Risk Factors for Postoperative Pneumonia After Cardiac Surgery and Development of a Preoperative Risk Score* Nicolas Allou, MD1,2; Regis Bronchard, MD1; Jean Guglielminotti, MD, PhD1,2; Marie Pierre Dilly, MD1; Sophie Provenchere, MD1; Jean Christophe Lucet, MD, PhD2,3; Cédric Laouénan, MD2,4; Philippe Montravers, MD, PhD1,2
Objectives: The aims of this study were, first, to identify risk factors for microbiology-proven postoperative pneumonia after cardiac surgery and, second, to develop and validate a preoperative scoring system for the risk of postoperative pneumonia. Design and Setting: A single-center cohort study.
*See also p. 1302. 1 Département d’Anesthésie-Réanimation, CHU Bichat-Claude Bernard, Université Paris VII, Assistance Publique Hopitaux de Paris, France. 2 Université Paris Diderot, Sorbonne Paris Cité, Paris, France. 3 Unité d’Hygiène et de lutte Contre les Infections Hospitalières, CHU Bichat-Claude Bernard, Université Paris VII, Assistance Publique Hopitaux de Paris, France. 4 Service de Biostatistique, F-75018, CHU Bichat-Claude Bernard, Université Paris VII, Assistance Publique Hopitaux de Paris, France. Drs. Allou, Bronchard, and Guglielminotti had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Allou, Bronchard, Guglielminotti, and Montravers contributed to study concept and design. Drs. Bronchard, Allou, Provenchere, Dilly, and Lucet contributed to acquisition of data. Drs. Allou, Bronchard, Montravers, and Guglielminotti contributed to analysis and interpretation of data. Drs. Allou, Montravers, Guglielminotti, and Bronchard contributed to drafting of the article. Drs. Allou, Bronchard, Guglielminotti, and Montravers contributed to critical revision of the article for important intellectual content. Drs. Guglielminotti and Laouénan contributed to statistical analysis. Drs. Allou, Bronchard, Guglielminotti, Dilly, Provenchere, Lucet, and Montravers contributed to administrative, technical, or material support. Drs. Allou, Montravers, Guglielminotti, and Bronchard contributed to study supervision. Supported, in part, by internal funds. Presented, in part, at the 24th annual congress of the European Society of Intensive Care Medicine, Berlin, Germany, October, 2011. Dr. Montravers consulted for Pfizer, Merck, Astellas, and AstraZeneca; received support for participation in review activity from Astellas; served as a board member for Pfizer, Merck, and Astellas; provided expert testimony for Aguettant; and lectured for Pfizer. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: [email protected] Copyright © 2013 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins DOI: 10.1097/CCM.0000000000000143
1150
www.ccmjournal.org
Patients: All consecutive patients undergoing cardiac surgery between January 2006 and July 2011. Interventions: None. Measurements and Main Results: Multivariate analysis of risk factors for postoperative pneumonia was performed on data from patients operated between January 2006 and December 2008 (training set). External temporal validation was performed on data from patients operated between January 2009 and July 2011 (validation set). Preoperative variables identified in multivariate analysis of the training set were then used to develop a preoperative scoring system that was validated on the validation set. Postoperative pneumonia occurred in 174 of the 5,582 patients (3.1%; 95% CI, 2.7–3.6). Multivariate analysis identified four risk factors for postoperative pneumonia: age (odds ratio, 1.02; 95% CI, 1.01–1.03), chronic obstructive pulmonary disease (odds ratio, 2.97; 95% CI, 1.8–4.71), preoperative left ventricular ejection fraction (odds ratio, 0.98; 95% CI, 0.96–0.99), and the interaction between RBC transfusion during surgery and duration of cardiopulmonary bypass (odds ratio, 2.98; 95% CI, 1.96–4.54). A 6-point score including the three preoperative variables then defined two risk groups corresponding to postoperative pneumonia rates of 1.8% (score < 3) and 6.5% (score ≥ 3). Conclusion: Assessing preoperative risk factors for postoperative pneumonia with the proposed scoring system could help to implement a preventive policy in high-risk patients with a risk of postoperative pneumonia greater than 4% (i.e., patients with a score ≥3). (Crit Care Med 2014; 42:1150–1156) Key Words: cardiac surgery; postoperative pneumonia; risk score
P
ostoperative pneumonia (POP) is the most common infection after cardiac surgery (CS), with a prevalence rate between 2% and 10% (1–6), especially during the first postoperative week (5, 6). It is associated with increased mortality (3, 5, 6). May 2014 • Volume 42 • Number 5
Clinical Investigations
Several risk factors for POP after CS have been identified (3, 6), but previous studies investigating risk factors for POP after CS confined their analysis to patients with ventilatorassociated pneumonia (VAP) (1–6). However, VAP is not the only presentation of POP, as illustrated by previous studies in which non-VAP accounted for 64% and 43% of POP in general and CS patients, respectively (7, 8). Furthermore, many studies investigating risk factors for POP after CS were based on small sample sizes (1, 3, 5, 6). Identification of risk factors for POP after CS on a large cohort of patients including both VAP and non-VAP POP is therefore warranted. Targeting preoperative interventions to patients at high-risk of POP may decrease POP frequency and mortality. High-risk patients could be identified during the preoperative period with a score based on a combination of individual preoperative risk factors. The objectives of this single-center cohort study were therefore to identify risk factors for POP after CS and to develop and validate a preoperative scoring system of the risk of POP.
MATERIALS AND METHODS The study was approved by the local ethics committee of HotelDieu Hospital, which waived the need for informed consent, because of the observational nature of the study. It complied with the Strengthening the Reporting of Observational studies in Epidemiology statement recommendations statement for reporting (9). Patient Sample Data from consecutive patients who underwent CS with cardiopulmonary bypass (CPB) from January 2006 to July 2011 were included in this single-center cohort study. Exclusion criteria were mechanical ventilation before surgery and death before the second postoperative day. Study Setting During the study period, perioperative care, including anesthesia, monitoring techniques, and normothermic CPB, were performed according to local standardized protocols for all patients. Cefamandole was used for surgical antibiotic prophylaxis (allergic patients received vancomycin and gentamicin). Prevention of POP was in accordance with the American Thoracic Surgery guidelines (10). Diagnosis of POP All consecutive episodes of POP were identified by prospective surveillance by the ICU and infection control team blinded to the study details. Only the first episode of pneumonia diagnosed during the first 7 days after surgery was studied and was defined as POP. The first postoperative week was chosen to cover the period at highest risk of POP (5, 6, 8). The diagnosis of POP was based on usual clinical and microbiological criteria, with bronchoalveolar lavage yielding bacteria at a concentration of greater than 104 colony-forming units (CFU)/mL or protected distal bronchial specimen samples yielding greater than 103 CFU/mL (10). Critical Care Medicine
Statistical Analysis Results are expressed as mean ± sd, median (range), or number of patients (%). When indicated, 95% CIs were calculated. Statistical analysis was performed with R software, version 2.12.0 (R foundation for Statistical Computing, Vienna, Austria). Identification of Risk Factors Risk factors for POP were identified on data from patients operated between January 1, 2006, and December 31, 2008 (training set). External temporal validation was performed with data from patients operated from January 1, 2009, to July 30, 2011 (validation set). Patients with and without POP were compared in the training set by univariate analysis using the Wilcoxon test for continuous variables and Fisher exact test for discrete variables. Variables with a p value less than 0.05 on univariate analysis were entered in a multivariate logistic regression analysis with backward selection. Continuous candidate variables were kept continuous. Variables with a p value greater than or equal to 0.05 at each step of regression analysis were rejected. Missing values for candidate variables entered in the multivariate analysis were imputed with the median of the variable. Two-way interactions between significant variables in multivariate analysis were studied. Significant interactions (p value < 0.05) were entered in the model. The final model with interactions was evaluated for discrimination with the c-index and for calibration with Hosmer-Lemeshow’s test. The model constructed in the training set was applied to the validation set and evaluated for discrimination with the c-index and for calibration with Hosmer-Lemeshow’s test. Score-Based Prediction Rule and Creation of Risk Groups A score-based prediction rule of the occurrence of POP was developed from the prediction model built in the training set. Since the goal of the score-based prediction rule was to identify patients at risk of POP before surgery, only variables of the prediction model that could be collected preoperatively were used. Continuous variables were binarized according to cutoffs previously used in the literature and according to clinical relevance. Logistic regression coefficients of the final model including only preoperative variables were multiplied by 10 and then rounded to the nearest integer. The number of points for each preoperative variable was attributed after identification of a common denominator across rounded coefficients. The score, ranging from 0 to 6, was the sum of the points corresponding to each variable. Evaluation of the score in the training and validation sets used the c-index and Hosmer-Lemeshow’s test of logistic regression with occurrence of pneumonia as the outcome and score as the continuous predictor. The goal of risk stratification according to risk groups was to identify a preoperative group of patients with a high risk of POP. Two groups were created: a low-risk group with an observed pneumonia rate less than 4% (i.e., score < 3) and a high-risk group with an observed pneumonia rate greater www.ccmjournal.org
1151
Allou et al
than or equal to 4% (i.e., score ≥ 3). Predicted pneumonia rate calculated by the score was reestimated in each risk group with bootstrap resampling (n = 2,000) in the training and validation sets to determine whether they were included in the 95% CI of the pneumonia rate observed in each risk group.
