Intensive Care Med (2011) 37:1136–1142 DOI 10.1007/s00134-011-2170-z
ORIGINA L
Khalid Abidi Jihane Belayachi Youssef Derras Mina El Khayari Tarek Dendane Naoufel Madani Ibtissam Khoudri Amine Ali Zeggwagh Redouane Abouqal
Eosinopenia, an early marker of increased mortality in critically ill medical patients
Received: 15 April 2010 Accepted: 7 January 2011 Published online: 3 March 2011 Ó Copyright jointly held by Springer and ESICM 2011
Abstract Purpose: Inflammatory markers may have a role in predicting severity of illness of intensive care unit (ICU) patients. The aim of this study is to determine whether low eosinophil count can predict 28-day mortality in medical ICU. Methods: A prospective study over a 4-month period. To evaluate the prognosis information provided by eosinophil count, we compared the variations in eosinophil count from ICU admission to seventh day between patients who survived and those who died. The best cutoff value was chosen using Younden’s index for identification of patients with high risk of mortality. The patient outcome was 28-day mortality. Results: A total of 200 patients were eligible. Overall 28-day ICU mortality was 28% (n = 56). At ICU admission, the median eosinophil count was significantly different in survivors [30 cells/ mm3; interquartile range (IQR), 0–100 cells/mm3] and nonsurvivors (0 cells/mm3; IQR, 0–30 cells/mm3; P = 0.004). Absolute eosinophil counts remained significantly lower in nonsurvivors from admission to seventh day. The 28-day mortality
Drs. Abidi Khalid and Belayachi Jihane contributed equally to the work.
K. Abidi J. Belayachi Y. Derras M. E. Khayari T. Dendane N. Madani I. Khoudri A. A. Zeggwagh R. Abouqal ()) Medical Intensive Care Unit, Ibn Sina University Hospital, 10000 Rabat, Morocco e-mail:
[email protected] Tel.: ?212-61224739 Fax: ?212-37672558 A. A. Zeggwagh R. Abouqal Laboratory of Biostatistics, Clinical and Epidemiological Research, Faculte´ de Me´decine et Pharmacie, Universite´ Mohamed V, 10000 Rabat, Morocco
Introduction
was significantly higher in patients with eosinopenia \40 cells/mm3 (P = 0.011). Multivariate analysis by Cox model with time-dependent covariates demonstrated that eosinophil count \40 cells/mm3 [hazard ratio (HR), 1.85; 95% confidence interval (CI), 1.01–3.42; P = 0.046], high Acute Physiology and Chronic Health Evaluation (APACHE) II score (HR, 1.08; 95% CI, 1.01–1.14; P = 0.014), high Sequential Organ Failure Assessment (SOFA) score (HR, 1.14; 95% CI, 1.03–1.25; P = 0.008), and use of mechanical ventilation (HR, 27.48; 95% CI, 12.12–62.28; P \ 0.001) were independent predictors of 28-day allcause mortality. Conclusion: This study suggests the possibility to use eosinophil cell count at admission and during the first 7 days as a prognosis marker of mortality in medical ICU. Keywords Eosinopenia Intensive care unit Mortality Prognosis
To assess the severity of illness of intensive care unit (ICU) patients, several scoring systems have been valiEarly identification of critically ill patients at high risk of dated [1–5]. Effective assessment scores may help mortality on admission to medical intensive care units intensivists to assess the prognosis of critically ill patients and to improve treatment decisions [6]. Several studies remains at present a real challenge for intensivists.
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suggested an important role of biomarkers (C-reactive protein, platelet count, and procalcitonin) [7–13] in the prediction of ICU mortality. Because resources are expensive and limited, use of procalcitonin in developing countries such as Morocco, however, remains inaccessible in our ICUs. An ideal marker of outcomes in ICU would be highly specific, highly sensitive, easy to measure, rapid, inexpensive, and correlated with severity and prognosis. It is already known that eosinopenia typically accompanies the response to acute infection [14]. This marked reduction in the number of circulating eosinophil leukocytes in acute infection was first described by Zappert [15], and was utilized during the first quarter of the last century as a useful diagnostic sign [16]. Abidi and colleagues [17] demonstrated for the first time that eosinopenia is a good diagnostic marker of infection on ICU admission with good sensitivity and specificity. Recently, Holland et al. [18] reported that eosinopenia might be a useful marker of severity in patients admitted with exacerbation of chronic obstructive pulmonary disease. To the best of our knowledge, no work has looked at the incidence of low eosinophil counts for reasons other than infection and exacerbation of chronic pulmonary disease. Potential association between eosinopenia and survival has not been assessed in critically ill patients. This study is the first to evaluate the prognosis value of the eosinophil cell count in ICU. The aim of the present study is to establish if eosinopenia can predict 28-day mortality in critically ill patients.
