Predictive model for bacteremia in adult patients with blood cultures performed at the emergency department: a preliminary report

Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results

BACKGROUND

Despite advancements in antimicrobial therapies, bacteremia remains a life-threatening condition. Appropriate antimicrobials must be promptly administered to ensure patient survival. However, diagnosing bacteremia based on blood cultures is time-consuming and not something emergency department (ED) personnel are routinely trained to do.

METHODS

This retrospective cohort study developed several machine learning (ML) models to predict bacteremia in adults initially presenting with fever or hypothermia, comprising logistic regression, random forest, extreme gradient boosting, support vector machine, k-nearest neighbor, multilayer perceptron, and ensemble models. Random oversampling and synthetic minority oversampling techniques were adopted to balance the dataset. The variables included demographic characteristics, comorbidities, immunocompromised status, clinical characteristics, subjective symptoms reported during ED triage, and laboratory data. The study outcome was an episode of bacteremia.

RESULTS

Of the 5063 patients with initial fever or hypothermia from whom blood cultures were obtained, 128 (2.5 %) were diagnosed with bacteremia. We combined 36 selected variables and 10 symptoms subjectively reported by patients into features for analysis in our models. The ensemble model outperformed other models, with an area under the receiver operating characteristic curve (AUROC) of 0.930 and an F1-score of 0.735. The AUROC of all models was higher than 0.80.

CONCLUSION

The ML models developed effectively predicted bacteremia among febrile or hypothermic patients in the ED, with all models demonstrating high AUROC values and rapid processing times. The findings suggest that ED clinicians can effectively utilize ML techniques to develop predictive models for addressing clinical challenges.

Utility of shaking chills as a diagnostic sign for bacteremia in adults: a systematic review and meta-analysis

BACKGROUND

Accurate prediction of bacteremia is essential for guiding blood culture collection and optimal antibiotic treatment. Shaking chills, defined as a subjective chill sensation with objective body shivering, have been suggested as a potential predictor of bacteremia; however, conflicting findings exist. To address the evidence gap, we conducted a systematic review and meta-analysis of studies to assess the diagnostic accuracy of shaking chills for predicting bacteremia among adult patients.

METHODS

We included studies reporting the diagnostic accuracy of shaking chills or chills for bacteremia. Adult patients with suspected bacteremia who underwent at least one set of blood cultures were included. Our main analysis focused on studies that assessed shaking chills. We searched these studies through CENTRAL, MEDLINE, Embase, the World Health Organization ICTRP Search Portal, and ClinicalTrials.gov. Study selection, data extraction, evaluation for risk of bias, and applicability using the QUADAS-2 tool were conducted by two independent investigators. We estimated a summary receiver operating characteristic curve and a summary point of sensitivity and specificity of the index tests, using a hierarchical model and the bivariate model, respectively.

RESULTS

We identified 19 studies with a total of 14,641 patients in which the accuracy of shaking chills was evaluated. The pooled sensitivity and specificity of shaking chills were 0.37 (95% confidence interval [CI], 0.29 to 0.45) and 0.87 (95% CI, 0.83 to 0.90), respectively. Most studies had a low risk of bias in the index test domain and a high risk of bias and a high applicability concern in the patient-selection domain.

CONCLUSIONS

Shaking chills are a highly specific but less sensitive predictor of bacteremia. Blood cultures and early initiation of antibiotics should be considered for patients with an episode of shaking chills; however, the absence of shaking chills must not lead to exclusion of bacteremia and early antibiotic treatment.

Do all Emergency Room Patients With Influenza-like Symptoms Need Blood Cultures? A Retrospective Cohort Study of 2 Annual Influenza Seasons

In this retrospective cohort study, we evaluated risk factors for bacteremia in emergency department patients presenting with influenza-like symptoms during influenza epidemic seasons. In patients without fever, chronic heart or chronic liver disease, blood culture collection might be omitted.

Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients

BACKGROUND

Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we conducted a study to provide clarity on this issue.

METHODS

Adult febrile ED patients with blood cultures at Chi Mei Medical Center were divided into derivation (January 2017 to June 2019) and validation groups (July 2019 to December 2020). The derivation group was utilized to develop AI models using twenty-one feature variables and five algorithms to predict bacteremia. The performance of these models was compared with qSOFA score. The AI model with the highest area under the receiver operating characteristics curve (AUC) was chosen to implement the AI prediction system and tested on the validation group.

RESULTS

The study included 5,647 febrile patients. In the derivation group, there were 3,369 patients with a mean age of 61.4 years, and 50.7% were female, including 508 (13.8%) with bacteremia. The model with the best AUC was built using the random forest algorithm (0.761), followed by logistic regression (0.755). All five models demonstrated better AUC than the qSOFA score (0.560). The random forest model was adopted to build a real-time AI prediction system integrated into the hospital information system, and the AUC achieved 0.709 in the validation group.

CONCLUSION

The AI model shows promise to predict bacteremia in adult febrile ED patients; however, further external validation in different hospitals and populations is necessary to verify its effectiveness.

A simplified scoring model for predicting bacteremia in the unscheduled emergency department revisits: The SADFUL score

BACKGROUND

Bacteremia is a severe complication of infectious disease. Patients with a high bacteremia risk in the emergency department (ED) but misidentified would lead to the unscheduled revisits. This study aimed to develop a simplified scoring model to predict bacteremia in patients with unscheduled ED revisits.

METHODS

Adult patients with unscheduled ED revisits within 72 h with a final diagnosis of infectious disease were retrospectively included. The development cohort included patients visiting the ED from January 1, 2019 to December 31, 2021. Internal validation was performed in patients visiting the ED from January 1, 2022 to March 31, 2022. Variables including demographics, pre-comorbidities, triage levels, vital signs, chief complaints, and laboratory data in the index visit were analyzed. Bacteremia was the primary outcome determined by blood culture in either index visits or revisits.

