Diagnostic value of serum procalcitonin, lactate, and high-sensitivity C-reactive protein for predicting bacteremia in adult patients in the emergency department

Sepsis Biomarkers: Advancements and Clinical Applications-A Narrative Review

Sepsis is now defined as a life-threatening syndrome of organ dysfunction triggered by a dysregulated host response to infection, posing significant challenges in critical care. The main objective of this review is to evaluate the potential of emerging biomarkers for early diagnosis and accurate prognosis in sepsis management, which are pivotal for enhancing patient outcomes. Despite advances in supportive care, traditional biomarkers like C-reactive protein and procalcitonin have limitations, and recent studies have identified novel biomarkers with increased sensitivity and specificity, including circular RNAs, HOXA distal transcript antisense RNA, microRNA-486-5p, protein C, triiodothyronine, and prokineticin 2. These emerging biomarkers hold promising potential for the early detection and prognostication of sepsis. They play a crucial role not only in diagnosis but also in guiding antibiotic therapy and evaluating treatment effectiveness. The introduction of point-of-care testing technologies has brought about a paradigm shift in biomarker application, enabling swift and real-time patient evaluation. Despite these advancements, challenges persist, notably concerning biomarker variability and the lack of standardized thresholds. This review summarizes the latest advancements in sepsis biomarker research, spotlighting the progress and clinical implications. It emphasizes the significance of multi-biomarker strategies and the feasibility of personalized medicine in sepsis management. Further verification of biomarkers on a large scale and their integration into clinical practice are advocated to maximize their efficacy in future sepsis treatment.

Clinical evaluation of droplet digital PCR in the early identification of suspected sepsis patients in the emergency department: a prospective observational study

Background

Rapid and accurate diagnosis of the causative agents is essential for clinical management of bloodstream infections (BSIs) that might induce sepsis/septic shock. A considerable number of suspected sepsis patients initially enter the health-care system through an emergency department (ED), hence it is vital to establish an early strategy to recognize sepsis and initiate prompt care in ED. This study aimed to evaluate the diagnostic performance and clinical value of droplet digital PCR (ddPCR) assay in suspected sepsis patients in the ED.

Methods

This was a prospective single-centered observational study including patients admitted to the ED from 25 October 2022 to 3 June 2023 with suspected BSIs screened by Modified Shapiro Score (MSS) score. The comparison between ddPCR and blood culture (BC) was performed to evaluate the diagnostic performance of ddPCR for BSIs. Meanwhile, correlative analysis between ddPCR and the inflammatory and prognostic-related biomarkers were conducted to explore the relevance. Further, the health economic evaluation of the ddPCR was analyzed.

Results

258 samples from 228 patients, with BC and ddPCR performed simultaneously, were included in this study. We found that ddPCR results were positive in 48.13% (103 of 214) of episodes, with identification of 132 pathogens. In contrast, BC only detected 18 positives, 88.89% of which were identified by ddPCR. When considering culture-proven BSIs, ddPCR shows an overall sensitivity of 88.89% and specificity of 55.61%, the optimal diagnostic power for quantifying BSI through ddPCR is achieved with a copy cutoff of 155.5. We further found that ddPCR exhibited a high accuracy especially in liver abscess patients. Among all the identified virus by ddPCR, EBV has a substantially higher positive rate with a link to immunosuppression. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity as well as prognosis. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs.

Conclusions

The multiplexed ddPCR delivers precise and quantitative load data on the causal pathogen, offers the ability to monitor the patient's condition and may serve as early warning of sepsis in time-urgent clinical situations as ED.

Importance

Early detection and effective administration of antibiotics are essential to improve clinical outcomes for those with life-threatening infection in the emergency department. ddPCR, an emerging tool for rapid and sensitive pathogen identification used as a precise bedside test, has developed to address the current challenges of BSI diagnosis and precise treatment. It characterizes sensitivity, specificity, reproducibility, and absolute quantifications without a standard curve. ddPCR can detect causative pathogens and related resistance genes in patients with suspected BSIs within a span of three hours. In addition, it can identify polymicrobial BSIs and dynamically monitor changes in pathogenic microorganisms in the blood and can be used to evaluate antibiotic efficacy and survival prognosis. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs.

