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The Utility of Acute Physiology and Chronic Health Evaluation II Scores for Prediction of Mortality among Intensive Care Unit (ICU) and Non-ICU Patients with Methicillin-Resistant Staphylococcus aureus Bacteremia

Published online by Cambridge University Press:  02 January 2015

Vanessa Stevens
Affiliation:
Department of Pharmacy Practice, State University of New York School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York Department of Pharmacy, University of Rochester School of Medicine and Dentistry, Rochester, New York
Thomas P. Lodise
Affiliation:
Department of Pharmacy Practice, Albany College of Pharmacy, Albany, New York
Brian Tsuji
Affiliation:
Department of Pharmacy Practice, State University of New York School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
Meagan Stringham
Affiliation:
Department of Pharmacy Practice, State University of New York School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
Jill Butterfield
Affiliation:
Department of Pharmacy Practice, Albany College of Pharmacy, Albany, New York
Elizabeth Dodds Ashley
Affiliation:
Department of Pharmacy, University of Rochester School of Medicine and Dentistry, Rochester, New York
Kristen Brown*
Affiliation:
Department of Pharmacy, University of Rochester School of Medicine and Dentistry, Rochester, New York
Alan Forrest
Affiliation:
Department of Pharmacy Practice, State University of New York School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York Institute for Clinical Pharmacodynamics, Albany, New York
Jack Brown
Affiliation:
Department of Pharmacy Practice, State University of New York School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York Department of Pharmacy, University of Rochester School of Medicine and Dentistry, Rochester, New York
*
Center for Health Outcomes, Pharmacoinformatics, and Epidemiology, State University of New York School of Pharmacy and Pharmaceutical Sciences, 315 Hochstetter Hall, Buffalo, NY 14260 ([email protected])

Abstract

Objective.

Bloodstream infections due to methicillin-resistant Staphylococcus aureus (MRSA) have been associated with significant risk of in-hospital mortality. The acute physiology and chronic health evaluation (APACHE) II score was developed and validated for use among intensive care unit (ICU) patients, but its utility among non-ICU patients is unknown. The aim of this study was to determine the ability of APACHE II to predict death at multiple time points among ICU and non-ICU patients with MRSA bacteremia.

Design.

Retrospective cohort study.

Participants.

Secondary analysis of data from 200 patients with MRSA bacteremia at 2 hospitals.

Methods.

Logistic regression models were constructed to predict overall in-hospital mortality and mortality at 48 hours, 7 days, 14 days, and 30 days using APACHE II scores separately in ICU and non-ICU patients. The performance of APACHE II scores was compared with age adjustment alone among all patients. Discriminatory ability was assessed using the c-statistic and was compared at each time point using X2 tests. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.

Results.

APACHE II was a significant predictor of death at all time points in both ICU and non-ICU patients. Discrimination was high in all models, with c-statistics ranging from 0.72 to 0.84, and was similar between ICU and non-ICU patients at all time points. APACHE II scores significantly improved the prediction of overall and 48-hour mortality compared with age adjustment alone.

Conclusions.

The APACHE II score may be a valid tool to control for confounding or for the prediction of death among ICU and non-ICU patients with MRSA bacteremia.

