Open Access

Persistent lymphopenia is a risk factor for ICU-acquired infections and for death in ICU patients with sustained hypotension at admission

  • Christophe Adrie1, 2Email author,
  • Maxime Lugosi3,
  • Romain Sonneville4,
  • Bertrand Souweine5,
  • Stéphane Ruckly6,
  • Jean-Charles Cartier3,
  • Maité Garrouste-Orgeas7,
  • Carole Schwebel3,
  • Jean-François Timsit4, 6 and
  • On behalf of the OUTCOMEREA study group
Annals of Intensive Care20177:30

https://doi.org/10.1186/s13613-017-0242-0

Received: 21 July 2016

Accepted: 4 February 2017

Published: 17 March 2017

Abstract

Background

Severely ill patients might develop an alteration of their immune system called post-aggressive immunosuppression. We sought to assess the risk of ICU-acquired infection and of mortality according to the absolute lymphocyte count at ICU admission and its changes over 3 days.

Methods

Adults in ICU for at least 3 days with a shock or persistent low blood pressure were extracted from a French ICU database and included. We evaluated the impact of the absolute lymphocyte count at baseline and its change at day 3 on the incidence of ICU-acquired infection and on the 28-day mortality rate. We categorized lymphocytes in 4 groups: above 1.5 × 103 cells/µL; between 1 and 1.5 × 103 cells/µL; between 0.5 and 1 × 103 cells/µL; and below 0.5 × 103 cells/µL.

Results

A total of 753 patients were included. The median lymphocyte count was 0.8 × 103 cells/µL [0.51–1.29]. A total of 174 (23%) patients developed infections; the 28-day mortality rate was 21% (161/753). Lymphopenia at admission was associated with ICU-acquired infection (p < 0.001) but not with 28-day mortality. Independently of baseline lymphocyte count, the absence of lymphocyte count increase at day 3 was associated with ICU-acquired infection (sub-distribution hazard ratio sHR: 1.37 [1.12–1.67], p = 0.002) and with 28-day mortality (sHR: 1.67 [1.37–2.03], p < 0.0001).

Conclusion

Lymphopenia at ICU admission and its persistence at day 3 were associated with an increased risk of ICU-acquired infection, while only persisting lymphopenia predicted increased 28-day mortality. The lymphocyte count at ICU admission and at day 3 could be used as a simple and reproductive marker of post-aggressive immunosuppression.

Keywords

Immunosuppression Shock ICU Nosocomial Infection Survival Absolute lymphocyte count

Background

Lymphopenia is defined as a decrease below normal value (often 1.5 × 103 cells/µL) of the blood circulating lymphocyte count; it reflects an impairment of the adaptive immune system. Several diseases can cause lymphopenia; they are associated with a higher risk of infection and adverse outcome [1, 2].

In critically ill patients, especially those with septic shock, after an initial phase of immune system hyperstimulation, dysfunction could appear secondarily. This is often called post-aggressive immunosuppression or compensatory anti-inflammatory response syndrome (CARS). It affects the innate and adaptive immune system [3, 4]. There is an increase in the level of anti-inflammatory cytokines, e.g., interleukin (IL)-10, in contrast to the decrease in pro-inflammatory cytokines levels, such as IL-6 or TNF-α. Immune cells are altered in both dimensions, qualitatively, and also quantitatively, as demonstrated with cells of innate immunity [57]. Persistence of CARS is associated with the risk of ICU-acquired infections and adverse outcome [7, 8].

Studies have shown the impact of critical illness on lymphocyte apoptosis and anergy [912]; however, there are few reports about the prognostic value in ICU of total lymphocyte count at admission and its evolution. These studies often evaluated the association between adverse outcome and other biomarkers of lymphocyte dysfunction than the lymphocyte count. However, the lymphocyte count would be a simple and reproducible marker of CARS. It was shown that low absolute lymphocyte counts are predictive of postoperative sepsis and a better predictor of bacteremia than conventional markers in patients admitted in emergency care units [13, 14]. Furthermore, a very recent study showed that persistent lymphopenia on the fourth day after bacteremia diagnosis predicts early and late mortality in those patients, including in the subgroup of patients with sepsis [15].

The main objective of this study was to evaluate the risk of development of an ICU-acquired infection according to the absolute lymphocyte blood count at admission and its evolution at day 3. The second objective was to evaluate how these parameters impact the 28-day mortality.

Methods

We performed a retrospective study on data prospectively collected within the cohort study conducted with centers participating to the OUTCOMEREA database (OutcomeRea®).

Ethical issues

This study was approved by our institutional review board (CECIC Clermont-Ferrand—IRB n°5891; Ref: 2007–2016), which waived the need for signed informed consent of the participants, in accordance with French legislation on non-interventional studies. However, the patients and their next of kin were asked whether they were willing to participate in the database, and none declined participation.

