• Deep learning
• Ensemble model
• Interpretable prediction model
• Machine learning
• TabNet
• Unilateral pyeloplasty
• Urinary tract infection

• Humans
• Urinary Tract Infections/etiology
• Urinary Tract Infections/diagnosis
• Male
• Female
• Child
• Retrospective Studies
• Postoperative Complications/etiology
• Postoperative Complications/diagnosis
• Machine Learning
• Child, Preschool
• Ureteral Obstruction/surgery
• Infant
• Kidney Pelvis/surgery
• Adolescent
• Urologic Surgical Procedures/adverse effects
• Algorithms

• LCYJ-2023-12 Research Foundation of Capital Institute of Pediatrics
• PX2025046 Beijing Municipal Administration of Hospitals Incubating Program
• 2021RU015 Research Unit of Minimally Invasive Pediatric Surgery on Diagnosis and Treatment, Chinese Academy of Medical Sciences2021RU015

• Antibiotic
• Gut microbiota
• Liver cirrhosis
• Liver disease
• Rifaximin

• Rifaximin/therapeutic use
• Rifaximin/pharmacology
• Humans
• Liver Diseases/drug therapy
• Liver Diseases/microbiology
• Animals
• Gastrointestinal Microbiome/drug effects
• Rifamycins/therapeutic use
• Rifamycins/pharmacology

• Bacteremia
• Carbapenem-resistant Acinetobacter baumannii
• Mortality
• Nosocomial pneumonia
• Risk factor

• Humans
• Retrospective Studies
• Male
• Bacteremia/microbiology
• Bacteremia/epidemiology
• Bacteremia/mortality
• Female
• Acinetobacter baumannii/drug effects
• Risk Factors
• Intensive Care Units
• Acinetobacter Infections/mortality
• Acinetobacter Infections/microbiology
• Acinetobacter Infections/epidemiology
• Acinetobacter Infections/drug therapy
• Middle Aged
• Aged
• Carbapenems/therapeutic use
• Carbapenems/pharmacology
• Hospital Mortality
• Anti-Bacterial Agents/therapeutic use
• Healthcare-Associated Pneumonia/microbiology
• Healthcare-Associated Pneumonia/epidemiology
• Healthcare-Associated Pneumonia/mortality
• Cross Infection/microbiology
• Cross Infection/epidemiology

• Bacteremia
• Chills
• Rigor
• Sepsis
• Shivering

• Humans
• Bacteremia/diagnosis
• Adult
• Chills
• Sensitivity and Specificity

• bacteremia prediction
• simple bacteremia score

• OMV
• activation
• bacteria
• inflammation
• macrophage

• R01 GM127596 NIGMS NIH HHS
• R01 HL142758 NHLBI NIH HHS

• Bactaeremia
• Bloodstream infection
• External validation
• Predictive models
• Systematic review

• Adult
• Humans
• Probability
• Sepsis/diagnosis
• Sepsis/epidemiology
• Bacteremia/diagnosis
• Bacteremia/epidemiology

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.

• bacteremia
• blood culture
• logistic regression
• machine learning
• net reclassification index

• bacteraemia
• pneumococcal pneumonia
• prediction model

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.

Pneumonia is a leading cause of hospitalization and death due to infection worldwide. Streptococcus pneumoniae and Legionella pneumophila remain among the most commonly identified bacterial pathogens. Unfortunately, more than half of all pneumonia cases today lack an etiologic diagnosis due to limitations in traditional microbiological methods like blood and sputum cultures, which are affected by poor sample collection, prior antibiotic administration, and delayed processing. Urinary antigen tests (UATs) for S. pneumoniae and L. pneumophila have emerged as powerful tools for improving the diagnosis of bacterial respiratory infections, enabling physicians to administer early directed therapy and improve antimicrobial stewardship. UATs are simple, rapid, and non-invasive diagnostic tests with high specificity (>90%) and moderate sensitivity (<80%). The potential impact of urinary antigen testing is especially significant for respiratory infections caused by Legionella. While all recommended community-acquired pneumonia (CAP) therapies are adequate for treating pneumococcal pneumonia, only certain antibiotics are effective against Legionella. Delayed therapy for Legionella is associated with worse clinical outcomes, which underscores the importance of rapid diagnostic methods like UATs. Despite their potential impact, current American Thoracic Society and Infectious Diseases Society of America (ATS/IDSA) guidelines argue against the routine use of urinary antigen testing for S. pneumoniae and L. pneumophila, except in patients with severe CAP and those with epidemiological risk factors for Legionella. Further research is necessary to evaluate the impact of early targeted treatment due to positive UAT results, as well as optimal strategies for UAT utilization. The purpose of this review is to summarize the UATs available for bacterial respiratory infections, describe current guidelines on their usage, and assess their impact on clinical outcomes and targeted therapy.