RESULTS

The SADFUL score for predicting bacteremia comprised the following predictors: "S"egmented neutrophil percentage (+3 points), "A"ge > 55 years (+1 point), "D"iabetes mellitus (+1 point), "F"ever (+2 points), "U"pper respiratory tract symptoms (-2 points), and "L"eukopenia (2 points). The area under receiver operating characteristic curve with 95% confidence interval in the development (1802 patients, 190 [11%] with bacteremia) and the validation cohort (134 patients, 17 [13%] with bacteremia) were 0.78 (0.74-0.81) and 0.79 (0.71-0.88), respectively.

CONCLUSIONS

The SADFUL score is a simplified useful tool for predicting bacteremia in patients with unscheduled ED revisits. The scoring model could help ED physicians decrease misidentification of patients at a high risk for bacteremia and potential complications.

Concurrent external validation of bloodstream infection probability models

OBJECTIVE

Accurately estimating the likelihood of bloodstream infection (BSI) can help clinicians make diagnostic and therapeutic decisions. Many multivariate models predicting BSI probability have been published. This study measured the performance of BSI probability models within the same patient sample.

METHODS

We retrieved validated BSI probability models included in a recently published systematic review that returned a patient-level BSI probability for adults. Model applicability, discrimination, and accuracy was measured in a simple random sample of 4485 admitted adults having blood cultures ordered in the emergency department or the initial 48 hours of hospitalization.

RESULTS

Ten models were included (publication years 1991-2015). Common methodological threats to model performance included overfitting and continuous variable categorization. Restrictive inclusion criteria caused seven models to apply to <15% of validation patients. Model discrimination was less than originally reported in derivation groups (median c-statistic 60%, range 48-69). The observed BSI risk frequently deviated from expected (median integrated calibration index 4.0%, range 0.8-12.4). Notable disagreement in expected BSI probabilities was seen between models (median (25th-75th percentile) relative difference between expected risks 68.0% (28.6-113.6%)).

DISCUSSION

In a large randomly selected external validation population, many published BSI probability models had restricted applicability, limited discrimination and calibration, and extensive inter-model disagreement. Direct comparison of model performance is hampered by dissimilarities between model-specific validation groups.

Development of a risk prediction model for bloodstream infection in patients with fever of unknown origin

BACKGROUND

Bloodstream infection (BSI) is a significant cause of mortality among patients with fever of unknown origin (FUO). Inappropriate empiric antimicrobial therapy increases difficulty in BSI diagnosis and treatment. Knowing the risk of BSI at early stage may help improve clinical outcomes and reduce antibiotic overuse.

METHODS

We constructed a multivariate prediction model based on clinical features and serum inflammatory markers using a cohort of FUO patients over a 5-year period by Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression.

RESULTS

Among 712 FUO patients, BSI was confirmed in 55 patients. Five independent predictors available within 24 h after admission for BSI were identified: presence of diabetes mellitus, chills, C-reactive protein level of 50-100 mg/L, procalcitonin > 0.3 ng/mL, neutrophil percentage > 75%. A predictive score incorporating these 5 variables has adequate concordance with an area under the curve of 0.85. The model showed low positive predictive value (22.6%), but excellent negative predictive value (97.4%) for predicting the risk of BSI. The risk of BSI reduced to 2.0% in FUO patients if score < 1.5.

CONCLUSIONS

A simple tool based on 5 variables is useful for timely ruling out the individuals at low risk of BSI in FUO population.

Current Status of Antibiotic Stewardship and the Role of Biomarkers in Antibiotic Stewardship Programs

The importance of antibiotic stewardship is increasingly emphasized in accordance with the increasing incidences of multidrug-resistant organisms and accompanying increases in disease burden. This review describes the obstacles in operating an antibiotic stewardship program (ASP), and whether the use of biomarkers within currently available resources can help. Surveys conducted around the world have shown that major obstacles to ASPs are shortages of time and personnel, lack of appropriate compensation for ASP operation, and lack of guidelines or appropriate manuals. Sufficient investment, such as the provision of full-time equivalent ASP practitioners, and adoption of computerized clinical decision systems are useful measures to improve ASP within an institution. However, these methods are not easy in terms of both time commitments and cost. Some biomarkers, such as C-reactive protein, procalcitonin, and presepsin are promising tools in ASP due to their utility in diagnosis and forecasting the prognosis of sepsis. Recent studies have demonstrated the usefulness of algorithmic approaches based on procalcitonin level to determine the initiation or discontinuation of antibiotics, which would be helpful in decreasing antibiotics use, resulting in more appropriate antibiotics use.

Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study

Introduction: Bacteremia is a common but life-threatening infectious disease. However, a well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is lacking. The aim of this study is to establish a predictive model for bacteremia in septic patients using available big data in the emergency department (ED) through logistic regression and other machine learning (ML) methods. Material and Methods: We conducted a retrospective cohort study at the ED of National Cheng Kung University Hospital in Taiwan from January 2015 to December 2019. ED adults (≥18 years old) with systemic inflammatory response syndrome and receiving blood cultures during the ED stay were included. Models I and II were established based on logistic regression, both of which were derived from support vector machine (SVM) and random forest (RF). Net reclassification index was used to determine which model was superior. Results: During the study period, 437,969 patients visited the study ED, and 40,395 patients were enrolled. Patients diagnosed with bacteremia accounted for 7.7% of the cohort. The area under the receiver operating curve (AUROC) in models I and II was 0.729 (95% CI, 0.718–0.740) and 0.731 (95% CI, 0.721–0.742), with Akaike information criterion (AIC) of 16,840 and 16,803, respectively. The performance of model II was superior to that of model I. The AUROC values of models III and IV in the validation dataset were 0.730 (95% CI, 0.713–0.747) and 0.705 (0.688–0.722), respectively. There is no statistical evidence to support that the performance of the model created with logistic regression is superior to those created by SVM and RF. Discussion: The advantage of the SVM or RF model is that the prediction model is more elastic and not limited to a linear relationship. The advantage of the LR model is that it is easy to explain the influence of the independent variable on the response variable. These models could help medical staff identify high-risk patients and prevent unnecessary antibiotic use. The performance of SVM and RF was not inferior to that of logistic regression. Conclusions: We established models that provide discrimination in predicting bacteremia among patients with sepsis. The reported results could inspire researchers to adopt ML in their development of prediction algorithms.

High prevalence of infections in non-COVID-19 patients admitted to the Emergency Department with severe lymphopenia

Background

In the Emergency Department (ED), early and accurate recognition of infection is crucial to prompt antibiotic therapy but the initial presentation of patients is variable and poorly characterized. Lymphopenia is commonly associated with bacteraemia and poor outcome in intensive care unit patients. The objective of this retrospective study was to assess the prevalence of community-acquired infection in a cohort of unselected patients admitted to the ED with undifferentiated symptoms and severe lymphopenia.

Methods

This is a retrospective single-center study conducted over a 1 year-period before the COVID-19 pandemic. Consecutive adult patients admitted to the ED with severe lymphopenia (lymphocyte count < 0.5 G/L) were studied. Patients with hematological or oncological diseases, HIV infection, hepato-cellular deficiency, immunosuppression, or patients over 85 years old were excluded. Diagnoses of infection were validated by an independent adjudication committee. The association between various parameters and infection was assessed using a multivariate logistic regression analysis.

Results

Of 953 patients admitted to the ED with severe lymphopenia, 245 were studied (148 men; mean age: 63 ± 19 years). Infection was confirmed in 159 patients (65%) (bacterial: 60%, viral: 30%, other: 10%). Only 61 patients (25%) were referred to the ED for a suspected infection. In the univariate analysis, SIRS criteria (OR: 5.39; 95%CI: 3.04–9.70; p < 0.001) and temperature ≥ 38.3 °C (OR: 10.95; 95%CI: 5.39–22.26; p < 0.001) were strongly associate with infection. In the multivariate analysis, only SIRS criteria (OR: 2.4; 95%CI: 1.48–3.9; p < 0.01) and fever (OR: 3.35; 95%CI: 1.26–8.93; p = 0.016) were independently associated with infection.

Conclusions

The prevalence of underlying infection is high in patients admitted to the ED with lymphopenia, irrespective of the reason for admission. Whether lymphopenia could constitute a valuable marker of underlying infection in this clinical setting remains to be confirmed prospectively in larger cohorts.

Trial registration: No registration required as this is a retrospective study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07295-5.

Validation of a predictive model for bacteraemia (MPB5-Toledo) in the patients seen in emergency departments due to infections

OBJECTIVE

To validate a simple risk score to predict bacteremia (MPB5-Toledo) in patients seen in the emergency departments (ED) due to infections.

METHODS

Prospective and multicenter observational cohort study of the blood cultures (BC) ordered in 74 Spanish ED for adults (aged 18 or older) seen from October 1, 2019, to February 29, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the cut-off values chosen for getting the sensitivity, specificity, positive predictive value and negative predictive value.

RESULTS

A total of 3.843 blood samples wered cultured. True cases of bacteremia were confirmed in 839 (21.83%). The remaining 3.004 cultures (78.17%) were negative. Among the negative, 172 (4.47%) were judged to be contaminated. Low risk for bacteremia was indicated by a score of 0-2 points, intermediate risk by 3-5 points, and high risk by 6-8 points. Bacteremia in these 3 risk groups was predicted for 1.5%, 16.8%, and 81.6%, respectively. The model's area under the receiver operating characteristic curve was 0.930 (95% CI, 0.916-0.948). The prognostic performance with a model's cut-off value of ≥5 points achieved 94.76% (95% CI: 92.97-96.12) sensitivity, 81.56% (95% CI: 80.11-82.92) specificity, and negative predictive value of 98.24% (95% CI: 97.62-98.70).

CONCLUSION

The 5MPB-Toledo score is useful for predicting bacteremia in patients attended in hospital emergency departments for infection.

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.

Cost impact analysis of novel host-response diagnostic for patients with community-acquired pneumonia in the emergency department

BACKGROUND

There is significant over-prescription of antibiotics for suspected community-acquired pneumonia (CAP) patients as bacterial and viral pathogens are difficult to differentiate. To address this issue, a host response diagnostic called MeMed BV (MMBV) was developed that accurately differentiates bacterial from viral infection at the point of need by integrating measurements of multiple biomarkers. A literature-based cost-impact model was developed that compared the cost impact and clinical benefits between using the standard of care diagnostics combined with MMBV relative to standard of care diagnostics alone.

METHODS

The patient population was stratified according to the pneumonia severity index, and cost savings were considered from payer and provider perspectives. Four scenarios were considered. The main analysis considers the cost impact of differences in antibiotic stewardship and resulting adverse events. The first, second, and third scenarios combine the impacts on antibiotic stewardship with changes in hospital admission probability, length of hospital stay and diagnosis related group (DRG) reallocation, and hospital admission probability, length of stay, and DRG reallocation in combination, respectively.

