Exact(36)
Importance of transitions in creep mechanisms with decreased stress levels in predicting in-service dimensional changes is emphasized.
Nevertheless, in the present study, the specificity and sensitivity, described by AUCROC, of admission lactate and lactate-time-integral were similar in predicting in-hospital mortality.
In the present study, lactate-time-integral was not superior in predicting in-hospital mortality compared with admission or maximum arterial lactate concentrations.
Conclusions: The APACHE IV score and SAPS 3 in predicting in-hospital mortality after liver transplantation showed revealed generally good discrimination and calibration.
The best accuracy in predicting in-hospital mortality (90 days) was achieved by POCAS.
In predicting in-hospital mortality, the Elixhauser models performed better when using the index hospitalization only.
Similar(24)
To determine the selected cut-off points for predicting in-hospital mortality, the sensitivity, specificity, and overall accuracy of prediction were determined (Table 5).
Figure 3 demonstrates the difference between admission lactate levels and lactate-derived variables with respect to predicting in-hospital mortality.
An external validation of the score among 149 critically ill cirrhotic patients showed a good accuracy for predicting in-ICU mortality.
The Rapid Emergency Medicine Score (REMS) and Worthing Physiological Scoring system (WPS) have been developed for predicting in-hospital mortality in nonsurgical emergency department (ED) patients.
However, an external validation of our local score showed a good accuracy for predicting in-ICU mortality in 149 critically ill cirrhotic patients.
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