Data collection

Data were prospectively collected daily by senior physicians in the participating ICUs. For each patient, the data were entered into electronic case report forms using VIGIREA® and RHEA® data capture software, and all case report forms were then entered into the OutcomeRea® data warehouse. All codes and definitions were established prior to study initiation. For each patient, age, sex, and McCabe score were recorded. Severity of illness was evaluated on the first ICU day using the Simplified Acute Physiology Score (SAPS II), Sequential Organ Failure Assessment (SOFA) score, and Glasgow Coma Scale (GCS) score, and Knaus’ scale definitions were used to record preexisting chronic organ failures including respiratory, cardiac, hepatic, renal, and immune system failures. Admission category (medical, scheduled surgery, or unscheduled surgery), admission diagnosis (cardiac, respiratory, or neurological failure, infection, and other), invasive procedures (arterial or venous central catheter, Swan-Ganz catheter, or endotracheal intubation), and treatment of organ failures (inotropic support, hemodialysis, and mechanical ventilation) and the use of corticosteroids, gastro-protective drugs, and antibiotics were also recorded. Daily lymphocyte counts were retrospectively collected from four ICUs participating to OUTCOMEREA database between July 2006 and May 2012. All patients with a lymphocyte count in the first day of admission were included in the study. In order to avoid confusion bias, we excluded patients with chronic lymphocytic leukemia (CLL), infection with the human immunodeficiency virus (HIV) or aplasia at admission. We also excluded patients with limitation of life-sustaining therapy in the four first days after admission. Patients with shock or persistent low blood pressure below 90 mmHg of systolic blood pressure in the first day of admission were included. Study variables were the first lymphocyte count on the first day of admission and its evolution at day 3 after admission. The lymphocyte count at admission was categorized in four predefined classes: normal (>1.5 × 103 cells/µL); subnormal (1 × 103 cells/µL < lymphocytes ≤1.5 × 103 cells/µL); low (0.5 × 103 cells/µL < lymphocytes ≤1 × 103 cells/µL); very low (≤0.5 × 103 cells/µL).

The evolution of lymphocyte count at day 3 versus baseline was defined as a binary variable: normal count (≥1.5 × 103 cells/µL) or relevant increase (more than 0.2 × 103 cells/µL) and decrease or no relevant increase (≤0.2 × 103 cells/µL). We handled missing values at day 3 (n = 166, 22.1%) by taking the value one day before or after.

Nosocomial infection was defined as bacteremia, pneumonia, or catheter-related infection occurring after 72 h from admission. Definition of nosocomial infection provided from the HELICS (Hospital in Europe Link for Infection Control through Surveillance) project [16]. Bacteraemia was defined as the presence of pathogenic bacteria in blood culture. Pneumonia was defined as a chest X-ray with suggestive image of pneumonia with clinical and biological signs of pulmonary infection associated with a positive quantitative bacteriological culture from a respiratory sample: a broncho-alveolar lavage [BAL ≥104 colony-forming unit (CFU)/ml]; a protected specimen brush (≥103 CFU/ml); a blind protected bronchial sampling (≥103 CFU/ml); a tracheal aspiration (≥105 CFU/ml). Catheter infection was defined as positive quantitative catheter culture (≥103 CFU/ml) treated by physicians in charge. Only the first event was considered for analysis.

Statistical analysis

Characteristics of patients were described as count (percent) or median [interquartile range, IQR] for qualitative and quantitative variables, respectively, and were compared between groups using Chi-square or Mann–Whitney tests, as appropriate.

In order to decrease the risk of confusion bias between lymphopenia and acquired-ICU infection, we developed a propensity score aimed to predict the probability to have a nosocomial infection conditionally on variables recorded in the first 2 days of admission [17].

A logistic regression was used to construct the propensity score including variables on clinical relevance or statistic comparison on univariate analysis. Linearity of the logit of continuous covariates was checked. The following clinically relevant variables were entered in the model: age, gender, admission category, center, Knaus definitions, McCabe score, main reason for ICU admission (multi-organ failure, cardiogenic shock, septic shock, coma, acute respiratory deficiency), diabetes with complications (binary variable), severity illness related to specific organ assessed by the sequential SOFA score categorized in 2 classes, lower or equal to two or higher (cardiovascular, neurological, hepatic, renal, coagulation failures), acute respiratory distress syndrome, mechanical ventilation, central venous catheter, arterial catheter or arterial pulmonary catheter, temperature, use of gastro-protective drugs, antibiotics, or corticosteroids.

Then, an inverse probability of treatment weighted (IPTW) [18] based on the propensity score was computed to create a pseudo-population in which the probability to develop or not an ICU-acquired infection was equal. We performed a model with covariates using for the construction of the propensity score weighted by the IPTW including the explicative variables, baseline lymphocyte count, and evolution at the third day [19]. We took the 5–95th percentiles of IPTW to create a new pseudo-population to assess the robustness of the model.

Sub-distribution hazard ratios (sHRs) were developed to assess the independent effects of lymphocyte count at admission and the evolution at day 3 on subsequent risk of ICU-acquired infection. Discharge alive from ICU was treated as competing events. Data were censored at 28 days since the fourth day after admission.

For the secondary objective, risk of death related to initial lymphocyte count and its evolution at day 3, the same protocol was used. We developed a specific propensity score aiming to predict the probability to die in ICU within 28 days of inclusion conditionally on variables recorded within the first 2 days of admission. The following clinically relevant variables were entered in the model: age, gender, admission category, center, Knaus definitions, cardiogenic shock as symptom at admission, continuous monitoring as reason of admission, complicated diabetes, severity illness related to specific organ assessed by the SOFA score categorized in two classes, lower, or equal to 2 or higher (cardiovascular, neurological, hepatic, renal, coagulation failure), respiratory failure severity reflected by acute respiratory distress syndrome, requiring invasive mechanical ventilation, central venous catheter, arterial catheter or arterial pulmonary catheter, temperature, use of corticosteroids.

