Cancer continues to be one of the leading causes of death worldwide [1, 2]. In Jordan, cancer is the second leading cause of death after chronic cardiovascular and circulatory disease with an estimated 4,000 new patients diagnosed annually [3, 4]. Most new cancer patients present at advanced stages III/IV, with majority of those dying within one year of diagnosis [3, 5]. Consequently, an increasing number of cancer patients is at risk of admission to intensive care unit (ICU) due to cancer-related complication or the infectious and toxic adverse effects of its treatments [6-8]. Despite the aggressive management provided in ICU, the mortality rate associated with cancer remains high [7, 9, 10]. Minimizing the admission of terminally ill cancer patients to ICU is considered a common practice in industrialized countries [11]. However, admitting those patients to ICUs is more common in Jordan [12-14]. Since ICUs in the developing countries have limited resources, an objective indicator predicting the mortality among cancer patients is needed to guide clinical decisions, avoid unnecessary procedures, and ensure optimal use of ICU resources [15-17].

Illness severity measures are widely used to predict mortality among the patients with wide range of health conditions in ICUs [18]. Acute Physiology and Chronic Health Evaluation (APACHE) is commonly utilized validated measure in research and clinical practices [19]. APACHE scores estimate disease severity based on the presence of several clinical conditions and physiological and laboratory parameters obtained at admission and/or during the first 24 hours after ICU admission. APACHE has been modified several times for an attempt to improve the prediction accuracy of patients’ outcomes. APACHE II is the simplest validated version in predicting mortality and still utilized in ICUs worldwide as well as in Jordan. [18, 20] APACHE IV is the latest updated version that has been designed to include other significant health status parameters to predict health outcomes. Studies validating the predictive performance of APACHE ӀӀ scoring system among ICU patients with cancer suggests that its predicting ability remains suboptimal [20-23]. A contrary result was also documented [24, 25]. APACHE ӀV model on the other hand was validated only in one study [24]. The study only included critically ill patients with non-haematological and non-metastatic cancer and demonstrated a good predicting ability

However, it remains unclear whether APACHE IV is superior to APACHE II in predicting mortality in the ICU among patients with different types and stages of cancer. Comparing the predictive performance of APACHE II and APACHE IV in predicting mortality will potentially help in risk stratification and utilization of proper resources for patients with cancer. The aim of this study was to compare the predictive performance of APACHE II and APACHE IV in predicting ICU mortality using data at the time of ICU admission among Jordanian patients with cancer.

Two hundred and fifty-one patients (survived=80; none-survived=171) were included in the study with an average age of 54.9±15.4 years (survived=53.4±16.8; none-survived= 55.7±14.7). The percentages of male and female in the sample were 56.3% and 43.7%, respectively. Compared to survivors, the proportion of patients with sepsis was higher among non-survived patients (p=.003). Patients who survived their ICU stay had significantly lower APACHE II and APACHE IV score (19.3±7.5 and 76.5±22.3, respectively) compared to those who did not survive (25.4±7.9 and 90.8±22.4, respectively), p<.001. The predicted mortality of APACHE II and APACHE IV were significantly lower among survivors (30.5±22.1% and 37.3±21.2%, respectively) compared to non-survivors (51.0±23.3% and 59.1±22.9%, respectively), p<.001. The characteristics of the study sample and comparison between survival and non-survival patients are provided in Table 1.

Figs. (1 and 2) show the actual mortality rate for each APACHE II and IV score category, respectively. For instance, there was only 1 patient in the category of APCHE II score of 0-4 and no survivors in this category which resulted in 100% mortality rate, while the category of 5-9 had no non-survivors which resulted in 0% mortality rate. The figure of APACHE II model shows that the higher the APCHE II score, the greater the mortality rate. For APACHE IV model, there was a general increase in mortality rate with increasing model scores.