Effect of preoperative prophylactic antibiotic use on postoperative infection after percutaneous nephrolithotomy in patients with negative urine culture: a single-center randomized controlled trial

PURPOSE

To compare the effects of different preoperative antibiotic prophylaxis (ABP) regimens on the incidence of sepsis after percutaneous nephrolithotomy (PCNL) in patients with negative urine culture.

METHODS

A single-center, randomized controlled trial (June 2022-December 2023) included 120 patients with negative preoperative urine cultures for upper urinary tract stones who underwent PCNL (chictr.org.cn; ChiCTR2200059047). The experimental group and the control group were respectively given different levofloxacin-based preoperative ABP regimes, including 3 days before surgery and no ABP before surgery. Both groups were given a dose of antibiotics before the operation. The primary outcome was differences in the incidence of postoperative sepsis.

RESULTS

A total of 120 subjects were included, including 60 patients in the experimental group and 60 patients in the control group. The baseline characteristics of the two groups were comparable and intraoperative characteristics also did not differ. The sepsis rate was not statistically different between the experimental and control groups (13.3% vs.13.3%, P = 1.0). A multivariate logistic regression analysis revealed that body mass index (BMI) (OR = 1.3; 95% CI = 1.1-1.6; P = 0.003) and operating time (OR = 1.1; 95% CI = 1.0-1.1; P = 0.012) were independent risk factors of sepsis.

CONCLUSION

Our study showed that prophylactic antibiotic administration for 3 days before surgery did not reduce the incidence of postoperative sepsis in patients with negative urine cultures undergoing PCNL. For this subset of patients, we recommend that a single dose of antibiotics be given prior to the commencement of surgery seems adequate.

The Value of Peripheral Blood Leukocyte Parameters in the Early Diagnosis and Clinical Prognosis of Sepsis

Background

Early diagnosis of sepsis is the key to timely, targeted treatment. Cell population data (CPD) has been widely used in many diseases, but its predictive value for early diagnosis and the clinical outcome of sepsis remains unclear. Therefore, this paper discusses whether peripheral blood leukocyte parameters can be used as predictive indicators for early diagnosis and the clinical outcome of sepsis.

Methods

A retrospective study of 45 patients with sepsis, 53 patients with nonseptic infections, and 86 healthy check-ups admitted to Gansu Provincial Hospital from January 2021 to June 2022 was done using a hematology analyzer.

Results

The results of LYMPH#, HFLC#, IG#, NE-WX, LY-WX, LY-WY, and MO-WX showed better diagnostic efficiency in the sepsis group and nonseptic infection group. When the seven differential leukocyte parameters were used to establish diagnostic models, the sensitivity and specificity were 82.20% and 77.40%, respectively. Correlation analysis showed that LYMPH# and HFLC# were positively correlated with PCT (P < 0.05). The clinical outcome of sepsis showed that the leukocyte parameters of discharged WBC and LY-X had better predictive efficacy. When the two differential leukocyte parameters were used to establish diagnostic models, the sensitivity and specificity were 90.90% and 100.00%. Cox regression analysis showed that leukocyte parameters of discharged WBC and LY-X were independent predictors of clinical outcomes (P < 0.05).

Conclusion

Leucocyte parameters HFLC#, IG#, NE-WX, LY-WX, LY-WY, and MO-WX had a certain auxiliary effect on the early diagnosis of sepsis leukocyte parameters of discharged WBC and LY-X were independent predictors of clinical outcomes in patients with sepsis. Therefore, peripheral blood leukocyte parameters may have predictive value for early diagnosis and the clinical outcome of sepsis, but large-scale retrospective studies are still needed to prove our preliminary results.

