The intensive care infection score - a novel marker for the prediction of infection and its severity

Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department

OBJECTIVE

To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.

METHODS

This retrospective study included adults who visited the ED afebrile at initial presentation. We developed four models based on artificial neural networks to identify suspected serious infection. Patient demographics, vital signs, laboratory test results and information extracted from initial ED physician notes using term frequency-inverse document frequency were used as model variables. Models were trained and internally validated with data from one hospital and externally validated using data from a different hospital. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs).

RESULTS

The training, internal validation, and external validation datasets comprised 150,699, 37,675, and 85,098 patients, respectively. The AUCs (95% CIs) for Models 1 (demographics + vital signs), 2 (demographics + vital signs + initial ED physician note), 3 (demographics + vital signs + laboratory tests), and 4 (demographics + vital signs + laboratory tests + initial ED physician note) in the internal validation dataset were 0.789 (0.782-0.796), 0.867 (0.862-0.872), 0.881 (0.876-0.887), and 0.911 (0.906-0.915), respectively. In the external validation dataset, the AUCs (95% CIs) of Models 1, 2, 3, and 4 were 0.824 (0.817-0.830), 0.895 (0.890-0.899), 0.879 (0.873-0.884), and 0.913 (0.909-0.917), respectively. Model 1 can be utilized immediately after ED triage, Model 2 can be utilized after the initial physician notes are recorded (median time from ED triage: 28 min), and Models 3 and 4 can be utilized after the initial laboratory tests are reported (median time from ED triage: 68 min).

CONCLUSIONS

We developed and validated models to identify suspected serious infection in the ED. Extracted information from initial ED physician notes using NLP contributed to increased model performance, permitting identification of suspected serious infection at early stages of ED visits.

Significant Variability in the Identification and Reporting of Band Neutrophils by Participants Enrolled in the College of American Pathologists Proficiency Testing Program: Time for a Change

CONTEXT.—

Increased band neutrophils in blood smear differential counts ("bandemia") are entrenched in medicine as a flag for sepsis. However, laboratory hematology experts have long advocated for discontinuation of reporting bands separately from segmented neutrophils because of poor sensitivity and specificity, poor interobserver agreement, and availability of alternative biomarkers for sepsis.

OBJECTIVE.—

To describe band neutrophil reporting practices and reproducibility of band classification among laboratories participating in the College of American Pathologists (CAP) proficiency testing (PT) program.

DESIGN.—

A survey questionnaire was distributed to hematology PT participants. A subsequent morphologic challenge included 12 preselected cell identifications of segmented neutrophils, bands, and metamyelocytes, and a 100-cell manual differential count of a digitally scanned blood smear.

RESULTS.—

Among laboratories that reported manual differentials, most respondents reported bands (4554 of 5268; 86.4%). Only 3222 of 4412 respondents (73.0%) provided band reference ranges. Though participants classified "easy" band neutrophils well (78.0%-98.3%), categorization of cell identifications for "moderate" and "difficult" bands was poor (3.1%-39.0% of laboratories), with classification instead as segmented neutrophils. This pattern was seen regardless of laboratory demographic characteristics. Marked variability in band counts was observed on the 100-cell differential count for both CAP PT participants and CAP Hematology and Clinical Microscopy Committee (HCMC) members (coefficients of variation, 55.8% and 32.9%, respectively). Variability was significantly improved when segmented and band neutrophils were grouped together (coefficients of variation, 6.2% and 5.0%, respectively).

CONCLUSIONS.—

Most CAP PT-participating laboratories report band counts, many without reference ranges. The survey confirms significant interlaboratory variability of band enumeration when bands are separately identified from segmented neutrophils. This study reaffirms the CAP Hematology and Clinical Microscopy Committee's strong recommendation to group segmented and band neutrophils together in manual differential counts.

Integrating Routine Hematological and Extended Inflammatory Parameters as a Novel Approach for Timely Diagnosis and Prognosis in Sepsis Management

Sepsis is one of the major causes of morbidity and mortality in hospitals, especially in low- and middle-income countries, and represents a challenge to health care providers to carry out early detection, and accurate diagnosis and prognosis with cost-effective diagnostic tools. An observational prospective study was conducted from December 2021 to December 2022 to investigate the extended inflammatory parameters (EIPs) for sepsis management and analyze the survival of septic patients in the emergency unit, intensive care unit (ICU) and inpatient ward. Patients suspected of having sepsis underwent a sequential organ failure assessment (SOFA) evaluation and had blood drawn for complete blood counts (CBCs). Significant changes were observed in various CBC parameters and EIPs, and the sepsis group was followed up with for 30-day mortality. The study highlighted a significant difference yet strong discriminatory power to differentiate sepsis with an AUC of 0.924 against the non-sepsis group and an AUC of 0.991 against the healthy control group using combination of white blood cells and EIPs. Furthermore, the study showed good predictive ability for 30-day mortality with a hazard ratio of 2.311. In summary, this study provides evidence that the utilization of EIPs may be valuable in diagnosing and predicting patient outcomes, and thus will be beneficial for sepsis management in the hospital.

Efficacy of blood parameters as indicators of the need for overdue urgent cholecystectomy in elderly patients with acute cholecystitis

BACKGROUND

Although early cholecystectomy is recommended for patients with acute cholecystitis, conservative treatment followed by delayed cholecystectomy (DC) is a highly preferred modality, especially in older adult patients. However, some severe cases require overdue urgent cholecystectomy (OC). This study aimed to evaluate the ability of laboratory findings and Tokyo severity classification (TokyoSC) to differentiate those with the need for OC among elderly patients.