We described the current incidence and risk factors of bacterial co-infection in hospitalized patients with COVID-19.

Observational cohort study was performed at the Hospital Clinic of Barcelona (February 2020–February 2021). All patients with COVID-19 who were admitted for >48 hours with microbiological sample collection and procalcitonin (PCT) determination within the first 48 hours were included.

A total of 1125 consecutive adults met inclusion criteria. Co-infections were microbiologically documented in 102 (9.1%) patients. Most frequent microorganisms were Streptococcus pneumoniae (79%), Staphylococcus aureus (6.8%), and Haemophilus influenzae (6.8%). Test positivity was 1% (8/803) for blood cultures, 10.1% (79/780) for pneumococcal urinary antigen test, and 11.4% (15/132) for sputum culture. Patients with PCT higher than 0.2, 0.5, 1, and 2 ng/mL had significantly more co-infections than those with lower levels (p=0.017, p=0.031, p<0.001, and p<0.001, respectively). In multivariate analysis, oxygen saturation ≤94% (OR 2.47, CI 1.57–3.86), ferritin levels <338 ng/mL (OR 2.63, CI 1.69–4.07), and PCT higher than 0.2 ng/mL (OR 1.74, CI 1.11–2.72) were independent risk factors for co-infection at hospital admission owing to COVID-19.

Bacterial co-infection in patients hospitalized for COVID-19 is relatively common. However, clinicians could spare antibiotics in patients with PCT values <0.2, especially with high ferritin values and oxygen saturation >94%.

• COVID-19
• SARS-CoV-2
• antibiotics
• bacterial infection
• co-infection

Nontyphoidal Salmonellae (NTS) often cause self-limiting gastroenteritis in adults, known as salmonellosis. Salmonellosis has remained controlled in the United States due to intensive measures. Infrequently, these patients develop bacteremia and local or disseminated infections after salmonellosis. NTS endovascular infections are frequent in patients with immunosuppression, indwelling prosthetic vascular grafts, atherosclerotic vascular disease, or aortic aneurysms. NTS endovascular infections are uncommon in immunocompetent adults. Similarly, other focal extraintestinal infections such as pneumonia are also rare. A PubMed review of the medical literature reveals few cases in healthy adults with bacteremia, pneumonia, and acute infectious thoracic aortitis with dissection due to Salmonella typhimurium. We present an elderly White male with salmonellosis followed by S. typhimurium bacteremia with pneumonia and an acute thoracic aortic dissection three weeks later. He was treated successfully with endovascular repair and antibiotics.

• Anti-Bacterial Agents/therapeutic use
• Bacteremia/drug therapy
• Humans
• Pneumonia/drug therapy

• Adult
• Anti-Bacterial Agents/therapeutic use
• Community-Acquired Infections/diagnosis
• Community-Acquired Infections/drug therapy
• Community-Acquired Infections/mortality
• Community-Acquired Infections/prevention & control
• Humans
• Influenza Vaccines/administration & dosage
• Influenza Vaccines/immunology
• Pneumonia/mortality
• Risk Factors
• Severity of Illness Index
• Tomography, X-Ray Computed
• Ultrasonography

• bacteremia
• blood cultures
• indications

• Adult
• Bacteremia/diagnosis
• Blood Culture
• Cellulitis
• Fever
• Humans
• Inpatients
• Retrospective Studies

• diagnostic microbiology
• molecular diagnostics
• respiratory infections

• Adult
• Anti-Bacterial Agents/therapeutic use
• COVID-19
• Clinical Trials, Phase IV as Topic
• Humans
• Molecular Diagnostic Techniques
• Pneumonia/drug therapy
• Randomized Controlled Trials as Topic
• SARS-CoV-2

• Fundació la Marató de TV3

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.

• Bacterial infections
• bloodstream infections
• chills
• emergency department
• septicaemia

• bacteremia
• sepsis
• septic shock
• severe sepsis

An adequate model for predicting bacteraemia has not yet been developed. This study aimed to evaluate the performance of an artificial neural network (ANN)-based prediction model in comparison with previous statistical models. The performance of multi-layer perceptron (MLP), a representative ANN model, was verified via comparison with a non-neural network model. A total of 1260 bacteraemia episodes were identified in 13,402 patients. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUC) of the prediction performance was 0.729 (95% confidence interval [CI]; 0.712–0.728), while in MLP with 256 hidden layer nodes, it was 0.727 (95% CI; 0.713–0.727). In a conventional Bayesian statistical method, the AUC was 0.7. The aforementioned two MLP models exhibited the highest sensitivity (0.810). The ranking of clinical variables was used to describe the influential power of the prediction. Serum alkaline phosphatase was one of the most influential clinical variables, and one-out search was the best ranking method for measuring the influence of the clinical variables. Furthermore, adding variables beyond the 10 top-ranking ones did not significantly affect the prediction of bacteraemia. The ANN model is not inferior to conventional statistical approaches. Bacteraemia can be predicted using only the top 10 clinical variables determined by a ranking method, and the model can be used in clinical practice by applying real-time monitoring.