RESULTS Study Population From January 2006 to July 2011, 5,678 cardiac procedures with CPB were performed. Ninety-six patients were excluded from the analysis: eight patients were on mechanical ventilation before surgery and 88 patients died during the first 48 hours. The remaining 5,582 patients constituted the cohort. One hundred seventy-four cases of POP were observed (3.1%; 95% CI, 2.7–3.6), including 101 cases of VAP (58%; 95% CI, 51–65). POP was diagnosed 4.3 ± 1.4 days after surgery. Ninety-six cases of POP were observed in the 3,008 patients of the training set (3.2%; 95% CI, 2.6–3.8) and 78
cases were observed in the 2,574 patients of the validation set (3.0%; 95% CI, 2.4–3.7). The microorganisms most commonly isolated belong to Enterobacteriaceae (40%). Pseudomonas aeruginosa represented 18% of all microorganisms, Haemophilus species 17%, Streptococcus species 10%, and S. aureus 5%. No statistically significant difference was observed on univariate analysis between patients with VAP and patients with non-VAP, except for the duration of CPB (101 ± 62 min in the VAP group vs 72 ± 33 min in the non-VAP group; p = 0.0003) and the proportion of New York Heart Association class IV patients (60% in the VAP vs 40% in the non-VAP group; p = 0.009). Duration of mechanical ventilation was longer in the POP group than in the non-POP group (108 ± 273 hr vs 15 ± 57 hr; p < 0.0001), and duration of ICU stay was also longer in the POP group (14 ± 19 d vs 4 ± 5 d; p < 0.0001). Seventy-four deaths were observed among the 174 POP patients (42.5%; 95% CI, 35.2–49.9) and 312 deaths were
Table 1. Univariate Analysis of Risk Factors for Postoperative Pneumonia in the Training Set Patients Without POP
Patients with POP
n = 2,912
n = 96
p
0
1,990 (68)
62 (65)
0.44
0.84
0.55–1.30
0
63 ± 14
68 ± 13
0.0004
1.03
1.01–1.04
Body mass index, mean ± sd, kg/m
3
26.3 ± 4.5
26.3 ± 5.1
0.88
1.00
0.95–1.04
Current smoker, n (%)
2
444 (15)
19 (20)
0.29
1.37
0.80–2.24
New York Heart Association class IV, n (%)
0
722 (25)
45 (47)
< 0.0001
2.68
1.77–4.03
Hypertension, n (%)
0
1,592 (55)
54 (56)
0.83
1.07
0.71–1.61
Statin treatment, n (%)
0
1,687 (58)
53 (55)
0.60
0.89
0.59–1.35
Neurologic diseasea, n (%)
0
69 (2)
5 (5)
0.08
2.26
0.78–5.22
Diabetes mellitus, n (%)
3
720 (25)
22 (23)
0.83
0.91
0.55–1.45
Chronic obstructive pulmonary diseaseb, n (%)
0
291 (10)
28 (29)
< 0.0001
3.71
2.32–5.79
Immunodeficiency , n (%)
0
55 (2)
4 (4)
0.12
2.26
0.67–5.65
Hemoglobin concentration, mean ± sd, g/dL
6
13.4 ± 1.8
13.0 ± 1.9
0.03
0.90
0.81–1.01
Creatinine clearance, mean ± sd, mL/min
9
77.3 ± 32.0
66.3 ± 33.6
0.0002
0.98
0.97–0.99
Left ventricular ejection fraction, mean ± sd, %
58
58.3 ± 12.2
54.5 ± 12.6
0.003
0.98
0.96–0.99
Redo surgery, n (%)
21
193 (7)
15 (16)
0.003
2.60
1.42–4.48
Nonscheduled surgery, n (%)
0
91 (3)
5 (5)
0.60
1.22
0.43–2.78
Duration of cardiopulmonary bypass, mean ± sd, min
0
73 ± 37
96 ± 57
< 0.0001
1.009
RBC transfusion during surgery, n (%)
0
1,058 (4)
57 (59)
< 0.0001
2.56
1.69–3.90
Antibiotic prophylaxis with cefamandole, n (%)
0
2,513 (86)
85 (88)
0.65
0.81
0.41–1.47
Missing Data
Patient Characteristics
Male, n (%) Age, mean ± sd, yr 2
c
OR
95% CI
1.006–1.010
POP = postoperative pneumonia, OR = odds ratio. a Neurologic disease associated with altered functional disability. b Chronic obstructive pulmonary disease requiring medical therapy or with forced expiratory volume in 1 s < 75% predicted. c Immunodeficiency status was defined as steroid therapy > 20 mg prednisone/d/2 mo.
1152
www.ccmjournal.org
May 2014 • Volume 42 • Number 5
Clinical Investigations
Table 2. Multivariate Analysis Without Interactions of Risk Factors for Postoperative Pneumonia in the Training Set Patient Characteristics
p
OR
95% CI
Age (yr)
1.02
1.01–1.04
0.013
Chronic obstructive pulmonary disease (yes)
3.08
1.89–4.89
< 0.0001
Preoperative left ventricular ejection fraction (%)
0.98
0.96–0.99
0.009
Duration of cardiopulmonary bypass (min)
1.01
1.006–1.013
< 0.0001
RBC transfusion during surgery (yes)
1.67
1.07–2.62
0.03
OR = odds ratio. The c-index was 0.75 (95% CI, 0.70–0.79) and the Hosmer-Lemeshow’s test p value was 0.74.
observed among the 5,408 non-POP patients (5.8%; 95% CI, 5.8–6.4) (p < 0.0001). Risk Factors for POP Univariate analysis of risk factors for POP in the training set is presented in Table 1. Nine variables had a p value less than 0.05 and were entered in the logistic regression: age, New York Heart Association class IV, chronic obstructive pulmonary disease (COPD), hemoglobin, creatinine clearance, left ventricular ejection fraction (LVEF), redo surgery, duration of CPB, and RBC transfusion. Multivariate analysis without interactions identified five independent risk factors of POP (Table 2). After inclusion and selection of interactions, four independent risk factors were identified (Table 3). Three were preoperative factors: age (odds ratio [OR], 1.02; 95% CI, 1.01–1.03), COPD (OR, 2.97; 95% CI, 1.8–4.71), and LVEF (OR, 0.98; 95% CI, 0.96–0.99). One variable was an intraoperative factor: the interaction between RBC transfusion during surgery and duration of CPB (OR, 2.98; 95% CI, 1.96–4.54). The c-index of the prediction model and the Hosmer-Lemeshow’s test p value were 0.72 (95% CI, 0.67–0.78) and 0.15 and 0.78 (95% CI, 0.73–0.83) and 0.27 in the training and validation sets, respectively. Score-Based Prediction Rule and Risk Groups Only variables of the previous prediction model that could be collected preoperatively were used to develop the score-based prediction rule (i.e., age, COPD, and LVEF). The coefficients and points of the logistic regression model using the three
variables as independent variables and the occurrence of POP as the dependent variable are presented in Table 4. The c-index of this model and the Hosmer-Lemeshow’s test p value were 0.67 (95% CI, 0.62–0.72) and 0.83 and 0.72 (95% CI, 0.66–0.7) and 0.84 in the training and validation sets, respectively. The score ranged from 0 to 6 with a median of 1, in both the training and validation sets (Fig. 1). Predicted and observed POP rates for each score in the training and validation sets are presented in Table 5. In the training set, the low-risk group (score of 0–2) and the high-risk group (score of 3–6) comprised 2,348 (78%) and 660 (22%) patients, respectively, and accounted for 56 and 40 cases of POP, respectively. Compared with the low-risk group, the OR for the occurrence of POP was 2.64 (95% CI, 1.73–3.99) for the high-risk group (p < 0.0001). Calibration of the score obtained in the training set is shown in Table 6. In the validation set, the low- and high-risk groups comprised 1,981 (77%) and 593 (23%) patients, respectively, and accounted for 35 and 43 cases of POP, respectively. Predicted POP rates were 2.1% and 6.5% in the low- and high-risk groups, respectively. The good calibration of the score was confirmed by the fact that predicted rates were within the 95% CI of observed POP rates for the two risk groups (Table 6).