Materials and methods Study design and setting A prospective study was performed of all patients consecutively admitted to a 12-bed medical ICU of Rabat University Hospital between April and July 2007. Patients who died or were discharged within 24 h after admission were excluded from the study. Rabat University Hospital is the referral venue for habitants in Northwestern Morocco. The 12-bed medical ICU admits approximately 550 patients annually. Surgery patients, coronary patients, neonates, and burn patients are treated in specialized units. The study protocol was approved by the hospital ethics committee. Informed consent was obtained from all patients or from their close relatives. Data collection and definitions At time of ICU admission, for each patient we evaluated their age, gender, principal diagnosis, and corticosteroids therapy. Prior health status evaluated by Charlson comorbidity index (CCI) [19], Glasgow coma scale
(GCS), Acute Physiology and Chronic Health Evaluation II (APACHE II) score [1], and Sequential Organ Failure Assessment (SOFA) score [2] were also recorded on admission. Other data collected included the requirement for mechanical ventilation and length of ICU stay, and patient outcome was 28-day mortality. Infected patients were classified as having sepsis, severe sepsis, and septic shock at time of admission, according to the Criteria of the American College of Chest Physicians/Society of Critical Care Medicine [20]. Immunocompromise was defined as primary immunodeficiency or immunodeficiency secondary to radiation treatment, use of cytotoxic drugs or steroids (daily dose [20 mg prednisolone or equivalent for [2 weeks), or acquired immune-deficiency syndrome (AIDS) [21]. Blood samples Blood samples were obtained by venipuncture on admission, and subsequently each morning at 07:00 h. Blood samples were collected in microtubes containing ethylenediamine tetraacetic acid anticoagulant. The white blood cell count and the eosinophil cell count were performed by Coulter (GenS) hematology analyzer (Beckman Coulter, Fullerton, CA, USA). Eosinophil daily change analysis Total white blood cell counts, neutrophil counts, and eosinophil counts were assessed. To evaluate the prognostic information provided by eosinophil level, we compared the variations in eosinophil level from day 1 to day 7 between patients who survived and those who died. Comparison of patients with and without eosinopenia According to the best eosinophil cutoff value, two groups of patients with and without eosinopenia at day 1 were compared for identification of patients with high risk of ICU mortality. Statistical analyses Data are presented as mean ± standard deviation for variables with normal distribution, and as median and interquartile range for variables with skewed distributions. Parametric or nonparametric tests were used for continuous variables as appropriate after the normality of the distribution was tested by the Kolmogorov–Smirnov test with Lilliefors correction. Receiver operating characteristics (ROC) curve and the area under the curve were calculated for eosinophil counts at admission. The best
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eosinophil cutoff value was chosen using Younden’s index. Statistical differences between groups were evaluated by the chi-square test for categorical variables. Comparison of group differences for continuous variables was carried out by Student test or Mann–Whitney U test. Survival curves were generated by the Kaplan–Meier method, and differences in survival between subgroups of patients were evaluated using the log-rank test. Variables with P value \0.10 on univariate analysis were tested in multivariate analysis. Multivariate analysis was performed with Cox proportional-hazard regression models with time-dependent covariates [22–24]. Eosinopenia was included in the Cox regression model as a time-dependent variable. A two-tailed P value \0.05 was considered significant. Statistical analyses were carried out using SPSS 13 (SPSS, Inc., Chicago, IL, USA) for Windows.