METHODS

Laboratory/radiological/clinical findings of geriatric patients with acute cholecystitis on admission and TokyoSC were retrospectively analyzed. The DC and OC groups were compared.

RESULTS

The mean age of the 164 patients was 72.3±6.4 years. White blood cell, neutrophil (NEU), immature granulocyte (IG), C-reactive protein, neutrophil-to-lymphocyte ratio (NLR), and TokyoSC parameters were all significant at P<0.001 in differentiation. NLR had a specificity of 98%, and TokyoSC had a sensitivity of 98%.

CONCLUSION

NLR, NEU, IG, and TokyoSC were effective in differentiating patients who needed OC while planning conservative treatment + DC for older adult patients who were followed up due to acute cholecystitis. If the NLR is >9.9 and TokyoSC is moderate/high, early cholecystectomy should be preferred instead of conservative treatment + DC in aged patients.

Exploring Extended White Blood Cell Parameters for the Evaluation of Sepsis among Patients Admitted to Intensive Care Units

Sepsis is a major cause of mortality and morbidity in intensive care units. This case-control study aimed to investigate the haematology cell population data and extended inflammatory parameters for sepsis management. The study included three groups of patients: sepsis, non-sepsis, and healthy controls. Patients suspected of having sepsis underwent a Sequential Organ Failure Assessment (SOFA) evaluation and had blood drawn for blood cultures, complete peripheral blood counts (CBC), and measurements of various markers such as C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6). We observed significant changes in numerous CBC parameters and extended inflammation parameters (EIPs), in addition to significant biochemical analysis markers CRP and IL-6 in sepsis cohorts. Multiple logistic regression analyses showed that combining different CBC parameters and EIPs were effective to profile these patients. Two different models have been developed using white blood cell counts and their extended parameters. Our findings indicate that the absolute counts of white blood cells, and the EIPs which reflect their activation states, are important for the prediction and assessment of sepsis, as the body responds to an insult that triggers an immune response. In an emergency situation, having timely updates on patient conditions becomes crucial for guiding the management process. Identifying trends in these specific patient groups will aid early diagnosis, complementing clinical signs and symptoms, especially as CBC is the most commonly ordered test in a diagnostic workup.

Value of new advanced hematological parameters in early prediction of severity of COVID-19

INTRODUCTION

COVID-19 usually presents with upper respiratory tract infection in varying severity which can lead to sepsis. Early prediction of sepsis may reduce mortality by timely interventions. The intended purpose of this study was to determine whether the advanced parameters like the extended inflammation parameters (EIPs) can predict prognosis and early progression to sepsis as a sequel of COVID-19 infection and can be used as a screening profile. Also, to evaluate the Intensive Care Infection Score (ICIS) and the COVID-19 prognostic score and validate the scores for our population.

METHODS

Prospective observational study of 50 reverse transcription- polymerase chain reaction (RT-PCR) proven admitted COVID-19 patients. The data assessed included complete blood counts (CBC) with EIP measurements, from Day 1 of admission to Day 10. The following groups were studied: noncritical (NC) and critical illness (CI) in COVID-19 positive cases, COVID negative sepsis and nonsepsis cases, and healthy volunteers for reference range.

RESULTS

The parameters that showed statistically significant higher mean in CI group compared to the NC group are reactive lymphocyte number and percentage (RE-LYMPH#, RE-LYMPH%), antibody synthesizing lymphocyte number and percentage (AS-LYMPH#, AS-LYMPH%), Reactive monocyte count and percentage (RE-MONO#, RE-MONO%/M), ICIS, COVID-19 prognostic score (p-value <0.05). The AUC confirmed the diagnostic accuracy of all these parameters. From the multivariate logistic regression, the significant risk factor was RE-LYMPH# with cut-off >0.10 (p value: 0.011).

CONCLUSION

The new EIP parameters, RE-MONO#, RE-MONO%/M, ICIS score and COVID-19 prognostic score are useful for early prediction of critical illness. AS-LYMPH is the most useful predictor of critical illness on multivariate analysis. RE-MONO# and RE-MONO%/M parameter are useful in distinguishing critical and noncritical non-COVID and COVID-19 patients.

Immunoprofiling of Severity and Stage of Bacterial Infectious Diseases by Ultrabright Fluorescent Nanosphere-Based Dyad Test Strips

Bacterial infectious diseases are common clinical diseases that seriously threaten human health, especially in countries and regions with poor environmental hygiene. Due to the lack of characteristic clinical symptoms and signs, it is a challenge to distinguish a bacterial infection from other infections, leading to misdiagnosis and antibiotic overuse. Therefore, there is an urgent need to develop a specific method for detection of bacterial infections. Herein, utilizing ultrabright fluorescent nanospheres (FNs) as reporters, immunochromatographic dyad test strips are developed for the early detection of bacterial infections and distinction of different stages of bacterial infectious diseases in clinical samples. C-reactive protein (CRP) and heparin-binding protein (HBP) are quantified and assayed because their levels in plasma are varied dynamically and asynchronously during the progression of the disease. The detection limits of CRP and HBP can reach as low as 0.51 and 0.65 ng/mL, respectively, due to the superior fluorescence intensity of each FN, which is 570 times stronger than that of a single quantum dot. The assay procedure can be achieved in 22 min, fully meeting the needs of rapid and ultrasensitive detection in the field. This constructed strip has been successfully used to profile the stage and severity of bacterial infections by monitoring the levels of CRP and HBP in human plasma samples, showing great potential as a point-of-care biosensor for clinical diagnosis. In addition to bacterial infections, the developed ultrabright FN-based point-of-care testing can be readily expanded for rapid, quantitative, and ultrasensitive detection of other trace substances in complex systems.