DISCUSSION Four independent risk factors for POP after CS were identified in this cohort of 5,582 patients: age, COPD, preoperative LVEF, and the interaction between RBC transfusion during surgery and duration of CPB. A user-friendly preoperative scoring
Multivariable Analysis With Interactions of Risk Factors for Postoperative Pneumonia in the Training Set Table 3.
OR
95% CI
p
Age (yr)
1.02
1.01–1.03
0.049
Chronic obstructive pulmonary disease (yes)
2.97
1.8–4.71
< 0.0001
Preoperative left ventricular ejection fraction (%)
0.98
0.96–0.99
0.01
RBC transfusion during surgery (yes) and duration of cardiopulmonary bypass > 60 min (yes)
2.98
1.96–4.54
< 0.0001
Patient Characteristics
OR = odds ratio. The c-index was 0.72 (95% CI, 0.67–0.78) and the Hosmer-Lemeshow’s test p value was 0.15.
Critical Care Medicine
www.ccmjournal.org
1153
Allou et al
Table 4. Coefficients and Points of the Logistic Regression Model Using the Three Variables That Can Be Collected Preoperatively as Independent Variables and the Prevalence of Postoperative Pneumonia as the Dependent Variable in the Training Set OR
95% CI
p
Age > 70 yr (yes)
1.98
1.31–3.00
0.001
0.68
2
Chronic obstructive pulmonary disease (yes)
3.21
1.99–5.05
< 0.0001
1.167
3
Preoperative left ventricular ejection fraction< 60% (yes)
1.59
1.05–2.41
0.03
0.46
1
Patient Characteristics
Coefficient
Points
OR = odds ratio. The c-index was 0.67 (95% CI, 0.62–0.72) and the Hosmer-Lemeshow’s test p value was 0.83.
system was derived and validated to identify high-risk patients who may benefit from preoperative preventive measures. POP and Mortality Rates The observed POP rate in this study (3.1%) was in the low range of previously reported rates, ranging between 2% and 9.7% (1–3, 5, 6). Mortality in the group of patients with POP was higher than in the group without POP and was consistent with the rates usually reported in this complication (3, 5, 6). The high mortality associated with POP emphasizes the need to identify high-risk patients. Risk Factors for POP Age more than 70 (11, 12) and COPD (11, 13–16) are two preoperative risk factors for POP frequently reported in the literature. Low preoperative ejection fraction has been previously reported to be a risk factor for POP after CS, but only
Figure 1. Density histogram of the score in the training and validation sets. The median value was 1 (range, 0–6) in the training set and 1 (range, 1–6) in the validation set.
1154
www.ccmjournal.org
in one study (17). Two intraoperative risk factors were also identified: RBC transfusion and duration of CPB with a significant interaction between these two risk factors. Several studies have suggested that perioperative RBC transfusion was associated with POP (2, 3, 6, 11, 18). In a recent randomized controlled trial following CS, Hajjar et al (19) found that each unit of RBC transfused increased the risk of infectious complications. Unfortunately, this intraoperative variable is difficult to predict. However, procedure duration, age, impaired LVEF, and COPD could simply reflect the severity of comorbidities and the complexity of surgery, as previously suggested by Arozullah et al (11) and Delgados-Rodrigues et al (13). Score-Based Prediction Rule and Risk Groups Assessment of individual patient outcome is difficult because of the number of risk factors and their different predictive strengths, as reflected by ORs. One way to assess individual outcome is to use a simple score combining risk factors for POP according to their own predictive strength. Since the goal of the score-based prediction rule was to identify patients at risk of POP before surgery, only risk factors that could be collected preoperatively were used. This score was elaborated and validated by dividing the population into two sets. The 6-point score defined two risk groups. More than two risk groups could have been defined, but the nature of medical decision is binary and the creation of two risk groups would facilitate the clinician’s decision. The good discrimination and calibration of the score in the training and validation sets demonstrate its robustness, which constitutes a strong argument to support its application in clinical practice. Several perioperative measures can be proposed for patients of the high-risk group (i.e., score ≥ 3). First, preoperative physiotherapy to decrease the prevalence of POP (20–22), especially in elderly or COPD patients, is strongly recommended in some studies (22). In a randomized controlled trial, Hulzebos et al (22) demonstrated that intensive preoperative inspiratory muscle training in patients with COPD or aged more than 75 was associated with a significantly lower risk of POP following CS. In a randomized trial after CS, Zarbock et al (23) demonstrated that systematic use of noninvasive ventilatory support following extubation was associated with a significant reduction of pulmonary complications including pneumonia. The use of subglottic secretion drainage can also be proposed as May 2014 • Volume 42 • Number 5
Clinical Investigations
Table 5. Predicted and Observed Rates of Postoperative Pneumonia in the Training and Validation Sets Training Set
Score
No. (%) of Patients (n = 3,008)
Number and Observed Rate of POP (n = 96)
Validation Set Predicted Rate of POP (%)
No. (%) of Patients (n = 2,574)
Number and Observed Rate of POP (n = 78)
Predicted Rate of POP (%)
0
1,065 (35.4%)
13 (1.2)
1.5
944 (36.7%)
8 (0.8)
1.5
1
742 (24.6%)
23 (3.1)
2.2
569 (22.1%)
15 (2.6)
2.2
2
541 (17.9%)
20 (3.7)
3.3
468 (18.2%)
12 (2.5)
3.3
3
424 (14.1%)
15 (3.5)
4.9
429 (16.7%)
26 (6.1)
4.9
4
78 (2.6%)
6 (7.7)
7.3
59 (2.3%)
6 (10.2)
7.3
5
83 (2.7%)
9 (10.8)
10.6
57 (2.2%)
7 (12.3)
10.6
6
75 (2.5%)
10 (13.3)
15.2
48 (1.9%)
4 (8.3)
15.2
POP = postoperative pneumonia.
