Predictive perioperative factors for developing severe sepsis after major surgery

D. Mokart1,*, M. Leone2, A. Sannini1, J. P. Brun1, A. Tison1, J. R. Delpero3, G. Houvenaeghel3, J. L. Blache1 and C. Martin2

1 Intensive Care Unit and Department of Anesthesiology, Institut Paoli-Calmettes, Marseille, France. 2 Department of Anesthesiology and Intensive Care, Hôpital Nord, Marseille, France. 3 Department of Surgery, Institut Paoli-Calmettes, Marseille, France

* Corresponding author. E-mail: mokartd{at}marseille.fnclcc.fr

Accepted for publication September 13, 2005.


    Abstract
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Background. Early identification of high-risk patients undergoing major surgery can result in an aggressive management affecting the outcome.

Methods. We designed a prospective cohort study of 93 adult patients undergoing major oncological surgery to identify the predictive risk factors for developing postoperative severe sepsis.

Results. Nineteen of 93 patients developed a severe sepsis after surgery; seven of the septic patients died in intensive care unit. Multivariate analysis discriminated preoperative and postoperative (first and second day after surgery) predictive risk factors. The postoperative severe sepsis was independently associated with preoperative factors like male gender (OR 4.7, 95% CI between 1.5 and 15.5, P<0.01) and Charlson co-morbidity index (OR 1.3, 95% CI between 1.07 and 1.6, P<0.01). After the surgery, the presence of systemic inflammatory response syndrome (OR 4.0, 95% CI between 1.02 and 15.7, P<0.05) and a logistic organ dysfunction score on day 2 (OR 3.3, 95% CI between 1.9 and 5.7, P<0.001) were found as independent predictive factors.

Conclusion. We have shown that some of the markers that can be easily collected in the preoperative or postoperative visits can be used to screen the patients at high risk for developing severe sepsis after major surgery.

Keywords: complications, postoperative sepsis ; sepsis, predictors ; surgery, major, cancer


    Introduction
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Sepsis after major surgery is common in patients admitted to intensive care units (ICU). It is fast becoming the most common cause of mortality in surgical ICU.1 Recent therapeutic advances have enabled clinicians to reduce early postoperative mortality and/or morbidity.2 3 Despite these advances, patients remain at high risk for infection and the associated increased morbidity and mortality. The suppression of the immune system after surgery predisposes the patients to develop sepsis.4 The post-surgical immunosuppression may be related to the direct effects of anaesthetic drugs, hormonal changes related to stress, effects of hemorrhage and transfusion, occurrence of ischaemia–reperfusion, and extent of surgical trauma.5 The underlying illness, co-morbidity, and factors like age or gender also play a pivotal role in modulating the immune system.6 7 The postoperative period is characterized by development of systemic inflammatory response syndrome (SIRS).8 9 SIRS, sepsis, severe sepsis, and septic shock represent a clinical continuum, with an increasing mortality from SIRS to septic shock.7 The early identification of markers to this progression may reduce postoperative morbidity and mortality. Many tools like Charlson co-morbidity index,10 American Society of Anesthesiologists (ASA) physical status,11 and logistic organ dysfunction (LOD) system can be used to determine the outcome of patients undergoing surgery.12 The Charlson co-morbidity index and ASA physical status are determined at the preoperative consultation, although the LOD system identifies organ failure in the postoperative period.

In the present study, our aim was to determine early predictive significant and independent risk factors for developing severe sepsis in patients undergoing major oncological surgery. These factors were collected at the preoperative consultation, and during the early postoperative period.


    Methods
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The study was conducted prospectively over an 18-month period in a cancer hospital. After approval from our institutional Ethics Committee and obtaining the informed consent of the patients, the study was completed in 93 consecutive patients undergoing elective major surgery. The criteria for inclusion were major surgery (oesophageal or digestive tract or gynaecological cancers) with an expected duration of surgery of at least 5 h.13 The exclusion criteria were age less than 18 yr, emergency surgery, and presence of preoperative infection or clinical inflammatory syndrome. All patients received standardized pre-medication and anaesthesia, and they were admitted to the ICU postoperatively. Pre-medication (the night before and on the morning of surgery) consisted of oral hydroxizine 100 mg. Anaesthesia was induced with i.v. propofol 3 mg kg–1 and fentanyl 3 µg kg–1. For muscle relaxation and tracheal intubation atracurium 0.5 mg kg–1 was given. Subsequently, anaesthesia was maintained with inspiratory concentration of desflurane at 1–1.5 MAC and nitrous oxide 50% in oxygen along with repeated doses of fentanyl. Muscle relaxation was maintained by repeated doses of atracurium as required. Antibiotic prophylaxis consisted of amoxicillin 2 g and clavulanic acid 200 mg with half that dose repeated every 2 h.14 All the patients received hypothermia protection (fluid warmer and forced-air cover over the upper or lower body delivering air at 40°C). Epidural analgesia and inotropes (for non-sepsis reasons) were not used in this cohort of patients. The patients who developed severe sepsis during the 10 days after surgery were allocated to the sepsis group, and the others to the control group.