Risk Estimation for Infection in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention: Development and Validation of a Predictive Score

Background

Infection during hospitalization is a serious complication among patients who suffered from acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI); however, there are no suitable and accurate means to assess risk. This study aimed to develop and validate a simple scoring system to predict post-AMI infection in such patients.

Methods

All patients with ST-segment elevation myocardial infarction (STEMI) undergoing PCI consecutively enrolled from January 2010 to May 2016 were served as derivation cohort, and those from June 2016 to May 2018 as validation cohort, respectively. The primary endpoint was post-AMI infection during hospitalization, and all-cause death and major adverse cardiovascular events (MACE) were considered as secondary endpoints. The simplified risk model was established using logistic regression. The area under the receiver operating curve and calibration of predicted and observed infection risk were calculated.

Results

A 24-point risk score was developed, with infection risk ranging from 0.7 to 99.6% for patients with the lowest and highest score. Seven variables including age, Killip classification, insulin use, white blood cell count, serum albumin, diuretic use, and transfemoral approach were included. This model achieved the same high discrimination in the development and validation cohort (C-statistic:0.851) and revealed adequate calibration in both datasets. The incidences of post-AMI infection increased steadily across risk score groups in both development (1.3, 5.1, 26.3, and 69.1%; P < 0.001) and validation (1.8, 5.9, 27.2, and 79.2%; P < 0.001) cohort. Moreover, the risk score demonstrated good performance for infection, in-hospital all-cause death, and MACE among these patients, as well as in patients with the non-ST-elevation acute coronary syndrome.

Conclusion

This present risk score established a simple bedside tool to estimate the risk of developing infection and other in-hospital outcomes in patients with STEMI undergoing PCI. Clinicians can use this risk score to evaluate the infection risk and subsequently make evidence-based decisions.

Intensive Care Infection Score (ICIS) is elevated in patients with moderate and severe COVID-19 in the early stages of disease

Background

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is still a very dangerous and life-threatening disease with an extremely heterogeneous course. Older patients and those with comorbidities are at increased risk of death from the disease but young patients can develop potentially lethal complications too. For those reasons, numerous recent studies focus on the analysis of markers associated with early assessment of COVID-19 prognosis. Previous publications provided evidence for the Intensive Care Infection Score (ICIS) as an easy to use tool to assess the risk for bacterial infection in ICU patients based on a combination of haematologic parameters. This study evaluated the performance of ICIS as a prognostic marker of stages of disease in COVID-19 patients.

Methods

A total of 205 COVID-19 patients admitted to the University Hospital Hradec Kralove, Czech Republic, with symptoms of respiratory tract infection and a positive RT-PCR test for SARS-CoV-2 virus were enrolled in this study. Forty-nine patients developed mild COVID-19 symptoms (no oxygen therapy needed), 156 patients developed moderate or severe symptoms (supplemental oxygen therapy or death).

Results

ICIS predicted the mild or moderate/severe course with the highest AUC (0.773). The cut-off value (ICIS = 3.5) was selected as the value with the highest Youden index (0.423). The cut-off value could predict a mild or moderate/severe course of the disease with the highest specificity (77.6%) and positive predictive value (90.2%) of all markers used in this study. Sensitivity was 64.7%.

Conclusion

ICIS is a reliable, cheap, fast and simply interpretable score for the early identification of moderate/severe course of COVID-19 in an early stage of the disease. ICIS> 3 predicts a severe course of the disease with high specificity and positive predictive value.

Mining of a Clinical Database: The Interpretation of Intense Serial Procalcitonin in the Prediction for Bloodstream Infection

Background: Procalcitonin (PCT) is a promising biomarker for predicting infection. Bloodstream infection (BSI) is usually a deteriorating stage of sepsis. The purpose of this study was to explore the predictive value of intense serial PCT assays for BSI in the intensive care unit (ICU). Methods: This study was a retrospective study based on a clinical database. We analyzed the data of critically ill patients from February 2016 to May 2020. The patients who received PCT assays and blood cultures (BCs) were classified into four groups according to the BCs: (i) BC negative, (ii) bacteria positive, (iii) fungi-positive, and (iv) combined-positive, and the patients with bacteremia were further subdivided into Gram+ and Gram- bacteremia. Results: The database included 11,219 patients. There were 3,593 patients who met the criteria for the analysis. The PCT concentration differed significantly across BC groups (p < 0.0001). The fluctuation of PCT significantly increased in the BC positive groups (p < 0.0001). According to the receiver operating characteristic (ROC), the optimum cutoff of the fluctuation of PCT was around 8 ng/ml for predicting BSI. Conclusion: Our study indicated that the fluctuation of PCT could be an indicator for screening BSI, but less accurate for Gram-positive infections. With a fluctuation of PCT less than 8 ng/ml, BSI should not be a rational cause for sepsis exacerbating.

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.

Biochemical Analysis of Leukocytes after In Vitro and In Vivo Activation with Bacterial and Fungal Pathogens Using Raman Spectroscopy

Biochemical information from activated leukocytes provide valuable diagnostic information. In this study, Raman spectroscopy was applied as a label-free analytical technique to characterize the activation pattern of leukocyte subpopulations in an in vitro infection model. Neutrophils, monocytes, and lymphocytes were isolated from healthy volunteers and stimulated with heat-inactivated clinical isolates of Candida albicans, Staphylococcus aureus, and Klebsiella pneumoniae. Binary classification models could identify the presence of infection for monocytes and lymphocytes, classify the type of infection as bacterial or fungal for neutrophils, monocytes, and lymphocytes and distinguish the cause of infection as Gram-negative or Gram-positive bacteria in the monocyte subpopulation. Changes in single-cell Raman spectra, upon leukocyte stimulation, can be explained with biochemical changes due to the leukocyte’s specific reaction to each type of pathogen. Raman spectra of leukocytes from the in vitro infection model were compared with spectra from leukocytes of patients with infection (DRKS-ID: DRKS00006265) with the same pathogen groups, and a good agreement was revealed. Our study elucidates the potential of Raman spectroscopy-based single-cell analysis for the differentiation of circulating leukocyte subtypes and identification of the infection by probing the molecular phenotype of those cells.