POP. In our study, all patients were included in the analysis, including those not mechanically ventilated at the time of diagnosis of POP (42% were non-VAP). The mortality rate in the present study was 41% in non-VAP patients and 44% in VAP patients (p = 0.76). Consequently, we can assume that all types of POP, including non-VAP cases, are of importance. This registry was not specifically designed to evaluate POP, and certain factors associated with POP in the literature were not collected. Only documented cases of POP were taken into account; this could lead to underestimation of our real rate of POP. Nevertheless, Fagon et al (26) and Singh et al (27) suggested that many noninfectious processes in postoperative patients are associated with lung infiltrates falsely attributed to pneumonia that could overestimate their prevalence. Similar to other preoperative scoring systems such as the European System for Cardiac Operative Risk Evaluation score (28), the score proposed here may be of limited value in the case of unexpected intraoperative events such as massive RBC transfusion or prolonged CPB. The clinical usefulness of the score was not formally demonstrated. Such a demonstration would require an impact study comparing the effect of implementing preventive strategies in high-risk patients defined by the score with routine clinical care on the prevalence of POP (29). We expect to
it is the only system that has been demonstrated to reduce the prevalence of VAP (24) in a large cohort of patients. Our score could limit the use of this device to selected high-risk patients. To our knowledge, no study has demonstrated that improvement of preoperative LVEF can decrease the prevalence of POP, but such management might decrease the prevalence of postoperative mechanical ventilation and consequently VAP. Two previous studies have built scores to assess risk factors for POP after CS. The first study by Hortal et al (3) developed a preoperative and perioperative POP risk score, but only included patients with VAP and did not validate the score in a second cohort of patients. In the second study, Kinlin et al (25) developed and validated a POP risk score, but microbiologic documentation was not performed. In addition, they included intraoperative and postoperative factors that limit the practical value of this score. Limitations The number of events in the cohort may be considered to be too small. However, the ratio of the number of candidate variables to the number of events in the training set was greater than 10. Furthermore, this cohort analyzed the largest number of cases of documented POP in CS patients ever reported (1–6). In addition, some of these studies confined their analysis to patients with VAP (1–6). However, VAP is not the only presentation of
Calibration of the Scoring System: Predicted and Observed Rates of Postoperative Pneumonia According to the Two Risk Groups Defined by the Score Table 6.
Training Set (n = 3,008) Postoperative Pneumonia
Number of cases of postoperative pneumonia Predicted rate of postoperative pneumonia Observed rate of postoperative pneumonia (95% CI)
Score 0–2 (n = 2,348)
56 1.8 2.4 (1.7–3.0)
Score 3–6 (n = 660)
40 7.1 6.1 (4.2–7.9)
Validation Set (n = 2,574) Score 0–2 (n = 1,981)
Score 3–6 (n = 593)
35
43
2.1
6.5
1.8 (1.2–2.3)
7.2 (5.2–9.3)
The predicted rate of postoperative pneumonia was reestimated with bootstrapping. The predicted rate is included in the 95% CI of the observed rate.
Critical Care Medicine
www.ccmjournal.org
1155
Allou et al
design a controlled trial randomizing high-risk patients (score ≥ 3) to standard care or to preventive measures combining preoperative physiotherapy and subglottic secretion drainage while the patient is on mechanical ventilation and noninvasive ventilatory support after extubation. However, development and validation of a preoperative scoring system is the first and mandatory step before launching such a randomized controlled trial.
CONCLUSIONS POP following CS is a frequent complication associated with increased mortality (> 40%). Assessing preoperative risk factors for POP with the user-friendly score validated in this study could help to target the preventive preoperative policy to highrisk patients with a risk of POP greater than 4% (i.e., patients with a score ≥3).
REFERENCES
1. Bouza E, Hortal J, Muñoz P, et al; European Study Group on Nosocomial Infections; European Workgroup of Cardiothoracic Intensivists: Postoperative infections after major heart surgery and prevention of ventilator-associated pneumonia: A one-day European prevalence study (ESGNI-008). J Hosp Infect 2006; 64:224–230 2. El Solh AA, Bhora M, Pineda L, et al: Nosocomial pneumonia in elderly patients following cardiac surgery. Respir Med 2006; 100:729–736 3. Hortal J, Giannella M, Pérez MJ, et al: Incidence and risk factors for ventilator-associated pneumonia after major heart surgery. Intensive Care Med 2009; 35:1518–1525 4. Hortal J, Muñoz P, Cuerpo G, et al; European Study Group on Nosocomial Infections; European Workgroup of Cardiothoracic Intensivists: Ventilator-associated pneumonia in patients undergoing major heart surgery: An incidence study in Europe. Crit Care 2009; 13:R80 5. Kollef MH, Sharpless L, Vlasnik J, et al: The impact of nosocomial infections on patient outcomes following cardiac surgery. Chest 1997; 112:666–675 6. Leal-Noval SR, Marquez-Vácaro JA, García-Curiel A, et al: Nosocomial pneumonia in patients undergoing heart surgery. Crit Care Med 2000; 28:935–940 7. Allou N, Kermarrec N, Muller C, et al: Risk factors and prognosis of post-operative pneumonia due to Pseudomonas aeruginosa following cardiac surgery. J Antimicrob Chemother 2010; 65:806–807 8. Montravers P, Veber B, Auboyer C, et al: Diagnostic and therapeutic management of nosocomial pneumonia in surgical patients: Results of the Eole study. Crit Care Med 2002; 30:368–375 9. Von Elm E, Altman D, Egger M, et al: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Lancet 2007; 104: 434–437 10. American Thoracic Society; Infectious Diseases Society of America: Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med 2005; 171:388–416 11. Arozullah AM, Khuri SF, Henderson WG, et al; Participants in the National Veterans Affairs Surgical Quality Improvement Program:
1156
www.ccmjournal.org
Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery. Ann Intern Med 2001; 135:847–857 12. Thompson DA, Makary MA, Dorman T, et al: Clinical and economic outcomes of hospital acquired pneumonia in intra-abdominal surgery patients. Ann Surg 2006; 243:547–552 13. Delgado-Rodríguez M, Medina-Cuadros M, Martínez-Gallego G, et al: Usefulness of intrinsic surgical wound infection risk indices as predictors of postoperative pneumonia risk. J Hosp Infect 1997; 35:269–276 14. Schussler O, Alifano M, Dermine H, et al: Postoperative pneumonia after major lung resection. Am J Respir Crit Care Med 2006; 173:1161–1169 15. Ephgrave KS, Kleiman-Wexler R, Pfaller M, et al: Postoperative pneumonia: A prospective study of risk factors and morbidity. Surgery 1993; 114:815–819; discussion 819–821 16. Gaynes R, Bizek B, Mowry-Hanley J, et al: Risk factors for nosocomial pneumonia after coronary artery bypass graft operations. Ann Thorac Surg 1991; 51:215–218 17. Santos Mda S, Tura BR, Rouge A, et al: External validation of models for predicting pneumonia after cardiac surgery. Surg Infect (Larchmt) 2011; 12:365–372 18. Leal-Noval SR, Rincón-Ferrari MD, García-Curiel A, et al: Transfusion of blood components and postoperative infection in patients undergoing cardiac surgery. Chest 2001; 119:1461–1468 19. Hajjar LA, Vincent JL, Galas FR, et al: Transfusion requirements after cardiac surgery: The TRACS randomized controlled trial. JAMA 2010; 304:1559–1567 20. Fagevik Olsén M, Hahn I, Nordgren S, et al: Randomized controlled trial of prophylactic chest physiotherapy in major abdominal surgery. Br J Surg 1997; 84:1535–1538 21. Morran CG, Finlay IG, Mathieson M, et al: Randomized controlled trial of physiotherapy for postoperative pulmonary complications. Br J Anaesth 1983; 55:1113–1117 22. Hulzebos EH, Helders PJ, Favié NJ, et al: Preoperative intensive inspiratory muscle training to prevent postoperative pulmonary complications in high-risk patients undergoing CABG surgery: A randomized clinical trial. JAMA 2006; 296:1851–1857 23. Zarbock A, Mueller E, Netzer S, et al: Prophylactic nasal continuous positive airway pressure following cardiac surgery protects from postoperative pulmonary complications: A prospective, randomized, controlled trial in 500 patients. Chest 2009; 135:1252–1259 24. Muscedere J, Rewa O, McKechnie K, et al: Subglottic secretion drainage for the prevention of ventilator-associated pneumonia: A systematic review and meta-analysis. Crit Care Med 2011; 39:1985–1991 25. Kinlin LM, Kirchner C, Zhang H, et al: Derivation and validation of a clinical prediction rule for nosocomial pneumonia after coronary artery bypass graft surgery. Clin Infect Dis 2010; 50:493–501 26. Fagon JY, Chastre J, Hance AJ, et al: Evaluation of clinical judgment in the identification and treatment of nosocomial pneumonia in ventilated patients. Chest 1993; 103:547–553 27. Singh N, Falestiny MN, Rogers P, et al: Pulmonary infiltrates in the surgical ICU: Prospective assessment of predictors of etiology and mortality. Chest 1998; 114:1129–1136 28. Nashef SA, Roques F, Michel P, et al: European system for cardiac operative risk evaluation (EuroSCORE). Eur J Cardiothorac Surg 1999; 16:9–13 29. Reilly BM, Evans AT: Translating clinical research into clinical practice: Impact of using prediction rules to make decisions. Ann Intern Med 2006; 144:201–209
May 2014 • Volume 42 • Number 5
Objectives: The aims of this study were, first, to identify risk factors for microbiology-proven postoperative pneumonia after cardiac surgery and, second, to develop and validate a preoperative scoring system for the risk of postoperative pneumonia. Design and Setting: A single-center cohort study.