All data were collected during the intra-operative period (day 0) and on days 1 and 2 after surgery. Haemodynamic variables were electronically recorded preoperatively and at 5-min intervals throughout the surgery. Intra-operative systolic hypotension was defined as systolic arterial pressure below 80 mm Hg lasting for at least 5 min. The Charlson co-morbidity index,10 Karnofsky performance status,15 ASA physical status,11 Acute Physiology and Chronic Health Evaluation (APACHE II),16 Simplified Acute Physiology Score (SAPS II),17 and LOD score12 were measured everyday at the bedside by the same physician (D.M.). These scores were chosen because they are easy to determine at the bedside, or require only basic biological values. Intensive care unit physicians met every week to complete the data of patients who were discharged from the ICU, and then, to check the scores that were determined during the daily rounds. The Charlson co-morbidity index was originally designed to quantify underlying diseases and to classify prognostic co-morbidity, and can be divided into four co-morbidity grades (Table 1). The Karnofsky performance status allows patients to be classified according to their functional impairment. It is an attempt to measure the more ‘subjective’ side of the outcome of cancer treatment. The LOD score was derived from a multiple logistic regression model with physiological variables defining the dysfunction in six organ systems. The data required to evaluate the Charlson co-morbidity index, Karnofsky score, ASA physical status, APACHE II, SAPS II, and LOD score were recorded preoperatively and then during the first 2 days after surgery. The occurrence of septic events was recorded postoperatively until day 10.


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Table 1 Charlson co-morbidity index. Assigned weights for each condition that a patient has. The total equals the score. Example: chronic pulmonary disease (1) and lymphoma (2)=total score of 3

 
Sepsis was defined as SIRS associated with infection according to the criteria defined by Bone and colleagues.18 The SIRS was defined by the presence of at least two of the following: body temperature less than 36 or more than 38°C, tachycardia (>90 beats min–1), ventilatory frequency more than 20 b.p.m. or less than 4.27 kPa (unless the patient was mechanically ventilated), white cell count more than 12 000 cells mm–3 or less than 4000 cells mm–3 or more than 10% immature neutrophils (bands). Severe sepsis was defined as sepsis with the evidence of at least one organ dysfunction as defined by acute alteration of mental status, elevated plasma lactate, unexplained metabolic acidosis (arterial pH <7.3), hypoxaemia ( <9.33 kPa breathing room air, or an acute >2 kPa drop in below baseline breathing room air or hypoxaemia requiring mechanical ventilation), prolonged prothrombin time or platelet count ≤100 000 mm–3 or decrease of the platelet count of more than 50%, oliguria, and hypotension defined as systolic arterial pressure less than 90 mm Hg or a decrease of more than 40 mm Hg from baseline. Septic shock was defined as hypotension, in addition to sepsis syndrome, persisting despite adequate fluid resuscitation and requiring catecholamine support. Standard supportive care, surgical procedures (drainage of abscesses) and broad-spectrum antibiotics were provided to all septic patients. In mechanically ventilated patients postoperative pneumonia was diagnosed by the presence of bronchial purulent sputum, body temperature more than 38 or less than 36°C, worsening of arterial oxygenation, white blood cells more than 12 000 cells mm–3 and less than 4000 cells mm–3, chest radiograph showing new or progressive infiltrates, and presence of at least one microorganism at a concentration of at least 104 colony-forming units (cfu) ml–1 on broncho-alveolar lavage or a bacterial culture of the protected specimen brush more than 103 cfu ml–1. In patients breathing spontaneously, the diagnosis was considered if they had a compatible chest radiograph and purulent sputum with Gram's stain and sputum culture documenting the presence of microorganisms. Abscesses and peritonitis were diagnosed by ultrasonography or computed tomography (CT-scan) together with growth of pathogenic bacteria from aspirated pus. Urinary tract infections were diagnosed by the evidence of growth of a pathogen in urine culture.

