Diagnostic accuracy and clinical relevance of an inflammatory biomarker panel for sepsis in adult critically ill patients

Heparin-binding protein as a biomarker for the diagnosis of sepsis in the intensive care unit: a retrospective cross-sectional study in China

OBJECTIVES

This study aims to investigate the diagnostic value of heparin-binding protein (HBP) in sepsis and develop a sepsis diagnostic model incorporating HBP with key biomarkers and disease-related scores for rapid, and accurate diagnosis of sepsis in the intensive care unit (ICU).

DESIGN

Clinical retrospective cross-sectional study.

SETTING

A comprehensive teaching tertiary hospital in China.

PARTICIPANTS

Adult patients (aged ≥18 years) who underwent HBP testing or whose blood samples were collected when admitted to the ICU.

MAIN OUTCOME MEASURES

HBP, C reactive protein (CRP), procalcitonin (PCT), white blood cell count (WBC), interleukin-6 (IL-6), lactate (LAC), Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) score were recorded.

RESULTS

Between March 2019 and December 2021, 326 patients were enrolled in this study. The patients were categorised into a non-infection group (control group), infection group, sepsis group and septic shock group based on the final diagnosis. The HBP levels in the sepsis group and septic shock group were 45.7 and 69.0 ng/mL, respectively, which were significantly higher than those in the control group (18.0 ng/mL) and infection group (24.0 ng/mL) (p<0.001). The area under the curve (AUC) value of HBP for diagnosing sepsis was 0.733, which was lower than those corresponding to PCT, CRP and SOFA but higher than those of IL-6, LAC and APACHE II. Multivariate logistic regression analysis identified HBP, PCT, CRP, IL-6 and SOFA as valuable indicators for diagnosing sepsis. A sepsis diagnostic model was constructed based on these indicators, with an AUC of 0.901, a sensitivity of 79.7% and a specificity of 86.9%.

CONCLUSIONS

HBP could serve as a biomarker for the diagnosis of sepsis in the ICU. Compared with single indicators, the sepsis diagnostic model constructed using HBP, PCT, CRP, IL-6 and SOFA further enhanced the diagnostic performance of sepsis.

21st century critical care medicine: An overview

Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units (ICUs). This abstract provides a concise summary of the latest developments in critical care, highlighting key areas of innovation. Recent advancements in critical care include Precision Medicine: Tailoring treatments based on individual patient characteristics, genomics, and biomarkers to enhance the effectiveness of therapies. The objective is to describe the recent advancements in Critical Care Medicine. Telemedicine: The integration of telehealth technologies for remote patient monitoring and consultation, facilitating timely interventions. Artificial intelligence (AI): AI-driven tools for early disease detection, predictive analytics, and treatment optimization, enhancing clinical decision-making. Organ Support: Advanced life support systems, such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support. Infection Control: Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections. Ventilation Strategies: Precision ventilation modes and lung-protective strategies to minimize ventilator-induced lung injury. Sepsis Management: Early recognition and aggressive management of sepsis with tailored interventions. Patient-Centered Care: A shift towards patient-centered care focusing on psychological and emotional well-being in addition to medical needs. We conducted a thorough literature search on PubMed, EMBASE, and Scopus using our tailored strategy, incorporating keywords such as critical care, telemedicine, and sepsis management. A total of 125 articles meeting our criteria were included for qualitative synthesis. To ensure reliability, we focused only on articles published in the English language within the last two decades, excluding animal studies, in vitro/molecular studies, and non-original data like editorials, letters, protocols, and conference abstracts. These advancements reflect a dynamic landscape in critical care medicine, where technology, research, and patient-centered approaches converge to improve the quality of care and save lives in ICUs. The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.

