Admission factors associated with hospital mortality in patients with haematological malignancy admitted to UK adult, general critical care units: a secondary analysis of the ICNARC Case Mix Programme Database

Organ system failures were identified from physiological data according to the definitions of Knaus and colleagues [22]. Severity of illness was measured by the APACHE II Acute Physiology Score, the APACHE II score [22], the ICNARC physiology score [18], the SAPS II score [23] and the number of organ system failures. Both the APACHE II Acute Physiology Score and the ICNARC physiology score encompass a weighting for acute physiology (defined by derangement from the normal range for 12 physiological variables in the first 24 hours following admission to the ICU). The APACHE II score and the SAPS II additionally encompass a weighting for age and for a past medical history of specified conditions.

Patients who were ventilated at any time during the first 24 hours in the ICU include both patients who were receiving mechanical ventilation on admission to the ICU and those for which ventilation was initiated at any time during the first 24 hours of their stay.

Patients were defined as having severe sepsis if they met at least three of the four systemic inflammatory response syndrome criteria, if they had evidence of infection, and by the presence of at least one organ dysfunction during the first 24 hours following admission to the ICU. Physiological definitions of the systemic inflammatory response syndrome criteria and organ dysfunctions were matched as closely as possible to those used in the PROWESS trial, as has been reported previously [24].

Survival data were extracted at discharge from the Case Mix Programme unit and at ultimate discharge from the acute hospital.

Readmissions to the unit within the same hospital stay were identified from the postcode, date of birth and sex of the patient, and were confirmed by the participating units.

The case mix, outcome and activity were described for all haematological malignancy admissions.

The effect of case mix factors on acute hospital mortality was assessed by multivariable logistic regression modelling for the admissions that were identified as having a haematological malignancy. The past medical history as recorded in the CMPD does not distinguish between individual haematological diagnoses, but groups together the following diagnoses: acute myelogenous leukaemia or lymphocytic leukaemia or multiple myeloma; chronic lymphocytic leukaemia or chronic myelogenous leukaemia; and Hodgkin's lymphoma or non-Hodgkin's lymphoma. To assess the effect of specific haematological diagnoses on outcome, therefore, only admissions with a haematological diagnosis as the primary, secondary or ultimate reason for admission were included in the regression analysis of diagnosis on outcome.

For all physiology variables, all measurements were from the first 24 hours following ICU admission. The variables entered into the model, selected a priori, were as follows: age; sex; haematological diagnosis on admission (only admissions with a haematological diagnosis as the primary, secondary or ultimate reason for admission were included in this analysis); highest central temperature (or noncentral temperature + 1°C if no central temperature was recorded); lowest systolic blood pressure; highest heart rate; lowest respiratory rate; PaO₂:FiO₂ (with additional weightings for patients who were ventilated at any time during the first 24 hours of their admission to the unit); lowest arterial pH; serum sodium (most extreme value from the normal range); serum potassium (most extreme value from the normal range); serum urea (most extreme value from the normal range); serum creatinine (most extreme value from the normal range); urine output in the first 24 hours of admission to the unit (if the length of stay in the unit was less than 24 hours the urine output during their stay is scaled up to give the equivalent urine output in 24 hours); haematocrit (most extreme value from the normal range; if there were no haematocrit measurements available then three times the recorded haemoglobin values were used instead); lowest white blood cell count; lowest total Glasgow Coma Score (GCS); IMV; severe sepsis; cardiopulmonary resuscitation within 24 hours prior to admission; and acute hospital length of stay before ICU admission in days.

Continuous variables were divided into categories for modelling, except for age and hospital length of stay before ICU admission, which were assumed to have a linear effect on the log odds.

The SAPS II, the APACHE II score and the ICNARC physiology score were evaluated for discrimination (the ability of the model to distinguish survivors from nonsurvivors), and the APACHE II mortality probability (using coefficients from the model that has been calibrated using the CMPD [25]), the ICNARC model mortality probability and the SAPS II mortality probability were evaluated for discrimination and calibration (the accuracy of the estimated probability of survival). The APACHE II and ICNARC models are used to predict the probability of ultimate acute hospital mortality. The SAPS II model is used to predict the probability of mortality within the same hospital that houses the ICU where the admission occurred.

Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC) [26]. Calibration was assessed by the standardised mortality ratio (SMR), the Hosmer–Lemeshow C statistic [27] and Cox's regression calibration [28].

The AUROC (also called the concordance statistic) measures the probability that a randomly selected nonsurvivor has a higher prediction than a randomly selected survivor. A value of 0.5 indicates no discrimination, and a value of 1 indicates perfect discrimination. Values higher than 0.8 are generally considered to demonstrate good discrimination, values between 0.6 and 0.8 are considered moderate, and values lower than 0.6 are considered poor.

The SMR can be used to compare the discrepancy between observed and expected deaths between groups. The ratio is calculated as the number of observed deaths divided by the number of deaths predicted by the model.

The Hosmer–Lemeshow test divides the data into 10 groups and compares the observed mortality in these groups with the expected mortality predicted by the model. The C statistic is a chi-squared statistic for testing the hypothesis of perfect calibration (observed mortality = expected mortality). A significant result indicates that calibration is not perfect [27].

Cox's regression calibration tests for a systematic lack of calibration by performing a linear recalibration of the log odds. The log odds are given by log(p/(1 - p)), where p is the mortality probability. The following model is fitted:

If the model is perfectly calibrated then the slope will be 1 and the intercept will be 0; that is, true log odds = predicted log odds. This is tested with a likelihood ratio chi-squared test, with a significant result indicating lack of calibration.

Readmissions within the same acute hospital stay were excluded from all analyses of acute hospital mortality. Patients who stayed less than 8 hours in the ICU were excluded from the calculation of APACHE II scores and probabilities. In addition, patients transferred from another ICU and admissions following coronary artery bypass graft or for primary burns were excluded from the calculation of APACHE II and SAPS II probabilities. In addition, patients were excluded from the calculation of SAPS II probability if no respiratory rates were recorded or no measurements from blood gases were taken. There are no exclusions from the ICNARC model.

All analyses were performed using Stata 9.2 (Stata Corporation, College Station, TX, USA).

The SAPS II and APACHE II models have been assessed in small studies of critically ill haematological malignancy patients. The AUROC for SAPS II was found to be 0.765 [32] and 0.78 [12]. Benoit and colleagues found that the SAPS II model had better discrimination than the APACHE II model (AUROC = 0.712) [32]; however, both studies included only small numbers of patients. Both the ICNARC and SAPS II models underestimated actual mortality in this study. Underestimation of mortality in haematological malignancy patients by the SAPS II model has been previously described [6, 11]. The APACHE II model has also been found to underestimate mortality in these patients [8, 11], but we found that the APACHE II model gave the most accurate mortality prediction (Table 3) with an SMR of 1.01.

We found that patients diagnosed with Hodgkin's lymphoma had an increased risk of hospital mortality (adjusted OR = 2.38). This is an unexpected finding, since patients with Hodgkin's lymphoma have a relatively good long-term prognosis. Groeger and colleagues found in cancer patients receiving IMV that those with leukaemia had a higher mortality [33]. Massion and colleagues found that acute myeloid leukaemia and non-Hodgkin's lymphoma had a negative effect on 6-month survival [11]. In the present study, patients with acute myeloid leukaemia had a higher mortality than those with acute lymphocytic leukaemia, perhaps partially explained by the difference in mean age between these admissions (Table 2). Patients with chronic lymphocytic leukaemia had the best prognosis in the study by Peters and colleagues [7]. Other researchers, however, have found that the type of haematological malignancy was not predictive for mortality [13, 16].

Admission after bone marrow transplantation (HSCT) was a risk factor for acute hospital mortality in the present study. The hospital mortality in patients who become critically ill after HSCT is 54 to 67% [1, 34, 35], with higher mortality rates reported in intubated patients [3, 16, 36, 37]. Patients who receive autologous HSCT transplants undergo less intensive preparative regimens and faster marrow engraftment, so a lower mortality would be expected. Khassawneh and colleagues found that the hospital mortality rate in such patients who were intubated was 74% [38]. Indeed, allogeneic HSCT has been identified as a risk factor for mortality [33, 34]. Other studies, however, have found that the type of HSCT has no influence on mortality [3, 35, 39]. Dividing HSCT patients into those receiving autologous and allogeneic transplants is not possible in the present study, as the type of HSCT received is not recorded in the CMPD.