Reticulocyte and Erythrocyte Hemoglobin Parameters for Iron Deficiency and Anemia Diagnostics in Patient Blood Management. A Narrative Review

Anemia, iron deficiency and other hematinic deficiencies are a major cause of perioperative transfusion needs and are associated with increased morbidity and mortality. Anemia can be caused either by decreased production of hemoglobin or red blood cells or by increased consumption and blood loss. Decreased production can involve anything from erythropoietin or vitamin B12 insufficiency to absolute or functional lack of iron. Thus, to achieve the goal of patient blood management, anemia must be addressed by addressing its causes. The traditional parameters to diagnose anemia, despite offering elaborate options, are not ideally suited to giving a simple overview of the causes of anemia, e.g., iron status for erythropoiesis, especially during the acute phase of inflammation, acute blood loss or iron deficiency. Reticulocyte hemoglobin can thus help to uncover the cause of the anemia and to identify the main factors inhibiting erythropoiesis. Regardless of the cause of anemia, reticulocyte hemoglobin can also quickly track the success of therapy and, together with the regular full blood count it is measured alongside, help in clearing the patient for surgery.

Development and Validation of a Risk Score for Predicting Post-acute Myocardial Infarction Infection in Patients Undergoing Percutaneous Coronary Intervention: Study Protocol for an Observational Study

Background: Post-acute myocardial infarction (post-AMI) infection is an infrequent but important and serious complication in patients with ST-segment elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI). Predicting its occurrence is essential for future prevention. However, little is known about the prediction of post-AMI infection in such patients to date. This study aims to develop and validate a new risk score based on risk factors for early prediction of infection in STEMI patients undergoing PCI. Methods: This prospective, multi-center and observational study assesses the predictive value of risk score for post-AMI infection among a cohort of patients hospitalized due to STEMI. The STEMI patients undergoing PCI enrolled between January 1st 2010 and May 31st 2016 were served as a development cohort while those enrolled from June 1st 2016 to May 31st 2018 were served as validation cohort. The primary endpoint was post-AMI infection during hospitalization, defined as infection requiring antibiotics (reflecting the clinical influence of infection compatible with the necessity for additional treatment), and all-cause death and major adverse cardiovascular events (MACE) including all-cause death, recurrent myocardial infarction, target vessel revascularization, and stroke were considered as secondary endpoints. The risk score model based on risk factors was established using stepwise logistic regression, and will be validated in other centers and external patients with non-ST-elevation acute coronary syndrome (NSTE-ACS). Results: This study will provide evidence on prognostic property, reliability of scoring, comparative performance, and suitability of the novel model for screening purpose in order to be recommended for clinical practice. Discussion: Our study is designed to develop and validate a clinical risk score for predicting infection in participants with STEMI who have undergone PCI. This simple tool may therefore improve evaluation of post-AMI infection and enhance future researches into the best practices to prevent or reduce infection in such patients. Clinical Trial Registration: www.chictr.org.cn, identifier: ChiCTR1900028278.

Usefulness of sialic acid for diagnosis of sepsis in critically ill patients: a retrospective study

Background

Early diagnosis of sepsis is very important. It is necessary to find effective and adequate biomarkers in order to diagnose sepsis. In this study, we compared the value of sialic acid and procalcitonin for diagnosing sepsis.

Methods

Newly admitted intensive care unit patients were enrolled from January 2019 to June 2019. We retrospectively collected patient data, including presence of sepsis or not, procalcitonin level and sialic acid level. Receiver operating characteristic curves for the ability of sialic acid, procalcitonin and combination of sialic acid and procalcitonin to diagnose sepsis were carried out.

Results

A total of 644 patients were admitted to our department from January 2019 to June 2019. The incomplete data were found in 147 patients. Finally, 497 patients data were analyzed. The sensitivity, specificity and area under the curve for the diagnosis of sepsis with sialic acid, procalcitonin and combination of sialic acid and procalcitonin were 64.2, 78.3%, 0.763; 67.9, 84.0%, 0.816 and 75.2, 84.6%, 0.854. Moreover, sialic acid had good values for diagnosing septic patients with viral infection, with 87.5% sensitivity, 82.2% specificity, and 0.882 the area under the curve.

Conclusions

Compared to procalcitonin, sialic acid had a lower diagnostic efficacy for diagnosing sepsis in critically ill patients. However, the combination of sialic acid and procalcitonin had a higher diagnostic efficacy for sepsis. Moreover, sialic acid had good value for diagnosing virus-induced sepsis.