*See also p. 1302. 1 Département d’Anesthésie-Réanimation, CHU Bichat-Claude Bernard, Université Paris VII, Assistance Publique Hopitaux de Paris, France. 2 Université Paris Diderot, Sorbonne Paris Cité, Paris, France. 3 Unité d’Hygiène et de lutte Contre les Infections Hospitalières, CHU Bichat-Claude Bernard, Université Paris VII, Assistance Publique Hopitaux de Paris, France. 4 Service de Biostatistique, F-75018, CHU Bichat-Claude Bernard, Université Paris VII, Assistance Publique Hopitaux de Paris, France. Drs. Allou, Bronchard, and Guglielminotti had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Allou, Bronchard, Guglielminotti, and Montravers contributed to study concept and design. Drs. Bronchard, Allou, Provenchere, Dilly, and Lucet contributed to acquisition of data. Drs. Allou, Bronchard, Montravers, and Guglielminotti contributed to analysis and interpretation of data. Drs. Allou, Montravers, Guglielminotti, and Bronchard contributed to drafting of the article. Drs. Allou, Bronchard, Guglielminotti, and Montravers contributed to critical revision of the article for important intellectual content. Drs. Guglielminotti and Laouénan contributed to statistical analysis. Drs. Allou, Bronchard, Guglielminotti, Dilly, Provenchere, Lucet, and Montravers contributed to administrative, technical, or material support. Drs. Allou, Montravers, Guglielminotti, and Bronchard contributed to study supervision. Supported, in part, by internal funds. Presented, in part, at the 24th annual congress of the European Society of Intensive Care Medicine, Berlin, Germany, October, 2011. Dr. Montravers consulted for Pfizer, Merck, Astellas, and AstraZeneca; received support for participation in review activity from Astellas; served as a board member for Pfizer, Merck, and Astellas; provided expert testimony for Aguettant; and lectured for Pfizer. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: [email protected] Copyright © 2013 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins DOI: 10.1097/CCM.0000000000000143
1150
www.ccmjournal.org
Patients: All consecutive patients undergoing cardiac surgery between January 2006 and July 2011. Interventions: None. Measurements and Main Results: Multivariate analysis of risk factors for postoperative pneumonia was performed on data from patients operated between January 2006 and December 2008 (training set). External temporal validation was performed on data from patients operated between January 2009 and July 2011 (validation set). Preoperative variables identified in multivariate analysis of the training set were then used to develop a preoperative scoring system that was validated on the validation set. Postoperative pneumonia occurred in 174 of the 5,582 patients (3.1%; 95% CI, 2.7–3.6). Multivariate analysis identified four risk factors for postoperative pneumonia: age (odds ratio, 1.02; 95% CI, 1.01–1.03), chronic obstructive pulmonary disease (odds ratio, 2.97; 95% CI, 1.8–4.71), preoperative left ventricular ejection fraction (odds ratio, 0.98; 95% CI, 0.96–0.99), and the interaction between RBC transfusion during surgery and duration of cardiopulmonary bypass (odds ratio, 2.98; 95% CI, 1.96–4.54). A 6-point score including the three preoperative variables then defined two risk groups corresponding to postoperative pneumonia rates of 1.8% (score < 3) and 6.5% (score ≥ 3). Conclusion: Assessing preoperative risk factors for postoperative pneumonia with the proposed scoring system could help to implement a preventive policy in high-risk patients with a risk of postoperative pneumonia greater than 4% (i.e., patients with a score ≥3). (Crit Care Med 2014; 42:1150–1156) Key Words: cardiac surgery; postoperative pneumonia; risk score
P
ostoperative pneumonia (POP) is the most common infection after cardiac surgery (CS), with a prevalence rate between 2% and 10% (1–6), especially during the first postoperative week (5, 6). It is associated with increased mortality (3, 5, 6). May 2014 • Volume 42 • Number 5
Clinical Investigations
Several risk factors for POP after CS have been identified (3, 6), but previous studies investigating risk factors for POP after CS confined their analysis to patients with ventilatorassociated pneumonia (VAP) (1–6). However, VAP is not the only presentation of POP, as illustrated by previous studies in which non-VAP accounted for 64% and 43% of POP in general and CS patients, respectively (7, 8). Furthermore, many studies investigating risk factors for POP after CS were based on small sample sizes (1, 3, 5, 6). Identification of risk factors for POP after CS on a large cohort of patients including both VAP and non-VAP POP is therefore warranted. Targeting preoperative interventions to patients at high-risk of POP may decrease POP frequency and mortality. High-risk patients could be identified during the preoperative period with a score based on a combination of individual preoperative risk factors. The objectives of this single-center cohort study were therefore to identify risk factors for POP after CS and to develop and validate a preoperative scoring system of the risk of POP.
MATERIALS AND METHODS The study was approved by the local ethics committee of HotelDieu Hospital, which waived the need for informed consent, because of the observational nature of the study. It complied with the Strengthening the Reporting of Observational studies in Epidemiology statement recommendations statement for reporting (9). Patient Sample Data from consecutive patients who underwent CS with cardiopulmonary bypass (CPB) from January 2006 to July 2011 were included in this single-center cohort study. Exclusion criteria were mechanical ventilation before surgery and death before the second postoperative day. Study Setting During the study period, perioperative care, including anesthesia, monitoring techniques, and normothermic CPB, were performed according to local standardized protocols for all patients. Cefamandole was used for surgical antibiotic prophylaxis (allergic patients received vancomycin and gentamicin). Prevention of POP was in accordance with the American Thoracic Surgery guidelines (10). Diagnosis of POP All consecutive episodes of POP were identified by prospective surveillance by the ICU and infection control team blinded to the study details. Only the first episode of pneumonia diagnosed during the first 7 days after surgery was studied and was defined as POP. The first postoperative week was chosen to cover the period at highest risk of POP (5, 6, 8). The diagnosis of POP was based on usual clinical and microbiological criteria, with bronchoalveolar lavage yielding bacteria at a concentration of greater than 104 colony-forming units (CFU)/mL or protected distal bronchial specimen samples yielding greater than 103 CFU/mL (10). Critical Care Medicine
Statistical Analysis Results are expressed as mean ± sd, median (range), or number of patients (%). When indicated, 95% CIs were calculated. Statistical analysis was performed with R software, version 2.12.0 (R foundation for Statistical Computing, Vienna, Austria). Identification of Risk Factors Risk factors for POP were identified on data from patients operated between January 1, 2006, and December 31, 2008 (training set). External temporal validation was performed with data from patients operated from January 1, 2009, to July 30, 2011 (validation set). Patients with and without POP were compared in the training set by univariate analysis using the Wilcoxon test for continuous variables and Fisher exact test for discrete variables. Variables with a p value less than 0.05 on univariate analysis were entered in a multivariate logistic regression analysis with backward selection. Continuous candidate variables were kept continuous. Variables with a p value greater than or equal to 0.05 at each step of regression analysis were rejected. Missing values for candidate variables entered in the multivariate analysis were imputed with the median of the variable. Two-way interactions between significant variables in multivariate analysis were studied. Significant interactions (p value < 0.05) were entered in the model. The final model with interactions was evaluated for discrimination with the c-index and for calibration with Hosmer-Lemeshow’s test. The model constructed in the training set was applied to the validation set and evaluated for discrimination with the c-index and for calibration with Hosmer-Lemeshow’s test. Score-Based Prediction Rule and Creation of Risk Groups A score-based prediction rule of the occurrence of POP was developed from the prediction model built in the training set. Since the goal of the score-based prediction rule was to identify patients at risk of POP before surgery, only variables of the prediction model that could be collected preoperatively were used. Continuous variables were binarized according to cutoffs previously used in the literature and according to clinical relevance. Logistic regression coefficients of the final model including only preoperative variables were multiplied by 10 and then rounded to the nearest integer. The number of points for each preoperative variable was attributed after identification of a common denominator across rounded coefficients. The score, ranging from 0 to 6, was the sum of the points corresponding to each variable. Evaluation of the score in the training and validation sets used the c-index and Hosmer-Lemeshow’s test of logistic regression with occurrence of pneumonia as the outcome and score as the continuous predictor. The goal of risk stratification according to risk groups was to identify a preoperative group of patients with a high risk of POP. Two groups were created: a low-risk group with an observed pneumonia rate less than 4% (i.e., score < 3) and a high-risk group with an observed pneumonia rate greater www.ccmjournal.org
1151
Allou et al
than or equal to 4% (i.e., score ≥ 3). Predicted pneumonia rate calculated by the score was reestimated in each risk group with bootstrap resampling (n = 2,000) in the training and validation sets to determine whether they were included in the 95% CI of the pneumonia rate observed in each risk group.