Tachypnoea, a low PaO₂:FiO₂ and hypotension were associated with increased hospital mortality. The relationship between respiratory failure and increased risk of mortality has already been demonstrated [11]. Requirement for vasopressors has also been associated with a worse outcome in these patients [12, 32, 39].

In the present study oliguria, hyponatraemia and uraemia correlated with mortality. These abnormalities may result from acute kidney injury, which is strongly associated with mortality in haematological malignancy patients [14, 32].

Acidaemia was associated with lower survival. Acute kidney injury or high serum lactate may cause acidosis, and both conditions predicted higher mortality in previous studies [1, 14, 32, 40, 41].

Most studies report a strong relationship between IMV and increased mortality [8, 12, 41]. Using noninvasive ventilation rather than IMV reduces mortality in immunocompromised patients [42, 43]. IMV during the first 24 hours of admission was not associated with an increased risk of mortality in this study, although a large absolute difference in mortality exists between patients who received IMV and those who did not.

Depudyt and colleagues found that IMV within 24 hours of ICU admission was related to a lower mortality rate in haematological malignancy patients with respiratory failure [44]. Considering those patients who had PaO₂:FiO₂ lower than 200 mmHg in the present study, a similar conclusion may be drawn, as those who were invasively ventilated within 24 hours had lower mortality rates than those who were not. Indeed, we found that severe respiratory failure (PaO₂:FiO₂ < 100 mmHg) without IMV is associated with increased hospital mortality. This may reflect reluctance to use IMV in such patients, as less than one-third of patients who had a PaO₂:FiO₂ < 100 mmHg received IMV (Table 5).

We did not find an association between leukopaenia and mortality, although in previous studies leukopaenia was identified as a significant risk factor [8, 32] and leukopaenia is a component of the APACHE II score and the SAPS II. We were unable to assess the effect of neutropaenia on mortality, as this is not a recorded parameter in the CMPD.

A low GCS was independently associated with mortality. Gordon and colleagues [6] found that central nervous system failure was associated with higher mortality.

The retrospective nature of the present study should lead to caution in interpreting the factors identified as being associated with mortality. Severe sepsis – together with cardiorespiratory and renal dysfunction – was associated with increased mortality, however, and this implies that early, aggressive treatment aimed at reversing organ dysfunction may improve survival, particularly in those patients with severe sepsis.

We also found that acute hospital mortality was associated with a longer stay in hospital prior to ICU admission. This is probably due to delayed recognition and suboptimal treatment of patients who have become acutely unwell on the ward, suggesting that earlier admission to the ICU may benefit some patients. Groeger and colleagues found a correlation between hospital length of stay prior to ICU admission and mortality in critically ill cancer patients [33, 45, 46], and this has also been found in critically ill patients as a whole [47].

A low haematocrit of between 20% and 45% was associated with higher mortality, compared with admissions with a haematocrit of between 50% and 59. Anaemia has not previously been associated with an increased risk of hospital mortality in critically ill patients with haematological malignancy. Current practice in intensive care patients is to tolerate a fall in haematocrit, excepting certain groups of patients, based on evidence that this practice is not harmful [48]. On the other hand, in the early phase of severe sepsis, targeting a haematocrit of at least 30% is associated with improved survival [49]. In patients with a haematological malignancy a low haematocrit is common, and its causes are multifactorial. The low haematocrit may be chronic (resulting from bone marrow suppression or more aggressive chemotherapy) or acute (secondary to haemorrhage). One interpretation of our results would suggest that if haematology malignancy patients were managed with a higher haematocrit, at least initially, then their mortality may be reduced.

The association between hospital mortality and sedation may be partially explained by confounding with the adverse effect on mortality of mechanical ventilation. The relationship between hospital mortality and anaemia on admission in these patients is surprising given previous evidence of safety in tolerating a low haematocrit in intensive care patients. Further studies should be conducted to refute or confirm this relationship.