In this study, the actual mortality rate in our sample was 68.1%. APACHE II and APACHE IV models demonstrated similar predicted mortality rates (43.3% vs. 43%), sensitivity (52.6% vs. 52%), and specificity (76.3%, 76.2%), respectively. Table 2 shows APACHE II and APACHE IV predicted outcome versus observed outcome cross tabulation. The area under the curve (AUC) in APACHE II score (71.4%) was slightly higher than the AUC for APACHE IV score (66.5%) (Table 3). Multivariate binary logistic regression revealed that APACHE II score (OR 1.11, 95% CI 1.073-1.17, p = .03) and APACHE IV score (OR 1.03, 95%CI 1.01-1.05, p = .01) were independently associated with higher mortality in cancer patients who needed ICU support (Table 4).

This retrospective cohort study of 251 cancer patients evaluated the performance of APACHE ӀӀ and APACHE ӀV scores in predicting mortality in cancer patients. This is one of the few studies on assessing the performance of these models among critically ill cancer patients; the first in Middle East. The results revealed that higher scores of both APACHE II and IV at ICU admission are associated with higher mortality rate. However, the predictive and performance of both scores was insufficient for predicting ICU mortality. Both prognostic models had similar predictive sensitivity and specificity and provided similar ability prediction for mortality in this sample. Furthermore, the AUC of the ROC curve for both models demonstrate a comparable moderate predicting ability. Even though there a statistical significance in the regression models, OR were very small suggesting weak clinical significance for both APACHE II and APACHE IV in predicting mortality in this sample.

Our findings regarding APACHE ӀӀ score corroborates other findings in the literature [21-24]. Evidence suggests that while APACHE ӀӀ score is a significant predictor for ICU mortality, it can be used to prognosticate about cancer patients in ICU and remains suboptimal predictor of mortality in patients with cancer. To a lesser extent, other studies reported a good predictive performance for APACHE ӀӀ model [25, 26].

Fig. (1). Mortality Rate for Each APACHE II Score Category.

Fig. (2). Mortality Rate for Each APACHE VI Score Category.

There is a paucity of information in the literature about APACHE ӀV score in critically ill cancer patients. In a recent study in China [25], the predictive performance of APACHE ӀӀ and APACHE ӀV scores was evaluated in 981 cancer patients admitted to ICU and both scores demonstrated good discrimination and calibration ability. However, this study did not include patients with haematological cancer and the majority of patients had non-metastatic tumors. In our study, more than one third of patients had haematological cancer and the majority had stage III and IV tumor. These differences may explain the discrepancy in findings and also suggest the need for modified version of APACHE scores specific for patients with cancer. To our knowledge, this is the only study that validated APACHE ӀV model in critically ill cancer patients. Validating this score among Jordanian patients with specific cancer characteristics might yield a different result.

In this study, sepsis was found to have a significantly unfavourable association with cancer mortality in ICU with an OR of 4.1 and 3.8 in APACHE II and APACHE IV models, respectively. Other prognostic variables in addition to sepsis were reported in the literature including poor patient performance status, stage or nature of malignancy, provision of intensive treatments (ventilator support, vasoactive medication, and renal-replacement therapy), and the time of last chemotherapy [21, 23]. Adjusting for these variables may improve the predictive performance of APACHE scores. For example, Martos-Benítez et al. adjusted APACHE II prediction model for clinical stage of cancer, use of mechanical ventilator, and reason for admission (malignancy and non-malignancy) [21] The adapted model was validated with 522 patients admitted to ICU with solid cancer and demonstrated a better discrimination and calibration compared to the original one.

There are several limitations for our study. For example this was retrospective study which restricted the ability to collect further data about the sample. Also, only a single medical center was utilized. Lastly, we did not collect data about variables that could have been important in our prediction models such as the stage of cancer and the length of ICU stay.

Predicting mortality in ICU cancer patients is difficult due to variability in stages, performance status, and other cancer specific criteria. Even though the predictive ability of APACHE ӀӀ and APACHE ӀV models was established, their performances were insufficient. There is a need to further explore and consider other typical prognostic variables related to cancer patients. A prospective study that may adjust for cancer specific variables is recommended to improve the predictability of APACHE scores.