Download PDF
Methods Inf Med 2018; 57(01/02): 81-88
DOI: 10.3414/ME17-01-0097
Original Articles
Schattauer GmbH

Application of Accelerated Time Models to Compare Performance of Two Comorbidity-adjusting Methods with APACHE II in Predicting Short-term Mortality Among the Critically Ill

George Mnatzaganian
1   La Trobe Rural Health School, College of Science, Health and Engineering, La Trobe University, Victoria, Australia
,
Melanie Bish
1   La Trobe Rural Health School, College of Science, Health and Engineering, La Trobe University, Victoria, Australia
,
Jason Fletcher
2   Intensive Care Unit, Bendigo Health, Barnard Street, Bendigo, Victoria, Australia
,
Cameron Knott
2   Intensive Care Unit, Bendigo Health, Barnard Street, Bendigo, Victoria, Australia
3   Monash Rural Health Bendigo, Monash University, Bendigo, Victoria, Australia
4   Department of Intensive Care, Austin Health, Heidelberg, Victoria, Australia
5   Honorary Clinical Fellow, Austin Clinical School, University of Melbourne, Heidelberg, Victoria, Australia
,
John Stephenson
6   School of Human and Health Sciences, University of Huddersfield, Queensgate, Huddersfield, United Kingdom
› Author AffiliationsFunding This study had no funding.
Further Information

Publication History

received: 17 September 2017

accepted: 19 December 2017

Publication Date:
05 April 2018 (online)

    Permissions and Reprints

Summary

Objective: This study aimed to determine how the abilities of the Charlson Index and Elixhauser comorbidities compared with the chronic health components of the Acute Physiology and Chronic Health Evaluation (APACHE II) to predict in-hospital 30 day mortality among adult critically ill patients treated inside and outside of Intensive Care Unit (ICU).

Methods: A total of 701 critically ill patients, identified in a prevalence study design on four randomly selected days in five acute care hospitals, were followed up from the date of becoming critically ill for 30 days or until death, whichever occurred first. Multiple data sources including administrative, clinical, pathology, microbiology and laboratory patient records captured the presence of acute and chronic illnesses. The exponential, Gompertz, Weibull, and log-logistic distributions were assessed as candidate parametric distributions available for the modelling of survival data. Of these, the log-logistic distribution provided the best fit and was used to construct a series of parametric survival models.

Results: Of the 701 patients identified in the initial prevalence study, 637 (90.9%) had complete data for all fields used to calculate APACHE II score. Controlling for age, sex and Acute Physiology Score (APS), the chronic health components of the APACHE II score, as a group, were better predictors of survival than Elixhauser comorbidities and Charlson Index. Of the APACHE II chronic health components, only the relatively uncommon conditions of liver failure (3.4%) and immunodeficiency (9.6%) were statistically associated with inferior patient survival with acceleration factors of 0.35 (95% CI 0.17, 0.72) for liver failure, and 0.42 (95% CI 0.26, 0.72) for immunodeficiency. Sensitivity analyses on an imputed dataset that also included the 64 individuals with imputed APACHE II score showed identical results.

Conclusion: Our study suggests that, in acute critical illness, most co-existing comorbidities are not major determinants of shortterm survival, indicating that observed variations in ICU patient 30-day mortality may not be confounded by lack of adjustment to pre-existing comorbidities.

Keywords

Accelerated time modelling - APACHE II - Charlson Index - critically ill - Elixhauser comorbidities - in-hospital mortality

 