Sub-distribution hazard ratios (sHRs) were developed with covariates using for the construction of the propensity score weighted by the IPTW. Discharge alive from ICU was treated as competing events. For all analyses, p < .05 was considered to statistically significant. All analyses were performed using SAS, version 9.3 (SAS Institute, Cary, NC, USA).

Results

Population description

Of the 2402 patients recorded within the 4 participating ICUs (Fig. 1), 753 patients were included. The mean age was 68 [56; 78] years, 467 patients (62%) were males, and the median SOFA score at admission was 8 [5, 11]. Medical admission represented the most frequent cases [596 patients (79%)], and septic shock was the first diagnosis at admission in 154 patients (21%). Mechanical ventilation was required for 559 patients (74%) and vasoactive agents at day 1 or 2 for 480 patients (63.8%). The median length of stay in ICU was 9 days [618]. A total of 174 (23%) patients had ICU-acquired infection and 161 (21%) patients died in ICU during the study period (Table 1).
Fig. 1

Flowchart

Table 1

Patients’ characteristics at admission

Variable

Population

N = 753

No ICU-acquired infection (N = 579)

With ICU-acquired infection (N = 174)

P value

Alive

(N = 592)

Dead

(N = 161)

P value

Age

68 [56–78]

67.6 [56–78]

69 [55–77]

0.4106

66.5 [55–77]

71.5 [59–79]

0.02

Men

467 (62)

342 (59)

125 (72)

0.0023

359 (61)

108 (67)

0.13

Length of stay (days)

9 [6–18]

7 [5–13]

23 [14–37]

<.0001

9 [5–19]

10 [7–17]

0.18

Center

 A

501 (66.5)

402 (69)

99 (57)

0.0030

406 (69)

95 (59.0)

0.002

 B

105 (14)

80 (14)

25 (14)

 

86 (14)

19 (12)

 

 C

35 (4.6)

21 (3.6)

14 (8.0)

 

27 (4.6)

8 (5.0)

 

 D

112 (15)

76 (13)

36 (21)

 

73 (12)

39 (24)

 

Admission category

   

0.7400

  

0.003

 Medical

596 (79)

457 (19)

139 (80)

 

454 (77)

142 (88)

 

 Unscheduled surgery

104 (14)

79 (14)

25 (14)

 

94 (16)

10 (6)

 

 Scheduled surgery

53 (7)

43 (7)

10 (6)

 

44 (7)

9 (6)

 

Co-morbidities (Knaus definitions)

 Chronic hepatic failure

45 (6.0)

41 (7)

4 (2.3)

0.0196

30 (5)

15 (9)

0.044

 Chronic cardiovascular failure

101 (13.4)

70 (12)

31 (18)

0.0519

70 (12)

31 (19)

0.014

 Chronic respiratory failure

157 (20.8)

120 (21)

37 (21)

0.8780

126 (21)

31 (19)

0.57

 Chronic renal failure

61 (8.1)

47 (8.1)

14 (8.0)

0.9758

44 (7.4)

17 (10.6)

0.19

 Immunosuppression

69 (9.2)

54 (9.3)

15 (8.6)

0.7772

59 (10.0)

10 (6.2)

0.14

Long-term corticosteroids use

24 (3.2)

19 (3.3)

5 (2.9)

0.7882

20 (3.4)

4 (2.5)

0.57

History of chemotherapy

40 (5.3)

31 (5)

9 (5.2)

0.9254

31 (5)

9 (5)

0.86

Main reason of admission

 Coma

106 (14)

81 (14)

25 (14)

0.8999

81 (14)

25 (15)

0.55

 Acute respiratory failure

211 (28.0)

150 (26)

61 (35)

0.0184

164 (27.7)

47 (29)

0.71

 Septic shock

154 (20.4)

123 (21)

31 (18)

0.3257

121 (20)

33 (20)

0.99

 Cardiogenic shock

39 (5)

14 (4)

15 (8)

0.0195

22 (4)

17 (11)

0.0005

 Hemorrhage shock

50 (6.6)

40 (7)

10 (6)

0.5895

43 (7)

7 (4)

0.19

 Multi-organ failure

21 (3)

11 (2)

10 (6)

0.0069

14 (2)

7 (4)

0.17

 Shock (other)

27 (3.6)

23 (4)

4 (2)

0.2978

21 (3.5)

6 (4)

0.91

 Other

145 (19)

127 (22)

18 (10)

0.0007

126 (21)

19 (12)

0.007

SAPS II score

49 [37–60]

48 [3–59]

51 [40–62]

0.0374

47 [36–57]

57 [46–66]

<0.0001

SOFA score

8 [5–11]

8 [5–11]

10 [7–12]

<.0001

7.5 [5–11]

10 [7–12]

<0.0001

Cardiovascular SOFA score (>2)

462 (61)

333 (57)

129 (74)

<.0001

333 (56)

129 (80)

<0.0001

Mechanical ventilation

559 (74)