Routine laboratory biomarkers used to predict Gram-positive or Gram-negative bacteria involved in bloodstream infections

This study evaluated routine laboratory biomarkers (RLB) to predict the infectious bacterial group, Gram-positive (GP) or Gram-negative (GN) associated with bloodstream infection (BSI) before the result of blood culture (BC). A total of 13,574 BC of 6787 patients (217 BSI-GP and 238 BSI-GN) and 68 different RLB from these were analyzed. The logistic regression model was built considering BSI-GP or BSI-GN as response variable and RLB as covariates. After four filters applied total of 320 patients and 16 RLB remained in the Complete-Model-CM, and 4 RLB in the Reduced-Model-RM (RLB p > 0.05 excluded). In the RM, only platelets, creatinine, mean corpuscular hemoglobin and erythrocytes were used. The reproductivity of both models were applied to a test bank of 2019. The new model presented values to predict BSI-GN of the area under the curve (AUC) of 0.72 and 0.69 for CM and RM, respectively; with sensitivity of 0.62 and 0.61 (CM and RM) and specificity of 0.67 for both. These data confirm the discriminatory capacity of the new models for BSI-GN (p = 0.64). AUC of 0.69 using only 4 RLB, associated with the patient's clinical data could be useful for better targeted antimicrobial therapy in BSI.

Diagnostic Value and Prognostic Significance of Procalcitonin Combined with C-Reactive Protein in Patients with Bacterial Bloodstream Infection

Objective

To study the clinical values and implications for the prognosis of procalcitonin (PCT) combined with C-reactive protein (hs-CRP) in patients with bacterial bloodstream infection.

Methods

One hundred and twenty patients with infection hospitalized from Mar. 2020 to Jun. 2021 were chosen as subjects. All participants were tested for serum PCT, hs-CRP, and blood culture. According to the types of pathogenic bacteria, they were divided into the gram-negative bacteria bloodstream infection group (n = 53) and the gram-positive bacteria bloodstream infection group (n = 31). Depending on the prognostic outcome of the participants after 28 days, they were categorized into survival and fatality cohorts. The PCT and hs-CRP levels were compared to explore diagnostic value implications for the prognosis of the cases with bacterial bloodstream infection.

Results

Serum PCT and hs-CRP values in the positive cohort were higher than those in the negative cohort. The levels of serum PCT and hs-CRP in pulmonary infection were higher than those in the group with negative cases, and the difference was statistically significant (P < 0.05). There were 27 gram-positive participants and 9 gram-negative cases in the positive cohort. The serum PCT value of gram-negative bacterial infection was greater than that of gram-positive bacterial infection. The value of serum PCT in the gram-negative bacterial infection group was higher than that in the gram-positive bacterial infection group, and the difference was statistically significant (P < 0.05). The areas under the curve (AUCs) of PCT, combination of hs-CRP and PCT, and hs-CRP were 0.946, 0.783, and 0.991, respectively. The combined examination of PCT and hs-CRP was the largest, PCT was the second, and hs-CRP was the lowest. These results indicated that the accuracy of combined detection of PCT and hs-CRP in the diagnostic bloodstream infection was the highest (0.991), followed by PCT (0.946) and the lowest (0.783). The PCT and hs-CRP levels of the survival cohort were lower than those in the death cohort. AUCs of PCT, hs-CRP and PCT, and hs-CRP were 0.848, 0.826, and 0.934, respectively. The combined examination of PCT and hs-CRP was the largest, followed by PCT and hs-CRP. The accuracy of the combination of PCT and hs-CRP was the highest (0.934), followed by PCT (0.848), and the diagnostic accuracy of hs-CRP was the lowest (0.826).

Conclusion

There were significant differences in the levels of PCT and CRP between the gram-positive bacteria group and the gram-positive bacteria group. PCT and CRP have high diagnostic values in predicting the short-term prognosis of patients. PCT and CRP assist clinical diagnosis and guide treatment and play a positive role in early treatment and prognosis evaluation of patients.

Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission

BACKGROUND AND OBJECTIVES

Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics.

METHODS

We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the number of SBI-negative children subsequently provided antibiotics in the PICU identified as low-risk by each model.