Results
ventilation: 46 (23%) were invasively ventilated and 14 (7%) were noninvasively ventilated. The most common causes of admission were infection (sepsis, severe sepsis, and septic shock) (n = 79), intoxication (n = 37), acute respiratory failure (n = 36), metabolic disease (n = 26), neurological disease (n = 18), and others (n = 4). Survival status in the population The 28-day mortality was 28% (n = 56). Stratifying patients according to their survival status revealed that, of the various causes of admission, sepsis was significantly associated with high mortality. Patients who died had significantly lower GCS (P \ 0.001), higher APACHE II and SOFA scores (P \ 0.001), were more likely to require mechanical ventilation specifically invasive ventilation (P \ 0.001), and their eosinophil cell count was significantly lower (P = 0.004) (Table 1).
Patient characteristics A total of 220 patients were admitted to the ICU; 20 patients died or were discharged within 24 h of admission, leaving 200 patients for the study. Demographic and clinical characteristics of the study patients are shown in Table 1. The mean age of the study population was 43 ± 19 years, and 56% were male. Sixteen (8%) patients had immunosuppression. Sixty (30%) patients required mechanical
Comparison of patients with and without eosinopenia Area under the ROC curve of eosinophil count at ICU admission for discrimination of survivors and nonsurvivors was 0.82 [95% confidence interval (CI), 0.77–0.88] (Fig. 1). The best eosinophil cell count cutoff value was 40 cell/mm3.
Table 1 Clinical characteristics of study patients on admission to the intensive care unit Parameter
All (n = 200)
Survivors (n = 144)
Nonsurvivors (n = 56)
Age, years, mean ± SD Male, n (%) CCI n (%) 0 1 2 C3 Median (IQR) GCS, mean ± SD APACHE II, median (IQR) SOFA, median (IQR) Corticosteroids therapy, n (%) Infection, n (%) Mechanical ventilation, n (%) ICU length of stay, days, median (IQR) WBC (cells/mm3), median (IQR) Neutrophil (cells/mm3), median (IQR) Eosinophil (cells/mm3), median (IQR)
43 ± 19 112 (56)
42 ± 19 78 (54.2)
45 ± 19 34 (61)
115 (57.5) 57 (28.5) 15 (7.5) 13 (6.5) 0 (0–1) 13.0 ± 1.5 12.5 (9–17) 1 (0–4) 15 (7.5) 79 (39.5) 60 (30) 5 (3–30) 12,700 (9,000–17,200) 9,600 (6,000–14,000) 10 (0–100)
90 (62.5) 39 (27.1) 8 (5.6) 7 (4.9) 0 (0–1) 13.3 ± 1.2 11 (7–15) 1 (0–3) 12 (8.3) 29 (20) 11 (7.6) 5 (3–7) 12,000 (8,000–15,000) 9,600 (6,000–13,000) 30 (0–100)
25 (44.6) 18 (32.1) 7 (12.5) 6 (10.8) 1 (0–1) 12.2 ± 1.9 17 (14–21) 4 (2–7) 3 (5.4) 50 (89.3) 49 (87.5) 5 (3–30) 14,700 (9,000–19,000) 11,200 (8,000–16,000) 0 (0–30)
P value* 0.41 0.41 0.12
n (%) number of patient (percentage). Infection included sepsis, severe sepsis, and septic shock SD standard deviation, GCS Glasgow coma scale, APACHE II Acute Physiology and Chronic Health Evaluation II, SOFA Sequential Organ Failure Assessment, CCI Charlson comorbidity
\0.001 \0.001 \0.001 0.470 0.027 \0.001 0.240 0.430 0.410 0.004
index, WBC white blood cells, IQR interquartile range, ICU intensive care unit *P values are from the chi-square test, Student test, or Mann– Whitney U test to compare the difference between survivors and nonsurvivors
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(P = 0.044), more often were infected (P \ 0.001), more often needed invasive ventilation (P = 0.043), had longer ICU stay (P = 0.008), and had higher 28-day ICU mortality (P = 0.011) (Table 2). Kaplan–Meier survival curves according to eosinophil count at admission are shown in Fig. 2.