Advances in sepsis biomarkers

Sepsis, a dysregulated host immune response to an infectious agent, significantly increases morbidity and mortality for hospitalized patients worldwide. This chapter reviews (1) the basic principles of infectious diseases, pathophysiology and current definition of sepsis, (2) established sepsis biomarkers such lactate, procalcitonin and C-reactive protein, (3) novel, newly regulatory-cleared/approved biomarkers, such as assays that evaluate white blood cell properties and immune response molecules, and (4) emerging biomarkers and biomarker panels to highlight future directions and opportunities in the diagnosis and management of sepsis.

Study of Phospholipase A2 Levels and Its Comparison With Procalcitonin Levels in Patients With Sepsis Admitted in a Tertiary Care Hospital, Karnataka, India

INTRODUCTION

Sepsis is a complicated host response to infection involving organ failure which ultimately causes death of the host. Procalcitonin (PCT) is an effective marker used to diagnose sepsis but until now, there has been no ideal marker for sepsis. Phospholipase A2 (PLA2) also increases infections; however, only a few studies have assessed its capacity as a biomarker to diagnose sepsis. Thus, we aimed to examine PLA2 and compare its diagnostic capacity and accuracy with PCT as a biomarker of sepsis.

MATERIAL AND METHODS

Our study was a hospital-oriented cross-sectional study. Our study group included 80 patients of both sexes older than 18 years, meeting the quick sequential organ failure assessment (qSOFA) or systemic inflammatory response syndrome (SIRS) criteria of ≥2, hospitalized in a tertiary care hospital in Karnataka, India from January 2021 to December 2021. Out of them, 59 were found to have sepsis. Samples of all the patients were evaluated for relevant parameters, and data were statistically analyzed using SPSS v21 running on Windows 10. The statistical significance was set at p-value <0.05.

RESULTS

The mean PCT and PLA2 were significantly raised in sepsis patients compared to non-sepsis patients. Out of 59 septic patients, 45.76% had positive blood cultures, and 16.95% had positive urine culture reports. In blood cultures, the most common Gram-positive organism found was Staphylococcus, and the most common Gram-negative organism was Enterobacter. In urine cultures, Escherichia coli was the most common species. PLA2 was significantly higher in patients with bacterial etiology and Gram-positive cultures. The diagnostic capability, sensitivity, specificity, and accuracy of PLA2 were demonstrably higher than those of PCT.

CONCLUSION

Our study proves that PLA2 is a much better and more efficient biomarker in sepsis than PCT. The diagnostic capacity and accuracy of PLA2 clearly surpass PCT, so using PLA2 in sepsis as a biomarker can help clinicians in deciding on timely and appropriate management to speed the recovery of patients.

Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning

Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.

Combined CD25, CD64, and CD69 Biomarker in 3D-Printed Multizone Millifluidic Device for Sepsis Detection in Clinical Samples

Sepsis is a serious medical condition that arises from a runaway response to an infection, which triggers the immune system to release chemicals into the bloodstream. This immune response can result in widespread inflammation throughout the body, which may cause harm to vital organs and, in more severe cases, lead to organ failure and death. Timely and accurate diagnosis of sepsis remains a challenge in analytical diagnostics. In this work, we have developed and validated a sepsis detection device, utilizing 3D printing technology, which incorporates multiple affinity separation zones. Our device requires minimal operator intervention and utilizes CD64, CD69, and CD25 as the biomarker targets for detecting sepsis in liquid biopsies. We assessed the effectiveness of our 3D-printed multizone cell separation device by testing it on clinical samples obtained from both septic patients (n = 35) and healthy volunteers (n = 8) and validated its performance accordingly. Unlike previous devices using poly(dimethyl siloxane), the 3D-printed device had reduced nonspecific binding for anti-CD25 capture, allowing this biomarker to be assayed for the first time in cell separations. Our results showed a statistically significant difference in cell capture between septic and healthy samples (with p values of 0.0001 for CD64, CD69, and CD25), suggesting that 3D-printed multizone cell capture is a reliable method for distinguishing sepsis. A receiver operator characteristic (ROC) analysis was performed to determine the accuracy of the captured cell counts for each antigen in detecting sepsis. The ROC area under the curve (AUC) values for on-chip detection of CD64+, CD69+, and CD25+ leukocytes were 0.96, 0.92, and 0.88, respectively, indicating our diagnostic test matches clinical outcomes. When combined for sepsis diagnosis, the AUC value for CD64, CD69, and CD25 was 0.99, indicating an improved diagnostic performance due to the use of multiple biomarkers.

