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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 morbidity 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-morbidities 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 assessed by measuring the difference in the Harrell's C estimates between pairs of models 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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare that they have no competing interests., (Schattauer GmbH.)