Performance of six severity-of-illness scores in cancer patients requiring admission to the intensive care unit: a prospective observational study

Advances in oncological and supportive care have improved survival rates in cancer patients to the point that many of them can now be cured or have their disease controlled. However, such advances have often been achieved through aggressive therapies and support, at high expense [1]. Some of these patients require admission to the intensive care unit (ICU) for acute concurrent illness, postoperative care, or complications of their cancer or its therapy [2]. In general hospitals, intensivists frequently consider these patients as having a poor prognosis and tend to oppose their admission to the ICU [3]. Recent studies [4, 5] have indicated that this reluctance to offer ICU care to cancer patients with severe illness is unjustified, and is usually based on inequitable parameters in comparison with other severe and chronic diseases that share a similarly poor prognosis [6, 7]. Hence, efforts have been made to identify parameters that are associated with worse prognosis and to improve allocation of ICU resources [4, 5, 8‐11].

Prognostic scores have been used to predict outcome in patients admitted to ICUs. Although none of these models should be used to predict individual outcomes, they can assist physicians in discussions of prognosis and in clinical decision making to improve allocation of resources in intensive care [12]. Stratification of patients for clinical research and assessment of quality of intensive care are other potential applications [12‐14].

However, the performance of a prognostic score must be validated before it may be used in an ICU, where there is a specific mix of patients such as cancer patients. Few studies have addressed adequately the performance (calibration and discrimination properties) of prognostic scores in cancer patients [4, 9, 15‐17]. Some years ago a specific model with which to predict outcome among critically ill cancer patients – the Cancer Mortality Model (CMM) – was developed by Groeger and coworkers [9]. To the best of our knowledge, only one external validation for the CMM has been conducted recently [17], and few studies have compared different scores in cancer patients [4, 16, 17]. The present study evaluated the performance of five general prognostic models and validated the CMM in predicting outcome in a large prospective cohort of cancer patients requiring intensive care.

During the period of study 1357 adult patients were admitted to the ICU, and of those 1257 (92.6%) met the eligibility criteria. Sources of admission were distributed as follows: wards (n = 234 [18.6%]), emergency room (n = 156 [12.4%]), operating room (n = 853 [67.9%]) and other hospital (n = 14 [1.1%]). Patients were referred from another hospital because of a severe medical complication and had to have a prior diagnosis of malignancy. Based on the reason for ICU admission, there were 404 (32.1%) medical, 715 (56.9%) scheduled surgical and 138 (11.0%) emergency surgical patients. At admission and during the first day of ICU stay, 468 (37.2%) patients required mechanical ventilation (MV), 302 (24.0%) received therapy with vasopressors, and 64 (5.1%) received haemodialysis. Within 2 hours before and the first 24 hours after ICU admission, 39 (3.1%) patients presented with cardiac arrest and 362 (28.8%) patients had a definite or probable diagnosis of infection. Median (25–75% interquartile range) lead time was 2 (1–5) days, hospital stay was 12 (8–25) days and ICU stay was 2 (1–6) days. The patients' demographical and clinical characteristics are shown in Table 1 and their cancer related data are summarized in Table 2. Global hospital mortality was 28.6% (360/1257) and the global ICU mortality rate was 20.8% (261/1257). As expected, hospital mortality was significantly higher for medical (69.4%) and emergency surgical patients (49.3%) than for scheduled surgical ones (5.7%; P < 0.001).

The performance of each individual mortality prediction system among all patients is presented in Table 3. All models exhibited excellent discriminatory power but calibration was poor. General prognostic scores underestimated the observed mortality (SMR > 1). By contrast, the CMM tended to overestimate (SMR = 0.51, 95% confidence interval 0.46–0.57).

To better compare the performances of the CMM and of the general prognostic scores, all scheduled surgical patients were excluded, and therefore 542 (43.1%) patients were included in this analysis. A total of 411 (75.8%) patients had solid tumours and 131 (24.2%) had haematological malignancies. Their mean age was 58.7 ± 16.7 years and their mean Sequential Organ Failure Assessment score was 7.6 ± 4.2 points. Out of these patients, 380 (70.1%) had acute respiratory failure. Hospital and ICU mortality rates were 58.7% (318/542) and 43.9% (238/542), respectively. The ICU (47.8% versus 32.6%; P = 0.003) and hospital (61.9% versus 49.3%; P = 0.013) mortality rates for medical patients were significantly greater than for emergency surgical patients. Patients with haematological malignancies had higher mortality than did those with solid tumours (67.2% versus 56.0%; P = 0.030). Their median scores were 18 (25–75% interquartile range 13–25, range 4–48) for APACHE II, 74 (55–99, range 7–199) for APACHE III-J and 50 (37–64, range 6–121) for SAPS II. Results for the performance of the six prognostic scores are shown in Table 4. As was observed for all patients combined, among medical and emergency surgical patients SAPS II exhibited the best discriminative ability (AUROC = 0.815) and MPM II₀ the poorest (AUROC = 0.729), and all of the scores were poorly calibrated. Statistically significant differences between observed and predicted mortality rates, using goodness-of-fit H statistics, were obeserved for all scores. Significant underestimation of actual mortality by general scores and overestimation by the CMM were again observed. The impacts of the differences between actual and predicted mortality rates are demonstrated in the calibration curves (Figs 1 and 2).

