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

Added Value of Shaking Chills for Predicting Bacteremia in Patients with Suspected Infection

BACKGROUND

Detailed grading of chills is more useful for diagnosing bacteremia than simply classifying the presence or absence of chills. However, its value added to other clinical information has not been evaluated.

OBJECTIVE

To evaluate the value of adding chills grading to other clinical information compared to simply noting the presence or absence of chills for predicting bacteremia in patients with suspected infection.

DESIGN

Prospective observational study.

PARTICIPANTS

Adult patients admitted to two acute-care hospitals with suspected infection from April 2018 to March 2019.

MAIN MEASURES

Two types of categorization for chills were applied: "presence" or "absence" (dichotomized chills); and "no chills", "mild/moderate chills", and "shaking chills" (trichotomized chills). Three multivariable logistic regression models incorporating each of dichotomized chills, trichotomized chills, and C-reactive protein (CRP) with other clinical information were developed and compared. To assess the potential consequences of using each model to identify patients with high risk of bacteremia (i.e., requiring prompt intervention), we applied a cut-off point of an estimated probability of 60%. The number of patients with bacteremia correctly identified by each model was compared.

KEY RESULTS

Among the 2,013 patients, 327 (16.2%) were diagnosed with bacteremia. The three models showed comparable discrimination and calibration performance. At the 60% cut-off, the dichotomized chills model correctly identified 11 patients (3.4% [95% confidence interval (CI) 1.9-3.4] of patients with bacteremia). The trichotomized chills model and CRP model correctly identified an additional 15 patients (4.6% [95% CI 2.8-7.4]) and 2 patients (0.6% [95% CI 0.1-2.3]) with bacteremia, respectively.

CONCLUSIONS

Differentiating shaking chills in comparison with dichotomized chills for predicting bacteremia allowed the correct identification of an additional 4.6% of patients with bacteremia. Detailed grading of chills can be assessed without additional time, cost, or burden on patients and can be recommended in the routine history taking.

Clinical predictors of hospital-acquired bloodstream infections: A healthcare system analysis

INTRODUCTION

This study was performed to identify patient factors associated with hospital-acquired bloodstream infections (HABSI) to guide blood culture collection and empiric antibiotic therapy.

METHODS

A retrospective case-control study reviewed the medical records of 350 patients admitted to our health system from September 2017 to April 2020. The patients were 18 years and older and had at least one set of new positive non-contaminant blood cultures collected after 48 hours of admission, defined as HABSI. We developed clinical variables through a literature review associated with it. Univariate relationships between each variable and bacteremia were evaluated by chi-square test. A predictive model was developed through stepwise multivariate logistic regression.

RESULTS

The univariate analysis and stepwise regression analysis showed that temperature >100.4° F (OR: 1.9, CI 1.1 to 3.4), male sex (OR: 1.8, CI 1.0 to 3.0), and platelet count <150,000/µL (OR: 1.8, CI 1.0 to 3.2) were statistically associated with a positive blood culture.

CONCLUSIONS

This model helps identify patients with clinical characteristics associated with the likelihood of HABSI. This model can help guide the appropriate initiation of empiric antibiotics in clinical situations and assist with antibiotic stewardship.

Timing and prediction of secondary bacteremia in patients with COVID-19: A retrospective cohort study

Background

We aimed to aid the appropriate use of antimicrobial agents by determining the timing of secondary bacteremia and validating and updating clinical prediction models for bacteremia in patients with COVID-19.

Methods

We performed a retrospective cohort study on all hospitalized patients diagnosed with COVID-19 who underwent blood culture tests from January 1, 2020, and September 30, 2021, at an urban teaching hospital in Japan. The primary outcome measure was secondary bacteremia in patients with COVID-19.

Results

Of the 507 patients hospitalized with COVID-19, 169 underwent blood culture tests. Eleven of them had secondary bacteremia. The majority of secondary bacteremia occurred on or later than the 9th day after symptom onset. Positive blood culture samples collected on day 9 or later after disease onset had an odds ratio of 22.4 (95% CI 2.76-181.2, p < 0.001) compared with those collected less than 9 days after onset. The area under the receiver operating characteristic curve of the modified Shapiro rule combined with blood culture collection on or after the 9th day from onset was 0.919 (95% CI, 0.843-0.995), and the net benefit was high according to the decision curve analysis.