Type
Original Article
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2012 

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References

1. Cosgrove, SE, Qi, Y, Kaye, KS, Harbarth, S, Karchmer, AW, Carmeli, Y. The impact of methicillin resistance in Staphylococcus aureus bacteremia on patient outcomes: mortality, length of stay, and hospital charges. Infect Control Hosp Epidemiol 2005;26:166174.Google Scholar
2. Albur, M, Bowker, K, Weir, I, MacGowan, A. Factors influencing the clinical outcome of methicillin-resistant Staphylococcus aureus bacteraemia. Eur J Clin Microbiol Infect Dis 2012;31: 295301.Google Scholar
3. Society, AT. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med 2005;171:388416.Google Scholar
4. Brookhart, MA, Rassen, JA, Schneeweiss, S. Instrumental variable methods in comparative safety and effectiveness research. Pharmacoepidemiol Drug Saf 2010;19:537554.Google Scholar
5. Sturmer, T, Jonsson Funk, M, Poole, C, Brookhart, MA. Non-experimental comparative effectiveness research using linked healthcare databases. Epidemiology 2011;22:298301.Google Scholar
6. Marchaim, D, Kaye, KS, Fowler, VG, et al. Case-control study to identify factors associated with mortality among patients with methicillin-resistant Staphylococcus aureus bacteraemia. Clin Microbiol Infect 2010;16:747752.Google Scholar
7. Tumbarello, M, de Gaetano Donati, K, Tacconelli, E, et al. Risk factors and predictors of mortality of methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia in HIV-infected patients. J Antimicrob Chemother 2002;50:375382.Google Scholar
8. Fowler, VG Jr, Justice, A, Moore, C, et al. Risk factors for hematogenous complications of intravascular catheter-associated Staphylococcus aureus bacteremia. Clin Infect Dis 2005;40: 695703.Google Scholar
9. Brookhart, MA, Sturmer, T, Glynn, RJ, Rassen, J, Schneeweiss, S. Confounding control in healthcare database research: challenges and potential approaches. Med Care 2010;48(suppl):S114S120.Google Scholar
10. Vincent, JL, Moreno, R, Takala, J, et al. The SOFA (sepsis-related organ failure assessment) score to describe organ dysfunction/failure. Intensive Care Med 1996;22:707710.Google Scholar
11. Higgins, TL. Quantifying risk and benchmarking performance in the adult intensive care unit. J Intensive Care Med 2007;22: 141156.Google Scholar
12. Strand, K, Flaatten, H. Severity scoring in the ICU: a review. Acta Anaesthesiol Scand 2008;52:467478.Google Scholar
13. Bernard, A, Rivera, C, Pages, PB, Falcoz, PE, Vicaut, E, Dahan, M. Risk model of in-hospital mortality after pulmonary resection for cancer: a national database of the French Society of Thoracic and Cardiovascular Surgery (Epithor). J Thorac Cardiovasc Surg 2011;141:449458.Google Scholar
14. Thompson, HJ, Rivara, FP, Nathens, A, Wang, J, Jurkovich, GJ, Mackenzie, EJ. Development and validation of the mortality risk for trauma comorbidity index. Ann Surg 2010;252:370375.Google Scholar
15. van Walraven, C, Dhalla, IA, Bell, C, et al. Derivation and validation of an index to predict early death or unplanned read-mission after discharge from hospital to the community. CMAJ 2010;182:551557.Google Scholar
16. Knaus, WA, Draper, EA, Wagner, DP, Zimmerman, JE. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13:818829.Google Scholar
17. Ho, KM, Lee, KY, Williams, T, Finn, J, Knuiman, M, Webb, SAR. Comparison of acute physiology and chronic health evaluation (APACHE) II score with organ failure scores to predict hospital mortality. Anaesthesia 2007;62:466473.Google Scholar
18. Dupont, BF, Lortholary, O, Ostrosky-Zeichner, L, Stucker, F, Yeldandi, V. Treatment of candidemia and invasive candidiasis in the intensive care unit: post hoc analysis of a randomized, controlled trial comparing micafungin and liposomal amphotericin B. Crit Care 2009;13:R159.Google Scholar
19. Singh, N, Yee Chang, F, Gayowski, T, Wagener, M, Marino, IR. Fever in liver transplant recipients in the intensive care unit. Clin Transplantation 1999;13:504511.Google Scholar
20. Khan, AA, Parekh, D, Cho, Y, et al. Improved prediction of outcome in patients with severe acute pancreatitis by the APACHE II score at 48 hours after hospital admission compared with the APACHE II score at admission. Arch Surg 2002;137:11361140.Google Scholar
21. Hamilton, KW, Bilker Warren, B, Lautenbach, E. Controlling for severity of illness in assessment of the association between antimicrobial-resistant infection and mortality: impact of calculation of acute physiology and chronic health evaluation (APACHE) II scores at different time points. Infect Control Hosp Epidemiol 2007;28:832836.Google Scholar
22. Inouye, SK, Peduzzi, PN, Robison, JT, Hughes, JS, Horwitz, RI, Concato, J. Importance of functional measures in predicting mortality among older hospitalized patients. JAMA 1998;279: 11871193.Google Scholar
23. Rozzini, R, Trabucchi, M. Pneumonia and mortality beyond hospital discharge in elderly patients. Chest 2011;139:473474.Google Scholar
24. Ranieri, P, Bianchetti, A, Margiotta, A, Virgilio, A, Clini, EM, Trabucchi, M. Predictors of 6-month mortality in elderly patients with mild chronic obstructive pulmonary disease discharged from a medical ward after acute nonacidotic exacerbation. J Am Geriatr Soc 2008;56:909913.Google Scholar
25. Fernandes, NM, Pinto Pdos, S, Lacet, TB, et al. APACHE II and ATN-ISS in acute renal failure (ARF) in intensive care unit (ICU) and non-ICU. Rev Assoc Med Bras 2009;55:434441.Google Scholar
26. Aslar, AK, Kuzu, MA, Elhan, AH, Tanik, A, Hengirmen, S. Admission lactate level and the APACHE II score are the most useful predictors of prognosis following torso trauma. Injury 2004;35:746752.Google Scholar
27. Blot, SI, Vandewoude, KH, Hoste, EA, Colardyn, FA. Outcome and attributable mortality in critically ill patients with bacteremia involving methicillin-susceptible and methicillin-resistant Staphylococcus aureus . Arch Intern Med 2002;162:22292235.Google Scholar
28. Olsson, T, Lind, L. Comparison of the rapid emergency medicine score and APACHE II in nonsurgical emergency department patients. Acad Emerg Med 2003;10:10401048.Google Scholar
29. Chatzicostas, C, Roussomoustakaki, M, Notas, G, et al. A comparison of Child-Pugh, APACHE II and APACHE III scoring systems in predicting hospital mortality of patients with liver cirrhosis. BMC Gastroenterol 2003;3:7.Google Scholar
30. Von Korff, M, Wagner, EH, Saunders, K. A chronic disease score from automated pharmacy data. J Clin Epidemiol 1992;45: 197203.Google Scholar
31. Schneeweiss, S, Maclure, M. Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 2000;29:891898.Google Scholar
32. Butterfield, J, Tsuji, B, Brown, J, et al. Predictors of agr dysfunction in methicillin-resistant Staphylococcus aureus (MRSA) isolates among patients with MRSA bloodstream infections in the “15-20 mg/L” target vancomycin trough era. Antimicrob Agents Chemother 2011.-AAC.00407-00411.Google Scholar
33. Thom, KA, Shardell, MD, Osih, RB, et al. Controlling for severity of illness in outcome studies involving infectious diseases: impact of measurement at different time points. Infect Control Hosp Epidemiol 2008;29:10481053.Google Scholar
34. Hanley, JA, McNeil, BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:2936.Google Scholar
35. 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: 837845.Google Scholar
36. Lemeshow, S, Hosmer, DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol 1982;115:92106.Google Scholar