RESULTS

A total of 15 074 children met inclusion criteria; 4788 (32%) received antibiotics before PICU admission. Of these antibiotic-exposed patients, 2325 of 4788 (49%) had an SBI. Of the 10 286 antibiotic-unexposed patients, 2356 of 10 286 (23%) had an SBI. In antibiotic-exposed children, a radial support vector machine model had the highest AUROC (0.80) for evaluating SBI, identifying 48 of 442 (11%) SBI-negative children provided antibiotics in the PICU who could have been spared a median 3.7 (interquartile range 0.9-9.0) antibiotic-days per patient. In antibiotic-unexposed children, a random forest model performed best, but was less accurate overall (AUROC 0.76), identifying 33 of 469 (7%) SBI-negative children provided antibiotics in the PICU who could have been spared 1.1 (interquartile range 0.9-3.7) antibiotic-days per patient.

CONCLUSIONS

Among children who received antibiotics before PICU admission, machine learning models can identify children at low risk of SBI and potentially reduce antibiotic exposure.

Combination of Prehospital NT-proBNP with qSOFA and NEWS to Predict Sepsis and Sepsis-Related Mortality

Background. The aim of this study was to assess the role of prehospital point-of-care N-terminal probrain natriuretic peptide to predict sepsis, septic shock, or in-hospital sepsis-related mortality. Methods. A prospective, emergency medical service-delivered, prognostic, cohort study of adults evacuated by ambulance and admitted to emergency department between January 2020 and May 2021. The discriminative power of the predictive variable was assessed through a prediction model trained using the derivation cohort and evaluated by the area under the curve of the receiver operating characteristic on the validation cohort. Results. A total of 1,360 patients were enrolled with medical disease in the study. The occurrence of sepsis, septic shock, and in-hospital sepsis-related mortality was 6.4% (67 cases), 4.2% (44 cases), and 6.1% (64 cases). Prehospital National Early Warning Score 2 had superior predictive validity than quick Sequential Organ Failure Assessment and N-terminal probrain natriuretic peptide for detecting sepsis and septic shock, but N-terminal probrain natriuretic peptide outperformed both scores in in-hospital sepsis-related mortality estimation. Application of N-terminal probrain natriuretic peptide to subgroups of the other two scores improved the identification of sepsis, septic shock, and sepsis-related mortality in the group of patients with low-risk scoring. Conclusions. The incorporation of N-terminal probrain natriuretic peptide in prehospital care combined with already existing scores could improve the identification of sepsis, septic shock, and sepsis-related mortality.

Prediction of bacteremia at the emergency department during triage and disposition stages using machine learning models

INTRODUCTION

Bacteremia is a common but critical condition with high mortality that requires timely and optimal treatment in the emergency department (ED). The prediction of bacteremia at the ED during triage and disposition stages could support the clinical decisions of ED physicians regarding the appropriate treatment course and safe ED disposition. This study developed and validated machine learning models to predict bacteremia in the emergency department during triage and disposition stages.

METHODS

This study enrolled adult patients who visited a single tertiary hospital from 2016 to 2018 and had at least two sets of blood cultures during their ED stay. Demographic information, chief complaint, triage level, vital signs, and laboratory data were used as model predictors. We developed and validated prediction models using 10 variables at the time of ED triage and 42 variables at the time of disposition. The extreme gradient boosting (XGB) model was compared with the random forest and multivariable logistic regression models. We compared model performance by assessing the area under the receiver operating characteristic curve (AUC), test characteristics, and decision curve analysis.

RESULTS

A total of 24,768 patients were included: 16,197 cases were assigned to development, and 8571 cases were assigned to validation. The proportion of bacteremia was 10.9% and 10.4% in the development and validation datasets, respectively. The Triage XGB model (AUC, 0.718; 95% confidence interval (CI), 0.701-0.735) showed acceptable discrimination performance with a sensitivity over 97%. The Disposition XGB model (AUC, 0.853; 95% CI, 0.840-0.866) showed excellent performance and provided the greatest net benefit throughout the range of thresholds probabilities.

CONCLUSIONS

The Triage XGB model could be used to identify patients with a low risk of bacteremia immediately after initial ED triage. The Disposition XGB model showed excellent discriminative performance.