Diagnostic Value of Neutrophil CD64 in Sepsis Patients in the Intensive Care Unit: A Cross-Sectional Study

Little is known about the role of neutrophil CD64 (nCD64) in detecting sepsis early in Asian populations. We examined the cut-off and predictive values of nCD64 for diagnosing sepsis in Vietnamese intensive care units (ICU) patients. A cross-sectional study was conducted at the ICU of Cho Ray Hospital between January 2019 and April 2020. All 104 newly admitted patients were included. Sensitivity (Sens), specificity (Spec), positive and negative predictive values (PPV and NPV), and receiver operating characteristic (ROC) curves were calculated to compare the diagnostic values of nCD64 with those of procalcitonin (PCT) and white blood cell (WBC) for sepsis. The median nCD64 value in sepsis patients was statistically higher than that of non-sepsis patients (3106 [1970-5200] vs. 745 [458-906] molecules/cell, p < 0.001). ROC analysis found that the AUC value of nCD64 was 0.92, which was higher than that of PCT (0.872), WBC (0.637), and nCD64 combined, with WBC (0.906) and nCD64 combined with WBC and PCT (0.919), but lower than that of nCD64 combined with PCT (0.924). With an AUC value of 0.92, the nCD64 index of 1311 molecules/cell-detected sepsis with 89.9% Sens, 85.7% Spec, 92.5% PPV, and 81.1% NPV. nCD64 can be a useful marker for early sepsis diagnosis in ICU patients. nCD64 combined with PCT may improve the diagnostic accuracy.

Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections

The early identification of bacteremia is critical for ensuring appropriate treatment of nosocomial infections in intensive care unit (ICU) patients. The aim of this study was to use flow cytometric data of myeloid cells as a biomarker of bloodstream infection (BSI). An eight-color antibody panel was used to identify seven monocyte and two dendritic cell subsets. In the learning cohort, immunophenotyping was applied to (1) control subjects, (2) postoperative heart surgery patients, as a model of noninfectious inflammatory responses, and (3) blood culture-positive patients. Of the complex changes in the myeloid cell phenotype, a decrease in myeloid and plasmacytoid dendritic cell numbers, increase in CD14⁺CD16⁺ inflammatory monocyte numbers, and upregulation of neutrophils CD64 and CD123 expression were prominent in BSI patients. An extreme gradient boosting (XGBoost) algorithm called the “infection detection and ranging score” (iDAR), ranging from 0 to 100, was developed to identify infection-specific changes in 101 phenotypic variables related to neutrophils, monocytes and dendritic cells. The tenfold cross-validation achieved an area under the receiver operating characteristic (AUROC) of 0.988 (95% CI 0.985–1) for the detection of bacteremic patients. In an out-of-sample, in-house validation, iDAR achieved an AUROC of 0.85 (95% CI 0.71–0.98) in differentiating localized from bloodstream infection and 0.95 (95% CI 0.89–1) in discriminating infected from noninfected ICU patients. In conclusion, a machine learning approach was used to translate the changes in myeloid cell phenotype in response to infection into a score that could identify bacteremia with high specificity in ICU patients.