RESULTS

The main analysis results show overall per-patient savings of $37 for payers and $223 for providers. Scenarios 1, 2, and 3 produced savings of $137, $189, and $293 for payers, and $339, $713, and $809 for providers, respectively.

LIMITATIONS

Models are simulations of real-world clinical processes, and are not sensitive to variations in clinical practice driven by differences in physician practice styles, differences in facility-level practice patterns, and patient comorbidities expected to exacerbate the clinical impact of CAP. Hospital models are limited to costs and do not consider differences in revenue associated with each approach.

CONCLUSIONS

Introducing MMBV to the current SOC diagnostic process is likely to be cost-saving to both hospitals and payers when considering impacts on antibiotic distribution, hospital admission rate, hospital LOS, and DRG reallocation.

A bacteraemia risk prediction model: development and validation in an emergency medicine population

OBJECTIVE

Design a risk model to predict bacteraemia in patients attended in emergency departments (ED) for an episode of infection.

METHODS

This was a national, prospective, multicentre, observational cohort study of blood cultures (BC) collected from adult patients (≥ 18 years) attended in 71 Spanish EDs from October 1 2019 to March 31, 2020. Variables with a p value < 0.05 were introduced in the univariate analysis together with those of clinical significance. The final selection of variables for the scoring scale was made by logistic regression with selection by introduction. The results obtained were internally validated by dividing the sample in a derivation and a validation cohort.

RESULTS

A total of 4,439 infectious episodes were included. Of these, 899 (20.25%) were considered as true bacteraemia. A predictive model for bacteraemia was defined with seven variables according to the Bacteraemia Prediction Model of the INFURG-SEMES group (MPB-INFURG-SEMES). The model achieved an area under the curve-receiver operating curve of 0.924 (CI 95%:0.914-0.934) in the derivation cohort, and 0.926 (CI 95%: 0.910-0.942) in the validation cohort. Patients were then split into ten risk categories, and had the following rates of risk: 0.2%(0 points), 0.4%(1 point), 0.9%(2 points), 1.8%(3 points), 4.7%(4 points), 19.1% (5 points), 39.1% (6 points), 56.8% (7 points), 71.1% (8 points), 82.7% (9 points) and 90.1% (10 points). Findings were similar in the validation cohort. The cut-off point of five points provided the best precision with a sensitivity of 95.94%, specificity of 76.28%, positive predictive value of 53.63% and negative predictive value of 98.50%.

CONCLUSION

The MPB-INFURG-SEMES model may be useful for the stratification of risk of bacteraemia in adult patients with infection in EDs, together with clinical judgement and other variables independent of the process and the patient.

Clinical factors associated with bloodstream infection at the emergency department

BACKGROUND

Bloodstream infection (BSI) is a common urgent condition at the emergency department (ED). However, current guidelines for diagnosis do not specify the juncture at which blood cultures should be taken. The decision whether or not to obtain hemoculture is based solely upon clinical judgment and potential outcomes of inappropriately ordered cultures. This study aimed to find clinical factors present on ED arrival that are predictive of bloodstream infection.

METHODS

This study was conducted retrospectively at the ED of a single tertiary care hospital in Thailand. We included adult patients with suspected infection based on blood culture who were treated with intravenous antibiotics during their ED visit. Independent positive predictors for positive blood culture were calculated by logistic regression analysis.

RESULTS

A total of 169,578 patients visited the ED during the study period, 12,556 (7.40%) of whom were suspected of infection. Of those, 8177 met the study criteria and were categorized according to blood culture results (741 positive; 9.06%). Six clinical factors, including age over 55 years, moderate to severe CKD, solid organ tumor, liver disease, history of chills, and body temperature of over 38.3 °C, were associated with positive blood culture.

CONCLUSIONS

Clinical factors at ED arrival can be used as predictors of bloodstream infection.

Validation of a predictive model for bacteraemia (MPB5-Toledo) in the patients seen in emergency departments due to infections

OBJECTIVE

To validate a simple risk score to predict bacteremia (MPB5-Toledo) in patients seen in the emergency departments (ED) due to infections.

METHODS

Prospective and multicenter observational cohort study of the blood cultures (BC) ordered in 74 Spanish ED for adults (aged 18 or older) seen from from October 1, 2019, to February 29, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the cut-off values chosen for getting the sensitivity, specificity, positive predictive value and negative predictive value.

RESULTS

A total of 3.843 blood samples wered cultured. True cases of bacteremia were confirmed in 839 (21.83%). The remaining 3.004 cultures (78.17%) were negative. Among the negative, 172 (4.47%) were judged to be contaminated. Low risk for bacteremia was indicated by a score of 0 to 2 points, intermediate risk by 3 to 5 points, and high risk by 6 to 8 points. Bacteremia in these 3 risk groups was predicted for 1.5%, 16.8%, and 81.6%, respectively. The model's area under the receiver operating characteristic curve was 0.930 (95% CI, 0.916-0.948). The prognostic performance with a model's cut-off value of ≥ 5 points achieved 94.76% (95% CI: 92.97-96.12) sensitivity, 81.56% (95% CI: 80.11-82.92) specificity, and negative predictive value of 98.24% (95% CI: 97.62-98.70).

CONCLUSION

The 5MPB-Toledo score is useful for predicting bacteremia in patients attended in hospital emergency departments for infection.