Clinical and microbiological characteristics of patients with bacteremia and normal procalcitonin

Procalcitonin is a biomarker of bacterial infection used to guide antimicrobial therapy. However, emerging studies have highlighted bacteremic patients with low procalcitonin, potentially limiting its clinical utility. Here, we conducted an observational, retrospective study analyzing clinical and microbiological parameters of adult patients with bacteremia and procalcitonin <2 ng/mL. High proportions of patients required intensive care (31.2%) with vasopressor (14.9%) or ventilatory (17.7%) support, developed renal injury (30.7%), or had in-hospital mortality (14.4%). When divided into subgroups by procalcitonin level, patients with procalcitonin 0.5 to 2.0 ng/mL had significantly higher rates of in-hospital mortality, vasopressor requirement, and renal injury than those with procalcitonin <0.5 ng/mL. Altogether, bacteremic patients had significant morbidity and mortality despite low procalcitonin. While subgroup analysis suggested that higher procalcitonin may correlate with illness severity, a more sensitive procalcitonin cutoff did not eliminate patients with significant disease. Procalcitonin-based algorithms may not be clinically appropriate for management of bacteremia.

Clinical Outcome Predictive Value of Procalcitonin in Patients Suspected with Infection in the Emergency Department

Procalcitonin (PCT) may be useful for early risk stratification in the emergency department (ED), but the transposition of published data to routine emergency practice is sometimes limited. An observational retrospective study was conducted in the adult ED of the Reims University Hospital (France). Over one year, 852 patients suspected of infection were included, of mean age 61.7 years (SD: 22.6), and 624 (73.2%) were hospitalized following ED visit. Overall, 82 (9.6%) patients died during their hospitalization with an odds ratio (OR) of 5.10 (95% CI: 2.19-11.87) for PCT ≥ 0.5, in multivariate logistic regression analyses. Moreover, 78 (9.2%) patients were admitted to an ICU, 74 (8.7%) had attributable bacteremia, and 56 (6.6%) evolved toward septic shock with an OR of 4.37 (2.08-9.16), 6.38 (2.67-15.24), and 6.38 (2.41-16.86), respectively, for PCT ≥ 0.5. The highest discriminatory values were found for patients with age <65 years, but PCT lost its discrimination power for in-hospital mortality in patients with a bronchopulmonary infection site or a temperature ≥37.8°C and for ICU admission in patients with severe clinical presentations. PCT could be helpful in risk stratification, but several limitations must be considered, including being sometimes outperformed by a simple clinical examination.

The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data

OBJECTIVES

Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results at the time of the blood culture order using routine data in the electronic health record.

DESIGN

Retrospective analysis of a large, multicenter inpatient data.

SETTING

Two academic tertiary medical centers between the years 2007 and 2018.

SUBJECTS

All hospitalized patients who received a blood culture during hospitalization.

INTERVENTIONS

The dataset was partitioned temporally into development and validation cohorts: the logistic regression and gradient boosting machine models were trained on the earliest 80% of hospital admissions and validated on the most recent 20%.

MEASUREMENTS AND MAIN RESULTS

There were 252,569 blood culture days-defined as nonoverlapping 24-hour periods in which one or more blood cultures were ordered. In the validation cohort, there were 50,514 blood culture days, with 3,762 cases of bacteremia (7.5%) and 370 cases of fungemia (0.7%). The gradient boosting machine model for bacteremia had significantly higher area under the receiver operating characteristic curve (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p < 0.001). The model identified a high-risk group with over 30 times the occurrence rate of bacteremia in the low-risk group (27.4% vs 0.9%; p < 0.001). Using the low-risk cut-off, the model identifies bacteremia with 98.7% sensitivity. The gradient boosting machine model for fungemia had high discrimination (area under the receiver operating characteristic curve 0.88 [95% CI 0.86-0.90]). The high-risk fungemia group had 252 fungemic cultures compared with one fungemic culture in the low-risk group (5.0% vs 0.02%; p < 0.001). Further, the high-risk group had a mortality rate 60 times higher than the low-risk group (28.2% vs 0.4%; p < 0.001).

CONCLUSIONS

Our novel models identified patients at low and high-risk for bacteremia and fungemia using routinely collected electronic health record data. Further research is needed to evaluate the cost-effectiveness and impact of model implementation in clinical practice.