Inflammation and Cell Death of the Innate and Adaptive Immune System during Sepsis

Sepsis is a life-threatening medical condition that occurs when the host has an uncontrolled or abnormal immune response to overwhelming infection. It is now widely accepted that sepsis occurs in two concurrent phases, which consist of an initial immune activation phase followed by a chronic immunosuppressive phase, leading to immune cell death. Depending on the severity of the disease and the pathogen involved, the hosts immune system may not fully recover, leading to ongoing complications proceeding the initial infection. As such, sepsis remains one of the leading causes of morbidity and mortality world-wide, with treatment options limited to general treatment in intensive care units (ICU). Lack of specific treatments available for sepsis is mostly due to our limited knowledge of the immuno-physiology associated with the disease. This review will provide a comprehensive overview of the mechanisms and cell types involved in eliciting infection-induced immune activation from both the innate and adaptive immune system during sepsis. In addition, the mechanisms leading to immune cell death following hyperactivation of immune cells will be explored. The evaluation and better understanding of the cellular and systemic responses leading to disease onset could eventuate into the development of much needed therapies to combat this unrelenting disease.

Diagnostic Accuracy of Procalcitonin and C-reactive Protein Is Insufficient to Predict Proven Infection: A Retrospective Cohort Study in Critically Ill Patients Fulfilling the Sepsis-3 Criteria

BACKGROUND

New Sepsis-3 definitions facilitate early recognition of patients with sepsis. In this study we investigated whether a single initial determination of procalcitonin (PCT) or C-reactive protein (CRP) in plasma can predict proven sepsis in Sepsis-3 criteria-positive critically ill patients. We also investigated whether a decline in serial PCT or CRP can predict outcome in 28-day mortality.

METHODS

Patients, ≥18 years of age, at the intensive care unit with a suspected infection, a Sequential Organ Failure Assessment (SOFA) score of ≥2 points, and an index test PCT and CRP at admission were selected from a prospectively collected cohort. PCT and CRP were studied retrospectively with the Mann-Whitney U-test and ROC analysis.

RESULTS

In total, 157 patients were selected; 63 of the 157 had proven sepsis, and sepsis could not be detected in 94 of the 157. Neither a single PCT nor CRP at admission was able to discriminate proven sepsis from nonproven sepsis (PCT, 1.8 μg/L and 1.5 μg/L, respectively, P = 0.25; CRP, 198 mg/L and 186 mg/L, respectively, P = 0.53). Area under the curve for both PCT and CRP for detecting proven sepsis was low (0.55 and 0.53). Furthermore, neither a decline from baseline to day 5 PCT nor CRP could predict 28-day mortality (PCT, 50% vs 46%, P = 0.83; CRP, 30% vs 40%, P = 0.51).

CONCLUSION

PCT and CRP at admission were not able to discern patients with proven sepsis in Sepsis-3 criteria-positive critically ill patients. A decline of PCT and CRP in 5 days was not able to predict 28-day mortality.

Diagnostic value of neutrophil CD64, procalcitonin, and interleukin-6 in sepsis: a meta-analysis

Background

The aim of the study was to conduct a meta-analysis to evaluate the accuracy of neutrophil CD64, procalcitonin (PCT), and interleukin-6 (IL-6) as markers for the diagnosis of sepsis in adult patients.

Methods

Various databases were searched to collect published studies on the diagnosis of sepsis in adult patients using neutrophil CD64, PCT, and IL-6 levels. Utilizing the Stata SE 15.0 software, forest plots and the area under the summary receiver operating characteristic curves were drawn. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve (AUC) were calculated.

Results

Fifty-four articles were included in the study. The pooled sensitivity, specificity, and AUC of neutrophil CD64 for the diagnosis of sepsis were 0.88 (95% confidence interval [CI], 0.81–0.92), 0.88 (95% CI, 0.83–0.91), and 0.94 (95% CI, 0.91–0.96), respectively. The pooled sensitivity, specificity, and AUC of PCT for the diagnosis of sepsis were 0.82 (95% CI, 0.78–0.85), 0.78 (95% CI, 0.74–0.82), and 0.87 (95% CI, 0.83–0.89), respectively. Subgroup analysis showed that the AUC for PCT diagnosis of intensive care unit (ICU) sepsis was 0.86 (95% CI, 0.83–0.89) and the AUC for PCT diagnosis of non-ICU sepsis was 0.82 (95% CI, 0.78–0.85). The pooled sensitivity, specificity, and AUC of IL-6 for the diagnosis of sepsis were 0.72 (95% CI, 0.65–0.78), 0.70 (95% CI, 0.62–0.76), and 0.77 (95% CI, 0.73–0.80), respectively.