Update on diagnosis and management of sepsis in cirrhosis: Current advances

Sepsis and septic shock are catastrophic disease entities that portend high mortality in patients with cirrhosis. In cirrhosis, hemodynamic perturbations, immune dysregulation, and persistent systemic inflammation with altered gut microbiota in the background of portal hypertension enhance the risk of infections and resistance to antimicrobials. Patients with cirrhosis develop recurrent life-threatening infections that progress to multiple organ failure. The definition, pathophysiology, and treatment options for sepsis have been ever evolving. In this exhaustive review, we discuss novel advances in the understanding of sepsis, describe current and future biomarkers and scoring systems for sepsis, and delineate newer modalities and adjuvant therapies for the treatment of sepsis from existing literature to extrapolate the same concerning the management of sepsis in cirrhosis. We also provide insights into the role of gut microbiota in initiation and progression of sepsis and finally, propose a treatment algorithm for management of sepsis in patients with cirrhosis.

Reviewing the value of leukocytes cell population data (CPD) in the management of sepsis

Sepsis is a medical emergency that describes the body's systemic immune response to an infection and can lead to end-stage organic dysfunction and death. Despite the advances in understanding the pathophysiology of this syndrome and therapies, sepsis remains one of the leading causes of morbidity and mortality in critically ill patients. Early diagnosis and rapid intervention are essential to improve outcomes, which inspired the concept "golden hour," during which the correction of shock and organic dysfunction can improve the patients' outcomes. But the initial presentation of sepsis is often nonspecific and its severity is difficult to assess. Anomalies in temperature, heart and respiratory rates and leukocyte counts are manifestations of systemic inflammatory response syndrome (SIRS). Diagnosis, management and follow-up of patients with sepsis remains a challenge, and diverse biomarkers have been proposed for the timely diagnosis and prognosis of septic patients: lactic acid, procalcitonin (PCT), C-reactive protein, immature granulocytes. The host's initial response to infection is a humoral, cellular and neuroendocrine reaction to infection, and leukocytes interact with endothelial cells. The new generation of hematological analyzers incorporates technological innovations allowing to expand the information derived from the complete blood count: new leukocyte derived parameters are emerging as potentially useful markers in different clinical situations. Additional research parameters cell population data (CPD), characterizing different leukocyte populations have become available, and preliminary observations suggest their utility in the diagnosis of sepsis. This review emphasizes the value of CPD, reported by modern cellular counters for early recognition of sepsis, and therefore the potential improvement in patient outcomes.

Validating a prediction tool to determine the risk of nosocomial multidrug-resistant Gram-negative bacilli infection in critically ill patients: A retrospective case-control study

BACKGROUND

The Singapore GSDCS score was developed to enable clinicians predict the risk of nosocomial multidrug-resistant Gram-negative bacilli (RGNB) infection in critically ill patients. We aimed to validate this score in a UK setting.

METHOD

A retrospective case-control study was conducted including patients who stayed for more than 24h in intensive care units (ICUs) across two tertiary National Health Service hospitals in London, UK (April 2011-April 2016). Cases with RGNB and controls with sensitive Gram-negative bacilli (SGNB) infection were identified.

RESULTS

The derived GSDCS score was calculated from when there was a step change in antimicrobial therapy in response to clinical suspicion of infection as follows: prior Gram-negative organism, Surgery, Dialysis with end-stage renal disease, prior Carbapenem use and intensive care Stay of more than 5 days. A total of 110 patients with RGNB infection (cases) were matched 1:1 to 110 geotemporally chosen patients with SGNB infection (controls). The discriminatory ability of the prediction tool by receiver operating characteristic curve analysis in our validation cohort was 0.75 (95% confidence interval 0.65-0.81), which is comparable with the area under the curve of the derivation cohort (0.77). The GSDCS score differentiated between low- (0-1.3), medium- (1.4-2.3) and high-risk (2.4-4.3) patients for RGNB infection (P<0.001) in a UK setting.

CONCLUSION

A simple bedside clinical prediction tool may be used to identify and differentiate patients at low, medium and high risk of RGNB infection prior to initiation of prompt empirical antimicrobial therapy in the intensive care setting.

Prediction of blood culture outcome using hybrid neural network model based on electronic health records

Background

Blood cultures are often performed to detect patients who has a serious illness without infections and patients with bloodstream infections. Early positive blood culture prediction is important, as bloodstream infections may cause inflammation of the body, even organ failure or death. However, existing work mainly adopts statistical models with laboratory indicators, and fails to make full use of textual description information from EHRs.

Methods

We study the problem of positive blood culture prediction by using neural network model. Specifically, we first construct dataset from raw EHRs. Then we propose a hybrid neural network which incorporates attention based Bi-directional Long Short-Term Memory and Autoencoder networks to fully capture the information in EHRs.

Results

In order to evaluate the proposed method, we constructe a dataset which consists of totally 5963 patients who had one or more blood cultures tests during hospitalization. Experimental results show that the proposed neural model gets 91.23% F-measure for this task.

Conclusions

The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.

Inverse Correlation Between Plasma Sphingosine-1-Phosphate and Ceramide Concentrations in Septic Patients and Their Utility in Predicting Mortality

Supplemental Digital Content is available in the text

Introduction:

The aim of this study was to investigate the correlation between plasma sphingosine-1-phosphate (S1P) and ceramide concentrations in sepsis, and the possible mechanisms for altered expression.

Methods:

Plasma S1P and ceramide concentrations were measured by HPLC-ESI-MS/MS. HLA-DR (human leukocyte antigen-DR) expression on peripheral blood mononuclear cells was examined by flow cytometry. Platelet sphingosine kinases 1/2 (SphK1/2) mRNA expression, protein content, and enzyme activities were determined by qRT-PCR, western blot, and commercial enzyme assay kits, respectively.