Early prediction of blood stream infection in a prospectively collected cohort

Background

Blood stream infection (BSI) and sepsis are serious clinical conditions and identification of the disease-causing pathogen is important for patient management. The RISE (Rapid Identification of SEpsis) study was carried out to collect a cohort allowing high-quality studies on different aspects of BSI and sepsis. The aim of this study was to identify patients at high risk for BSI who might benefit most from new, faster, etiological testing using neutrophil to lymphocyte count ratio (NLCR) and Shapiro score.

Methods

Adult patients (≥ 18 years) presenting at the emergency department (ED) with suspected BSI were prospectively included between 2014 and 2016 at Örebro University Hospital. Besides extra blood sampling, all study patients were treated according to ED routines. Electronic patient charts were retrospectively reviewed. A modified Shapiro score (MSS) and NLCR were extracted and compiled. Continuous score variables were analysed with area under receiver operator characteristics curves (AUC) to evaluate the ability of BSI prediction.

Results

The final cohort consisted of 484 patients where 84 (17%) had positive blood culture judged clinically significant. At optimal cut-offs, MSS (≥3 points) and NLCR (> 12) showed equal ability to predict BSI in the whole cohort (AUC 0.71/0.74; sensitivity 69%/67%; specificity 64%/68% respectively) and in a subgroup of 155 patients fulfilling Sepsis-3 criteria (AUC 0.71/0.66; sensitivity 81%/65%; specificity 46%/57% respectively). In BSI cases only predicted by NLCR> 12 the abundance of Gram-negative to Gram-positive pathogens (n = 13 to n = 4) differed significantly from those only predicted by MSS ≥3 p (n = 7 to n = 12 respectively) (p < 0.05).

Conclusions

MSS and NLCR predicted BSI in the RISE cohort with similar cut-offs as shown in previous studies. Combining the MSS and NLCR did not increase the predictive performance. Differences in BSI prediction between MSS and NLCR regarding etiology need further evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05990-3.

Lack of admission biomarkers' clinical utility in outcomes prediction in patients suspected with infection in the emergency department

INTRODUCTION

Initial procalcitonin (PCT) levels may fail in mortality and septic shock prediction and raise cost-effectiveness issues. Since measurement of lactate, C-reactive protein (CRP), white blood cells and neutrophils is common in the emergency department (ED), we compared prediction abilities of these biomarkers to PCT.

METHODS

From January 1st to December 31st, 2018, an observational, single center, retrospective study was conducted in the adult ED of the Reims University Hospital (France). Endpoints were bacteremia, septic shock, and in-hospital mortality, related to the same ED visit.

RESULTS

Over one year, 459 patients suspected with infection were included, of mean age 60.4 years (SD: 22.0), with 50.8% male, and 364 (79.3%) were hospitalized following ED visit. Overall, 45 (9.8%) patients had a bacteremia, 39 (8.5%) a septic shock and 54 (11.8%) died during their hospitalization. PCT and CRP showed the best discrimination for bacteremia, with an area under curve (AUC) of 0.68 for PCT and 0.65 for CRP. PCT and lactate showed similar good discriminative power for septic shock, with an AUC of 0.78 for both, and poor discrimination for in-hospital mortality, with an AUC of 0.62 for PCT and 0.69 for lactate. Systolic blood pressure and pulse oximetry showed similar discrimination for septic shock as PCT or lactate, while they showed higher discrimination for in-hospital mortality than PCT.

CONCLUSION

Usual admission biomarkers lack clinical utility in predicting septic shock or in-hospital mortality. CRP and PCT are poorly efficient in predicting bacteremia.

Blood cultures of adult patients discharged from the emergency department-is the safety net reliable?