Conclusions

Of the three biomarkers studied, neutrophil CD64 showed the highest diagnostic value for sepsis, followed by PCT, and IL-6. On the other hand, PCT showed a better diagnostic potential for the diagnosis of sepsis in patients with severe conditions compared with that in patients with non-severe conditions.

A 2020 review on the role of procalcitonin in different clinical settings: an update conducted with the tools of the Evidence Based Laboratory Medicine

Biomarkers to guide antibiotic treatment decisions have been proposed as an effective way to enhancing a more appropriate use of antibiotics. As a biomarker, procalcitonin (PCT) has been found to have good specificity to distinguish bacterial from non-bacterial inflammations. Decisions regarding antibiotic use in an individual patient are complex and should be based on the pre-test probability for bacterial infection, the severity of presentation and the results of PCT serum concentration. In the context of a high pre-test probability for bacterial infections and/or a high-risk patient with sepsis, monitoring of PCT over time helps to track the resolution of infection and decisions regarding early stop of antibiotic treatment. As outlined by the Evidence Based Laboratory Medicine (EBLM), not only the pre-test probability but also the positive likelihood ratio influence the performance of a test do be really diagnostic. This aspect should be taken into account in the interpretation of the results of clinical trials evaluating the performance of PCT in guiding antibiotic therapy.

Biomarkers for Point-of-Care Diagnosis of Sepsis

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. In 2017, almost 50 million cases of sepsis were recorded worldwide and 11 million sepsis-related deaths were reported. Therefore, sepsis is the focus of intense research to better understand the complexities of sepsis response, particularly the twin underlying concepts of an initial hyper-immune response and a counter-immunological state of immunosuppression triggered by an invading pathogen. Diagnosis of sepsis remains a significant challenge. Prompt diagnosis is essential so that treatment can be instigated as early as possible to ensure the best outcome, as delay in treatment is associated with higher mortality. In order to address this diagnostic problem, use of a panel of biomarkers has been proposed as, due to the complexity of the sepsis response, no single marker is sufficient. This review provides background on the current understanding of sepsis in terms of its epidemiology, the evolution of the definition of sepsis, pathobiology and diagnosis and management. Candidate biomarkers of interest and how current and developing point-of-care testing approaches could be used to measure such biomarkers is discussed.

Effect of neutrophil CD64 for diagnosing sepsis in emergency department

BACKGROUND

The aim of this study is to investigate the diagnostic and prognostic value of neutrophil CD64 (nCD64) as a novel biomarker in sepsis patients.

METHODS

One hundred fifty-one adult patients diagnosed with sepsis and 20 age-matched healthy controls were enrolled in the study. Patients with sepsis were further subdivided into a sepsis group and a septic shock group. nCD64 expression, serum procalcitonin (PCT) level, C-reactive protein (CRP) level, and white blood cell (WBC) count were obtained for each patient, and Sequential Organ Failure Assessment (SOFA) scores were calculated.

RESULTS

nCD64 expression was higher in the sepsis group with confirmed infection than in the control group. The receiver operating characteristic (ROC) curve of nCD64 was higher than those of SOFA score, PCT, CRP and WBC for diagnosing infection. The area under the curve (AUC) of nCD64 combined with SOFA score was the highest for all parameters. The AUC of nCD64 for predicting 28-day mortality in sepsis was significantly higher than those of PCT, CRP, and WBC, but slightly lower than that of SOFA score. The AUC of nCD64 or PCT combined with SOFA score was significantly higher than that of any single parameter for predicting 28-day mortality.

CONCLUSION

nCD64 expression and SOFA score are valuable parameters for early diagnosis of infection and prognostic evaluation of sepsis patients.