Conclusions

The timings of symptom onset and hospital admission may be valuable indicators for making a clinical decision to perform blood cultures in patients hospitalized with COVID-19.

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.

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.

Evaluating Clinical Prediction Rules for Bacteremia Detection in the Emergency Department: A Retrospective Review

BACKGROUND

Bacteremia is a major cause of morbidity. Blood cultures are the gold standard for diagnosing bacteremia.

OBJECTIVE

To compare previously published clinical decision rules for predicting a true positive blood culture (bacteremia) in the emergency department.

METHODS

Retrospective analysis of medical records of patients who had a blood culture performed in a tertiary hospital emergency department in 2020 (12 months). Positive blood cultures were compared with randomly selected negative blood cultures (1:4 ratio). Blood cultures were analyzed per patient presentation. Clinical data from patient presentations were extracted and appraised against the modified-Shapiro (mShapiro) rule and systemic inflammatory response syndrome (SIRS) criteria to calculate diagnostic accuracy to detect bacteremia.

RESULTS

During the study period, 3870 blood cultures were taken from 2921 patients: 476 (12.3%) cultures were positive for bacterial growth, from 421 individual patient presentations (10 excluded as incomplete data). Of included patients, 338 were true positives and 73 contaminates, these were compared with 1446 patients with negative blood culture presentations. Evaluating mShapiro's rule and SIRS criteria to detect bacteremia vs. no bacteremia (negative + contaminated cultures) had a sensitivity of 94.4% (95% confidence interval [CI] 91.4-96.4%) and 84.9% (95% CI 80.7-88.3%), respectively, and a specificity of 37.9% (95% CI 35.5-40.1%) and 33.8% (95% CI 31.5-36.3%), respectively. Both had a high negative predictive value for bacteremia of 96.8% (95% CI 95.1-98.0) and 91.0% (95% CI 88.3-93.1) for mShapiro's rule and SIRS criteria, respectively.

CONCLUSIONS

In this cohort, mShapiro's rule performed better than the SIRS criteria at predicting bacteremia.

External validation of ID-BactER and Shapiro scores for predicting bacteraemia in the emergency department

Introduction

The blood culture positivity rate in the emergency department (ED) is <20%; however, the mortality associated with Community-acquired bacteraemia (CAB) is as high as 37.8%. For this reason, several models have been developed to predict blood culture positivity for the diagnosis of CAB.

Objective

To validate two bacteraemia prediction models in a high-complexity hospital in Colombia.

Design

External validation study of the ID-BactER and Shapiro scores based on a consecutive cohort of patients who underwent blood culture within 48 h of ED admission.

Methods

Scale calibration was assessed by comparing expected and observed events (calibration belt). Discriminatory ability was assessed by area under the ROC curve (AUC-ROC).

Results

We included 1347 patients, of whom 18.85% were diagnosed with CAB. The most common focus of infection was the respiratory tract (36.23%), and the most common microorganism was Escherichia coli (52.15%). The Shapiro score underestimated the risk in all categories and its discriminatory ability was poor (AUC 0.68 CI 95% 0.64-0.73). In contrast, the ID-BactER score showed an adequate observed/expected event ratio of 1.07 (CI 0.85-1.36; p = 0.018) and adequate calibration when expected events were greater than 20%, in addition to good discriminatory ability (AUC 0.74 95% CI 0.70-0.78).

Conclusion

The Shapiro score is not calibrated, and its discriminatory ability is poor. ID-BactER has an adequate calibration when the expected events are higher than 20%. Limiting blood culture collection to patients with an ID-BactER score ⩾4 could reduce unnecessary blood culture collection and thus health care costs.

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.

Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty

Prediction of bacteremia is a clinically important but challenging task. An artificial intelligence (AI) model has the potential to facilitate early bacteremia prediction, aiding emergency department (ED) physicians in making timely decisions and reducing unnecessary medical costs. In this study, we developed and externally validated a Bayesian neural network-based AI bacteremia prediction model (AI-BPM). We also evaluated its impact on physician predictive performance considering both AI and physician uncertainties using historical patient data. A retrospective cohort of 15,362 adult patients with blood cultures performed in the ED was used to develop the AI-BPM. The AI-BPM used structured and unstructured text data acquired during the early stage of ED visit, and provided both the point estimate and 95% confidence interval (CI) of its predictions. High AI-BPM uncertainty was defined as when the predetermined bacteremia risk threshold (5%) was included in the 95% CI of the AI-BPM prediction, and low AI-BPM uncertainty was when it was not included. In the temporal validation dataset (N = 8,188), the AI-BPM achieved area under the receiver operating characteristic curve (AUC) of 0.754 (95% CI 0.737-0.771), sensitivity of 0.917 (95% CI 0.897-0.934), and specificity of 0.340 (95% CI 0.330-0.351). In the external validation dataset (N = 7,029), the AI-BPM's AUC was 0.738 (95% CI 0.722-0.755), sensitivity was 0.927 (95% CI 0.909-0.942), and specificity was 0.319 (95% CI 0.307-0.330). The AUC of the post-AI physicians predictions (0.703, 95% CI 0.654-0.753) was significantly improved compared with that of the pre-AI predictions (0.639, 95% CI 0.585-0.693; p-value < 0.001) in the sampled dataset (N = 1,000). The AI-BPM especially improved the predictive performance of physicians in cases with high physician uncertainty (low subjective confidence) and low AI-BPM uncertainty. Our results suggest that the uncertainty of both the AI model and physicians should be considered for successful AI model implementation.

Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments

BACKGROUND

Bacteremia is a life-threatening complication of infectious diseases. Bacteremia can be predicted using machine learning (ML) models, but these models have not utilized cell population data (CPD).

METHODS

The derivation cohort from emergency department (ED) of China Medical University Hospital (CMUH) was used to develop the model and was prospectively validated in the same hospital. External validation was performed using cohorts from ED of Wei-Gong Memorial Hospital (WMH) and Tainan Municipal An-Nan Hospital (ANH). Adult patients who underwent complete blood count (CBC), differential count (DC), and blood culture tests were enrolled in the present study. The ML model was developed using CBC, DC, and CPD to predict bacteremia from positive blood cultures obtained within 4 h before or after the acquisition of CBC/DC blood samples.

RESULTS

This study included 20,636 patients from CMUH, 664 from WMH, and 1622 patients from ANH. Another 3143 patients were included in the prospective validation cohort of CMUH. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.844 in the derivation cross-validation, 0.812 in the prospective validation, 0.844 in the WMH external validation, and 0.847 in the ANH external validation. The most valuable predictors of bacteremia in the CatBoost model were the mean conductivity of lymphocytes, nucleated red blood cell count, mean conductivity of monocytes, and neutrophil-to-lymphocyte ratio.

CONCLUSIONS

ML model that incorporated CBC, DC, and CPD showed excellent performance in predicting bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

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.

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.

Diagnostic accuracy of quick SOFA score and inflammatory biomarkers for predicting community-onset bacteremia

The potential use of quick SOFA (qSOFA) score and inflammatory biomarkers as bacteremia predictors is unelucidated. Herein the aim of this study was to evaluate the diagnostic accuracy of the qSOFA score and biomarkers for predicting community-onset bacteremia. We enrolled adult outpatients with blood culture samples drawn between 2018 and 2020. Contamination, intensive care unit admission, and hemodialysis were excluded. We performed a case-control study, and analyzed 115 patients (58 with bacteremia and 57 without bacteremia). The positive likelihood ratio (LR) for bacteremia was 2.46 (95% confidence interval [CI] 0.76–9.05) for a qSOFA score ≥ 2, and 4.07 (95% CI 1.92–9.58) for tachypnea (≥ 22/min). The highest performing biomarkers were procalcitonin (area under the curve [AUC] 0.80; 95% CI 0.72–0.88), followed by presepsin (AUC 0.69; 95% CI 0.60–0.79), and C-reactive protein (AUC 0.60; 95% CI 0.49–0.70). The estimated optimal cut-off value of procalcitonin was 0.377 ng/mL, with a sensitivity of 74.1%, a specificity of 73.7%, and a positive LR of 2.82. Presepsin was 407 pg/mL, with a sensitivity of 60.3%, a specificity of 75.4%, and a positive LR of 2.46. Procalcitonin was found to be a modestly useful biomarker for predicting non-severe community-onset bacteremia. Tachypnea (≥ 22/min) itself, rather than the qSOFA score, can be a diagnostic predictor. These predictors may aid decision-making regarding the collection of blood culture samples in the emergency department and outpatient clinics.