411 (71)

148 (85)

0.0002

422 (71)

137 (85)

0.0004

Antibiotic day 1 or 2

581 (77)

448 (77)

133 (76)

0.80

455 (78)

126 (78)

0.71

Data are expressed as number (%) or median [interquartile]. ICU: intensive care unit; SAPS II: Simplified Acute Physiology Score; SOFA: Sequential Organ Failure Assessment. Of note, in some cases, septic shock was not the cause of admission in ICU, but developed within the first hours of ICU admission

The median number of lymphocyte counts measurements was 6 [413]. The percentage of day with a lymphocyte count by patient during ICU stay was 75%, and the median range between two blood samples with lymphocyte count was 1 day. The median of the lymphocyte count at admission was 0.80 [0.51–1.29] × 103 cells/µL. The distribution in 4 classes was as follows: 149 patients (20%) had a normal lymphocyte count with a median of 1.97 [1.70–2.80] × 103 cells/µL; 141 patients (19%) had a lymphocyte count ranging between 1 and 1.5 × 103 cells/µL with a median of 1.19 [1.10–1.30] × 103 cells/µL; 278 patients (37%) had a lymphocyte count ranging between 0.5 and 1 × 103 cells/µL with a median of 0.72 [0.61–0.84] × 103 cells/µL; 185 patients (24%) had a lymphocyte count lower than 0.5 × 103 cells/µL with a median of 0.34 [0.24–0.43] × 103 cells/µL.

Among the total of 174 (24%) ICU-acquired infections, pneumonia was diagnosed in 113 (64.9%) patients, bacteremia in 37 (21.3%) and catheter-associated infection in 36 (20.7%). In 13 patients, 2 sites of infection were diagnosed the same day. Enterobacteriaceae bacteria were the most frequent pathogens isolated, followed by Pseudomonas spp. and Staphylococcus aureus (Tables 2, 3).
Table 2

Description of ICU-acquired infection related to site of infection and time to event

 

No (%)

Time to event (median [IQ]) or days of event

Total

174

8

Pneumonia

113 (64.9)

10 [6–15]

Bacteremia

37 (21.3)

8 [6–13]

Catheter-associated infection

36 (20.7)

8 [5–13]

Pneumonia with bacteremia

6 (3.4)

11.5 [7–23]

Catheter infection with bacteremia

3 (1.7)

13 [7–22]

Pneumonia with catheter-associated infection

3 (1.7)

13 [4–14]

Data are expressed as number (%) or median [interquartile]

Table 3

Description of ICU-acquired infection related to site of infection and microorganism (percentage of the total of pathogens isolated in a site)

Pathogens

Pneumonia

(n = 113)

Bacteremia

(n = 37)

Catheter infection

(n = 36)

Staphylococcus aureus

21 (18.6)

6 (16.2)

3 (8.3)

Coagulase-negative Staphylococci

8 (7.1)

5 (13.5)

9 (25.0)

Other GPB

16 (14.2)

9 (24.3)

8 (22.2)

Fermenting GNP

46 (40.7)

13 (35.1)

14 (38.9)

Non-fermenting GNP

40 (35.4)

6 (16.2)

7 (19.4)

Anaerobes

1 (0.9)

1 (2.7)

0

Fungi

5 (4.4)

5 (13.5)

1 (2.8)

Polymicrobial

21 (18.6)

8 (21.6)

5 (13.9)

MDR pathogens

47 (45.6)

10 (27.0)

9 (25.0)

Data are expressed as number (%) or median [interquartile]. MDR: multi-drug-resistant, including methicillin-resistant Staphylococcus aureus, Enterobacteriaceae resistant to third-generation cephalosporins, Pseudomonas aeruginosa resistant to ticarcillin and/or imipenem and/or ceftazidime, Stenotrophomonas maltophilia, Burkholderia cepacia, and Acinetobacter baumannii. GPB; Gram-positive bacteria, GNB; Gram-negative Bacteria; non-fermenting GNB (Pseudomonas spp., Acinetobacter baumannii, Stenotrophomonas maltophilia, Burkholderia cepacia)

There were no relationships between the lymphocyte count and the SOFA score, and between the delta of the SOFA score and the variations in the lymphocyte counts. This result is consistent with our results about the independent role of immune paralysis and organ failures.

Risk of ICU-acquired infection

Comparisons between patients with ICU-acquired infection and the others are shown in Table 1. The final logistic model used to calculate propensity score is given in Additional file 1: Table E1. The cumulative incidence curve of ICU-acquired infection is shown in Fig. 2a.
Fig. 2

Cumulative incidence curves of ICU-acquired infection a according to baseline lymphocyte count categorized in 4 classes; cumulative incidence curve of ICU-acquired infection (b) and incidence curve of death (c) according to the increase from baseline of the lymphocyte count at day 3 (increase in lymphocyte count was considered significant if greater than 0.2 × 103 cells/µL). Numbers below each figure represent the number of patients still at risk of event at a particular time point. No patient were lost to follow-up at day 28