Many severity-of-illness scores have been developed and used to predict outcome in critically ill patients. During the past few years a series of studies dealing with the application of outcome prediction models in general critically ill patients demonstrated a similar pattern – good discrimination with poor calibration. This pattern has been observed in different settings and with different instruments [27]. Information regarding the usefulness of these general scores in cancer patients requiring ICU care is still restricted and most reports are limited by relatively small sample sizes and/or the statistical analyses used in the assessment of models' performance [28‐32]. In order to better address these issues, we conducted the present study to evaluate simultaneously the performance of five general prognostic scores and to validate the CMM in a large prospective cohort of cancer patients requiring ICU admission. The hospital mortality (28.6%) for the group of ICU cancer patients evaluated here seems to be low at a first glance. However, two thirds of our patients were admitted for routine postoperative care following elective surgery. When these patients were excluded, the hospital mortality (58.7%) was similar to that in previous studies dealing with large cohorts of critically ill cancer patients (33–58.7%) [8‐11, 16, 17]. Staudinger and coworkers [5] reported that ICU mortality was 47% and 1-year mortality was 77%. Mortality may vary with respect to the mix of patients (e.g. type of tumour, number of BMT patients, disease status and extent, and level of ICU support). In particular, the prognosis for cancer patients receiving MV is very poor. In a large prospective study conducted in 782 patients requiring MV, 76% died in the hospital [33]. In the present cohort about 37% of patients received MV.

Whether studying the entire population or the subgroup of nonscheduled surgical patients, all of the general models tested in the present study had comparatively similar levels of performance. As expected, they significantly underestimated the mortality rate. In general, discrimination was satisfactory (especially for the SAPS II and the APACHE III-J scores), but calibration was inadequate. Studying all patients, AUROC values were remarkably high (>0.850). The higher proportion of scheduled surgical patients (very low mortality), in contrast to patients with a severe illness (whether medical or emergency surgical), could be responsible for this finding. When those patients were excluded, AUROC values were similar to those reported in the literature [4, 9, 15‐17]. To our knowledge, there is no conventional method for comparing goodness-of-fit χ² tests, but it seems that this statistic was considerably lower for the SAPS II score than for the other models. This can be better appreciated in the calibration curves, which indicate significant underestimates in practically all of the strata of predicted mortality. Nevertheless, the line of observed mortality for the SAPS II score was closer to the line of equality when compared with other general scores. Assessments of both calibration and discriminatory abilities of general prognostic scores in cancer patients were reported in recent years, and yielded conflicting results [4, 9, 15‐17]. These scores usually tend to underestimate the observed mortality [9, 15, 16, 34]. Groeger and coworkers [9] tested the MPM II₀ model in the first 805 patients included in the sample from which the CMM was developed. The MPM II₀ model exhibited both poor calibration and poor discrimination, and underestimated the mortality. Sculier and coworkers [16] reported similar findings from their evaluation of the APACHE II and the SAPS II scores in a cohort of 261 patients. Guiguet and coworkers [15], studying 98 neutropenic cancer patients, found a reasonable discrimination (AUROC = 0.78) and good calibration for SAPS II. In a retrospective study conducted in 124 patients with haematological cancer, Benoit and colleagues [4] recently reported similar results for the SAPS II (AUROC = 0.765) and the APACHE II (AUROC = 0.712) scores. However, the results of calibration analyses in the latter two studies should be interpreted with caution because of the relatively small numbers of patients included, so that differences between predicted and observed mortalities may not reach statistical significance. In an elegant study, Zhu and coworkers [14] analyzed the impact of sample size on the accuracy of MPM II models by performing computer simulations. They showed that the smaller the sample size, the better the model calibration, as demonstrated by lower values of the goodness-of-fit χ² statistics. In contrast, discrimination was not affected by sample size.