Bandemia as an Early Predictive Marker of Bacteremia: A Retrospective Cohort Study

This single-center retrospective observational study aimed to verify whether a diagnosis of bandemia could be a predictive marker for bacteremia. We assessed 970 consecutive patients (median age 73 years; male 64.8%) who underwent two or more sets of blood cultures between April 2015 and March 2016 in both inpatient and outpatient settings. We assessed the value of bandemia (band count > 10%) and the percentage band count for predicting bacteremia using logistic regression models. Bandemia was detected in 151 cases (15.6%) and bacteremia was detected in 188 cases (19.4%). The incidence of bacteremia was significantly higher in cases with bandemia (52.3% vs. 13.3%; odds ratio (OR) = 7.15; 95% confidence interval (CI) 4.91–10.5). The sensitivity and specificity of bandemia for predicting bacteremia were 0.42 and 0.91, respectively. The bandemia was retained as an independent predictive factor for the multivariable logistic regression model (OR, 6.13; 95% CI, 4.02–9.40). Bandemia is useful for establishing the risk of bacteremia, regardless of the care setting (inpatient or outpatient), with a demonstrable relationship between increased risk and bacteremia. A bandemia-based electronic alert for blood-culture collection may contribute to the improved diagnosis of bacteremia.

Bacteremia in Adults Admitted from the Emergency Department with Laboratory-Confirmed Respiratory Syncytial Virus

BACKGROUND

Collecting blood cultures from patients admitted from the emergency department (ED) with acute respiratory infection (ARI) is common, but the rate of secondary bacteremia in adult patients admitted from the ED with ARI associated with respiratory syncytial virus (RSV) is unknown. Indiscriminate collection of blood cultures can be associated with contaminated blood cultures and increased inappropriate antimicrobial use and health care costs.

OBJECTIVE

This study sought to determine the rate and etiology of secondary bacteremia, factors associated with secondary bacteremia, and factors associated with collecting blood cultures in the ED, in adults hospitalized with RSV.

METHODS

We performed a retrospective substudy using data from a prospective study of adults admitted with RSV infections during two respiratory seasons (October 2017 to April 2018 and October 2018 to April 2019). Blood cultures were collected at the discretion of ED providers. We compared demographic and clinical characteristics among those with and without secondary bacteremia and among those with and without blood cultures collected using multivariate logistic regression models.

RESULTS

Of the 365 hospitalized RSV-positive patients (mean age 68.8 years), 269 (73.7%) had blood cultures collected in the ED and 18 (6.7%) patients had secondary bacteremia, most commonly from a nonrespiratory source (n = 13). Patients with asthma and chronic obstructive pulmonary disease were significantly less likely to have secondary bacteremia. Patients who were immunocompromised, met systemic inflammatory response syndrome criteria, or had pneumonia described on chest x-ray reports were more likely to have blood cultures collected.

CONCLUSIONS

Overall, 6.7% of adults hospitalized with RSV infections had secondary bacteremia, more commonly from nonrespiratory sources.

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.

Diagnostic performance of food consumption for bacteraemia in patients admitted with suspected infection: a prospective cohort study

OBJECTIVES

A previous study reported that food consumption is useful to rule out bacteraemia in hospitalised patients. We aimed to validate the diagnostic performance of (1) food consumption and (2) a previously reported algorithm using food consumption and shaking chills for bacteraemia in patients admitted to hospital with suspected infection.

DESIGN

Prospective cohort study.

SETTING

Department of General Medicine in two acute care hospitals in Japan.

PARTICIPANTS

A total of 2009 adult patients who underwent at least two blood cultures on admission.

PRIMARY OUTCOME MEASURES

The reference standard for bacteraemia was judgement by two independent specialists of infectious diseases. Food consumption was evaluated by the physician in charge asking the patient or their caregivers the following question on admission: 'What percentage of usual food intake were you able to eat during the past 24 hours?'

RESULTS

Among 2009 patients, 326 patients were diagnosed with bacteraemia (16.2%). Diagnostic performance of food consumption was sensitivity of 84.4% (95% CI 80.1 to 88), specificity of 19.8% (95% CI 18 to 21.8), positive predictive value (PPV) of 16.9% (95% CI 15.2 to 18.9) and negative predictive value (NPV) of 86.8% (95% CI 83.1 to 89.8). The discriminative performance was an area under the curve of 0.53 (95% CI 0.50 to 0.56). The performance of the algorithm using food consumption and shaking chills was sensitivity of 89% (95% CI 85.1 to 91.9), specificity of 18.8% (95% CI 17 to 20.7), PPV of 17.5% (95% CI 15.7 to 19.4) and NPV of 89.8% (95% CI 86.2 to 92.5).