Day-by-day changes of eosinophil cells count in the ICU population and mortality The comparison of the daily eosinophil cell count in ICU from admission to seventh day showed that median eosinophil count in nonsurvivors was significantly lower than 40 cells/mm3 compared with survivors (P \ 0.05) (Fig. 3). Fig. 1 Receiver operating curve (ROC) of eosinophil cell count at ICU admission for discrimination of survivors and nonsurvivors. Eosinophil count predictors of ICU mortality Area under the ROC curve was 0.82 (95% confidence interval, 0.77–0.88) In multivariate analysis by Cox model with time-depen-
The admission diagnoses of patients with eosinophil count \40 cell/mm3 were infection (n = 60), acute respiratory failure (n = 22), metabolic disease (n = 9), neurological disease (n = 17), intoxication (n = 12), and others (n = 2). At admission, patients with eosinopenia \40 cells/ mm3 had significantly higher APACHE II score (P = 0.016), SOFA score (P \ 0.001), and CCI
dent covariates, the eosinopenia was included in the Cox regression model as a time-dependent variable. Eosinophil count \40 cells/mm3 [hazard ratio (HR), 1.85; 95% CI, 1.01–3.42; P = 0.046], high APACHE II score (HR, 1.08; 95% CI, 1.01–1.14; P = 0.014), high SOFA score (HR, 1.14; 95% CI, 1.03–1.25; P = 0.008), and use of mechanical ventilation (HR, 27.48; 95% CI, 12.12–62.28; P \ 0.001) were independent predictors of 28-day allcause mortality (Table 3).
Table 2 Clinical characteristics Variables of study patients according to eosinophil cell count on day 1 Age, years, mean ± SD Male, n (%) CCI n (%) 0 1 2 C3 Median (IQR) GCS, mean ± SD APACHE II, median (IQR) SOFA, median (IQR) Corticosteroids therapy, n (%) Infection, n (%) Mechanical ventilation, n (%) ICU length of stay, days, median (IQR) 28-day ICU mortality, n (%)
Eosinophil count \40 cells/mm3 (n = 122)
Eosinophil count C40 cells/mm3 (n = 78)
45 ± 19 69 (56.6)
41 ± 19 43 (55.1)
66 (54.1) 43 (35.2) 7 (5.7) 6 (4.9) 0 (0–1) 12.9 ± 1.7 13.5 (10–18) 2 (1–4) 10 (8.2) 60 (49.2) 43 (35.2) 5.5 (3–9) 42 (34.4)
49 (62.8) 14 (17.9) 8 (10.3) 7 (9) 0 (0–1) 13.1 ± 1.3 11 (7–17) 1 (0–3) 5 (6.4) 19 (24.4) 17 (21.8) 4 (2–7) 14 (17.9)
P value*
0.24 0.84 0.044
0.306 0.016 \0.001 0.64 \0.001 0.043 0.008 0.011
n (%) number of patient (percentage). Infection included sepsis, severe sepsis, and septic shock SD standard deviation, GCS Glasgow coma scale, APACHE II Acute Physiology and Chronic Health Evaluation II, SOFA Sequential Organ Failure Assessment, CCI Charlson comorbidity index, WBC white blood cells, IQR interquartile range, ICU intensive care unit *P values are from the chi-squared test, Student test, or Mann–Whitney U test to compare the difference between survivors and nonsurvivors
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Table 3 Multivariate analysis by Cox model with time-dependent covariates HR
EC \40 cells/mm3 a Charlson comorbidity index 0 1 C2 GCS (per point decrease) APACHE II score (per point increase) SOFA score (per point increase) Mechanical ventilation Infection
Fig. 2 Survival curve according to eosinophil count at admission among patients with eosinophil count C40 cells/mm3 (solid line) and patients with eosinophil count \40 cells/mm3 (dotted line) (P = 0.020)
Fig. 3 Daily eosinophil cell count in intensive care unit for survivors (shaded bars) and nonsurvivors (white bars). Data are presented as median values (line) with 25th and 75th percentiles (boxes) and 5th and 95th percentiles (whiskers). (P \ 0.05). The dotted line corresponds to 40 eosinophil cells/mm3
Discussion The present study is the first to suggest the value of eosinopenia for prediction of mortality in the ICU. Our results show that nonsurvivors had a persistently low eosinophil count from day 1 to day 7. In addition, eosinopenia \40 cells/mm3 at ICU admission was an independent predictor of 28-day mortality. Therefore, eosinophil count may be helpful for intensivists to identify patients with high risk of mortality. Although eosinophils account for only a very small proportion of the peripheral white blood cells, their
95% confidence interval
P value
1.85
1.01–3.42
0.046
0.91 1.01 1.15 1.08
0.48–1.72 0.47–2.15 0.98–1.34 1.01–1.14
1.00 0.768 0.986 0.095 0.014
1.14 27.48 0.94
1.03–1.25 12.12–62.28 0.53–1.67
0.008 \0.001 0.831
Infection included sepsis, severe sepsis, and septic shock EC eosinophil count, HR hazard ratio, GCS Glasgow coma scale, APACHE II Acute Physiology and Chronic Health Evaluation II 1.00 reference group a Time-dependent variable
