Research and Opinion in Anesthesia & Intensive Care

REVIEW ARTICLE
Year
: 2017  |  Volume : 4  |  Issue : 3  |  Page : 99--107

Serial heparin-binding protein compared with sequential organ failure assessment, acute physiological and chronic health evaluation ii, multiple organ dysfunction and charlson scores as predictors of mortality in critically ill septic patients


Gamal Hamid Ibrahim1, Dalia Mohamed Ragab1, Amal Foad Rizk1, Nora Ismail Abbas1, Talal Ibrahim Hagag2,  
1 Department of Critical Care Medicine, Cairo University, Cairo, Egypt
2 Department of Critical Care Medicine, Shebin Elkom Teaching Hospital, Al Minufiyah, Egypt

Correspondence Address:
Nora Ismail Abbas
Department of Critical Care Medicine, Cairo University, Cairo
Egypt

Abstract

Introduction Early detection and management of severe sepsis is crucial for successful outcome. We hypothesized that the progression of sepsis to severe sepsis is preceded by vascular leakage, which is caused by neutrophil-derived mediators, as heparin-binding protein (HBP). Aim The aim of the study was to identify the role of serial HBP measurement as a predictor of morbidity and mortality in critically ill septic patients in comparison with sequential organ failure assessment (SOFA), acute physiological and chronic health evaluation II (APACHE II) score, multiple organ dysfunction (MODS) scores, and Charlson scores. Settings and design This was an observational prospective controlled study. Materials and methods Patients were classified into two groups: group I, which included 40 patients with evident sepsis; and group II (control group), which included 10 critically ill nonseptic patients. Results Statistically significant difference was detected between survivors and nonsurvivors in max SOFA score, APACHE II, MODS, white blood cells, and serial HBP. Receiver-operating characteristic curve using admission HBP for prediction of severe sepsis showed a sensitivity of 94.7% and specificity of 100% at cut-off level more than 1.9 ng/ml, and for prediction of mortality the sensitivity was 91.6% and specificity 100% at cut-off level more than 1.9 ng/ml. Conclusion Serial plasma HBP levels can predict severe sepsis and mortality in ICU septic patients without statistically significant difference compared with SOFA, APACHE, and MODS scores.



How to cite this article:
Ibrahim GH, Ragab DM, Rizk AF, Abbas NI, Hagag TI. Serial heparin-binding protein compared with sequential organ failure assessment, acute physiological and chronic health evaluation ii, multiple organ dysfunction and charlson scores as predictors of mortality in critically ill septic patients.Res Opin Anesth Intensive Care 2017;4:99-107


How to cite this URL:
Ibrahim GH, Ragab DM, Rizk AF, Abbas NI, Hagag TI. Serial heparin-binding protein compared with sequential organ failure assessment, acute physiological and chronic health evaluation ii, multiple organ dysfunction and charlson scores as predictors of mortality in critically ill septic patients. Res Opin Anesth Intensive Care [serial online] 2017 [cited 2017 Nov 18 ];4:99-107
Available from: http://www.roaic.eg.net/text.asp?2017/4/3/99/209669


Full Text

 Introduction



There still need of a biomarker that can effectively predict the development and prognosis of sepsis.

Microvascular dysfunction is well-known to be one of the hallmark of sepsis syndrome and early warning of progression to severe sepsis. Heparin-binding protein (HBP) (cationic antimicrobial protein CAP 57 or azorucidin) a bactericidal, chemo-attractant, potent inducer of increased vascular permeability and edema formation, is released from activated neutrophils in bacterial infections [1],[2]. We hypothesized that HBP can predict the early progression to severe sepsis as well as mortality outcome.

 Aim



In a first of its kind study we aimed to compare HBP with ICU scores [multiple organ dysfunction (MODS), acute physiological and chronic health evaluation (APACHE II), sequential organ failure assessment (SOFA)] as a predictor of mortality in critically ill septic patients.

 Materials and methods



The study received approval from Medical Ethical Committee of Cairo University. It included 50 patients admitted in the Critical Care Department, Cairo University in the time period from January 2013 to January 2014. The patients were divided into two groups: group I (n=40), comprising patients with evident sepsis; and group II (n=10), the control group, comprising patients who did not develop sepsis during their hospital stay. Two patients were excluded because they were transferred.