RESULTS Study Population From January 2006 to July 2011, 5,678 cardiac procedures with CPB were performed. Ninety-six patients were excluded from the analysis: eight patients were on mechanical ventilation before surgery and 88 patients died during the first 48 hours. The remaining 5,582 patients constituted the cohort. One hundred seventy-four cases of POP were observed (3.1%; 95% CI, 2.7–3.6), including 101 cases of VAP (58%; 95% CI, 51–65). POP was diagnosed 4.3 ± 1.4 days after surgery. Ninety-six cases of POP were observed in the 3,008 patients of the training set (3.2%; 95% CI, 2.6–3.8) and 78
cases were observed in the 2,574 patients of the validation set (3.0%; 95% CI, 2.4–3.7). The microorganisms most commonly isolated belong to Enterobacteriaceae (40%). Pseudomonas aeruginosa represented 18% of all microorganisms, Haemophilus species 17%, Streptococcus species 10%, and S. aureus 5%. No statistically significant difference was observed on univariate analysis between patients with VAP and patients with non-VAP, except for the duration of CPB (101 ± 62 min in the VAP group vs 72 ± 33 min in the non-VAP group; p = 0.0003) and the proportion of New York Heart Association class IV patients (60% in the VAP vs 40% in the non-VAP group; p = 0.009). Duration of mechanical ventilation was longer in the POP group than in the non-POP group (108 ± 273 hr vs 15 ± 57 hr; p < 0.0001), and duration of ICU stay was also longer in the POP group (14 ± 19 d vs 4 ± 5 d; p < 0.0001). Seventy-four deaths were observed among the 174 POP patients (42.5%; 95% CI, 35.2–49.9) and 312 deaths were
Table 1. Univariate Analysis of Risk Factors for Postoperative Pneumonia in the Training Set Patients Without POP
Patients with POP
n = 2,912
n = 96
p
0
1,990 (68)
62 (65)
0.44
0.84
0.55–1.30
0
63 ± 14
68 ± 13
0.0004
1.03
1.01–1.04
Body mass index, mean ± sd, kg/m
3
26.3 ± 4.5
26.3 ± 5.1
0.88
1.00
0.95–1.04
Current smoker, n (%)
2
444 (15)
19 (20)
0.29
1.37
0.80–2.24
New York Heart Association class IV, n (%)
0
722 (25)
45 (47)
< 0.0001
2.68
1.77–4.03
Hypertension, n (%)
0
1,592 (55)
54 (56)
0.83
1.07
0.71–1.61
Statin treatment, n (%)
0
1,687 (58)
53 (55)
0.60
0.89
0.59–1.35
Neurologic diseasea, n (%)
0
69 (2)
5 (5)
0.08
2.26
0.78–5.22
Diabetes mellitus, n (%)
3
720 (25)
22 (23)
0.83
0.91
0.55–1.45
Chronic obstructive pulmonary diseaseb, n (%)
0
291 (10)
28 (29)
< 0.0001
3.71
2.32–5.79
Immunodeficiency , n (%)
0
55 (2)
4 (4)
0.12
2.26
0.67–5.65
Hemoglobin concentration, mean ± sd, g/dL
6
13.4 ± 1.8
13.0 ± 1.9
0.03
0.90
0.81–1.01
Creatinine clearance, mean ± sd, mL/min
9
77.3 ± 32.0
66.3 ± 33.6
0.0002
0.98
0.97–0.99
Left ventricular ejection fraction, mean ± sd, %
58
58.3 ± 12.2
54.5 ± 12.6
0.003
0.98
0.96–0.99
Redo surgery, n (%)
21
193 (7)
15 (16)
0.003
2.60
1.42–4.48
Nonscheduled surgery, n (%)
0
91 (3)
5 (5)
0.60
1.22
0.43–2.78
Duration of cardiopulmonary bypass, mean ± sd, min
0
73 ± 37
96 ± 57
< 0.0001
1.009
RBC transfusion during surgery, n (%)
0
1,058 (4)
57 (59)
< 0.0001
2.56
1.69–3.90
Antibiotic prophylaxis with cefamandole, n (%)
0
2,513 (86)
85 (88)
0.65
0.81
0.41–1.47
Missing Data
Patient Characteristics
Male, n (%) Age, mean ± sd, yr 2
c
OR
95% CI
1.006–1.010
POP = postoperative pneumonia, OR = odds ratio. a Neurologic disease associated with altered functional disability. b Chronic obstructive pulmonary disease requiring medical therapy or with forced expiratory volume in 1 s < 75% predicted. c Immunodeficiency status was defined as steroid therapy > 20 mg prednisone/d/2 mo.
1152
www.ccmjournal.org
May 2014 • Volume 42 • Number 5
Clinical Investigations
Table 2. Multivariate Analysis Without Interactions of Risk Factors for Postoperative Pneumonia in the Training Set Patient Characteristics
p
OR
95% CI
Age (yr)
1.02
1.01–1.04
0.013
Chronic obstructive pulmonary disease (yes)
3.08
1.89–4.89
< 0.0001
Preoperative left ventricular ejection fraction (%)
0.98
0.96–0.99
0.009
Duration of cardiopulmonary bypass (min)
1.01
1.006–1.013
< 0.0001
RBC transfusion during surgery (yes)
1.67
1.07–2.62
0.03
OR = odds ratio. The c-index was 0.75 (95% CI, 0.70–0.79) and the Hosmer-Lemeshow’s test p value was 0.74.
observed among the 5,408 non-POP patients (5.8%; 95% CI, 5.8–6.4) (p < 0.0001). Risk Factors for POP Univariate analysis of risk factors for POP in the training set is presented in Table 1. Nine variables had a p value less than 0.05 and were entered in the logistic regression: age, New York Heart Association class IV, chronic obstructive pulmonary disease (COPD), hemoglobin, creatinine clearance, left ventricular ejection fraction (LVEF), redo surgery, duration of CPB, and RBC transfusion. Multivariate analysis without interactions identified five independent risk factors of POP (Table 2). After inclusion and selection of interactions, four independent risk factors were identified (Table 3). Three were preoperative factors: age (odds ratio [OR], 1.02; 95% CI, 1.01–1.03), COPD (OR, 2.97; 95% CI, 1.8–4.71), and LVEF (OR, 0.98; 95% CI, 0.96–0.99). One variable was an intraoperative factor: the interaction between RBC transfusion during surgery and duration of CPB (OR, 2.98; 95% CI, 1.96–4.54). The c-index of the prediction model and the Hosmer-Lemeshow’s test p value were 0.72 (95% CI, 0.67–0.78) and 0.15 and 0.78 (95% CI, 0.73–0.83) and 0.27 in the training and validation sets, respectively. Score-Based Prediction Rule and Risk Groups Only variables of the previous prediction model that could be collected preoperatively were used to develop the score-based prediction rule (i.e., age, COPD, and LVEF). The coefficients and points of the logistic regression model using the three
variables as independent variables and the occurrence of POP as the dependent variable are presented in Table 4. The c-index of this model and the Hosmer-Lemeshow’s test p value were 0.67 (95% CI, 0.62–0.72) and 0.83 and 0.72 (95% CI, 0.66–0.7) and 0.84 in the training and validation sets, respectively. The score ranged from 0 to 6 with a median of 1, in both the training and validation sets (Fig. 1). Predicted and observed POP rates for each score in the training and validation sets are presented in Table 5. In the training set, the low-risk group (score of 0–2) and the high-risk group (score of 3–6) comprised 2,348 (78%) and 660 (22%) patients, respectively, and accounted for 56 and 40 cases of POP, respectively. Compared with the low-risk group, the OR for the occurrence of POP was 2.64 (95% CI, 1.73–3.99) for the high-risk group (p < 0.0001). Calibration of the score obtained in the training set is shown in Table 6. In the validation set, the low- and high-risk groups comprised 1,981 (77%) and 593 (23%) patients, respectively, and accounted for 35 and 43 cases of POP, respectively. Predicted POP rates were 2.1% and 6.5% in the low- and high-risk groups, respectively. The good calibration of the score was confirmed by the fact that predicted rates were within the 95% CI of observed POP rates for the two risk groups (Table 6).