CONCLUSION

Our results did not show the usefulness of food consumption and the algorithm using food consumption and shaking chills for the diagnosis of bacteraemia in patients admitted to hospital with suspected infection.

Comparison of Systemic Inflammatory Response Syndrome and quick Sequential Organ Failure Assessment scores in predicting bacteremia in the emergency department

Aim

The emergency department requires simple and useful clinical indicators to identify bacteremia. This retrospective study explored the Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA) scores for predicting bacteremia.

Methods

Between April and September 2017, we assessed blood cultures of 307 patients in our emergency department. We calculated the SIRS and qSOFA scores for these patients and evaluated their correlation with bacteremia.

Results

Of 307 patients, 66 (21.5%) had bacteremia, 237 (77.2%) were SIRS-positive, and 123 (40.0%) were qSOFA-positive. The sensitivity and specificity of the SIRS score for predicting bacteremia were 87.9% and 25.7%, respectively. The sensitivity and specificity of the qSOFA score were 47.0% and 61.8%, respectively. Multivariate analysis revealed that body temperature (odds ratio, 2.16; 95% confidence interval, 1.22-3.84; P = 0.009) and blood pressure (odds ratio, 2.72; 95% confidence interval, 1.39-5.35; P = 0.004) significantly associated with bacteremia.

Conclusions

The SIRS score was a more sensitive indicator than the qSOFA score for predicting bacteremia.

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.

Added value of clinical prediction rules for bacteremia in hemodialysis patients: An external validation study

Introduction

Having developed a clinical prediction rule (CPR) for bacteremia among hemodialysis (HD) outpatients (BAC-HD score), we performed external validation.

Materials & methods

Data were collected on maintenance HD patients at two Japanese tertiary-care hospitals from January 2013 to December 2015. We enrolled 429 consecutive patients (aged ≥ 18 y) on maintenance HD who had had two sets of blood cultures drawn on admission to assess for bacteremia. We validated the predictive ability of the CPR using two validation cohorts. Index tests were the BAC-HD score and a CPR developed by Shapiro et al. The outcome was bacteremia, based on the results of the admission blood cultures. For added value, we also measured changes in the area under the receiver operating characteristic curve (AUC) using logistic regression and Net Reclassification Improvement (NRI), in which each CPR was added to the basic model.

Results

In Validation cohort 1 (360 subjects), compared to a Model 1 (Basic Model) AUC of 0.69 (95% confidence interval [95% CI]: 0.59–0.80), the AUC of Model 2 (Basic model + BAC-HD score) and Model 3 (Basic model + Shapiro’s score) increased to 0.8 (95% CI: 0.71–0.88) and 0.73 (95% CI: 0.63–0.83), respectively. In validation cohort 2 (96 subjects), compared to a Model 1 AUC of 0.81 (95% CI: 0.68–0.94), the AUCs of Model 2 and Model 3 increased to 0.83 (95% CI: 0.72–0.95) and 0.85 (95% CI: 0.76–0.94), respectively. NRIs on addition of the BAC-HD score and Shapiro’s score were 0.3 and 0.06 in Validation cohort 1, and 0.27 and 0.13, respectively, in Validation cohort 2.

Conclusion

Either the BAC-HD score or Shapiro’s score may improve the ability to diagnose bacteremia in HD patients. Reclassification was better with the BAC-HD score.

The Shapiro-Procalcitonin algorithm (SPA) as a decision tool for blood culture sampling: validation in a prospective cohort study

PURPOSE

Blood cultures (BC) are the gold standard for bacteremia detection despite a relatively low diagnostic yield and high costs. A retrospective study reported high predictive values for BC positivity when combining the clinical Shapiro score with procalcitonin (PCT).

METHODS

Single-center, prospective cohort study between 01/2016 and 02/2017 to validate SPA algorithm, including a modified Shapiro score ≥ 3 points (S) PLUS admission PCT > 0.25 µg/l (P), or presence of overruling safety criteria (A) in patients with systemic inflammatory response syndrome. The diagnostic yield of SPA compared to non-standardized clinical judgment in predicting BC positivity was calculated and results presented as odds ratios (OR) with 95% confidence intervals.