Results:

Compared with healthy and ICU controls, septic patients had significantly decreased plasma S1P but increased ceramide concentrations (P < 0.05). S1P concentration was negatively associated with the ceramide concentration in the septic patients (r = −0.36, P < 0.05). Linear regression analysis found that plasma S1P and ceramide were linked not only to sequential (sepsis-related) organ failure assessment (SOFA) score but also the HLA-DR expression on circulating monocytes. An receiver operating characteristic analysis, including S1P, ceramide, SOFA score and HLA-DR, showed integrated analysis of S1P and ceramide as the better powerful predictors of septic lethality with area under the curve value of 0.95. More importantly, we found the platelet SphKs activities and the expression levels of SphK1 were significantly decreased in septic patients (P < 0.05). Linear regression analysis revealed platelet SphKs activity was positively associated with the plasma S1P concentration of the septic patients (r = −0.41, P = 0.02).

Conclusions:

Integrated analysis of plasma S1P and ceramide predict septic mortality with high accuracy. The decreased platelet SphK1 expression and subsequent reduced SphKs activity might be responsible for the decreased plasma S1P levels during sepsis.

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.

Serum human neutrophil lipocalin: An effective biomarker for diagnosing bacterial infections

BACKGROUND

Human neutrophil lipocalin (HNL) is used as a novel biomarker for infections. However, only a few studies have focused on the usefulness of HNL. The purpose of this study was to evaluate the diagnostic efficiency of HNL for identifying bacterial infections and to compare HNL with procalcitonin (PCT) and C-reactive protein (CRP).

METHODS

Hospital patients with acute infections of bacterial origin (n = 439), viral origin (n = 71), and healthy volunteers (n = 67) were included in the study. The infection status of each patient was verified using microbiological, serological, and PCR testing. Additionally, CRP, HNL, and PCT levels were measured by established methods.

RESULTS

In distinguishing bacterial and viral infections, area under the curve (AUC) analysis showed that, with a value of 0.81 (95% CI, 0.76-0.86), HNL was superior to CRP at 0.73 (0.68-0.79) and PCT at 0.64 (0.58-0.70). Interestingly, the combination of HNL, PCT, and CRP improved the diagnostic potential significantly with an AUC of 0.86 (0.82-0.90, P < 0.05). Furthermore, when comparing different infection site subgroups with healthy patients, HNL levels were higher in all bacterial groups, albeit to widely varying degrees (P < 0.0001), and HNL reached a higher level in bloodstream and abdominal infections. CRP levels showed the same trend as HNL levels. PCT levels were significantly increased in bloodstream infections, abdominal infections, and in bacterial pneumonia (P < 0.0001), while no significant differences were found in soft tissue (P = 0.4378) or urinary tract infections (P = 0.423). There was no difference in HNL and CRP levels between patients with Gram-negative bacterial (GNB) or Gram-positive bacterial infections. However, compared with controls, PCT was only increased in GNB-infected patients.

CONCLUSION

HNL detection can help diagnose patients with infectious diseases, and the diagnostic efficacy of HNL is not affected by the infected site or by pathogenic bacterial species. The combination of HNL, PCT, and CRP has a superior performance at identifying bacterial infections compared with traditional biomarkers.

Changes of Biomarkers before and after Antibiotic Treatment in Spinal Infection

Objective

The laboratory biomarkers used to diagnose spinal infection include white blood cell (WBC) counts, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Recently, procalcitonin (PCT) has been used as a biomarker to distinguish between bacterial infection and non-bacterial infection. We aimed to compare the changes of conventional biomarker and PCT in patients with spinal infection before and after antibiotic treatment.

Methods

ESR, CRP, WBC counts, and PCT were measured in 29 patients diagnosed with pyogenic spinal infection at our hospital between May 2016 and December 2018 prior to antibiotic administration. After antibiotic administration, the values were followed up for 4 weeks at 1-week intervals.

Results

A total of 29 patients were enrolled, with a mean age of 67.8 years, consisting of 16 men and 13 women. Twenty-five patients had lumbar infections, and 2 each had cervical and thoracic infections. The mean ESR, CRP, PCT, and WBCs decreased at week 4 of antibiotic treatment compared to their baseline values. CRP and WBCs were significantly decreased after 4 weeks of treatment compared to before treatment. The mean ESR and PCT was not statistically significant compared to pretreatment and after antibiotic treatment (p-value>0.05).

Conclusion

Among several biomarker, CRP and WBCs are biomarkers that can aid early evaluation of the effects of antibiotic treatment in pyogenic spondylitis. Although PCT did not have statistical significance, it can be used as a biomarker that reflects the effect of antibiotic and severity of infection.

Diagnosing and Managing Sepsis by Probing the Host Response to Infection: Advances, Opportunities, and Challenges

Sepsis is a major source of mortality and morbidity globally. Accurately diagnosing sepsis remains challenging due to the heterogeneous nature of the disease, and delays in diagnosis and intervention contribute to high mortality rates. Measuring the host response to infection enables more rapid diagnosis of sepsis than is possible through direct detection of the causative pathogen, and recent advances in host response diagnostics and prognostics hold promise for improving outcomes. The current review discusses recent advances in the technologies used to probe the host response to infection, particularly those based on transcriptomics. These are discussed in the context of contemporary approaches to diagnosing and prognosing sepsis, and recommendations are made for successful development and validation of host response technologies.

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.

A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia

BACKGROUND

Distinguishing arboviral infections from bacterial causes of febrile illness is of great importance for clinical management. The Infection Manager System (IMS) is a novel diagnostic algorithm equipped on a Sysmex hematology analyzer that evaluates the host response using novel techniques that quantify cellular activation and cell membrane composition. The aim of this study was to train and validate the IMS to differentiate between arboviral and common bacterial infections in Southeast Asia and compare its performance against C-reactive protein (CRP) and procalcitonin (PCT).