production is tightly regulated by interleukin-3, interleukin5, and granulocyte–macrophage colony-stimulating factor [14, 25, 26]. The eosinopenia of acute infection has been assumed to be a secondary expression of adrenal glucocorticosteroid stimulation produced by the stress of the infection [27]. Bass showed that each of the infectious and noninfectious stimuli of acute inflammation markedly suppressed eosinophilia [28]. This suggests that eosinopenia is a response to the acute inflammatory process rather than to a specific type of pathogen [28]. This decline could be due to any of three processes: (a) peripheral sequestration of eosinophils by localization in sites such as the inflammatory region, presumably by chemotactic substances released during acute inflammation, the draining lymph nodes, or the spleen, by diffuse intravascular margination, or by destruction of eosinophils; (b) suppression of egress of mature eosinophils from the bone marrow; and (c) suppression of eosinophil production. The major chemotactic substances include C5a and fibrin fragments that have been detected in the circulation during acute inflammatory states [26]. It has been established that eosinopenia is associated with infection [29]. Abidi et al. [17] showed that eosinopenia is a good diagnostic marker in distinguishing between noninfection and infection, but is a moderate marker in discriminating between systemic inflammatory response syndrome (SIRS) and infection in newly admitted critically ill patients. Another study failed to demonstrate any association between eosinopenia and infection in ICU [30]. In addition, Setterberg et al. [31] found no utility associated with eosinopenia (defined as relative eosinophil level of 0%) for predicting bacteremia and concluded that the absence of peripheral blood eosinophils cannot be used as a clinically reliable marker of bloodstream infection.
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The ideal marker of mortality would be very easy to measure, rapid, inexpensive, correlate with severity of prognosis, and predict mortality in ICU. However, most studies evaluated the value of different prognosis markers in critically ill patients [7–13]. Effectively, Lobo et al. [12] reported that persistently high C-reactive protein concentrations are associated with ICU mortality. Several studies have shown that thrombocytopenia is associated with reduced ICU survival [7–9]. In addition, Moureau et al. [10] demonstrated that a 30% decline in platelet counts on day 4 strongly predicted ICU mortality. In another study, Lopes Ferreira et al. reported that an increase in SOFA score during the first 48 h in the ICU predicted a mortality rate of at least 50%, while a decreasing SOFA score is associated with a decrease in mortality rates from 50 to 27% [13]. Recently, the Simplified Acute Physiology Score (SAPS) 3 admission score’s general equation can be seen as a framework for addressing the issue of outcome prediction in a general ICU adult population [32]. Jensen et al. [11] showed that day-by-day procalcitonin changes can identify critically ill patients who are at high risk of ICU mortality. In the same study, the authors reported that levels or increases of C-reactive protein and white blood cell counts do not seem to predict mortality [11]. Recently, Holland et al. [18] demonstrated that eosinopenia might be a useful marker of mortality in patients admitted to respiratory department with exacerbation of chronic obstructive pulmonary disease. To the best of our knowledge, this is the first study to examine whether eosinopenia \40 cells/mm3 can predict all 28-day mortality and suggesting that daily consecutive eosinophil cell counts may be a new valuable tool in monitoring the mortality risk of critically ill patients. However, despite this interesting finding, the median eosinophil count in the survivors and the nonsurvivors were both less than 40 cell/mm3, as shown in Fig. 3. Effectively, the 28-day mortality was significantly higher in patients with eosinopenia, and absolute eosinophil count remained significantly lower in nonsurvivors from admission to seventh day. Multivariate analysis was performed with Cox proportionalhazard regression models but not classic Cox model in which eosinophil cell count\40 cells/mm3 was the time-dependent variable taking account of its evolution over time as a predictor of mortality. This variable was also associated with 28-day mortality. The HR value of 1.85 indicates an increased risk of mortality if eosinophil counts were\40 cells/mm3.