  • References

  • 1 Adhikari NKJ, Fowler RA, Bhagwanjee S, Rubenfeld GD. Critical care and the global burden of critical illness in adults. Lancet 2010; 376 9749 1339-1346.
    CrossrefPubMedGoogle Scholar
  • 2 Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13 (10) 818-829.
    CrossrefPubMedGoogle Scholar
  • 3 Bouch CD, Thompson JP. Severity scoring systems in the critically ill. Contin Educ Anaesth Crit Care Pain 2008; 08 (05) 181-185.
    CrossrefPubMedGoogle Scholar
  • 4 Papachristou GI, Muddana V, Yadav D, O’Connell M, Sanders MK, Slivka A. et al. Comparison of BISAP, Ranson’s, APACHE-II, and CTSI scores in predicting organ failure, complications, and mortality in acute pancreatitis. Am J Gastroenterol 2010; 105 (02) 435-441.
    CrossrefPubMedGoogle Scholar
  • 5 Rowan KM, Kerr JH, Major ED, McPherson K, Short A, Vessey MP. Intensive Care Society’s Acute Physiology and Chronic Health Evaluation (APACHE II) study in Britain and Ireland: A prospective, multicenter, cohort study comparing two methods for predicting outcome for adult intensive care patients. Crit Care Med 1994; 22 (09) 1392-1401.
    CrossrefPubMedGoogle Scholar
  • 6 Del Bufalo C, Morelli A, Bassein A, Fasano L, Quarta CC, Pcilli AM. et al. Severity scores in respiratory intensive care: APACHE II predicted mortality better than SAPS II. Resp Care 1995; 40 (10) 1042-1047.
    PubMedGoogle Scholar
  • 7 Mnatzaganian G, Sprung C, Zitser-Gurevich Y, Galai N, Goldschmidt N, Levi L. et al. Effect of infections on 30-day mortality among critically ill patients hospitalized in and out of the intensive care unit. Crit Care Med 2008; 36 (04) 1097-1104.
    CrossrefPubMedGoogle Scholar
  • 8 Simchen E, Sprung C, Galai N, Zister-Gurevich Y, Bar-Lavi Y, Levi L. et al. Survival of critically ill patients hospitalized in and out of intensive care. Crit Care Med 2007; 35 (02) 449-457.
    CrossrefPubMedGoogle Scholar
  • 9 Garland A, Olafson K, Ramsey CD, Yogendran M, Fransoo R. Epidemiology of critically ill patients in intensive care units: a population-based observational study. Crit Care 2013; 17 (05) R212.
    CrossrefPubMedGoogle Scholar
  • 10 Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med 2002; 162 (20) 2269-2276.
    CrossrefPubMedGoogle Scholar
  • 11 Goldhill DR, Withington PS. The effect of casemix adjustment on mortality as predicted by APACHE II. Intensive Care Med 1996; 22 (05) 415-419.
    CrossrefPubMedGoogle Scholar
  • 12 Johnston JA, Wagner DP, Timmons S, Welsh D, Tsevat J, Render ML. Impact of different measures of comorbid disease on predicted mortality of intensive care unit patents. Med Care 2002; 40 (10) 929-940.
    CrossrefPubMedGoogle Scholar
  • 13 Norena M, Wong H, Thompson WD, Keenan SP, Dodek PM. Adjustment of intensive care unit outcomes for severity of illness and comorbidity scores. J Crit Care 2006; 21 (02) 142-150.
    CrossrefPubMedGoogle Scholar
  • 14 Poses RM, McClish DK, Smith WR, Bekes C, Scott WE. Prediction of survival of critically ill patients by admission comorbidity. J Clin Epidemiol 1996; 49 (07) 743-747.
    CrossrefPubMedGoogle Scholar
  • 15 Quach S, Hennessy DA, Faris P, Fong A, Quan H, Doig C. A comparison between the APACHE II and Charlson Index Score in predicting hospital mortality in critically ill patients. BMC Health Serv Res 2009; 09: 129.
    CrossrefPubMedGoogle Scholar
  • 16 Ho KM, Finn J, Knuiman M, Webb SAR. Combining multiple comorbidities with Acute Physiology Score to predict hospital mortality of critically ill patients: a linked data cohort study. Anaesthesia 2007; 62 (11) 1095-1100.
    CrossrefPubMedGoogle Scholar
  • 17 Christensen S, Johansen MB, Christiansen RJ, Lemeshow S. Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care. Clin Epidemiol 2011; 03: 203-211.
    PubMedGoogle Scholar
  • 18 Mnatzaganian G, Davidson DC, Hiller JE, Ryan P. Propensity score matching and randomization. J Clin Epidemiol 2015; 68 (07) 760-768.
    CrossrefPubMedGoogle Scholar
  • 19 Pintado MC, Villa P, Gonzalez-Garcia N, Lujan J, Molina R, Trascasa M. et al. Characteristics and outcomes of elderly patients refused to ICU. ScientificWorldJournal 2013; 590837 doi: 10.1155/2013/590837..
