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Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments:
Magnolia Cardona1, Ebony T Lewis2, Mette R Kristensen3
1Centre for Research in Evidence-Based Practice, Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia. mcardona@bond.edu.au.
Abstract:
To determine the validity of the Australian clinical prediction tool Criteria for Screening and Triaging to Appropriate aLternative care (CRISTAL) based on objective clinical criteria to accurately identify risk of death within 3 months of admission among older patients.
Methods:
Prospective study of ≥ 65 year-olds presenting at emergency departments in five Australian (Aus) and four Danish (DK) hospitals. Logistic regression analysis was used to model factors for death prediction; Sensitivity, specificity, area under the ROC curve and calibration with bootstrapping techniques were used to describe predictive accuracy.
Results:
2493 patients, with median age 78-80 years (DK-Aus). The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% CI 7.7-8.6 vs. 5.8 95% CI 5.6-5.9) and Danish mean 7.1 (95% CI 6.6-7.5 vs. 5.5 95% CI 5.4-5.6). The model with Fried Frailty score was optimal for the Australian cohort but prediction with the Clinical Frailty Scale (CFS) was also good (AUROC 0.825 and 0.81, respectively). Values for the Danish cohort were AUROC 0.764 with Fried and 0.794 using CFS. The most significant independent predictors of short-term death in both cohorts were advanced malignancy, frailty, male gender and advanced age. CriSTAL's accuracy was only modest for in-hospital death prediction in either setting.
Conclusions:
The modified CriSTAL tool (with CFS instead of Fried's frailty instrument) has good discriminant power to improve prognostic certainty of short-term mortality for ED physicians in both health systems. This shows promise in enhancing clinician's confidence in initiating earlier end-of-life discussions.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