The association between eosinopenia \40 cells/mm3 and ICU mortality found in this survey could therefore be explained by increasing cortisol production induced by stress in ICU patients. Moreover, several studies reported that profound eosinopenia occurs after injury or burning and lasts up to a few days [33–35]. The present study has a potentially important implication for clinicians in developing countries. As a cheap test for early identification of critically ill patients at high risk of mortality on admission to ICU, eosinopenia might aid physicians in their initial decisions, and help to identify patients who may require more aggressive diagnostic and therapeutic interventions. However, the value of eosinopenia could be limited as compared with more sophisticated biomarkers or scores where available. Some limitations of the study merit consideration. First, this study was conducted in a single medical ICU, so our conclusions are limited to ICU medical patients and cannot be generalized to all ICU patients. Second, one should keep in mind the acute nature of the diseases diagnosed in this service. In effect, the acute forms of disease and acute exacerbation of chronic diseases represent stress, resulting in a decrease of eosinophils that is more evident during the first days. The results of this work must therefore also be evaluated in the chronic forms of these diseases and in patients who do not require hospitalization in intensive care. Third, corticosteroids are known to cause eosinopenia, but the proportion of patients taking corticosteroids was not significantly different between the groups of patients. Moreover, even with exclusion of steroids patients, we found the same statistical results. Fourth, bearing in mind the association between sepsis and eosinopenia [17], the high percentage of infection (39.5%) in our sample may be an important limitation of this study. In conclusion, eosinophil count \40 cells/mm3 is independently associated with high risk of mortality. Eosinophil cell count at admission and during the first 7 days can be used as a prognosis marker of mortality in newly admitted critically ill medical patients. It may become a helpful clinical tool in ICU practice. Conflict of interest of interests.
The authors declare that they have no conflict
References 1. Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II A severity of disease classification system. Crit Care Med 13:818–829
2. Vincent JL, De Mendonca A, Cantrauine F, Moreno R, Takala J, Suter PM, Sprung CL, Colardyn F, Blecher S (1998) Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of multicenter prospective study. Crit Care Med 26:1793–1800
3. Le Gall JR, Lemeshow S, Saulnier F (1993) A new simplified acute physiology score (SAPS II) based on European/Nort American multicenter study. JAMA 270:2957–2963
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4. Marshal JC, Cook DJ, Christou NV (1995) Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med 23:1638–1652 5. Le Gall JR, Kler J, Lemeshow S, Saulnier F, Alberti C, Artigas A, Teres D (1996) The logistic organ dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU scoring group. JAMA 276:802–810 6. Afessa B, Keegan MT, Mohammed Z, Finkielman JD, Peters SG (2004) Identifying potentially ineffective care in the sickest critically ill patients on the third ICU day. Chest 126:1905–1909 7. Baughman RP, Lower EE, Flessa HC, Tollerud DJ (1993) Thrombocytopenia in the intensive care unit. Chest 104:1243–1247 8. Stephan F, Hollande J, Richard O, Bonnet F (1999) Thrombocytopenia in a surgical ICU. Chest 115:1363–1370 9. Vanderschueren S, De Weerdt A, Malbrain M, Vankersschaever D, Frans E, Wilmer A, Bobbaers H (2000) Thrombocytopenia and prognosis in intensive care. Crit Care Med 28:1871–1876 10. Moreau D, Timsit JF, Vesin A, Garrouste-Orgeas