Statistical analysis
Categorical data are presented as number (%). Quantitative data are presented as mean (SD). Statistical analysis was performed using SPSS software (version 12.0; SPSS Inc., Chicago, IL). Univariate analysis was conducted to determine potential risk factors for the occurrence of severe sepsis. {chi}2 tests or Fisher's exact tests were used for qualitative variables. The required significance level was set at a P<0.05. A multivariate analysis was used to quantify the respective role of each variable on the occurrence of severe sepsis. A stepwise logistic regression was performed (Backward method, likehood ratio). The explanatory variables included in the logistic regression were the variables identified as potential risk factors by the univariate analysis (cut-off P<0.2). The condensed model was presented with crude odds ratio (OR) and 95% confidence interval (CI), the required significance level was set at P<0.05. Discrimination was assessed using the area under the receiver operating characteristic curve to evaluate how well the model distinguished patients with severe sepsis from controls.

Preliminary data showed that 20% of cancer patients undergoing major surgery developed postoperative sepsis. To provide 80% power to detect an increase of 1.0 point of the LOD score between the septic group and the control group ({alpha}=0.05), we needed to enroll at least 19 septic patients.


    Results
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 Methods
 Results
 Discussion
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Ninety-three consecutive patients were prospectively included in the study. No eligible patient was excluded. All patients underwent tracheal extubation after surgery. Nineteen patients developed severe sepsis (sepsis group) from day 4 to day 9; among these 19 patients, tracheal intubation was required in 15 patients for a mean duration of 26 (22) days. The primary source of infection was lung (n=8), kidney (n=3), abdomen (n=3), pelvis (n=1), or prostate (n=1), and bacteria were isolated in each case. Bacteraemia was found in three patients. Seventy-four patients did not show any clinical signs of severe sepsis (control group). Four patients had renal failure, requiring haemodialysis. The ICU mortality was 8% (n=7). Death occurred in the septic group only. No death occurred on the first postoperative day.

The univariate analysis showed that male gender, Charlson co-morbidity index and ASA more than II (all preoperative factors) were significantly associated with postoperative severe sepsis (Table 2). The multivariate analysis showed that male gender (OR 4.7, 95% CI between 1.5 and 15.5, P<0.01) and Charlson co-morbidity index (OR 1.3, 95% CI between 1.07 and 1.6, P<0.01) were independently associated with postoperative severe sepsis.


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Table 2 Univariate analysis of preoperative predicting factors. Data are given as mean (range), mean (SD) or absolute numbers

 
Surgery consisted of gastro-intestinal (n=56 including seven septic patients), genito-urinary (n=19 including six septic patients) and thoracic (oesophagus) (n=14 including six septic patients) procedures. Intra-operatively, duration of surgery (P<0.01), blood transfusion (P<0.05) and peroperative fluid volume (P<0.05) were associated with an increased risk for developing severe sepsis. On day 1, the APACHE II score (P<0.001) and the LOD system (P<0.001) were associated with an increased risk for developing severe sepsis. On day 2, four predictive factors were identified: SIRS (P<0.05), APACHE II score (P<0.01), the SAPS II score (P<0.05), and LOD system (P<0.001). The results of the univariate analysis are reported in Table 3. The multivariate analysis showed that the presence of SIRS (OR 4.0, 95% CI between 1.02 and 15.7, P<0.05) and LOD score (OR 3.3, 95% CI between 1.9 and 5.7, P<0.001) on day 2 were independently associated with the development of severe sepsis. All the threshold values of Charlson co-morbidity index and day 2 LOD score are displayed on the receiver operating characteristic curves (Figs 1 and 2). The area under the receiver operating characteristic curves was 0.82 for LOD score (95% CI between 0.70 and 0.94), and the highest value reached for sensitivity and specificity was the cut-off value of 1. This threshold was associated with a sensitivity of 80% and a specificity of 72%. The area under the receiver operating characteristic curves was 0.66 for Charlson co-morbidity index (95% CI between 0.52 and 0.80), and the highest value reached for sensitivity and specificity was the cut-off value of 6. This threshold was associated with a sensitivity of 70% and a specificity of 56%.


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Table 3 Univariate analysis of intra- and postoperative predicting factors. Data are given as mean (SD) or absolute numbers. APACHE II: Acute Physiology And Chronic Health Evaluation; SAPS: Simplified Acute Physiology Score; LOD: logistic organ dysfunction; SIRS: systemic inflammatory response syndrome

 


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Fig 1 Receiver operating characteristic curve for Charlson co-morbidity index.

 


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Fig 2 Receiver operating characteristic curve for LOD score on day 2.

 

    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The present study was conducted to determine predictive factors for severe sepsis after major oncological surgery. The results show that determining Charlson index preoperatively, and presence of SIRS and evaluation of LOD score early postoperatively, make it possible to identify patients at high-risk for developing postoperative severe sepsis.