Risk of bacteremia in patients presenting with shaking chills and vomiting - a prospective cohort study

Chills and vomiting have traditionally been associated with severe bacterial infections and bacteremia. However, few modern studies have in a prospective way evaluated the association of these signs with bacteremia, which is the aim of this prospective, multicenter study. Patients presenting to the emergency department with at least one affected vital sign (increased respiratory rate, increased heart rate, altered mental status, decreased blood pressure or decreased oxygen saturation) were included. A total of 479 patients were prospectively enrolled. Blood cultures were obtained from 197 patients. Of the 32 patients with a positive blood culture 11 patients (34%) had experienced shaking chills compared with 23 (14%) of the 165 patients with a negative blood culture, P = 0.009. A logistic regression was fitted to show the estimated odds ratio (OR) for a positive blood culture according to shaking chills. In a univariate model shaking chills had an OR of 3.23 (95% CI 1.35–7.52) and in a multivariate model the OR was 5.9 (95% CI 2.05–17.17) for those without prior antibiotics adjusted for age, sex, and prior antibiotics. The presence of vomiting was also addressed, but neither a univariate nor a multivariate logistic regression showed any association between vomiting and bacteremia. In conclusion, among patients at the emergency department with at least one affected vital sign, shaking chills but not vomiting were associated with bacteremia.

[Predictive factors of bacteraemia in the patients seen in emergency departments due to infections]

Objetivos

Analizar los factores predictivos de bacteriemia en los pacientes atendidos en el servicio de urgencias (SU) por un episodio de infección.

Pacientes y métodos

Estudio observacional, retrospectivo, descriptivo y analítico de todos los hemocultivos extraídos en un SU en los pacientes adultos (≥ 18 años) atendidos por infección desde el 1-1-2018 hasta el 1-7-2018. Se realizó seguimiento durante 30 días. Se analizaron 38 variables independientes (epidemiológicas, de comorbilidad, funcionales, clínicas y analíticas) que pudieran predecir la existencia de bacteriemia. Se realizó un estudio univariado y multivariante mediante regresión logística.

Resultados

Se incluyeron 1.425 episodios de hemocultivos extraídos. De ellos se consideraron como bacteriemias verdaderas 179 (12,6 %) y como HC negativos 1.246 (87,4 %). Entre los negativos, 1.130 (79,3%) no tuvieron crecimiento y 116 (8,1%) se consideraron contaminados. Cinco variables se asociaron de forma significativa como predictoras de bacteriemia verdadera: procalcitonina (PCT) sérica ≥ 0,51 ng/ml [odds ratio (OR): 4,52; intervalo de confianza (IC) al 95%: 4,20-4,84; p <0,001], temperatura > 38,3°C [OR: 1,60; IC al 95%: 1,29-1,90; p <0,001], presión arterial sistólica (PAS) < 100 mmHg [OR: 3,68; IC al 95%: 2,78-4,58; p <0,001], shock séptico [OR: 2,96; IC al 95%: 1,78-4,13; p <0,001] y la existencia de neoplasia [OR: 1,73; IC al 95%: 1,27-2,20; p <0,001].

Conclusiones

. Existen varios factores disponibles tras una primera valoración en el SU, entre ellos la PCT sérica, la temperatura, la hipotensión con/sin criterios de shock séptico y la existencia de neoplasia, que predicen la existencia de bacteriemia verdadera.

Prognostic roles of time to positivity of blood culture in children with Streptococcus pneumoniae bacteremia

We aimed to investigate the relationship between time to positivity (TTP) of blood cultures and clinical outcomes in children with S. pneumoniae bacteremia. Children with S. pneumoniae bacteremia hospitalized in Children's Hospital of Chongqing Medical University from May 2011 to December 2017 were enrolled retrospectively. Overall, 136 children with S. pneumoniae bacteremia were enrolled. The standard cutoff TTP was 12 h. We stated that in-hospital mortality is significantly higher in the early TTP (≤ 12 h) group than that in the late TTP (> 12 h) group (41.70% vs 8.00%, P < 0.001). Septic shock occurred in 58.30% of patients with early TTP and in 21.00% of patients with late TTP (P < 0.001). Independent risk factors of in-hospital mortality and septic shock in children with S. pneumoniae bacteremia included early TTP, need for invasive mechanical ventilation, and PRISM III score ≥ 10. Overall, TTP ≤ 12 h appeared to associate with the worse outcomes for children with S. pneumoniae bacteremia.

Dogma without Data: A Clinical Decision-Making Tool for Postoperative Blood Cultures

Bacteremia is a worrisome postoperative complication and blood cultures (BCx) are often nondiagnostic. We previously reported a 4 per cent overall yield of positive cultures in postoperative patients. To reduce unnecessary testing, we present a predictive model to identify patients in whom growth of pathogens is unlikely and provide a clinical decision-making guide. Retrospective analysis of nonpregnant patients ≥18 years who had BCx within 10 days postoperatively was performed. Generalized linear mixed models identified clinical predictors of high- and low-yield cultures. A clinical algorithm was created using significant predictors, and positive predictive value, negative predictive value, sensitivity, and specificity calculated. Among 1759 BCx, hypotension, maximum temperature ≥101.5 °F within 24 hours of culture, and culture collected after postoperative day (POD) two were statistically significant predictors of positive cultures. Forty nine per cent of BCx were sent ≤ POD 2, and <1 per cent of these were positive. When all three criteria were met, the probability of a positive culture increased to 17 per cent. When absent, the probability of a negative culture was 99 per cent. When applied to the initial data set, the model resulted in 85 per cent reduction of cultures with 9 per cent yield of positive cultures. Drawing BCx based on a single predictor is inadequate. Reducing the number of cultures reflexively ordered within the first two POD could significantly reduce the number of unnecessary BCx. Several clinical features identified patients most likely to have positive BCx within the first 10 POD and could reduce unnecessary BCx. This model should be validated in an independent, prospective cohort.