Diagnostic value of procalcitonin and presepsin for sepsis in critically ill adult patients: a systematic review and meta-analysis

Background

Early and accurate diagnosis of sepsis is challenging. Although procalcitonin and presepsin have been identified as potential biomarkers to differentiate between sepsis and other non-infectious causes of systemic inflammation, the diagnostic accuracy of these biomarkers remains controversial. Herein, we performed a comprehensive meta-analysis to assess the overall diagnostic value of procalcitonin and presepsin for the diagnosis of sepsis.

Methods

We searched three electronic databases (MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials) for relevant studies. Two authors independently screened articles on the basis of inclusion and exclusion criteria. The pooled sensitivity, specificity, and summary receiver operating characteristic curves were estimated. The quality of evidence for diagnostic accuracy in absolute effects, i.e., the number of true or false positives and true or false negatives, gave a particular pre-test probability.

Results

We included 19 studies (19 observational studies and no randomized controlled trials) that had enrolled 3012 patients. Analyses of summary receiver operating characteristic curves revealed areas under the receiver operating characteristic curves of 0.84 for procalcitonin and 0.87 for presepsin. The pooled sensitivities and specificities were 0.80 (95% confidence interval 0.75 to 0.84) and 0.75 (95% confidence interval 0.67 to 0.81) for procalcitonin. For presepsin, these values were 0.84 (95% confidence interval 0.80 to 0.88) and 0.73 (95% confidence interval 0.61 to 0.82), respectively. There were no statistically significant differences in both pooled sensitivities (p = 0.48) and specificities (p = 0.57) between procalcitonin and presepsin.

Conclusion

Our meta-analysis provided evidence that the diagnostic accuracy of procalcitonin and presepsin in detecting infection was similar and that both are useful for early diagnosis of sepsis and subsequent reduction of mortality in critically ill adult patients.

Systematic review registration

The study was registered in PROSPERO under the registration number CRD42016035784.

Electronic supplementary material

The online version of this article (10.1186/s40560-019-0374-4) contains supplementary material, which is available to authorized users.

Automated measurement of neutrophil CD64 expression for diagnosing sepsis in critically ill patients

BACKGROUND

Although early identification of sepsis improves outcome, prompt and correct diagnostic remains often challenging. The expression of the high affinity immunoglobulin-Fc fragment receptor I CD64 on neutrophils is upregulated during acute inflammation. We here aimed at determining the usefulness of its rapid measurement in diagnosing sepsis.

METHODS

Seventy-two consecutive patients were enrolled upon admission to Intensive Care Unit within a two-month period. Sequential determination of serum C-reactive protein (CRP) and procalcitonin (PCT) concentrations was obtained. The neutrophil CD64 index was measured using Accellix-CD64® device, an in vitro diagnosis system allowing for an automatic and standardized measure.

RESULTS

Serum concentrations of CRP and PCT as well as the neutrophil CD64 index were higher in septic patients compared to all others (P<0.05 for the three markers). Only CD64 index was an independent predictor of sepsis, though with modest sensitivity and specificity (78% and 70%, respectively). Repeat determination of CD64 index at day 2 correctly classified 85% of patients.

CONCLUSIONS

This prospective study demonstrates the moderate performance of the neutrophil CD64 index, assessed through the Accellix-CD64® device, in diagnosing sepsis in the critically ill patient. However, repeat measurements improve its accuracy and may help to predict ICU-acquired infections.

Comparison of the accuracy of neutrophil CD64, procalcitonin, and C-reactive protein for sepsis identification: a systematic review and meta-analysis

Background

Neutrophil CD64 is widely described as an accurate biomarker for the diagnosis of infection in patients with septic syndrome. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of neutrophil CD64, comparing it with C-reactive protein (CRP) and procalcitonin (PCT) for the diagnosis of infection in adult patients with septic syndrome, based on sepsis-2 criteria. We searched the PubMed and Embase databases and Google Scholar. Original studies reporting the performance of neutrophil CD64 for sepsis diagnosis in adult patients were retained. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchical summary receiver operating characteristic (SROC) curve were calculated.