In agreement with previous reports, our study suggests that male gender increases the risk for developing sepsis.6 After experimental trauma-haemorrhage, pre-treatment of female mice with testosterone depressed macrophage function, whereas blockade of testosterone receptors in males restored the depressed immune function.19 Estradiol appears to reduce the inflammatory response, as well as the lower expression of the disease in female than in male in regard to a similar exposition to a pathogen.20 Epidemiology of severe sepsis shows that women have lower age-adjusted severe sepsis rate.1 In addition, female gender has been associated with a decreased risk of severe sepsis in trauma patients.21 However, the exact cause for this gender difference in development of sepsis is unclear.

Our findings in the present study are in agreement with the previously demonstrated correlation between the Charlson co-morbidity index and the secretion of anti-inflammatory cytokines, which were responsible for postoperative immunosuppression in cancer surgical patients.22 In practice, a Charlson co-morbidity index of 6 or more during the preoperative visit can predict a risk for developing postoperative severe sepsis with an acceptable sensitivity. Although the ASA physical status is widely used to assess co-morbidity in high-risk surgical patients, a generally accepted co-morbidity measure is still lacking. We observed that the Charlson co-morbidity index is an interesting tool for the preoperative identification of high-risk surgical patients.10 In this index, co-morbid variables like metastatic cancer are weighted heavily. These variables may affect the development of postoperative sepsis. Hence, preoperatively, the ASA score and Karnofsky performance status, which are highly subjective, are ruled out from the multivariate analysis by the Charlson index. This finding underscores the need to implement a strategy for the reduction of perioperative risk to these patients.

We found that the early presence of SIRS after surgery was predictive of the development of severe sepsis. The quantification of SIRS is potentially overly sensitive in surgical populations. Surgical stress, anaesthesia, and postoperative pain can result in a systemic response that can mimic acute inflammation. However, an elevated SIRS score obtained between 24 and 48 h after admission, despite aggressive resuscitation, predicted an increased mortality,23 and persistent elevated SIRS score is predictive for nosocomial sepsis in trauma patients.24 On the other hand, patients recovering from early postoperative SIRS have lower incidence of multiple organ dysfunction than those with a persisting SIRS.9 In our study, SIRS score was determined only on day 2, minimizing the effect of surgical stress and pain.

We found that the LOD score is a good predictor for the postoperative severe sepsis. The LOD score was derived from a multiple logistic regression model with physiological variables defining the dysfunction in six organ systems. Daily LOD characterized the progression of multiple organ dysfunction during the first ICU week, and showed a good internal consistency.25 Moreover, the LOD score is as effective as sequential organ failure assessment score.25 In another study, the LOD score assessed before the diagnosis of catheter-related bacteraemia was associated with ICU mortality.26 In our study, 36% of the patients with severe sepsis did not survive, which is in agreement with previous data.18 27 Six patients died with criteria of SIRS and a LOD system greater than 0 on day 2 (data not shown). In fact, a LOD score of 1 or more is predictive for developing severe sepsis with a sensitivity of 80% and a specificity of 72%. The LOD is a better predictor than the APACHE II and SAPS II, which were designed to assess patients on their admission in ICU. The daily postoperative follow-up is a key point to determine the development of dysfunction of organs. We hypothesize that an early detection of these dysfunctions associated with an aggressive management, as described elsewhere,28 29 can reduce the ICU duration of stay and subsequently the mortality.

Our study has several limitations. First, only 93 cancer patients were studied of whom 19 developed severe sepsis, which is not enough to develop a predictive scoring system. Hence, further large studies are required to validate our findings. Secondly, our rate of severe sepsis (20%) can seem large following elective surgery, but it is in agreement with the findings of previous studies in cancer as well as non-cancer patients.13 30 Unfortunately, to our knowledge, the severe sepsis rate of cancer patients following major surgery has never been compared with that of non-cancer patients. The other limitation of this study is that few patients were treated with beta-blockers that could affect heart rate and SIRS diagnosis. However, these patients are not excluded from most studies, because they represent the real life conditions.

In conclusion, our study suggests that an early identification of patients at high risk of developing postoperative severe sepsis can be done with the use of easy-to-collect markers. The first step of this identification consists of the preoperative determination of the Charlson co-morbidity index. Next, the daily examination of patients must determine the criteria of SIRS and organ dysfunctions with the LOD score. This examination discriminates at the bedside the patients developing sepsis. These three markers represent an inexpensive way to detect the high-risk patients in whom an early aggressive goal therapy may be evaluated in future studies.


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 Introduction
 Methods
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 Discussion
 References
 
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