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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Annals of Intensive Care 1/2017

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

Zeitschrift:
Annals of Intensive Care > Ausgabe 1/2017
Autoren:
Christophe Adrie, Maxime Lugosi, Romain Sonneville, Bertrand Souweine, Stéphane Ruckly, Jean-Charles Cartier, Maité Garrouste-Orgeas, Carole Schwebel, Jean-François Timsit, On behalf of the OUTCOMEREA study group
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s13613-017-0242-0) contains supplementary material, which is available to authorized users.
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

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).
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.
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].

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.

Appendix: Members of the OUTCOMEREA study group

Scientific committee

Jean-François Timsit (Hôpital Bichat, Paris and INSERM U823, Grenoble, France), Elie Azoulay (Medical ICU, Hôpital Saint Louis, Paris, France), Yves Cohen (ICU, Hôpital Avicenne, Bobigny, France), Maïté Garrouste-Orgeas (ICU Hôpital Saint-Joseph, Paris, France), Lilia Soufir (ICU, Hôpital Saint-Joseph, Paris, France), Jean-Ralph Zahar (Microbiology Department, Hôpital Necker, Paris, France), Christophe Adrie (ICU, Hôpital Cochin, Paris, France), Michael Darmon (Medical ICU, University hospital St Etienne, France), Corinne Alberti (Robert Debré Hospital, Paris France) and Christophe Clec’h (ICU, Hôpital Avicenne, Bobigny, and INSERM U823, Grenoble, France).

Biostatistical and informatics expertise

Jean-Francois Timsit (Hôpital Albert Michallon and Integrated Research Center U823, Grenoble, France), Corinne Alberti (Medical Computer Sciences and Biostatistics Department, Robert Debré, Paris, France), Adrien Français (Integrated Research Center U823, Grenoble, France), Aurélien Vesin (Integrated Research Center U823, Grenoble, France), Stephane Ruckly INSERM U823, Grenoble, France), Christophe Clec’h (ICU, Hôpital Avicenne, Bobigny, and INSERM U823, Grenoble, France), Frederik Lecorre (Supelec, France), and Didier Nakache (Conservatoire National des Arts et Métiers, Paris, France), Aurélien Vannieuwenhuyze (Tourcoing, France).

Investigators of the Outcomerea database

Christophe Adrie (ICU, Hôpital Delafontaine, Saint Denis, France and Physiology, Hôpital Cochin, Paris, France), Bernard Allaouchiche (Surgical ICU, Edouard Herriot Hôpital, Lyon), Claire Ara-Somohano (CHU A Michallon, Grenoble, France), Laurent Argault (medical ICU Edouard Herriot Hôpital, Lyon), Agnès Bonadona (CHU A Michallon, Grenoble, France), Caroline Bornstain (ICU, Hôpital de Montfermeil, France), Lila Bouadma (ICU, hôpital Bichat Claude-Bernard, Paris, France), Alexandre Boyer (ICU, Hôpital Pellegrin, Bordeaux, France), Christine Cheval (SICU, Hôpital Saint-Joseph, Paris, France), Jean-Pierre Colin (ICU, Hôpital de Dourdan, Dourdan, France), Michael Darmon (ICU, CHU Saint Etienne), Anne-Sylvie Dumenil (Hôpital Antoine Béclère, Clamart France), Adrien Descorps-Declere (Hôpital Antoine Béclère, Clamart France), Jean-Philippe Fosse (ICU, Hôpital Avicenne, Bobigny, France), Rebecca Hamidfar-Roy (CHU A Michallon, Grenoble, France), Samir Jamali (ICU, Hôpital de Dourdan, Dourdan, France), Hatem Khallel (ICU, Cayenne General Hospital), Christian Laplace (ICU, Hôpital Kremlin-Bicêtre, Bicêtre, France), Alexandre Lautrette (ICU, CHU G Montpied, Clermont-Ferrand), Thierry Lazard (ICU, Hôpital de la Croix Saint-Simon, Paris, France), Eric Le Miere (ICU, Hôpital Louis Mourier, Colombes, France), Maxime Lugosi (CHU A Michallon, Grenoble, France), Guillaume Marcotte (surgical ICU, Edouard Herriot Hôpital, Lyon), Laurent Montesino (ICU, Hôpital Bichat, Paris, France), Bruno Mourvillier (ICU, Hôpital Bichat, France), Benoît Misset (ICU, Hôpital Saint-Joseph, Paris, France), Delphine Moreau (ICU, Hôpital Saint-Louis, Paris, France), Etienne Pigné (ICU, Hôpital Louis Mourier, Colombes, France), Stéphane Ruckly (Integrated Research Center U823, Grenoble, France), Bertrand Souweine (ICU, CHU G Montpied, Clermont-Ferrand), Carole Schwebel (CHU A Michallon, Grenoble, France), Gilles Troché (Hôpital Antoine, Béclère, Clamart France), Marie Thuong (ICU, Pontoise, France), Guillaume Thierry (ICU, Hôpital Saint-Louis, Paris, France), Dany Toledano (CH Gonnesse, France), and EricVantalon (SICU, Hôpital Saint-Joseph, Paris, France).

Study monitors

Caroline Tournegros, Loïc Ferrand, Nadira Kaddour, Boris Berthe, Kaouttar Mellouk, Veronique Deiler, Kelly Tiercelet, Sophie Letrou, Igor Théodose, and Julien Fournier.
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