All patients were subjected to the following:Detailed history taking including risk factors and comorbidities and thorough clinical examination and ICU scoring MODS, SOFA, and APACHE II.Assessment for evidence of infection (onset, site of infection, cultures, and causative organisms).Laboratory work-up:

Routine and specific: serial HBP and serial highly sensitive C-reactive protein (CRP) (baseline, 48, 96 h). Blood samples were collected in 5 ml tubes at the time of inclusion or in the morning of the following day (0 h) and after 48 and 96 h. Tubes were immediately centrifuged at 3000 rpm for 10 min and separate aliquots of the serum supernatants were stored at −20°C until analysis. The concentration of HBP was determined by enzyme-linked immunosorbent assay. The researcher was blind to patient samples.Inclusion criteria: all septic patients at any stage of sepsis. Exclusion criteria: patients less than 18 years old and patients receiving heparin, which could alter laboratory results.

We studied surgical trauma patients with short ICU stay as controls to decrease the probability of infection.

 Study design



This was a prospective case–control observational study.

Statistical analysis

Statistical analysis was carried out on a personal computer using IBM SPSS statistics version 22 (IBM Corp., Armonk, New York, USA). Numerical variables were presented as median and interquartile range and intergroup differences were compared using the Mann–Whitney U-test. Categorical variables were presented as number and percentage and inter-group differences were compared using the Pearson χ2-test or Fisher’s exact test, when appropriate.

Receiver-operating characteristic (ROC) curve analysis was used to examine the value of continuous variables for prediction of binary outcomes.

Survival analysis was carried out using the Kaplan–Meier method and the log-rank test was used to compare individual survival curves.

Multivariable binary logistic regression analysis was used to build up a predictive model for prediction of 28-day mortality using a combination of predictors. The predicted probability of 28-day mortality as estimated from the regression model was then used for plotting a ROC curve to assess the predictive value of the model. ROC curves derived from various combinations of predictors were compared using the DeLong method.

 Results



A statistically significant difference was found between patients and controls in age and number of organ dysfunctions, whereas no significant difference was detected as regards sex, history of smoking, diabetes, hypertension, cardiac diseases, or length of stay in ICU. A statistically significant difference was detected between survivors and nonsurvivors as regards age and number of dysfunctional organ ([Table 1] and [Table 2]).{Table 1}{Table 2}

Highly statistically significant difference existed between patients and controls in as regards MODS score, vasopressors used, ICU-free days, organ failure-free days, ventilator-free days, and 28-day mortality.

Statistically significant difference between survivors and nonsurvivors was detected in admission MODS, SOFA, APACHE II, and Charlson scores. While Glasgow coma scale was not significant at admission as most survivors are critically ill trauma controls ([Table 3] and [Table 4]).{Table 3}{Table 4}

A highly statistically significant difference was found between group I and group II in baseline HBP, HBP at 48 and 96 h.

We found a statistically significant difference between survivors and nonsurvivors in HBP, white blood cells at baseline, 48, and 96 h. CRP was not predictive of mortality at baseline, 48, and 96 h ([Table 5],[Table 6],[Table 7],[Table 8],[Table 9]).{Table 5}{Table 6}{Table 7}{Table 8}{Table 9}

[Figure 1] shows highly significant difference of HBP level at various times in cases (group I) and controls (group II). Central box represents the values from the lower to upper quartile (25th to 75th percentile). Middle line represents the median. Error bars extend from the minimum to the maximum value, excluding outside and far out values, which are displayed as separate points (rounded markers or asterisks, respectively). An outside value is defined as a value that is smaller than the lower quartile minus 1.5 times the interquartile range, or larger than the upper quartile plus 1.5 times the interquartile range (inner fences). A far out value is defined as a value that is smaller than the lower quartile minus three times the interquartile range, or larger than the upper quartile plus three times the interquartile range (outer fences).{Figure 1}

[Figure 2] shows highly significant difference of HBP level at various times in survivors and nonsurvivors. Central box represents the values from the lower to upper quartile (25th to 75th percentile). Middle line represents the median. Error bars extend from the minimum to the maximum value, excluding outside and far out values, which are displayed as separate points (rounded markers or asterisks, respectively). An outside value is defined as a value that is smaller than the lower quartile minus 1.5 times the interquartile range, or larger than the upper quartile plus 1.5 times the interquartile range (inner fences). A far out value is defined as a value that is smaller than the lower quartile minus three times the interquartile range, or larger than the upper quartile plus three times the interquartile range (outer fences).{Figure 2}