M, Lassence AD, Zahar JR, Adrie C, Vincent F, Cohen Y, Schlemmer B, Azoulay E (2007) Platelet count decline: an early prognostic marker in critically ill patients with prolonged ICU stays. Chest 131:1735–1741 11. Jensen JU, Heslet L, Jensen TH, Espersen K, Steffensen P, Tvede M (2006) Procalcitonin increase in early identification of critically ill patients at high risk of mortality. Crit Care Med 34:2596–2602 12. Lobo SMA, Lobo FRM, Bota DP, lopes-Ferreira F, Soliman HM, Me´lot C, Vincent JL (2003) C-reactive protein levels correlate with patients mortality and organ failure in critically ill. Chest 123:2043–2049
13. Lopes-Ferreira F, Peres D, Bross A (2000) Serial evaluations of the SOFA score to predict outcome. JAMA 286:1754–1758 14. Bass DA, Gonwa TA, Szejda P, Cousart MS, DeChatelet LR, McCall CE (1980) Eosinopenia of acute infection: production of eosinopenia by chemotactic factors of acute inflammation. J Clin Invest 65:1265–1271 15. Zappert J (1983) Ueber das vorkommen der eosinophilen zellen in menschlichen blute. Z Klin Med 23:227–308 16. Simon CE (1922) A manual of clinical diagnosis. Henry Klimpton, London 53 17. Abidi K, Khoudri I, Belayachi J, Madani N, Zekraoui A, Zeggwagh AA, Abouqal R (2008) Eosinopenia is a reliable marker of sepsis on admission to medical intensive care units. Crit Care 12:R59 18. Holland M, Alkhalil M, Chandromouli S, Janjua A, Babores M (2010) Eosinopenia as a marker of mortality and length of stay in patients admitted with exacerbations of obstructive pulmonary disease. Respirology 15:165–167 19. Charlson ME, Pompei P, Ales Kl, Mackenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383 20. Bone RC, Balk RA, Cerra Fb, Dellinger RP, Fein AM, Knaus WA, Schein RM, Sibbald WJ (1992) American college of chest physicians/society of critical care medicine consensus conference: definition for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 101:1644–1655 21. Ewig S, Ruiz M, Mensa J, Marcos MA, Martinez JA, Arancibia F, Niederman MS, Torres A (1998) Severe community-acquired pneumonia: assessment of severity criteria. Am J Respir Crit Care Med 158:1102–1108 22. Cox DR (1972) Regression models and life-tables. J R Stat Soc 34:187–220
23. Crowley J, Hu M (1977) Covariance analysis of heart transplant survival data. J Am Stat Assoc 72:27–36 24. Therneau TM, Grambsch PM (2000) Modelling survival data: extending the Cox model. Springer, New York 25. Hogan SP, Rosenberg HF, Moqbel R, Phipps S, Foster PS, Lacy P (2008) Eosinophils: biological properties and role in health and disease. Clin Exp Allergy 38:709–750 26. Bass DA (1977) Reproduction of the eosinopenia of acute infection by passive transfer of a material obtained from inflammatory exudate. Infect Immun 15:410–416 27. Rothenberg ME (1998) Eosinophilia. N Engl J Med 338:1592–1600 28. Bass DA (1975) Behavior of eosinophil leukocytes in acute inflammation. II. Eosinophil dynamics during acute inflammation. J Clin Invest 56:870–879 29. Gil H, Magy N, Mauny F, Dupond JL (2003) Value of eosinopenia in inflammatory disorders: an ‘old’ marker revisited. Rev Med Interne 24:431–435 30. Smithson A, Perello R, Nicolas JM (2009) Is eosinopenia a reliable marker of sepsis? Crit Care 13:409 31. Setterberg MJ, Newman W, Potti A, Smego RA (2004) Utility of eosinophil count as predictor of bacteremia. Clin Infect Dis 38:460–461 32. Metnitz B, Schaden E, Moreno R, Le Gall JR, Bauer P, Metnitz PG (2009) Austrian validation and customization of the SAPS 3 admission score. Intensive Care Med 35:616–622 33. Hardy JD (1950) The role of the adrenal cortex in the postoperative retention of salt and water. Ann Surg 132:189–197 34. Evans EI, Butterfield WJH (1951) The stress response in the severely burned: an interim report. Ann Surg 134:588–611 35. Sevitt S (1955) The spleen and blood eosinopenia. J Clin Pathol 8:42–46