RESULTS

Of 1438 patients with BC sampling, 215 (15%) had positive BC which increased to 31% (173/555) in patients fulfilling SP criteria (OR for BC positivity 9.07 [6.34-12.97]). When adding 194 patients with overruling safety criteria (i.e., SPA), OR increased to 11.12 (6.99-17.69), although BC positivity slightly decreased to 26%. With an area under the receiver operating curve of 0.742, SPA indicated better diagnostic performance than its individual components. Positive BC in 689 patients not fulfilling SPA (sampling according to non-standardized clinical judgment) were rare (3%; OR for BC positivity 0.09 [0.06-0.14]). Eight out of 21 missed pathogens were still identified by sampling the primary infection focus.

CONCLUSIONS

This study validates the high predictive value of SPA for bacteremia, increasing true BC positivity from 15 to 26%. Restricting BC sampling to SPA would have reduced BC sampling by 48%, while still detecting 194/215 organisms (90%), which makes SPA a valuable diagnostic stewardship tool.

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.

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.

Strategies for increasing diagnostic yield of community-onset bacteraemia within the emergency department: A retrospective study

Bloodstream infections (BSI) are associated with high mortality. Therefore, reliable methods of detection are of paramount importance. Efficient strategies to improve diagnostic yield of bacteraemia within the emergency department (ED) are needed. We conducted a retrospective analysis of all ED encounters in a high-volume, city-centre university hospital within Germany during a five-year study period from October 2013 to September 2018. A time-series analysis was conducted for all ED encounters in which blood cultures (BCs) were collected. BC detection rates and diagnostic yield of community-onset bacteraemia were compared during the study period (which included 45 months prior to the start of a new diagnostic Antibiotic Stewardship (ABS) bundle and 15 months following its implementation). BCs were obtained from 5,191 out of 66,879 ED admissions (7.8%). Bacteraemia was detected in 1,013 encounters (19.5% of encounters where BCs were obtained). The overall yield of true bacteraemia (defined as yielding clinically relevant pathogens) was 14.4%. The new ABS-related diagnostic protocol resulted in an increased number of hospitalised patients with BCs collected in the ED (18% compared to 12.3%) and a significant increase in patients with two or more BC sets taken (59% compared to 25.4%), which resulted in an improved detection rate of true bacteraemia (2.5% versus 1.8% of hospital admissions) without any decrease in diagnostic yield. This simultaneous increase in BC rates without degradation of yield was a valuable finding that indicated success of this strategy. Thus, implementation of the new diagnostic ABS bundle within the ED, which included the presence of a skilled infectious disease (ID) team focused on obtaining BCs, appeared to be a valuable tool for the accurate and timely detection of community-onset bacteraemia.

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.

Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study

Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (80:20 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC: 0.729, 95%CI: 0.679–0.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI: 0.679–0.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT.

Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings

Background

To our knowledge, no reliable clinical prediction rule (CPR) for identifying bacteremia in hemodialysis (HD) patients has been established. The aim of this study was to develop a CPR for bacteremia in maintenance HD patients visiting the outpatient department.

Methods

This multicenter cohort study involved consecutive maintenance HD patients who visited the outpatient clinic or emergency room of seven Japanese institutions between August 2011 and July 2013. The outcome measure was bacteremia diagnosed based on the results of blood cultures. The candidate predictors for bacteremia were extracted through a literature review. A CPR for bacteremia was developed using a coefficient-based multivariable logistic regression scoring method, and calibration was performed. The test performance was then assessed for the CPR.

Results

Of 507 patients eligible for the study, we analyzed the 293 with a complete dataset for candidate predictors. Of these 293 patients, 48 (16.4%) were diagnosed with bacteremia. At the conclusion of the deviation process, body temperature ≥ 38.3°C, heart rate ≥ 125 /min, C-reactive protein ≥ 10 mg/dL, alkaline phosphatase >360 IU/L, and no prior antibiotics use within the past week were retained and scored. The CPR had a good fit for the model on calibration. The AUC of the CPR was 0.76, and for score CPR ≥ 2, the sensitivity and specificity were 89.6% and 51.4%, respectively.

Conclusions

We established a simple CPR for bacteremia in maintenance HD patients using routinely obtained clinical information in an outpatient setting. This model may facilitate more appropriate clinical decision making.