Prediction of severe sepsis/septic shock

[Figure 3] shows prediction of severe sepsis septic shock using baseline HBP: area under the curve (AUC) is 0.982, P less than 0.0001 with sensitivity 94.7, specificity 100, and cut-off level more than 1.9 ng/ml.{Figure 3}

[Figure 4] shows ROC curve analysis for prediction of severe sepsis/septic shock using HBP level at 48h: AUC=0.993, P-value less than 0.0001, with sensitivity 94.12%, specificity 100%, positive predictive value (PPV) of 100%, negative predictive value (NPV) of 81.8%, and associated criterion more than 1.8 ng/ml.{Figure 4}

[Figure 5] shows ROC curve analysis for prediction of severe sepsis/septic shock using HBP level at 96 h: AUC of 1, P-value less than 0.0001, sensitivity 100%, specificity 100%, PPV of 100%, NPV of 100%, and associated criterion more than 1.6 ng/ml.{Figure 5}

[Figure 6] shows ROC curve analysis for prediction of mortality using baseline HBP level: AUC is 0.99, P-value less than 0.0001, sensitivity 91.6%, specificity 100%, PPV of 100%, NPV of 80%, and associated criterion more than 1.9 ng/ml.{Figure 6}

[Figure 7] shows ROC curve analysis for prediction of mortality using HBP level at 48 h: AUC of 0.99, P-value less than 0.0001, sensitivity 96.8%, specificity 100%, PPV of 100%, NPV of 91.7%, and associated criterion more than 1.8 ng/ml.{Figure 7}

[Figure 8] shows ROC curve analysis for prediction of mortality using HBP level at 96 h (AUC) of 1, P-value less than 0.0001, sensitivity of 100%, specificity of 100%, PPV of 100%, NPV of 100%, associated criterion more than 1.6 ng/ml.{Figure 8}

[Figure 9] shows ROC curve for prediction of 28-day mortality using the SOFA max: AUC of 1, P-value less than 0.0001, sensitivity 100%, specificity 100%, PPV of 100%, NPV of 100%, and associated criterion more than 8.{Figure 9}

[Figure 10] shows ROC curve for prediction of 28-day mortality using the APACHE II score: AUC=1, P-value less than 0.0001, sensitivity of 100%, specificity of 100%, PPV of 100%, NPV of 100%, and associated criterion more than 12.{Figure 10}

[Figure 11],[Figure 12], [Figure 13], [Figure 14] show ROC curve for prediction of 28-day mortality using the HBP max: AUC of 1, P-value less than 0.0001, sensitivity of 100%, specificity of 100%, PPV of 100%, NPV of 100%, and associated criterion 3.8 or less.{Figure 11}{Figure 12}{Figure 13}{Figure 14}

There was no statistically significant difference between variables as SOFA max and APACHE II or SOFA and APACHE II combination on one side and HBP on the other side in prediction of mortality.

[Figure 15] shows ROC curve analysis for prediction of 28-day mortality using the SOFA max plus the APACHE II score: AUC of 1, P-value less than 0.0001, sensitivity of 100%, specificity of 100%, PPV of 100%, NPV of 100%, and associated criterion more than 0.{Figure 15}

 Discussion



Septic patients (group I) had higher MODS score, vasopressors use, ICU length of stay, and ventilator days, as well as higher 28-day mortality compared with controls trauma patients.

Heparin-binding protein levels as predictors of mortality

Thirty-eight patients with, mainly, pulmonary and urinary tract infection were analyzed as group I; it was referred to as the sepsis group with only three patients who developed septic shock at admission. Group II, the control group, included 10 ICU patients who did not show evidence of infection.

Blood C and S was obtained from all patients, and only in 35 patients was it positive (92.1%) and no growth in three patients.

HBP levels were higher in cases (group I) than in controls (group II) at admission, 48, and 96 h.

In addition, HBP levels were higher in nonsurvivors compared with survivors for all serial measurements (admission, 48, and 96 h) with high statistical significance in contrast to highly sensitive CRP, which showed no statistical significance between groups I and II, as well as between survivors and nonsurvivors for all serial measurement.