Procalcitonin as a diagnostic biomarker for septic shock and bloodstream infection in burn patients from the Formosa Fun Coast dust explosion

BACKGROUND/PURPOSE

Infection is the most common cause of death following burn injury. The study was conducted to compare the diagnostic value of serum procalcitonin (PCT) with the other current benchmarks as early predictors of septic shock and bloodstream infection in burn patients.

METHODS

We included 24 patients admitted to the Burn Unit of a medical center from June 2015 to December 2015 from the Formosa Fun Coast dust explosion. We categorized all patients at initial admission into either sepsis or septic shock groups. Laboratory tests including the worst PCT and C-reactive protein (CRP) levels, platelet (PLT), and white blood cell (WBC) count were performed at <48 h after admission. Patients were also classified in two groups with subsequent bacteremia and non-bacteremia groups during hospitalization.

RESULTS

Significantly higher PCT levels were observed among participants with septic shock compared to those with sepsis (47.19 vs. 1.18 ng/mL, respectively; p < 0.001). Patients with bacteremia had significantly elevated PCT levels compared to patients without bacteremia (29.54 versus 1.81 ng/mL, respectively, p < 0.05). No significant differences were found in CRP levels, PLT, and WBC count between the two groups. PCT levels showed reasonable discriminative power (cut-off: 5.12 ng/mL; p = 0.01) in predicting of bloodstream infection in burn patients and the area under receiver operating curves was 0.92.

CONCLUSIONS

PCT levels can be helpful in determining the septic shock and bloodstream infection in burn patients but CRP levels, PLT, and WBC count were of little diagnostic value.

Skin and soft tissue infection management failure in the emergency department observation unit: a systematic review

Introduction

Skin and soft tissue infections (SSTIs) are commonly treated in ED observation units (EDOUs). The management failure rate in this setting is high, as evidenced by a large proportion of patients requiring inpatient admission. This systematic review sought to quantify the management failure rate and identify risk factors associated with management failure.

Methods

Searches of six databases and grey literature were conducted with no limits on publication year or language. Manuscripts describing patients admitted to an EDOU setting (≤24 hours planned admission to EDOU) with a primary diagnosis of cellulitis or other SSTIs were included. Variables associated with failure of management, defined as inpatient admission, stay >28 hours (4 hours in ED, 24 hours in EDOU) or death, were extracted. A narrative description of variables associated with failure of EDOU admission was conducted.

Results

There were 1119 unique articles identified through the literature search. Following assessment, 10 studies were included in the final systematic review, 9 of which reported the management failure rate (range 15%–38%). The presence of fever, a high total white blood cell count and known methicillin-resistant Staphylococcus aureus exposure were the most commonly reported variables associated with management failure.

Conclusion

A higher rate of EDOU management failure in SSTIs than the generally accepted rate of 15% was observed in most studies identified by this review. Risk factors identified were varied, but presence of a fever and elevated inflammatory markers were commonly associated with failure of EDOU admission by multiple studies. Recognition of risk factors and the increased application of clinical decision tools may help to improve disposition of patients at high risk for clinical deterioration or management failure.

Blood cultures and bacteraemia in an Australian emergency department: Evaluating a predictive rule to guide collection and their clinical impact

OBJECTIVE

The objective of the present study is to determine whether a predictive rule could safely reduce the number of negative blood cultures collected in an Australian ED and to assess the clinical impact of positive results from blood cultures taken in the ED.

METHODS

All positive blood cultures taken in the ED at a single facility were retrospectively identified for the calendar year 2012. Clinically significant bacteraemia episodes were assessed against a predictive rule using major and minor clinical and laboratory criteria gathered from medical records and pathology databases, and compared with a randomly generated sample of ED patient episode with negative blood cultures. The ED and final diagnoses and blood culture impact on clinical management were also collected.

RESULTS

The predictive rule has a high sensitivity (98.8%) and modest specificity (48.7%), and if applied stringently would have prevented almost half of all blood cultures in our ED but missed two positives. Blood cultures altered the clinical management of 94.3% bacteraemic patients, representing 3.4% of all ED patients with blood cultures performed. High discordance (54%) between ED diagnosis and discharge diagnosis of bacteraemic patients was noted.

CONCLUSIONS

Bacteraemia detected in the ED alters subsequent patient management. The predictive rule can be safely applied in the ED to determine need for blood culture collection. Blood cultures should not be omitted in the ED based entirely on preliminary diagnosis given the high discordance seen between ED and discharge diagnosis.

Best Clinical Practice: Blood Culture Utility in the Emergency Department

BACKGROUND

Bacteremia affects 200,000 patients per year, with the potential for significant morbidity and mortality. Blood cultures are considered the most sensitive method for detecting bacteremia and are commonly obtained in patients with fever, chills, leukocytosis, focal infections, and sepsis.

OBJECTIVE

We sought to provide emergency physicians with a review of the literature concerning blood cultures in the emergency department.

DISCUSSION

The utility of blood cultures has been a focus of controversy, prompting research evaluating effects on patient management. Bacteremia is associated with increased mortality, and blood cultures are often obtained for suspected infection. False-positive blood cultures are associated with harm, including increased duration of stay and cost. This review suggests that blood cultures are not recommended for patients with cellulitis, simple pyelonephritis, and community-acquired pneumonia, because the chance of a false-positive culture is greater than the prevalence of true positive cultures. Blood cultures are recommended for patients with sepsis, meningitis, complicated pyelonephritis, endocarditis, and health care-associated pneumonia. Clinical prediction rules that predict true positive cultures may prove useful. The clinical picture should take precedence. If cultures are obtained, two bottles of ≥7 mL should be obtained from separate peripheral sites.