METHODOLOGY/PRINCIPAL FINDINGS

600 adult Indonesian patients with acute febrile illness were enrolled in a prospective cohort study and analyzed using a structured diagnostic protocol. The IMS was first trained on the first 200 patients and subsequently validated using the complete cohort. A definite infectious etiology could be determined in 190 of 463 evaluable patients (41%), including 89 arboviral infections (81 dengue and 8 chikungunya), 94 bacterial infections (26 murine typhus, 16 salmonellosis, 6 leptospirosis and 46 cosmopolitan bacterial infections), 3 concomitant arboviral-bacterial infections, and 4 malaria infections. The IMS detected inflammation in all but two participants. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the IMS for arboviral infections were 69.7%, 97.9%, 96.9%, and 77.3%, respectively, and for bacterial infections 77.7%, 93.3%, 92.4%, and 79.8%. Inflammation remained unclassified in 19.1% and 22.5% of patients with a proven bacterial or arboviral infection. When cases of unclassified inflammation were grouped in the bacterial etiology group, the NPV for bacterial infection was 95.5%. IMS performed comparable to CRP and outperformed PCT in this cohort.

CONCLUSIONS/SIGNIFICANCE

The IMS is an automated, easy to use, novel diagnostic tool that allows rapid differentiation between common causes of febrile illness in Southeast Asia.

Adjusted Intensive Care Infection Score (ICISΔ)-A new approach for prediction of ascitic fluid infection in patients with cirrhosis

BACKGROUND

Early and accurate diagnosis is the key to improving survival in cirrhotic patients with ascitic fluid infection.

AIMS

To investigate the usefulness of adjusted Intensive Care Infection Score (ICIS_Δ) for diagnosis of ascites infection in cirrhotic patients.

METHODS

Cirrhotic patients with ascites (n = 125) were enrolled, and the efficacy of ICIS and ICIS_Δ for predicting ascites infection was evaluated. ICIS_Δ was created by using the weighted variation of each ICIS parameter.

RESULTS

The area under the curves (AUCs) of ICIS for the diagnosis of ascites infection were 0.90 (95% CI: 0.84-0.95), 0.85 (95% CI: 0.79-0.90), and 0.87 (95% CI: 0.81-0.93), for SBP, culture-negative SBP, and combined SBP/culture-negative SBP, respectively. ICIS was optimized and diagnostic accuracy was obviously improved. ICIS_Δ had high AUCs of 0.99 (95% CI: 0.93-1.00) for SBP, 0.98 (95% CI: 0.83-1.00) for culture-negative SBP, and 0.98 (95% CI: 0.94-1.00) for the combination group. The optimal cutoff was identified as ICIS_Δ > 2, which had >97.8% sensitivity and 100% specificity for diagnosis of both SBP and culture-negative SBP. The ICIS_Δ had significantly higher AUCs than PCT and CPR in both groups (P = 0.002-0.008). ICIS_Δ kinetics could differentiate between SBP and culture-negative SBP patients. From sterile ascites, through culture-negative SBP to SBP, three ICIS_Δ parameters showed an increasing trend.

CONCLUSIONS

ICIS and ICIS_Δ are simple, rapid, accurate and cost-effective methods for the diagnosis of ascites infection in cirrhotic patients.

Improvement in detecting sepsis using leukocyte cell population data (CPD)

Background

The cell population data (CPD) parameters reported by XN analyzers (Sysmex Corporation, Kobe, Japan) reflect the size and internal structure of leukocytes. We explored whether CPD values could contribute to recognize those patients with fever at risk to develop sepsis. A profile of sepsis was developed combining CPD parameters and other markers.

Methods

We recruited 295 patients at the onset of fever, with infection confirmed by positive cultures. We studied the diagnostic performance of the CPD parameters in the differential diagnosis of sepsis vs. non-systemic bacterial infection using receiver operating characteristic (ROC) curve analysis. Additionally, the K-means unsupervised clustering method was applied. Once the clusters had been defined, the relationship between them and the CPD parameter values was assessed with the non-parametric Wilcoxon test. Lastly, the relationship between the clusters obtained and the categorical variables was examined with the χ2-test (or Fisher’s exact test).

Results

ROC analysis demonstrated that NE-FSL, NE-WY, NE-WZ and MO-WZ had areas under the curve (AUCs) >0.700 for predicting sepsis. Using the K-means clustering algorithm, 80 patients (66.67%) were assigned to Cluster 1 and the others to Cluster 2. Out of 80 of patients in Cluster 1, 45 (56.25%) presented a PCT value ≥2 ng/mL, whereas almost 80% of Cluster 2 patients had a PCT <2 ng/mL. Cluster 1 was characterized by high NE-SFL, NE-WY, MO-X, MO-WX and MO-Z values (p<0.05).

Conclusions

CPD related to monocyte complexity and neutrophil activation were found to be significant, with high values suggesting sepsis.

Combination of PCT, sNFI and dCHC for the diagnosis of ascites infection in cirrhotic patients

BACKGROUND

It is difficult to diagnose ascites infection early in cirrhotic patients. The present study was to create and evaluate a new bioscore combined with PCT, sNFI and dCHC in the diagnosis of ascites infection in cirrhotic patients.

METHODS

Two hundred and fifty-nine consecutive patients were enrolled; of which 51 patients were culture-positive spontaneous bacterial peritonitis (culture-positive SBP) and 58 patients were culture-negative SBP. The efficacy of procalcitonin(PCT), c-reactive protein (CRP), white blood cell (WBC), mean fluorescence intensity of mature neutrophils(sNFI) and difference in hemoglobin concentration between newly formed and mature red blood cells(dCHC) for diagnosing ascites infection was examined. These parameters were used to create a scoring system. The scoring system was analyzed by logistic regression analysis to determine which parameters were statistically different between ascites infection and non-ascites infection patients. Receiver operating characteristic curve (ROC) was used to analyze the diagnostic ability of bioscore for ascites infection.