DISCUSSION Four independent risk factors for POP after CS were identified in this cohort of 5,582 patients: age, COPD, preoperative LVEF, and the interaction between RBC transfusion during surgery and duration of CPB. A user-friendly preoperative scoring
Multivariable Analysis With Interactions of Risk Factors for Postoperative Pneumonia in the Training Set Table 3.
OR
95% CI
p
Age (yr)
1.02
1.01–1.03
0.049
Chronic obstructive pulmonary disease (yes)
2.97
1.8–4.71
< 0.0001
Preoperative left ventricular ejection fraction (%)
0.98
0.96–0.99
0.01
RBC transfusion during surgery (yes) and duration of cardiopulmonary bypass > 60 min (yes)
2.98
1.96–4.54
< 0.0001
Patient Characteristics
OR = odds ratio. The c-index was 0.72 (95% CI, 0.67–0.78) and the Hosmer-Lemeshow’s test p value was 0.15.
Critical Care Medicine
www.ccmjournal.org
1153
Allou et al
Table 4. Coefficients and Points of the Logistic Regression Model Using the Three Variables That Can Be Collected Preoperatively as Independent Variables and the Prevalence of Postoperative Pneumonia as the Dependent Variable in the Training Set OR
95% CI
p
Age > 70 yr (yes)
1.98
1.31–3.00
0.001
0.68
2
Chronic obstructive pulmonary disease (yes)
3.21
1.99–5.05
< 0.0001
1.167
3
Preoperative left ventricular ejection fraction< 60% (yes)
1.59
1.05–2.41
0.03
0.46
1
Patient Characteristics
Coefficient
Points
OR = odds ratio. The c-index was 0.67 (95% CI, 0.62–0.72) and the Hosmer-Lemeshow’s test p value was 0.83.
system was derived and validated to identify high-risk patients who may benefit from preoperative preventive measures. POP and Mortality Rates The observed POP rate in this study (3.1%) was in the low range of previously reported rates, ranging between 2% and 9.7% (1–3, 5, 6). Mortality in the group of patients with POP was higher than in the group without POP and was consistent with the rates usually reported in this complication (3, 5, 6). The high mortality associated with POP emphasizes the need to identify high-risk patients. Risk Factors for POP Age more than 70 (11, 12) and COPD (11, 13–16) are two preoperative risk factors for POP frequently reported in the literature. Low preoperative ejection fraction has been previously reported to be a risk factor for POP after CS, but only
Figure 1. Density histogram of the score in the training and validation sets. The median value was 1 (range, 0–6) in the training set and 1 (range, 1–6) in the validation set.
1154
www.ccmjournal.org
in one study (17). Two intraoperative risk factors were also identified: RBC transfusion and duration of CPB with a significant interaction between these two risk factors. Several studies have suggested that perioperative RBC transfusion was associated with POP (2, 3, 6, 11, 18). In a recent randomized controlled trial following CS, Hajjar et al (19) found that each unit of RBC transfused increased the risk of infectious complications. Unfortunately, this intraoperative variable is difficult to predict. However, procedure duration, age, impaired LVEF, and COPD could simply reflect the severity of comorbidities and the complexity of surgery, as previously suggested by Arozullah et al (11) and Delgados-Rodrigues et al (13). Score-Based Prediction Rule and Risk Groups Assessment of individual patient outcome is difficult because of the number of risk factors and their different predictive strengths, as reflected by ORs. One way to assess individual outcome is to use a simple score combining risk factors for POP according to their own predictive strength. Since the goal of the score-based prediction rule was to identify patients at risk of POP before surgery, only risk factors that could be collected preoperatively were used. This score was elaborated and validated by dividing the population into two sets. The 6-point score defined two risk groups. More than two risk groups could have been defined, but the nature of medical decision is binary and the creation of two risk groups would facilitate the clinician’s decision. The good discrimination and calibration of the score in the training and validation sets demonstrate its robustness, which constitutes a strong argument to support its application in clinical practice. Several perioperative measures can be proposed for patients of the high-risk group (i.e., score ≥ 3). First, preoperative physiotherapy to decrease the prevalence of POP (20–22), especially in elderly or COPD patients, is strongly recommended in some studies (22). In a randomized controlled trial, Hulzebos et al (22) demonstrated that intensive preoperative inspiratory muscle training in patients with COPD or aged more than 75 was associated with a significantly lower risk of POP following CS. In a randomized trial after CS, Zarbock et al (23) demonstrated that systematic use of noninvasive ventilatory support following extubation was associated with a significant reduction of pulmonary complications including pneumonia. The use of subglottic secretion drainage can also be proposed as May 2014 • Volume 42 • Number 5
Clinical Investigations
Table 5. Predicted and Observed Rates of Postoperative Pneumonia in the Training and Validation Sets Training Set
Score
No. (%) of Patients (n = 3,008)
Number and Observed Rate of POP (n = 96)
Validation Set Predicted Rate of POP (%)
No. (%) of Patients (n = 2,574)
Number and Observed Rate of POP (n = 78)
Predicted Rate of POP (%)
0
1,065 (35.4%)
13 (1.2)
1.5
944 (36.7%)
8 (0.8)
1.5
1
742 (24.6%)
23 (3.1)
2.2
569 (22.1%)
15 (2.6)
2.2
2
541 (17.9%)
20 (3.7)
3.3
468 (18.2%)
12 (2.5)
3.3
3
424 (14.1%)
15 (3.5)
4.9
429 (16.7%)
26 (6.1)
4.9
4
78 (2.6%)
6 (7.7)
7.3
59 (2.3%)
6 (10.2)
7.3
5
83 (2.7%)
9 (10.8)
10.6
57 (2.2%)
7 (12.3)
10.6
6
75 (2.5%)
10 (13.3)
15.2
48 (1.9%)
4 (8.3)
15.2
POP = postoperative pneumonia.
POP. In our study, all patients were included in the analysis, including those not mechanically ventilated at the time of diagnosis of POP (42% were non-VAP). The mortality rate in the present study was 41% in non-VAP patients and 44% in VAP patients (p = 0.76). Consequently, we can assume that all types of POP, including non-VAP cases, are of importance. This registry was not specifically designed to evaluate POP, and certain factors associated with POP in the literature were not collected. Only documented cases of POP were taken into account; this could lead to underestimation of our real rate of POP. Nevertheless, Fagon et al (26) and Singh et al (27) suggested that many noninfectious processes in postoperative patients are associated with lung infiltrates falsely attributed to pneumonia that could overestimate their prevalence. Similar to other preoperative scoring systems such as the European System for Cardiac Operative Risk Evaluation score (28), the score proposed here may be of limited value in the case of unexpected intraoperative events such as massive RBC transfusion or prolonged CPB. The clinical usefulness of the score was not formally demonstrated. Such a demonstration would require an impact study comparing the effect of implementing preventive strategies in high-risk patients defined by the score with routine clinical care on the prevalence of POP (29). We expect to
it is the only system that has been demonstrated to reduce the prevalence of VAP (24) in a large cohort of patients. Our score could limit the use of this device to selected high-risk patients. To our knowledge, no study has demonstrated that improvement of preoperative LVEF can decrease the prevalence of POP, but such management might decrease the prevalence of postoperative mechanical ventilation and consequently VAP. Two previous studies have built scores to assess risk factors for POP after CS. The first study by Hortal et al (3) developed a preoperative and perioperative POP risk score, but only included patients with VAP and did not validate the score in a second cohort of patients. In the second study, Kinlin et al (25) developed and validated a POP risk score, but microbiologic documentation was not performed. In addition, they included intraoperative and postoperative factors that limit the practical value of this score. Limitations The number of events in the cohort may be considered to be too small. However, the ratio of the number of candidate variables to the number of events in the training set was greater than 10. Furthermore, this cohort analyzed the largest number of cases of documented POP in CS patients ever reported (1–6). In addition, some of these studies confined their analysis to patients with VAP (1–6). However, VAP is not the only presentation of
Calibration of the Scoring System: Predicted and Observed Rates of Postoperative Pneumonia According to the Two Risk Groups Defined by the Score Table 6.