CONCLUSIONS

Blood cultures are commonly obtained but demonstrate low yield in cellulitis, simple pyelonephritis, and community-acquired pneumonia. The Shapiro decision rule for predicting true bacteremia does show promise, but clinical gestalt should take precedence. To maximize utility, blood cultures should be obtained before antibiotic therapy begins. At least two blood cultures should be obtained from separate peripheral sites.

Identifying Patients with Bacteremia in Community-Hospital Emergency Rooms: A Retrospective Cohort Study

Objectives

(1) To develop a clinical prediction rule to identify patients with bacteremia, using only information that is readily available in the emergency room (ER) of community hospitals, and (2) to test the validity of that rule with a separate, independent set of data.

Design

Multicenter retrospective cohort study.

Setting

To derive the clinical prediction rule we used data from 3 community hospitals in Japan (derivation). We tested the rule using data from one other community hospital (validation), which was not among the three “derivation” hospitals.

Participants

Adults (age ≥ 16 years old) who had undergone blood-culture testing while in the ER between April 2011 and March 2012. For the derivation data, n = 1515 (randomly sampled from 7026 patients), and for the validation data n = 467 (from 823 patients).

Analysis

We analyzed 28 candidate predictors of bacteremia, including demographic data, signs and symptoms, comorbid conditions, and basic laboratory data. Chi-square tests and multiple logistic regression were used to derive an integer risk score (the “ID-BactER” score). Sensitivity, specificity, likelihood ratios, and the area under the receiver operating characteristic curve (i.e., the AUC) were computed.

Results

There were 241 cases of bacteremia in the derivation data. Eleven candidate predictors were used in the ID-BactER score: age, chills, vomiting, mental status, temperature, systolic blood pressure, abdominal sign, white blood-cell count, platelets, blood urea nitrogen, and C-reactive protein. The AUCs was 0.80 (derivation) and 0.74 (validation). For ID-BactER scores ≥ 2, the sensitivities for derivation and validation data were 98% and 97%, and specificities were 20% and 14%, respectively.

Conclusions

The ID-BactER score can be computed from information that is readily available in the ERs of community hospitals. Future studies should focus on developing a score with a higher specificity while maintaining the desired sensitivity.

Evaluation of procalcitonin, C-reactive protein, interleukin-6 & serum amyloid A as diagnostic biomarkers of bacterial infection in febrile patients

BACKGROUND & OBJECTIVES

Early identification of bacterial infection in patients with fever is important for prompt treatment. However, the available parameters such as C-reactive protein (CRP) and leukocyte counts are not very specific. This study was aimed to assess the diagnostic value of procalcitonin (PCT), CRP, interleukin-6 (IL-6) and serum amyloid A (SAA) for bacterial infection in febrile patients.

METHODS

Serum samples were collected from febrile patients between January and December 2012 and processed for blood cultures. PCT, IL-6, CRP and SAA levels were measured. The patients were divided into three groups according to the final diagnosis: bacteraemia group (group1), bacterial infection with negative blood culture (group 2) and non-bacterial infection group (group 3).

RESULTS

There were significant (P<0.05) difference in the levels of PCT, CRP, IL-6 and SAA among the three groups. The PCT levels of patients with g0 ram-positive bacterial infections were lower than g0 ram-negative bacterial infections (0.53 vs 2.13, P < 0.01). The best cut-off value to detect bacterial infections was 0.26 ng/ml for PCT. PCT, CRP, IL-6 and SAA had areas under the curve of 0.804, 0.693, 0.658 and 0.687, respectively.

INTERPRETATION & CONCLUSIONS

Our results showed PCT as a valuable marker of bacterial infections in febrile patients. PCT was superior to CRP, IL-6 or SAA in the early identification of bacterial infection. More prospective and large scale studies are warranted to confirm these findings.

Blood cultures taken from patients attending emergency departments in South Africa are an important antibiotic stewardship tool, which directly influences patient management

Background

Febrile illness with suspected blood stream infection (BSI) is a common reason for admission to hospital in Africa and blood cultures are therefore an important investigation. Data on the prevalence and causes of community acquired BSI in Africa are scarce and there are no studies from South Africa. There are no validated clinical prediction rules for use of blood cultures in Africa.

Methods

A prospective observational cohort study of patients attending 2 urban emergency departments in Cape Town, South Africa. The decision to take a blood culture was made by the attending clinician and information available at the time of blood draw was collected. Bottles were weighed to measure volume of blood inoculated.

Results

500 blood culture sets were obtained from 489 patients. 39 (7.8 %) were positive for pathogens and 13 (2.6 %) for contaminants. Significant independent predictors of positive cultures were diastolic blood pressure <60 mmHg, pulse >120 bpm, diabetes and a suspected biliary source of infection, but not HIV infection. Positive results influenced patient management in 36 of 38 (95 %) cases with the organism being resistant to the chosen empiric antibiotic in 9 of 38 (24 %). Taking <8 ml of blood was predictive of a negative culture. The best clinical prediction rule had a negative predictive value (NPV) of 92 % which is unlikely to be high enough to be clinically useful.

Discussion

Blood cultures taken from patients attending emergency departments in a high HIV prevalent city in South Africa are frequently positive and almost always influence patient management. At least 8 ml of blood should be inoculated into each bottle.

Conclusion

Blood cultures should be taken from all patients attending EDs in South Africa suspected of having BSI particularly if diabetic, with hypotension, tachycardia or if biliary sepsis is suspected.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-1127-1) contains supplementary material, which is available to authorized users.

Don't miss the diagnosis of sepsis!