The same results were obtained by a comparative study by Linder et al. [3]. At enrollment HBP levels were significantly higher in the sepsis group (n=151) compared with controls (n=28) as well as in nonsurvivors compared with survivors.In addition, Berkestdt [4] studied variable antimicrobial peptides and found HBP levels higher in septic patients compared with controls, as well as nonsurvivors compared with survivors.

Ristagno et al. [5] reported that HBP levels were also higher in postresuscitation patients with higher SOFA, cardiovascular SOFA, and MODS scores, as well as in patients who died early in ICU or survived with unfavorable neurological outcome compared with survivors with favorable outcome.

Linder et al. [6] reported in 2009 that in febrile patients, high plasma levels of HBP help to identify patients with an imminent risk for developing sepsis with circulatory failure.

In addition, Tydén et al. [7] concluded that a high concentration of HBP in plasma upon ICU admission is associated with respiratory and circulatory failure later during the ICU stay, as well as with increased 30-day mortality.

We also found that serial HBP sampling (baseline, 48, and 96 h) improves the sensitivity of both sepsis prediction from 94.7 to 100%, as well as mortality prediction from 91.6 to 100%.

HBP ROC analysis was carried out for prediction of mortality using baseline, 48, 96 h for HBP at cut-off levels more than 1.9, more than 1.8, more than 1.6 ng/ml, respectively (P<0.0001).

Heparin-binding protein compared with ICU scoring as predictors of mortality

Admission and discharge MODS and SOFA were significantly higher in nonsurvivors compared with survivors, as well as admission APACHE II and Charlson score were also higher in nonsurvivors.

Comparison of the ROC curves for prediction of 28-day mortality using the SOFA max plus the APACHE II score versus the HBP max did not show statistically significant difference.

ROC curves for 28-day mortality prediction with SOFA max alone, Apache II alone, or SOFA max and APACHE II in combination gave the same results, as HBP max with AUC of 1, sensitivity and specificity 100%.

In the present study there was no statistically significant difference between variables as SOFA max and APACHE II or SOFA max and APACHE II combination on one side and HBP on the other side in prediction of mortality, but HBP offered very high predictive value for prediction of morbidity. HBP predicts severe sepsis in the level of microvascular changes, which occur early in the pathogenesis of sepsis before the scenario of MODS starts, which is not available in ICU scores. Furthermore, HBP allows mortality prediction with comparable accuracy to ICU scores.

In conclusion, the serial measurements of HBP plasma levels can be a useful tool for close monitoring of critically ill septic patients with high sensitivity and specificity in comparison with different ICU scores.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1Linder A, Soehnlein O, Akesson P. Roles of heparin-binding protein in bacterial infections. J Innate Immun 2010; 2:431–438.
2Gautam N, Olofsson AM, Herwald H, Iversen LF, Lundgren-Akerlund E, Hedqvist P et al. Heparin-binding protein (HBP/CAP37): a missing link in neutrophil-evoked alteration of vascular permeability. Nat Med 2001; 7:1123–1127.
3Linder A, Akesson P, Inghammar M, Treutiger C-J., Linner A, Sunden-Cullberg J. Elevated plasma levels of heparin-binding protein in intensive care unit patients with severe sepsis and septic shock. Crit Care 2012; 16:R90.
4Berkestedt I, Herwald H, Ljunggren L, Nelson A, Bodelsson M. Elevated plasma levels of antimicrobial polypeptides in patients with severe sepsis. J Innate Immun 2010; 2:478–482.
5Ristagno G, Masson S, Tiainen M, Bendel S, Bernasconi R, Varpula T et al. Elevated plasma heparin-binding protein is associated with early death after resuscitation from cardiac arrest. Crit Care 2016; 20:251.
6Linder A, Christensson B, Herwald H, Bjorck L, Akesson P. Heparin-binding protein: an early marker of circulatory failure in sepsis. Clin Infect Dis 2009; 49:1044–1050.
7Tydén J, Herwald H, Sjöberg F, Johansson J. Increased plasma levels of heparin-binding protein on admission to intensive care are associated with respiratory and circulatory failure. PLoS One 2016; 11:e0152035.