We investigated the clinical implications of the practice in our emergency department (ED) of discharging patients with pending blood cultures. We reviewed the medical records of adults discharged with positive blood cultures from the ED of a 330-bed university hospital during a five-year period. Clinical characteristics, laboratory data, and antibiotic treatment prescribed in the ED and at discharge were accessed. Antimicrobial susceptibility profiles were used to determine whether antibiotic treatment was adequate. The outcomes assessed for 90 days following discharge were return to the ED, hospitalization, modified diagnosis, and death. Of 220,681 visits to the ED, 1362 showed positive blood cultures; of these, 307 (22.5%) were from discharged patients. More than half the isolates (56.3%) were considered contaminants. Of 124 visits with true bacteremia, Enterobacteriaceae were the most common pathogens (67.0%). This is concordant with urinary tract infection (UTI) being the most common diagnosis (52.4%). With antibiotic treatment, 69.4% had been discharged with antibiotic treatment, which was adequate in two-thirds of them. Among the 77 who returned to the ED, 27.5% had persistent bacteremia. The diagnosis was changed in 44.2% of them, mostly with brucellosis or bone and joint infections, and 84.4% were subsequently hospitalized. Within three months, 5.6% of bacteremic patients died, all after hospitalization. Bacteremia in discharged patients occurred mainly in association with UTI. Outcomes were generally favorable, although only about half received appropriate antibiotic treatment. Diagnoses were changed in a relatively high proportion of patients following culture results.

Procalcitonin: A promising tool or just another overhyped test?

Sepsis is the leading cause of death worldwide. Timely administration of antibiotics is recognized as the cornerstone in the management of sepsis. However, inappropriate use of antibiotics may lead to adverse effects and the selection of drug-resistant pathogens. Microbiological cultures remain the gold standard to diagnose infection despite their low sensitivity and the intrinsic delay to obtain the results. Certain biomarkers have the benefit of rapid turnover, potentially providing an advantage in timely diagnosis leading to accurate treatment. Over the last few decades, there is an ongoing quest for the ideal biomarker in sepsis. Procalcitonin (PCT), when used alone or alongside additional clinical information, has shown to be a promising tool to aid in the diagnosis and management of patients with sepsis. In February 2017, the Food and Drug Administration (FDA) approved the use of PCT to guide antibiotic treatment in lower respiratory tract infections and sepsis. Despite a good negative predictive value for bacterial infection, the utility of PCT-guided antibiotic initiation is conflicting at best. On the other hand, the use of PCT-guided antibiotic discontinuation has shown to reduce the duration of antibiotic use, the associated adverse effects, and to decrease the overall mortality. The current review discusses the history and pathophysiology of procalcitonin, synthesizes its utility in the diagnosis and management of sepsis, highlights its limitations and compares it with other biomarkers in sepsis.

High incidence of multiresistant bacterial isolates from bloodstream infections in trauma emergency department and intensive care unit in Serbia

We investigated the incidence of bloodstream infections (BSIs) in trauma emergency department (ED) and intensive care unit (ICU), to assess ED- and ICU-related predictors of BSI and to describe the most common bacteria causing BSI and their antimicrobial resistance markers. A prospective study was conducted in two trauma ICUs of the ED of Clinical Center of Serbia. Overall, 62 BSIs were diagnosed in 406 patients, of which 13 were catheter-related BSI (3.0/1,000 CVC-days) and 30 BSIs of unknown origin, while 15% were attributed to ED CVC exposure. Lactate ≥2 mmol/L and SOFA score were independent ED-related predictors of BSI, while CVC in place for >7 days and mechanical ventilation >7 days were significant ICU-related predictors. The most common bacteria recovered were Acinetobacter spp., Klebsiella spp., and Pseudomonas aeruginosa. All Staphylococcus aureus and coagulase-negative staphylococci isolates were methicillin-resistant, whereas 66% of Enterococcus spp. were vancomycin-resistant. All isolates of Enterobacteriaceae were resistant to third-generation cephalosporins, whereas 87.5% of P. aeruginosa and 95.8% of Acinetobacter spp. isolates were resistant to carbapenems. ED BSI contributes substantially to overall ICU incidence of BSI. Lactate level and SOFA score can help to identify patients with higher risk of developing BSI. Better overall and CVC-specific control measures in patients with trauma are needed.