Sub-distribution hazard ratios (sHRs) of ICU-acquired infection were significant for abnormal values at admission (Table 4), with no difference between subnormal and very low lymphocyte counts. The absence of relevant increase in the lymphocyte count at day 3 was associated with an increased risk of developing an infection (sHR of 1.37 [1.12–1.67], p = 0.002) (Fig. 2b). The interaction term between baseline lymphocyte count and lymphocyte increase at day 3 was not significant. Importantly, the onset of ICU-acquired infection was associated with an increased day-28 mortality (p < 0.001).
Table 4

Results of the sub-distribution Hazard ratio (sHR) of baseline lymphocyte count and its evolution at day 3 for the risk of ICU-acquired infection (adjusted with the covariates used in the propensity score of acquiring a nosocomial infection before day 28 using an IPTW estimator; see Additional file 2)

Variables

sHR

IC-95

p value

Baseline lymphocyte count categorized in 4 classes

   

0.001

 Normal value ≥1.5 × 103 cells/µL

Reference

 

 Subnormal class (<1.5 and ≥ 1 × 103 cells/µL)

1.60

1.24

2.08

0.0004

 Low class (<1 × 103 cells/µL and ≥0.5 × 103 cells/µL)

1.43

1.12

1.85

0.004

 Very low class (<0.5 × 103 cells/µL)

1.63

1.23

2.15

0.0006

 Non-significant increase (below 0.2 × 103 cells/µL) at day 3 and abnormal value

1.37

1.12

1.67

0.002

Risk of 28-day mortality

Comparisons between patients’ dead in ICU and others are shown in Table 1, using the final logistic model used to calculate propensity score (Additional file 1: Table E2). The incidences of 28-day mortality according to baseline lymphocyte count and its evolution at day 3 are shown in Table 5. The baseline count of lymphocyte had no impact on the 28-day mortality in ICU. However, the decrease or the non-significant increase on day 3 was significantly associated with the death in ICU [sHR of 1.67 [1.37–2.03], p < 0.0001 (Table 5)]. The cumulative incidence curve of death according to the evolution of lymphocyte count is represented in Fig. 2c.
Table 5

Results of the sub-distribution Hazard ratio (sHR) of baseline lymphocyte count and its evolution at day 3 for the risk of 28-day ICU mortality (adjusted with the covariates used in the propensity score of dying before day 28 using an IPTW estimator; see Additional file 2)

Variables

sHR

IC-95

p value

Baseline lymphocyte count categorized in 4 classes

   

0.15

 Normal value ≥1.5 × 103 cells/µL

Reference

 

 Subnormal class (<1.5 and ≥ 1 × 103 cells/µL)

0.84

0.658

1.08

0.176

 Low class (<1 × 103 cells/µL and ≥0.5 × 103 cells/µL)

1.09

0.891

1.36

0.377

 Very low class (<0.5 × 103 cells/µL)

0.99

0.773

1.28

0.969

 Non-significant increase (below 0.2 × 103 cells/µL) at day 3 and abnormal value

1.67

1.37

2.03

<0.0001

Discussion

To our knowledge, our study is the first large cohort study which evaluated the relation between the baseline lymphocyte count and its evolution at day 3, and the risk of ICU-acquired infection and death in patients admitted in ICU with sustained hypotension. We demonstrated the significant independent prognostic impact of a low lymphocyte count at baseline on the risk to develop an ICU-acquired infection. A persisting lymphopenia or a non-significant increase at day 3 is associated with a risk to develop a nosocomial infection and with increased 28-day mortality (Additional file 1).

Acute critical ill patients, particularly in case of sepsis, often present signs of systemic inflammatory response syndrome (SIRS) which could be related to pro-inflammatory response. Beside this pro-inflammatory response, an anti-inflammatory response occurs. In these patients, several studies showed increased secretion of anti-inflammatory cytokines, e.g., IL-10, and decreased activation of immunity cells, e.g., monocytes [20, 21]. Thus, the immune response can display various profiles: combined anti- and pro-inflammatory response; anti-inflammatory response; or global immune depression. This syndrome of acquired deficiency of immune system is called the post-aggressive immunosuppression or compensatory anti-inflammatory response syndrome (CARS) [3, 4]. This secondarily impaired immunity has been described for decades [9]; several studies correlated it with poor outcome [57, 22]. This could explain the onset of nosocomial infections with opportunistic microorganism in septic patients, e.g., viral reactivation or fungal infection [2325].

CARS involves both the innate and adaptive parts of immune system. It affects different cells involved in the innate immune system, such as polymorphonuclear neutrophils, dendritic cells, and monocytes. The link with a poor outcome was demonstrated in several studies [4, 26]. Monocytes dysfunction is now evaluated by a clinically validated surrogate marker: mHLA-DR expression [4, 27]. While biological testing of mHLA-DR expression is standardized [10] and then could offer a well-recognized biological test to select patients who would benefit of immune-adjuvant therapy, this test is not yet generalized in clinical practice.

The acquired immunity cells such as lymphocytes are also affected. Lymphocytes, particularly T-cells subset, are a cornerstone of the adaptive response to aggressions. An acquired or congenital lymphocyte deficiency increases the risk of infection and of death. CARS is correlated with lymphocyte function alteration, which has been described for 30 years. Function alteration is reflected by a decreased production of pro-inflammatory cytokines, such as IL-2, an increased production of anti-inflammatory cytokines, such as IL-10, an increased expression on cells membrane of inhibitory receptor such as programmed cell death-1, and a decreased expression of T-cell receptor repertoire diversity [2731]. While our understanding of the mechanism of lymphocyte alteration during sepsis progresses, the link with patient’s prognosis is not always established.