The limitations of general prognostic models in predicting outcome in cancer patients motivated investigators to develop a specific model. Reported in 1998, the CMM was developed in a multicentre study from a cohort of 1483 critically ill cancer patients to predict hospital mortality at admission to the ICU, and it was further validated in another 230 patients [9]. By containing variables specific to oncology (disease progression/recurrence, performance status and allogeneic BMT group), this model was expected to be a more accurate scoring system in cancer patients [5, 16]. SAPS II, APACHE II, APACHE III-J and MPM II₂₄ models also take into account the presence of some cancer diagnostic categories, but they were not derived exclusively from cancer patients. The performance of the CMM was studied in medical and emergency surgical patients separately (i.e. excluding elective surgical patients) in order to minimize selection bias. There was no mention in the intial CMM report that elective surgical patients had been included in its development. At our ICU, CMM exhibited good discrimination and the AUROC value (0.795) is similar to values observed in both generation (0.812) and validation (0.802) groups of patients. However, CMM was poorly calibrated and, in contrast to general scores, exhibited a tendency to overestimate the observed mortality. Recently, Schellongowski and coworkers [17] compared the levels of performance of CMM, SAPS II and APACHE II in 242 ICU cancer patients [17]. In that study, the ability of SAPS II to discriminate between survivors and nonsurvivors (AUROC = 0.825) was superior to those of APACHE II (AUROC = 0.776) and CMM (AUROC = 0.698). All scores had acceptable calibration, although the statistical significance for the Hosmer–Lemeshow goodness-of-fit tests was borderline. The authors emphasized the limitations imposed by relatively small sample size on the results of calibration analyses.

The present study also has potential limitations. Ideally, a prognostic score should be employed in populations with similar characteristics to the sample of patients in which it was developed. Because we did not study BMT patients, it can be argued that our patients were less severely ill than those studied by Groeger and coworkers [9]. In that study, 11.3% and 5.8% of the sample were allogeneic and autologous BMT patients, respectively. These patients are considered to have the worst prognosis among cancer patients requiring intensive care, and prognosis is particularly poor when such patients need MV [16, 35, 36]. Our patients (excluding elective surgical patients) actually had a higher hospital mortality rate (58.7% versus 42%), but it was not feasible to make reliable comparisons of acute physiological disturbances (e.g. organ failures) between groups. In addition, whenever case mix adjustments are attempted, possible selection bias – resulting from different approaches to care (e.g. do-not-resuscitate orders) and from ICU admission/discharge policies – cannot be ruled out, especially in a single centre. Decisions to forgo life-sustaining therapy were demonstrated to independently predict hospital death in ICU patients [37]. Our ICU policies, including decisions to offer end-of-life care, appear similar to those reported in the literature [16, 17].

Another issue that deserves mention is the impact of missing data on the performance of models; in the present study prothrombin time, and serum albumin and bilirubin were not obtained in all patients. The differences between the predicted mortality with each score and the observed mortality were considerable, but there is a possible impact of missing data in the unsatisfactory performance of the models. As stated above, the study did not interfere with clinical decisions, including request for laboratory tests. In particular, the poor performance of the CMM cannot be attributed to missing data because it significantly overestimated the mortality rate.

Finally, we should be cautious when using SMR findings to evaluate the quality of intensive care. The prognostic scores that are already available do not take into consideration multidimensional parameters (ICU organizational and economic aspects in addition to clinical variables) in evaluating ICU performance [38].

In conclusion, none of the severity-of-illness scores evaluated in the present study were accurate in predicting outcome for critically ill cancer patients. Moreover, similar to a recent report [17], we found no advantage of CMM over the general prognostic models. It must be re-emphasized that any prognostic model should not be the only parameter taken into account when predicting outcome, and neither should they be used for triage and cost containment in individual patients. After all, prognostic scores were constructed based on patients who have been effectively admitted to the ICU. Otherwise, an accurate score may be helpful in enroling patients in clinical trials and enriching discussions about prognosis in intensive care.

None of the severity-of-illness scores evaluated in the present study were accurate in predicting outcome for critically ill cancer patients. There was no advantage of CMM over the general prognostic models. Prognostic scores should not be the only parameters taken into account when predicting outcome, and neither should they be used for triage and cost containment in individual patients.

study concept and design: Márcio Soares and José R Rocco; acquisition of data: Márcio Soares, Flávia Nardes, Flávia Fontes, Daniela Gadelha, César Amorim, Joana Dantas, Paloma Cariello and Luisa Toscano; analysis and interpretation of data: Márcio Soares and José R Rocco; drafting of the manuscript: Márcio Soares and José R Rocco; critical revision of the manuscript for important intellectual content: Márcio Soares, José R Rocco, Flávia Nardes, Flávia Fontes, Daniela Gadelha, César Amorim, Joana Dantas, Paloma Cariello and Luisa Toscano; administrative, technical or material support: Flávia Nardes, Flávia Fontes, Daniela Gadelha, César Amorim, Joana Dantas, Paloma Cariello and Luisa Toscano; statistical expertise: Márcio Soares and José R Rocco; study supervision: Márcio Soares and José R Rocco.