The early detection and treatment of sepsis are the most important factors in improving the outcome of patients with this condition. However, many patients admitted to hospital experience a long delay in the diagnosis of sepsis. Furthermore, it is not uncommon for febrile patients to be sent home from the Emergency Department or the physician’s office with the diagnosis of ‘flu’ only to return hours or days later in overt septic shock. The early diagnosis of sepsis may be challenging as many of the signs and symptoms are non-specific. Clinical studies suggest that early diagnosis of sepsis requires a high index of suspicion and comprehensive clinical evaluation together with laboratory tests, including a complete blood count with differential, lactate and procalcitonin levels.

A risk prediction model for screening bacteremic patients: a cross sectional study

Background

Bacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia.

Methods

In this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699) was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286).

Results

The proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694). Applying the A2DE classifier, two models, model 1 (20 variables) and model 2 (10 variables) were established with an area under the receiver operating characteristic curve (ROC-AUC) of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%.

Conclusion

Applying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed for further appreciation of the usefulness of this tool.

Predictive factors of bacteremia in patients with febrile urinary tract infection: an experience at a tertiary care center

PURPOSE

To investigate the clinical features in adult patients with febrile urinary tract infection (UTI) who visited the emergency department (ED) and to determine the predictive factors of bacteremia among the initial presenting clinical features.

METHODS

This retrospective cohort study was conducted at the ED of a tertiary hospital in Korea from 1 January 2012 to 31 December 2012. All adult patients who were diagnosed with febrile UTI and for whom data on blood and urine cultures were available were included in the study. Clinical examinations and laboratory tests were performed at the initial presentation.

RESULTS

A total of the 325 patients with febrile UTI (median age: 60 years) were included for analysis, of whom 82 % were female. Bacteremia was detected in 106 of the 325 patients (32.6 %), with Escherichia coli the most frequent pathogen detected (59.7 % of cases). Between the bacteremic and non-bacteremic groups, there was significant difference in age (67 vs. 57 years, respectively), flank pain (16 vs. 7.8 %), suprapubic discomfort (0 vs. 4.6 %), body temperature (38.8 vs. 38.3 °C), respiratory rate (21 vs. 20/min), platelet count (170 vs. 186 × 10(3)/μL), C-reactive protein (10.2 vs. 8.3 mg/dL), and procalcitonin (1.5 vs. 0.3 ng/mL) (P < 0.05 for all). In the multivariate logistic regression analysis, age [odds ratio (OR) 1.03; 95 % confidence interval (CI) 1.01-1.05], systolic blood pressure of <90 mmHg (OR 3.27; 95 % CI 1.13-9.45), body temperature of >39 °C (OR 4.26; 95 % CI 2.28-7.96), and procalcitonin level of >0.5 ng/dL (OR 2.03; 95 % CI 1.07-3.86) were significantly associated with bacteremia.

CONCLUSION

Among our adult patients with febrile UTI, age, systolic blood pressure, body temperature, and procalcitonin were significantly associated with bacteremia. We therefore suggest that these factors should be considered when deciding upon treatment options for febrile UTI patients at the ED.

Clinical characteristics of children and adults hospitalized for influenza virus infection

BACKGROUND/PURPOSE

Influenza infection has different clinical presentations and outcomes in children and adults, and bacterial coinfection is associated with significantly higher morbidity and mortality. This study compared the clinical features in children and adults hospitalized for influenza virus infection and the role of concomitant bacteremia.

METHODS

A retrospective observational cohort study was conducted by a review of medical records of all consecutive patients admitted for influenza infection between April 1, 2009 and February 28, 2011.

RESULTS

Of the 1203 patients, 76.2% were children, and ranged in age from 1 month to 99 years, with a mortality of 3.1% for adults; no children died. Pneumonia, acute respiratory distress syndrome, acute respiratory failure, septic shock, and cardiovascular complications were more common in adults. Bacteremia was more common in adults than in children (3.5% vs. 0.4%). C-reactive protein (CRP) > 4 mg/dL and a longer hospital stay occurred more often in children with bacteremia than in the group without bacteremia. In adults with bacteremia, acute respiratory failure, septic shock, and cardiovascular complications were more common, with a mortality of 50% versus 1.4% compared with those without bacteremia, and thrombocytopenia and increased CRP were independent risk factors. Using receiver operating characteristic analysis, CRP ≥ 14 mg/dL had a sensitivity of 90.0% and a specificity of 80.0%.

CONCLUSION

Influenza infection in adults is associated with increased risk of complications, bacteremia, and mortality compared with that in children. Bacteremia in adults with influenza is associated with increased complications and mortality; thrombocytopenia and elevated CRP levels could identify those at risk.

Diagnostic challenges and opportunities in older adults with infectious diseases

Infections remain a major threat to the well-being of our growing aged population. The correct and timely diagnosis of infections in older adults is increasingly important in the current age of antimicrobial resistance. Urinary tract infection, pneumonia, and bacteremia present particular challenges. In older patients with bacteremia, blood cultures have comparable yield as compared with younger patients. However, the routine triggers for ordering blood cultures may not be appropriate in older adults. In addition, resistance patterns of isolated pathogens may change with age. The main difficulties in diagnosing urinary tract infections in older adults are caused by an increased prevalence of asymptomatic bacteriuria and frequent use of urinary catheters. However, a combined noninvasive approach that includes history, physical examination, urinary dipstick testing, urine cultures, and simple blood tests can provide direction. In addition, specific guidelines for specific populations are available. In older patients suspected of bacterial pneumonia, bedside pulse oximetry and urinary antigen testing for Streptococcus pneumoniae and Legionella pneumophila provide direction for the clinician. Although infected older adults pose specific and unique diagnostic challenges, a thorough history and physical examination combined with minimally invasive testing will lead to the correct diagnosis in most older adults with infectious diseases, limiting the need for empiric antibiotics in this age group.