    PubMedGoogle Scholar
  • 20 Simchen E, Sprung C, Galai N, Zitser-Gurevich Y, Bar-Lavi Y, Gurman G. et al. Survival of critically ill patients hospitalized in and out of intensive care units under paucity of intensive care unit beds. Crit Care Med 2004; 32 (08) 1654-1661.
    CrossrefPubMedGoogle Scholar
  • 21 Carroll GC, Herbert DA. Using population death rate to predict rate of admissions to the intensive care unit. Crit Care Med 2004; 32 (01) 70-76.
    CrossrefPubMedGoogle Scholar
  • 22 Franklin C, Rackow EC, Mandoni B, Burke G, Weil MH. Triage considerations in medical intensive care. Arch Intern Med 1990; 150 (07) 1455-1459.
    CrossrefPubMedGoogle Scholar
  • 23 Mnatzaganian G, Braitberg G, Hiller JE, Kuhn L, Chapman R. Sex differences in in-hospital mortality following a first acute myocardial infarction: symptomatology, delayed presentation, and hospital setting. BMC Cardiovasc Dis 2016; 16 (01) 109.
    CrossrefPubMedGoogle Scholar
  • 24 Valentine A, Jordan B, Lang T, Hiesmayr M, Metnitz PGH. Gender-related differences in intensive care: a multiple-center cohort study of therapeutic interventions and outcome in critically ill patients. Crit Care Med 2003; 31 (07) 1901-1907.
    CrossrefPubMedGoogle Scholar
  • 25 Mnatzaganian G, Galai N, Sprung C, Zitser-Gurevich Y, Mandel M, Ben-Hur D. et al. Increased risk of bloodstream and urinary infections in intensive care unit (ICU) patients compared with patients fitting ICU admission criteria treated in regular wards. J Hosp Infect 2005; 59 (04) 331-342.
    CrossrefPubMedGoogle Scholar
  • 26 Task Force of the American College of Critical Care Medicine. Guidelines for intensive care unit admission, discharge, and triage. Crit Care Med 1999; 27 (03) 633-638.
    CrossrefPubMedGoogle Scholar
  • 27 Miranda DR, Rijk AD, Schaufedic W. Simplified Therapeutic Intervention Scoring System: the TISS-28—Results from a multicentre study. Crit Care Med 1996; 24 (01) 64-73.
    CrossrefPubMedGoogle Scholar
  • 28 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 (11) 1130-1139.
    CrossrefPubMedGoogle Scholar
  • 29 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36 (01) 8-27.
    CrossrefPubMedGoogle Scholar
  • 30 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.
    CrossrefPubMedGoogle Scholar
  • 31 Hosmer DW, Lemeshow S, May S. Applied survival analysis. Regression modelling of time-toevent data. 2nd ed. A John Wiley & Sons, Inc., Publication; 2008
    Google Scholar
  • 32 Wei LJ. The accelerated failure time model: A useful alternative to the cox regression model in survival analysis. Stat Med 1992; 11 14–15 1871-1879.
    CrossrefPubMedGoogle Scholar
  • 33 Lambert P, Collett D, Kimber A, Johnson R. Parametric accelerated failure time models with random effects and an application to kidney transplant survival. Stat Med 2004; 23 (20) 3177-3192.
    CrossrefPubMedGoogle Scholar
  • 34 Sterne J, White I, Carlin J, Spratt M, Royston P, Kenward M. et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 2009; 338: b2393.
    CrossrefPubMedGoogle Scholar
  • 35 Rubin DB. Multiple Imputation for Nonresponse in Surveys. 99th ed. Michigan: John Wiley & Sons; 1987
    Google Scholar
  • 36 The public health (medical experiments involving human subjects) regulations, 1980. Int Dig Health Legis 1987; 38 (02) 270-272.
    PubMedGoogle Scholar
  • 37 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.
    CrossrefPubMedGoogle Scholar
  • 38 Mnatzaganian G, Ryan P, Hiller JE. Does co-morbidity provide significant improvement on age adjustment when predicting medical outcomes?. Methods Inf Med 2014; 53 (02) 115-120.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 39 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.
    CrossrefPubMedGoogle Scholar
  • 40 Ladha KS, Zhao K, Quraishi SA, Kurth T, Eikermann M, Kaafarani HMA. et al. The Deyo-Charlson and Elixhauser-van Walraven comorbidity indices as predictors of mortality in critically ill patients. BMJ Open 2015; 05 (09) e008990.
    CrossrefPubMedGoogle Scholar
  • 41 Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality?. JAMA 1992; 267 (16) 2197-2203.
    CrossrefPubMedGoogle Scholar