Results

We included 14 studies (2471 patients) from 2006 to 2017 in the meta-analysis. The pooled sensitivity and specificity of neutrophil CD64 for diagnosing infection in adult patients with septic syndrome were 0.87 (95% CI 0.80–0.92) and 0.89 (95% CI 0.82–0.93), respectively. The area under the SROC curve and the DOR were 0.94 (95% CI 0.92–0.96) and 53 (95% CI 22–128), respectively. There was significant heterogeneity between the studies included. Subgroup analyses showed that this heterogeneity was due to differences in sample size and the proportions of patients with sepsis included in the studies. Six studies (927 patients) compared neutrophil CD64 and CRP determinations, and six studies (744 patients) compared neutrophil CD64 and PCT determinations. The area under the SROC curve was larger for neutrophil CD64 than for CRP (0.89 [95% CI 0.87–0.92] vs. 0.84 [95% CI 0.80–0.88], P < 0.05) or PCT (0.89 [95% CI 0.84–0.95] vs. 0.84 [95% CI 0.79–0.89], P < 0.05).

Conclusions

In adult patients with septic syndrome, neutrophil CD64 levels are an excellent biomarker with moderate accuracy outperforming both CRP and PCT determinations.

Electronic supplementary material

The online version of this article (10.1186/s13613-018-0479-2) contains supplementary material, which is available to authorized users.

Flow Cytometry of CD64, HLA-DR, CD25, and TLRs for Diagnosis and Prognosis of Sepsis in Critically Ill Patients Admitted to the Intensive Care Unit: A Review Article

Sepsis is an important health problem with a high burden on health systems. Finding new aspects of immune system function in sepsis showed a new role for flow cytometry in the diagnosis of sepsis. We made a review on the role of CD64, HLA-DR, CD25, and TLRs as more useful flow cytometric tools in diagnosing sepsis, both in adults, and neonates. According to our results, we concluded that for diagnosis and treatment of the septic, flow cytometry can play an important role so that it can be used as a novel method in individualized treatment of septic patients based on their immune system situation.

A community approach to mortality prediction in sepsis via gene expression analysis

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

Correlation of Neutrophil CD64 with Clinical Profile and Outcome of Sepsis Patients during Intensive Care Unit Stay

Introduction:

Neutrophil CD64 (nCD64) has been found to identify sepsis from nonseptic patients. It is also reported to be a predictor of survival and severity of sepsis. The goal of this study was to correlate serial nCD64 with Intensive Care Unit (ICU) outcome and severity of sepsis.

Materials and Methods:

A prospective observational study was conducted in 12-bedded critical care unit of a tertiary care center. Adult patients with sepsis were included in this study. Demographics, illness severity scores, clinical parameters, laboratory data, and 28-day outcome were recorded. Serial nCD64 analysis was done (on days 0, 4, and 8) in consecutive patients.

Results:

Fifty-one consecutive patients were included in the study. Median Acute Physiology and Chronic Health Evaluation II was 16 (12–20) and mean Sequential Organ Failure Assessment was 9 (8–10). Compared to survivors, nonsurvivors had higher nCD64 on day 8 (P = 0.001). nCD64 was higher in the septic shock group compared to sepsis group on days 0 and 8 (P < 0.05). Survivors showed improving trend of nCD64 over time while nonsurvivors did not. This trend was similar in the presence or absence of septic shock. nCD64 count was a good predictor of the septic shock on day 0 (area under the curve [AUC] = 0.747, P = 0.010) and moderate predictor at day 8 (AUC = 0.679, P = 0.028).

Conclusion:

Monitoring serial nCD64 during ICU stay may be helpful in determining the clinical course of septic patients.

Detection of sepsis in patient blood samples using CD64 expression in a microfluidic cell separation device

A microfluidic affinity separation device was developed for the detection of sepsis in critical care patients.