 733-742.
    CrossrefPubMedSearch in Google Scholar
  • 5 Librero J, Peiro S, Ordinana R. Chronic comorbidity and outcomes of hospital care: length of stay, mortality, and readmission at 30 and 365 days. J Clin Epidemiol 1999; 52 (03) 171-179.
    CrossrefPubMedSearch in Google Scholar
  • 6 Greenfield S, Aronow HU, Elashoff RM, Watanabe D. Flaws in mortality data. The hazards of ignoring comorbid disease. JAMA 1988; 260 (015) 2253-2255.
    CrossrefPubMedSearch in Google Scholar
  • 7 Mnatzaganian G, Ryan P, Norman PE, Davidson DC, Hiller JE. Total joint replacement in men: old age, obesity and in-hospital complications. ANZ J Surg 2013; 83 (05) 376-381.
    CrossrefPubMedSearch in Google Scholar
  • 8 O’Connell RL, Lim LL. Utility of Charlson comorbidity index computed from routinely collected hospital discharge diagnosis codes. Methods Inf Med 2000; 39 (01) 7-11.
    Thieme ConnectPubMedSearch in Google Scholar
  • 9 D’Hoore W, Sicotte C, Tilquin C. Risk adjustment in outcome assessment: the Charlson comorbidity index. Methods Inf Med 1993; 32 (05) 382-387.
    Thieme ConnectPubMedSearch in Google Scholar
  • 10 Dreinhofer KE, Dieppe P, Sturmer T, Grober-Gratz D, Floren M, Gunther KP. et al Indications for total hip replacement: comparison of assessments of orthopaedic surgeons and referring physicians. Ann Rheum Dis 2006; 65 (010) 1346-1350.
    CrossrefPubMedSearch in Google Scholar
  • 11 Hamel MB, Toth M, Legedza A, Rosen MP. Joint replacement surgery in elderly patients with severe osteoarthritis of the hip or knee: decision making, postoperative recovery, and clinical outcomes. Arch Intern Med 2008; 168 (013) 1430-1440.
    CrossrefPubMedSearch in Google Scholar
  • 12 Mancuso CA, Ranawat CS, Esdaile JM, Johanson NA, Charlson ME. Indications for total hip and total knee arthroplasties. Results of orthopaedic surveys. J Arthroplasty 1996; 11 (01) 34-46.
    CrossrefPubMedSearch in Google Scholar
  • 13 Poses RM, Smith WR, McClish DK, Anthony M. Controlling for confounding by indication for treatment. Are administrative data equivalent to clinical data?. Med Care 1995; 33 (04) Suppl A S36-46.
    PubMedSearch in Google Scholar
  • 14 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 (09) 854-864.
    CrossrefPubMedSearch in Google Scholar
  • 15 Melfi C, Holleman E, Arthur D, Katz B. Selecting a patient characteristics index for the prediction of medical outcomes using administrative claims data. J Clin Epidemiol 1995; 48 (07) 917-926.
    CrossrefPubMedSearch in Google Scholar
  • 16 Mnatzaganian G, Ryan P, Norman PE, Hiller JE. Accuracy of the hospital morbidity data and performance of co-morbidity scores as predictors of mortality. J Clin Epidemiol 2012; 65 (01) 107-115.
    CrossrefPubMedSearch in Google Scholar
  • 17 Mnatzaganian G, Ryan P, Norman PE, Davidson DC, Hiller JE. Smoking, body weight, physical exercise and risk of lower limb total joint replacement in a population-based cohort of men. Arthritis Rheum 2011; 63 (08) 2525-2530.
    PubMedSearch in Google Scholar
  • 18 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 (05) 373-383.
    CrossrefPubMedSearch in Google Scholar
  • 19 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36 (01) 8-27.
    CrossrefPubMedSearch in Google Scholar
  • 20 Holman CD, Bass AJ, Rouse IL, Hobbs MS. Population-based linkage of health records in Western Australia: development of a health services research linked database. Aust NZ J Public Health 1999; 23 (05) 453-459.
    CrossrefPubMedSearch in Google Scholar
  • 21 Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC. et al Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005; 43 (011) 1130-1139.
    CrossrefPubMedSearch in Google Scholar
  • 22 Pencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 2004; 23 (013) 2109-2123.
    CrossrefPubMedSearch in Google Scholar
  • 23 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.
    CrossrefPubMedSearch in Google Scholar
  • 24 Kopelman PG. Obesity as a medical problem. Nature 2000; 404 6778 635-643.
    CrossrefPubMedSearch in Google Scholar
  • 25 Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med 2002; 162 (020) 2269-2276.
    CrossrefPubMedSearch in Google Scholar
  • 26 Kadam UT, Jordan K, Croft PR. Clinical comorbidity in patients with osteoarthritis: a case-control study of general practice consulters in England and Wales. Ann Rheum Dis 2004; 63 (04) 408-414.
    CrossrefPubMedSearch in Google Scholar
  • 27 Groot VD, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity: a critical review of available methods. J Clin Epidemiol 2003; 56 (03) 221-229.
    CrossrefPubMedSearch in Google Scholar
  • 28 Hsia DC. Ahern CA, Ritchie BP, Moscoe LM, Krushat WM. Medicare Reimbursement Accuracy under the Prospective Payment System, 1985 to 1988. JAMA 1992; 268 (07) 896-899.
    CrossrefPubMedSearch in Google Scholar
  • 29 Lorence DP. Ibrahim IA. Disparity in Coding Concordance: Do Physicians and Coders Agree?. J Health Care Finance 2003; 29 (04) 43-53.
    PubMedSearch in Google Scholar
  • 30 Santos S, Murphy G, Baxter K, Robinson KM. Organisational Factors Affecting the Quality of Hospital Clinical Coding. HIM J 2008; 37 (01) 25-37.
    CrossrefPubMedSearch in Google Scholar
  • 31 van Doorn C, Bogardus ST, Williams CS, Concato J, Towle VR, Inouye SK. Risk adjustment for older hospitalized persons: a comparison of two methods of data collection for the Charlson index. J Clin Epidemiol 2001; 54 (07) 694-701.
    CrossrefPubMedSearch in Google Scholar
  • 32 O’Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Serv Res 2005; 40: 1620-1639.
    CrossrefPubMedSearch in Google Scholar
  • 33 Goldman LE, Philip WC, Osmond D, Bindman A. The accuracy of present-on-admission reporting in administrative data. Health Serv Res 2011; 46: 1946-1962.
    CrossrefPubMedSearch in Google Scholar
  • 34 Bhalla R, Kalkut G. Could Medicare readmission policy exacerbate health care system inequity?. Ann Intern Med 2010; 152 (02) 114-117.
    CrossrefPubMedSearch in Google Scholar
  • 35 Milne RIG, Clarke A. Can readmission rates be used as an outcome indicator?. BMJ 1990; 301: 1139-1140.
    CrossrefPubMedSearch in Google Scholar