Performance enhancement of procalcitonin by high-sensitivity C-reactive protein at the optimal cutoff in predicting bacteremia in emergency department adult patients

Pathogenic bacteremia portends a high mortality risk in adult patients admitted to an Emergency Department (ED). This study aims to investigate the effect of adding high-sensitivity C-reactive protein (hs-CRP) to procalcitonin (PCT) and lactate in predicting bacteremia, Gram-negative (GNB) and Gram-positive bacteremia (GPB), using the optimal cutoff derived from the receiver operating characteristics analysis. We evaluated the diagnostic measures, including the positive-test likelihood (LR+), the negative-test likelihood (LR-), and the diagnostic odds ratio (DOR) using a single-center retrospective analysis design. This Standards for Reporting Diagnostic-compliant study comprised 886 consecutive adults who were admitted to the ED in 2010; to this cohort, a 22.2% prevalence of true bacteremia was subsequently confirmed. At the cutoff of 3.9 μg/L, PCT had a DOR of 5.3 (95% confidence interval [CI]: 3.76-7.61) and LR + of 2.8 (95% CI: 2.3-3.4) in predicting overall bacteremia. Elevated PCT and lactate (cutoff at 2 mmol/L), increased the DOR and LR + to 6.3 (95% CI: 4.27-9.29) and 4.0 (95% CI: 3.1-5.2). The DOR and LR + were further improved to 7.1 (95% CI: 4.2-11.95) and 5.6 (95% CI: 3.7-8.6), respectively, when hs-CRP at the cutoff of 1238 nmol/L was added to PCT plus lactate. High-sensitivity CRP at the cutoff of 1,255 nmol/L can enhance the discriminative power raising DOR and LR + values for GPB. The elevation of hs-CRP at the optimal cutoff might improve the diagnostic performance to predict unspecified bacteremia and GPB, but not GNB.

Using local clinical and microbiological data to develop an institution specific carbapenem-sparing strategy in sepsis: a nested case-control study

Background

From a stewardship perspective it is recommended that antibiotic guidelines are adjusted to the local setting, accounting for the local epidemiology of pathogens. In many settings the prevalence of Gram-negative pathogens with resistance to empiric sepsis therapy is increasing. How and when to escalate standard sepsis therapy to a reserve antimicrobial agent, is a recurrent dilemma. The study objective was to develop decision strategies for empiric sepsis therapy based on local microbiological and clinical data, and estimate the number needed to treat with a carbapenem to avoid mismatch of empiric therapy in one patient (NNTC).

Methods

We performed a nested case control study in patients (> 18 years) with Gram-negative bacteremia in 2013-2016. Cases were defined as patients with Gram-negative bacteremia with in vitro resistance to the combination 2nd generation cephalosporin AND aminoglycoside (C-2GC + AG). Control patients had Gram-negative bacteremia with in vitro susceptibility to cefuroxime AND/OR gentamicin, 1:2 ratio. Univariate and multivariable analysis was performed for demographic and clinical predictors of resistance. The adequacy rates of empiric therapy and the NNTC were estimated for different strategies.

Results

The cohort consisted of 486 episodes of Gram-negative bacteremia in 450 patients. Median age was 66 years (IQR 56-74). In vitro resistance to C-2GC + AG was present in 44 patients (8.8%). Independent predictors for resistance to empiric sepsis therapy were hematologic malignancy (adjusted OR 4.09, 95%CI 1.43-11.62, p < 0.01), previously cultured drug resistant pathogen (adjusted OR 3.72. 95%CI 1.72-8.03, p < 0.01) and antibiotic therapy during the preceding 2 months (adjusted OR 12.5 4.08-38.48, p < 0.01). With risk-based strategies, an adequacy rate of empiric therapy of 95.2-99.3% could be achieved. Compared to treating all patients with a carbapenem, the NNTC could be reduced by 82.8% (95%CI 78.5-87.5%) using the targeted approaches.

Conclusions

A risk-based approach in empiric sepsis therapy has the potential to better target the use of reserve antimicrobial agents aimed at multi-resistant Gram-negative pathogens. A structured evaluation of the expected antimicrobial consumption and antibiotic adequacy rates is essential to be able to weigh the costs and benefits of potential antibiotic strategies and select the most appropriate approach.