RESULTS

In ROC analysis, the area under the curves (AUC) for PCT was 0.852 (95% CI 0.803-0.921, P < 0.001), dCHC 0.837 (95% CI 0.773-0.923, P < 0.001), CRP 0.669 (95% CI 0.610-0.732, P = 0.0624), sNFI 0.838 (95% CI 0.777-0.903, P < 0.001), and WBC 0.624 (95% CI 0.500-0.722, P = 0.0881). Multivariate analysis revealed PCT, dCHC and sNFI to be statistically significant. The combination of these three parameters in the bioscore had an AUC of 0.937 (95% CI 0.901-0.994, P < 0.001). A bioscore of ≥3.40 was considered to be statistically significant in making a positive diagnosis of ascites infection. In different groups of ascites infection, bioscore also shown a high diagnostic value of AUC was 0.947(95% CI 0.882-0.988, P < 0.001) and 0.929 (95% CI 0.869-0.974, P < 0.001) for culture-positive SBP and culture-negative SBP group respectively.

CONCLUSION

The composite markers of combining PCT, dCHC and sNFI could be a valuable diagnostic score to early diagnose ascites infection in patients with cirrhosis.

Independent risk factors for the development of incontinence-associated dermatitis (category 2) in critically ill patients with fecal incontinence: A cross-sectional observational study in 48 ICU units

BACKGROUND

Critically ill patients suffering from fecal incontinence have a major risk of developing incontinence-associated dermatitis (IAD). The presence of moisture and digestive enzymes (lipase, protease) negatively influences skin barrier function. Additional risk factors will make some patients even more vulnerable than others. In order to provide (cost) effective prevention, this specific patient population should be identified timely.

OBJECTIVES

To identify independent risk factors for the development of IAD category 2 (skin loss) in critically ill patients with fecal incontinence.

DESIGN

A cross-sectional observational study.

SETTING AND PARTICIPANTS

The study was performed in 48 ICU wards from 27 Belgian hospitals. Patients of 18 years or older, with fecal incontinence at the moment of data collection, were eligible to participate. Patients with persistent skin redness due to incontinence (IAD category 1) were excluded.

METHODS

Potential risk factors were carefully determined based on literature and expert consultations. Data were collected over a period of eight months by trained researchers using patient records and observation of skin care practices. At the time a patient was included in the study, all relevant data from the past six days, or since admission at the ICU, were recorded. Simultaneously, direct skin observations were performed and high definition photographs were ratified by an expert IAD researcher. A multiple binary logistic regression model was composed to identify independent risk factors. Variables with P < .25 in single binary logistic regression analyses were added to the multiple model using a forward procedure. A cut-off value of P < .1 was established to retain variables in the final model. Nagelkerke's R² and Hosmer-Lemeshow statistic were calculated as measures of model fit.

RESULTS

The sample comprised of 206 patients, of which 95 presented with IAD category 2, and 111 were free of IAD. Seven independent risk factors were identified: liquid stool [odds ratio (OR) 4.69; 95% confidence interval (CI) 2.28-9.62], diabetes (OR 2.89; 95% CI 1.34-6.27), age (OR 1.05; 95% CI 1.02-1.08), smoking (OR 2.67; 95% CI 1.21-5.91), non-use of diapers (OR 2.97; 95% CI 1.39-6.33), fever (OR 2.60; 95% CI 1.23-5.53), and low oxygen saturation (OR 2.15; 95% CI 1.03-4.48). Nagelkerke's R² was 0.377. The Hosmer-Lemeshow statistic indicated no significant difference between the observed and expected values (p = .301).

CONCLUSIONS

Liquid stool, diabetes, age, smoking, non-use of diapers, fever, and low oxygen saturation were independently associated with IAD category 2 in critically ill patients with fecal incontinence.

Role of leucocytes cell population data in the early detection of sepsis

AIMS

The cell population data (CPD) parameters reported by XN analyser (Sysmex, Kobe, Japan) reflect the size and internal structure of leucocytes. We aimed to assess the clinical utility of these parameters as biomarkers for the early diagnosis of sepsis.

METHODS

The study group (G1) included 586 controls (no quantitative or morphological alterations in the complete blood count) and 137 patients diagnosed with sepsis. The reliability of the model was evaluated using a validation group (G2) of 212 controls and 60 patients with sepsis. The optimal cut-off for the diagnosis of sepsis and the OR for CPD were established using a univariate logistic regression. A multivariate logistic regression model was then created. The OR and area under the curve were recorded. A risk stratification scale (neutrophils and monocytes (NEMO)) for diagnosing sepsis was established on the basis of the coefficients of the multivariate model.

RESULTS

MO-X and neutrophils fluorescence intensity (NE-SFL) were found to be the most relevant of the CPD in predicting sepsis applying multivariate analysis to G1.NEMO score was composed using the above-mentioned CPD and subsequently stratified into three risk groups: mild (≤3), moderate (4≤NEMO≤5) and high (≥6). The OR for patients with a score of 4-5 was 10 and 249 for a score of ≥6. When applied to G2, the positive predictive value was 84.8 % and the negative predictive value was 96.0%.

CONCLUSIONS

CPD are potentially useful for the early diagnosis of sepsis. Their values were used to compose in NEMO score can help in rapid and reliable decision making.