An increased apoptosis was described in patients [12, 22]. Various pathways seem to be involved in the lymphocyte apoptosis in case of sepsis: an extrinsic pathway, mediated by the caspase-8, and an intrinsic pathway, mediated by the caspase-9 [10, 22]. In the study of Le Tulzo et al. [22], the magnitude of apoptosis was correlated with the persistence of multi-organ dysfunctions, duration of mechanical ventilation, and death. The correlation between the quantitative alteration of lymphocyte and a poor outcome was shown in two studies involving children [12, 32, 33]. In 21 adult patients with septic shock, Venet et al. [12] also described a median lymphocyte count within the first 24 h following admission for septic shock close to our results (0.5–0.7 × 103 cells/µL). Altered lymphocyte function with recombinant human IL-7 or anti-programmed cell death-1 antibody may be promising targets for future clinical studies [27].

In a retrospective study of bacteremic patients, an association was observed between persistent lymphopenia (defined as below of 0.6 × 103 cells/µLon the fourth day) with the 28-day mortality (primary endpoint), 1-year mortality, and subsequent hospital infection [15]. However, the low baseline total lymphocyte count (≤0.6 × 103 cells/µL) was not associated with any of them, conversely to what we observed in our study. This difference may be due to the lymphopenia threshold definitions, and also to the case mix, as we included all patients with sustained hypotension, whether or not they had sepsis and/or bacteremia. As a matter of fact, we included all patients with an unstable hemodynamic status, in order to take into account the severity of patient as a promoter of CARS. Indeed, dysfunction of immune system was observed not only in septic patients, but also in post-traumatic or severely burned patients [3437].

Although our study did not provide information on the link between the lymphocyte count and the qualitative alteration of lymphocyte function, it is the first one that demonstrated in a large cohort of patients, the impact of a low lymphocyte count at ICU admission and of its persistence on the risk to develop an ICU-acquired infection and of increased mortality. The interaction between the lymphocyte count at baseline and its evolution found in our study could reflect the persistent status of post-aggressive immunosuppression. Of course, our study did not preclude the absence of added prognostic value of the lymphocyte subsets, which has already been reported in the literature [22, 28, 38]; however, it highlights that the routinely measured total lymphocyte count may be taken into account. Indeed, the total lymphocyte count is simple to evaluate, without any special skill or laboratory equipment. However, further studies are warranted to figure out whether or not functional new markers would add more information that plain absolute lymphocyte counts.

Case mix varied between centers, which may explain significant differences between numbers of exams performed and mean lymphocyte counts between centers. However, we did not unmask heterogeneity between prognostic impacts of lymphocyte alterations between centers. We cannot make any causative relationship between mortality and ICU-acquired infection with low lymphocyte count, as they may all be related to the disease severity. Also, we do have any data on the immunoresponse and the anergy–apoptosis of this lymphocyte in general and lymphopenia in particular, as it could be expected from a retrospective study that requires a prospective confirmation using the functional activities of the different lymphocytes involved in the inflammation processes.

Conclusion

A large cohort of ICU patients with shock at admission, we demonstrated the independent impact of a low baseline lymphocyte count and its non-relevant increase at day 3 with the risk of ICU-acquired infection and, for persistent lymphopenia, its impact on 28-day mortality. Total lymphocyte count appears as a simple and routine marker of immune dysfunction, and might be useful for selecting patients that could benefit of potential immune-adjuvant therapies [27].

Abbreviations

sHR: 

sub-distribution hazard ratio

ICU: 

intensive care unit

CARS: 

compensatory anti-inflammatory response syndrome, IL-6, interleukin-6

TNF-α

tumor nuclear factor

SAPS II: 

Simplified Acute Physiology Score

SOFA: 

Sequential Organ Failure Assessment

GCS: 

Glasgow Coma Scale

CLL: 

chronic lymphocytic leukemia

HIV: 

infection with the human immunodeficiency virus

HELICS: 

Hospital in Europe Link for Infection Control through Surveillance project

BAL: 

broncho-alveolar lavage

CFU: 

coloning-forming unit

SIRS: 

systemic inflammatory response syndrome

IPTW: 

inverse probability of treatment weighted

IL-10: 

interleukin-10

mHLA-DR expression: 

monocytes dendritic cell HLA-D expression

Declarations

Authors’ contributions

JFT, ML, CA conceived and designed the study, and was involved in drafting the manuscript and provided research funding. RS, BS, JCC, MGO, CS collected data and critically revised the manuscript. SR collected and analyzed data, performed statistical analysis, and drafted and critically revised the manuscript. All authors gave final approval for manuscript publication and agree to be accountable for all aspects of this work. All authors read and approved the final manuscript.

Acknowledgements

The authors thank Celine Feger, MD (EMIBiotech) for her editorial assistance.

The study was set up and conducted by the Ou tcomerea network. The members of the OUTCOMEREA Study Group are listed in “Appendix".

