Predictive Performance of two Measures of Prognostic Mortality of Cancer Patients in Intensive Care Unit in Jordan: A Comparative Single-Centre Study

Information is presently insufficient about using Acute Physiology and Chronic Health Evaluation (APACHE) mortality predicting models for cancer patients in intensive care unit (ICU).

To evaluates the performance of APACHE II and IV in predicting mortality for cancer patients in ICU.

This was a retrospective study including adult patients admitted to an ICU in a medical center in Jordan. Actual mortality rate was determined and compared with mortality rates predicted by APACHE II and IV models. Receiver operating characteristic (ROC) analysis was used to assess the sensitivity, specificity and predictive performance of both scores. Binary logistic regression analysis was used to determine the effect that APACHE II, APACHE IV and other sample characteristics have on predicting mortality.

251 patients (survived=80; none-survived=171) were included in the study with an actual mortality rate of 68.1%. APACHE II and APACHE IV scores demonstrated similar predicted mortality rates (43.3% vs. 43.0%), sensitivity (52.6% vs. 52.0%), and specificity (76.3%, 76.2%), respectively. The area under (AUC), the ROC curve for APACHE II score was 0.714 (95% confidence interval [CI] 0.645–0.783), and AUC for APACHE IV score was 0.665 (95% CI 0.595–0.734).

As APACHE ӀӀ and ӀV mortality models demonstrate insufficient predicting performance, there is no need to consider APACHE IV in our ICU instead of using APACHE ӀӀ as it has more variables and need longer data extraction time.

We suggest that other approaches in addition to the available models should be attempted to improve the accuracy of cancer prognosis in ICU. Further, it is also required to adjust the available models.