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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 Affiliations
Funding This study had no funding.
Further Information

Publication History

received: 17 September 2017

accepted: 19 December 2017

Publication Date:
05 April 2018 (online)

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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