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Data and material are available on the website “lymphocyte_data-set”

Ethical approval

This study was approved by our institutional review board (CECIC Clermont-Ferrand—IRB n°5891; Ref: 2007–2016), which waived the need for signed informed consent of the participants, in accordance with French legislation on non-interventional studies. . However, the patients and their next of kin were asked whether they were willing to participate in the database, and none declined participation.

Funding

The study was funded by the non-profit OutcomeRea network. The OutcomeRea network takes full administrative responsibility for data management, analysis, and interpretation and for manuscript preparation, review, and approval.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Physiology Department, Cochin University Hospital, AP-HP, Paris Descartes University
(2)
Polyvalent ICU, Delafontaine Hospital
(3)
Medical ICU, Grenoble 1 University, Albert Michallon Hospital
(4)
Medical and Infectious Diseases ICU, Bichat University Hospital
(5)
Clermont-Ferrand University, Medical ICU, Gabriel Montpied Hospital
(6)
UMR 1137 IAME Inserm- Paris Diderot University
(7)
Polyvalent ICU, St Joseph Hospital

References

  1. Buckley RH. Primary immunodeficiency diseases due to defects in lymphocytes. N Eng J Med. 2000;343:1313–24.View ArticleGoogle Scholar
  2. Levy JA. Infection by human immunodeficiency virus–CD4 is not enough. N Eng J Med. 1996;335:1528–30.View ArticleGoogle Scholar
  3. Hotchkiss RS, Karl IE. The pathophysiology and treatment of sepsis. N Eng J Med. 2003;348:138–50.View ArticleGoogle Scholar
  4. Monneret G, Venet F, Pachot A, Lepape A. Monitoring immune dysfunctions in the septic patient: a new skin for the old ceremony. Mol Med. 2008;14:64–78.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Delano MJ, Thayer T, Gabrilovich S, Kelly-Scumpia KM, Winfield RD, Scumpia PO, et al. Sepsis induces early alterations in innate immunity that impact mortality to secondary infection. J Immuno. 2011;186:195–202.Google Scholar
  6. Grimaldi D, Louis S, Pene F, Sirgo G, Rousseau C, Claessens YE, et al. Profound and persistent decrease of circulating dendritic cells is associated with ICU-acquired infection in patients with septic shock. Intensive Care Med. 2011;37:1438–46.View ArticlePubMedGoogle Scholar
  7. Lukaszewicz AC, Grienay M, Resche-Rigon M, Pirracchio R, Faivre V, Boval B, et al. Monocytic HLA-DR expression in intensive care patients: interest for prognosis and secondary infection prediction. Crit Care Med. 2009;37:2746–52.View ArticlePubMedGoogle Scholar
  8. Guisset O, Dilhuydy MS, Thiebaut R, Lefevre J, Camou F, Sarrat A, et al. Decrease in circulating dendritic cells predicts fatal outcome in septic shock. Intensive Care Med. 2007;33:148–52.View ArticlePubMedGoogle Scholar
  9. Christou NV, Meakins JL, Gordon J, Yee J, Hassan-Zahraee M, Nohr CW, et al. The delayed hypersensitivity response and host resistance in surgical patients. 20 years later. Ann Surg. 1995;222:534–46.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Hotchkiss RS, Nicholson DW. Apoptosis and caspases regulate death and inflammation in sepsis. Nat Rev Immunol. 2006;6:813–22.View ArticlePubMedGoogle Scholar
  11. Hotchkiss RS, Tinsley KW, Swanson PE, Schmieg RE Jr, Hui JJ, Chang KC, et al. Sepsis-induced apoptosis causes progressive profound depletion of B and CD4+ T lymphocytes in humans. J Immunol. 2001;166:6952–63.View ArticlePubMedGoogle Scholar
  12. Venet F, Davin F, Guignant C, Larue A, Cazalis MA, Darbon R, et al. Early assessment of leukocyte alterations at diagnosis of septic shock. Shock. 2010;34:358–63.View ArticlePubMedGoogle Scholar
  13. de Jager CP, van Wijk PT, Mathoera RB, de Jongh-Leuvenink J, van der Poll T, Wever PC. Lymphocytopenia and neutrophil-lymphocyte count ratio predict bacteremia better than conventional infection markers in an emergency care unit. Crit Care. 2010;14:R192.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Lewis RT, Klein H. Risk factors in postoperative sepsis: significance of preoperative lymphocytopenia. J Surg Res. 1979;26:365–71.View ArticlePubMedGoogle Scholar
  15. Drewry AM, Samra N, Skrupky LP, Fuller BM, Compton SM, Hotchkiss RS. Persistent lymphopenia after diagnosis of sepsis predicts mortality. Shock. 2014;42:383–91.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Mertens R, Van Den Berg JM, Fabry J, Jepsen OB. HELICS: a European project to standardise the surveillance of hospital acquired infection, 1994-1995. Euro surveillance. 1996;1:28–30.PubMedGoogle Scholar
  17. Gayat E, Pirracchio R, Resche-Rigon M, Mebazaa A, Mary JY, Porcher R. Propensity scores in intensive care and anaesthesiology literature: a systematic review. Intensive Care Med. 2010;36:1993–2003.View ArticlePubMedGoogle Scholar