Training Set (n = 3,008) Postoperative Pneumonia
Number of cases of postoperative pneumonia Predicted rate of postoperative pneumonia Observed rate of postoperative pneumonia (95% CI)
Score 0–2 (n = 2,348)
56 1.8 2.4 (1.7–3.0)
Score 3–6 (n = 660)
40 7.1 6.1 (4.2–7.9)
Validation Set (n = 2,574) Score 0–2 (n = 1,981)
Score 3–6 (n = 593)
35
43
2.1
6.5
1.8 (1.2–2.3)
7.2 (5.2–9.3)
The predicted rate of postoperative pneumonia was reestimated with bootstrapping. The predicted rate is included in the 95% CI of the observed rate.
Critical Care Medicine
www.ccmjournal.org
1155
Allou et al
design a controlled trial randomizing high-risk patients (score ≥ 3) to standard care or to preventive measures combining preoperative physiotherapy and subglottic secretion drainage while the patient is on mechanical ventilation and noninvasive ventilatory support after extubation. However, development and validation of a preoperative scoring system is the first and mandatory step before launching such a randomized controlled trial.
CONCLUSIONS POP following CS is a frequent complication associated with increased mortality (> 40%). Assessing preoperative risk factors for POP with the user-friendly score validated in this study could help to target the preventive preoperative policy to highrisk patients with a risk of POP greater than 4% (i.e., patients with a score ≥3).
REFERENCES
1. Bouza E, Hortal J, Muñoz P, et al; European Study Group on Nosocomial Infections; European Workgroup of Cardiothoracic Intensivists: Postoperative infections after major heart surgery and prevention of ventilator-associated pneumonia: A one-day European prevalence study (ESGNI-008). J Hosp Infect 2006; 64:224–230 2. El Solh AA, Bhora M, Pineda L, et al: Nosocomial pneumonia in elderly patients following cardiac surgery. Respir Med 2006; 100:729–736 3. Hortal J, Giannella M, Pérez MJ, et al: Incidence and risk factors for ventilator-associated pneumonia after major heart surgery. Intensive Care Med 2009; 35:1518–1525 4. Hortal J, Muñoz P, Cuerpo G, et al; European Study Group on Nosocomial Infections; European Workgroup of Cardiothoracic Intensivists: Ventilator-associated pneumonia in patients undergoing major heart surgery: An incidence study in Europe. Crit Care 2009; 13:R80 5. Kollef MH, Sharpless L, Vlasnik J, et al: The impact of nosocomial infections on patient outcomes following cardiac surgery. Chest 1997; 112:666–675 6. Leal-Noval SR, Marquez-Vácaro JA, García-Curiel A, et al: Nosocomial pneumonia in patients undergoing heart surgery. Crit Care Med 2000; 28:935–940 7. Allou N, Kermarrec N, Muller C, et al: Risk factors and prognosis of post-operative pneumonia due to Pseudomonas aeruginosa following cardiac surgery. J Antimicrob Chemother 2010; 65:806–807 8. Montravers P, Veber B, Auboyer C, et al: Diagnostic and therapeutic management of nosocomial pneumonia in surgical patients: Results of the Eole study. Crit Care Med 2002; 30:368–375 9. Von Elm E, Altman D, Egger M, et al: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Lancet 2007; 104: 434–437 10. American Thoracic Society; Infectious Diseases Society of America: Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med 2005; 171:388–416 11. Arozullah AM, Khuri SF, Henderson WG, et al; Participants in the National Veterans Affairs Surgical Quality Improvement Program:
1156
www.ccmjournal.org
Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery. Ann Intern Med 2001; 135:847–857 12. Thompson DA, Makary MA, Dorman T, et al: Clinical and economic outcomes of hospital acquired pneumonia in intra-abdominal surgery patients. Ann Surg 2006; 243:547–552 13. Delgado-Rodríguez M, Medina-Cuadros M, Martínez-Gallego G, et al: Usefulness of intrinsic surgical wound infection risk indices as predictors of postoperative pneumonia risk. J Hosp Infect 1997; 35:269–276 14. Schussler O, Alifano M, Dermine H, et al: Postoperative pneumonia after major lung resection. Am J Respir Crit Care Med 2006; 173:1161–1169 15. Ephgrave KS, Kleiman-Wexler R, Pfaller M, et al: Postoperative pneumonia: A prospective study of risk factors and morbidity. Surgery 1993; 114:815–819; discussion 819–821 16. Gaynes R, Bizek B, Mowry-Hanley J, et al: Risk factors for nosocomial pneumonia after coronary artery bypass graft operations. Ann Thorac Surg 1991; 51:215–218 17. Santos Mda S, Tura BR, Rouge A, et al: External validation of models for predicting pneumonia after cardiac surgery. Surg Infect (Larchmt) 2011; 12:365–372 18. Leal-Noval SR, Rincón-Ferrari MD, García-Curiel A, et al: Transfusion of blood components and postoperative infection in patients undergoing cardiac surgery. Chest 2001; 119:1461–1468 19. Hajjar LA, Vincent JL, Galas FR, et al: Transfusion requirements after cardiac surgery: The TRACS randomized controlled trial. JAMA 2010; 304:1559–1567 20. Fagevik Olsén M, Hahn I, Nordgren S, et al: Randomized controlled trial of prophylactic chest physiotherapy in major abdominal surgery. Br J Surg 1997; 84:1535–1538 21. Morran CG, Finlay IG, Mathieson M, et al: Randomized controlled trial of physiotherapy for postoperative pulmonary complications. Br J Anaesth 1983; 55:1113–1117 22. Hulzebos EH, Helders PJ, Favié NJ, et al: Preoperative intensive inspiratory muscle training to prevent postoperative pulmonary complications in high-risk patients undergoing CABG surgery: A randomized clinical trial. JAMA 2006; 296:1851–1857 23. Zarbock A, Mueller E, Netzer S, et al: Prophylactic nasal continuous positive airway pressure following cardiac surgery protects from postoperative pulmonary complications: A prospective, randomized, controlled trial in 500 patients. Chest 2009; 135:1252–1259 24. Muscedere J, Rewa O, McKechnie K, et al: Subglottic secretion drainage for the prevention of ventilator-associated pneumonia: A systematic review and meta-analysis. Crit Care Med 2011; 39:1985–1991 25. Kinlin LM, Kirchner C, Zhang H, et al: Derivation and validation of a clinical prediction rule for nosocomial pneumonia after coronary artery bypass graft surgery. Clin Infect Dis 2010; 50:493–501 26. Fagon JY, Chastre J, Hance AJ, et al: Evaluation of clinical judgment in the identification and treatment of nosocomial pneumonia in ventilated patients. Chest 1993; 103:547–553 27. Singh N, Falestiny MN, Rogers P, et al: Pulmonary infiltrates in the surgical ICU: Prospective assessment of predictors of etiology and mortality. Chest 1998; 114:1129–1136 28. Nashef SA, Roques F, Michel P, et al: European system for cardiac operative risk evaluation (EuroSCORE). Eur J Cardiothorac Surg 1999; 16:9–13 29. Reilly BM, Evans AT: Translating clinical research into clinical practice: Impact of using prediction rules to make decisions. Ann Intern Med 2006; 144:201–209
May 2014 • Volume 42 • Number 5