  18. Bailly S, Pirracchio R, Timsit JF. What’s new in the quantification of causal effects from longitudinal cohort studies: a brief introduction to marginal structural models for intensivists. Intensive Care Med. 2016;42:576–9.View ArticlePubMedGoogle Scholar
  19. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med. 2004;23:2937–60.View ArticlePubMedGoogle Scholar
  20. Sinistro A, Almerighi C, Ciaprini C, Natoli S, Sussarello E, Di Fino S, et al. Downregulation of CD40 ligand response in monocytes from sepsis patients. Clin Vaccine Immuno. 2008;15:1851–8.View ArticleGoogle Scholar
  21. van Dissel JT, van Langevelde P, Westendorp RG, Kwappenberg K, Frolich M. Anti-inflammatory cytokine profile and mortality in febrile patients. Lancet. 1998;351:950–3.View ArticlePubMedGoogle Scholar
  22. Le Tulzo Y, Pangault C, Gacouin A, Guilloux V, Tribut O, Amiot L, et al. Early circulating lymphocyte apoptosis in human septic shock is associated with poor outcome. Shock. 2002;18:487–94.View ArticlePubMedGoogle Scholar
  23. Chiche L, Forel JM, Roch A, Guervilly C, Pauly V, Allardet-Servent J, et al. Active cytomegalovirus infection is common in mechanically ventilated medical intensive care unit patients. Crit Care Med. 2009;37:1850–7.View ArticlePubMedGoogle Scholar
  24. Hartemink KJ, Paul MA, Spijkstra JJ, Girbes AR, Polderman KH. Immunoparalysis as a cause for invasive aspergillosis? Intensive Care Med. 2003;29:2068–71.View ArticlePubMedGoogle Scholar
  25. Limaye AP, Kirby KA, Rubenfeld GD, Leisenring WM, Bulger EM, Neff MJ, et al. Cytomegalovirus reactivation in critically ill immunocompetent patients. JAMA. 2008;300:413–22.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Pillay J, Kamp VM, van Hoffen E, Visser T, Tak T, Lammers JW, et al. A subset of neutrophils in human systemic inflammation inhibits T cell responses through Mac-1. J Clin Invest. 2012;122:327–36.View ArticlePubMedGoogle Scholar
  27. Venet F, Lukaszewicz AC, Payen D, Hotchkiss R, Monneret G. Monitoring the immune response in sepsis: a rational approach to administration of immunoadjuvant therapies. Curr Opin Immuno. 2013;25:477–83.View ArticleGoogle Scholar
  28. Boomer JS, To K, Chang KC, Takasu O, Osborne DF, Walton AH, et al. Immunosuppression in patients who die of sepsis and multiple organ failure. JAMA. 2011;306:2594–605.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Guignant C, Lepape A, Huang X, Kherouf H, Denis L, Poitevin F, et al. Programmed death-1 levels correlate with increased mortality, nosocomial infection and immune dysfunctions in septic shock patients. Crit Care. 2011;15:R99.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Hotchkiss RS, Monneret G, Payen D. Immunosuppression in sepsis: a novel understanding of the disorder and a new therapeutic approach. Lancet Inf Dis. 2013;13:260–8.View ArticleGoogle Scholar
  31. Venet F, Filipe-Santos O, Lepape A, Malcus C, Poitevin-Later F, Grives A, et al. Decreased T-cell repertoire diversity in sepsis: a preliminary study. Critl Care Med. 2013;41:111–9.View ArticleGoogle Scholar
  32. Adamski JK, Arkwright PD, Will AM, Patel L. Transient lymphopenia in acutely unwell young infants. Arch Dis Child. 2002;86:200–1.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Felmet KA, Hall MW, Clark RS, Jaffe R, Carcillo JA. Prolonged lymphopenia, lymphoid depletion, and hypoprolactinemia in children with nosocomial sepsis and multiple organ failure. J Immunol. 2005;174:3765–72.View ArticlePubMedGoogle Scholar
  34. Cheron A, Floccard B, Allaouchiche B, Guignant C, Poitevin F, Malcus C, et al. Lack of recovery in monocyte human leukocyte antigen-DR expression is independently associated with the development of sepsis after major trauma. Crit Care. 2010;14:R208.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Pellegrini JD, De AK, Kodys K, Puyana JC, Furse RK, Miller-Graziano C. Relationships between T lymphocyte apoptosis and anergy following trauma. J Surg Res. 2000;88:200–6.View ArticlePubMedGoogle Scholar
  36. Spolarics Z, Siddiqi M, Siegel JH, Garcia ZC, Stein DS, Denny T, et al. Depressed interleukin-12-producing activity by monocytes correlates with adverse clinical course and a shift toward Th2-type lymphocyte pattern in severely injured male trauma patients. Crit Care Med. 2003;31:1722–9.View ArticlePubMedGoogle Scholar
  37. Venet F, Tissot S, Debard AL, Faudot C, Crampe C, Pachot A, et al. Decreased monocyte human leukocyte antigen-DR expression after severe burn injury: correlation with severity and secondary septic shock. Crit Care Med. 2007;35:1910–7.View ArticlePubMedGoogle Scholar
  38. Boomer JS, Shuherk-Shaffer J, Hotchkiss RS, Green JM. A prospective analysis of lymphocyte phenotype and function over the course of acute sepsis. Crit Care. 2012;16:R112.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s) 2017

Advertisement