• artificial intelligence
• artificial neural network
• bacteraemia
• machine learning
• prediction

• albumin: PCT ratio
• nosocomial blood stream infection
• spontaneous intracerebral hemorrhage

• albumin
• diagnostic
• febrile urinary tractinfection
• procalcitonin
• urosepsis

• Adult
• Aged
• Biomarkers
• Calcitonin/blood
• China
• Diagnosis, Differential
• Early Diagnosis
• Female
• Fever/etiology
• Fever/physiopathology
• Humans
• Male
• Middle Aged
• Predictive Value of Tests
• Sensitivity and Specificity
• Serum Albumin/analysis
• Shock, Septic/blood
• Shock, Septic/diagnosis
• Shock, Septic/etiology
• Urinary Tract Infections/complications
• Urinary Tract Infections/diagnosis
• Urinary Tract Infections/physiopathology

• Aged
• Aged, 80 and over
• Anti-Bacterial Agents/therapeutic use
• Bacteremia/diagnosis
• Community-Acquired Infections/diagnosis
• Community-Acquired Infections/mortality
• Controlled Before-After Studies
• Databases, Factual
• Emergency Service, Hospital
• Female
• Humans
• Length of Stay/statistics & numerical data
• Male
• Middle Aged
• Predictive Value of Tests
• Propensity Score
• Retrospective Studies
• Risk Factors

Pneumococcal pneumonia causes high morbidity and mortality among adults. This study aimed to identify risk factors for bacteremic pneumococcal pneumonia, and to construct a prediction model for the development of bacteremia in patients with community-acquired pneumococcal pneumonia.

We retrospectively analyzed data from patients hospitalized with community-acquired pneumococcal pneumonia between April 2007 and August 2015. Logistic regression models were applied to detect risk factors for pneumococcal bacteremia, and a receiver operating characteristic curve was used to devise a prediction model.

Based on the results of sputum cultures, urine antigen tests, and/or blood cultures, 389 patients were diagnosed with pneumococcal pneumonia, 46 of whom had bacteremia. In the multivariate analysis, age < 65 years, serum albumin level < 3.0 g/dL, need for intensive respiratory or vasopressor support (IRVS), and C-reactive protein level > 20 mg/dL were identified as independent risk factors for the development of pneumococcal bacteremia. The bacteremia prediction score based on receiver operating characteristic curve analysis had a sensitivity of 0.74 and a specificity of 0.78 in patients with two risk factors. The area under the receiver operating characteristic curve was 0.77 (95% confidence interval (CI), 0.70–0.85).

Age < 65 years, hypoalbuminemia, IRVS, and high C-reactive protein level on admission are independent risk factors for the development of bacteremia in patients with community-acquired pneumococcal pneumonia. A prediction model based on these four risk factors could help to identify patients with community-acquired pneumococcal pneumonia at high risk of developing bacteremia; this can be used to guide antibiotic choices.

UMIN-CTR UMIN 000004353. Registered 7 October 2010. Retrospectively registered.

• Bacteremia
• Blood culture
• Community-acquired pneumonia
• Pneumococcal pneumonia

• Pneumonia
• antibiotic stewardship
• antibiotic use
• bacterial disease
• pneumonia diagnostics

• Acute kidney injury
• Antibiotics
• Antifungals
• Dosing
• Obesity
• Resistance
• Sepsis

• diagnosis
• microbial etiology
• pneumonia

• bacteremia
• elderly
• emergency department
• old
• systemic inflammatory response syndrome
• young-old

• Adolescent
• Adult
• Age Distribution
• Aged
• Bacteremia/diagnosis
• Bacteremia/epidemiology
• Cause of Death
• Cohort Studies
• Emergency Service, Hospital
• Female
• Hospital Mortality
• Humans
• Incidence
• Male
• Middle Aged
• Retrospective Studies
• Risk Assessment
• Sepsis/diagnosis
• Sepsis/epidemiology
• Sex Distribution
• Systemic Inflammatory Response Syndrome/diagnosis
• Systemic Inflammatory Response Syndrome/epidemiology
• Taiwan/epidemiology
• Young Adult

• bacteremia
• health care
• microbiology
• pneumonia

• Aged
• Aged, 80 and over
• Bacteremia/diagnosis
• Bacteremia/etiology
• Bacteremia/microbiology
• Bacteria/isolation & purification
• Blood Culture/statistics & numerical data
• Community-Acquired Infections/complications
• Community-Acquired Infections/diagnosis
• Community-Acquired Infections/epidemiology
• Female
• Hospital Mortality/trends
• Humans
• Male
• Middle Aged
• Pneumonia, Bacterial/complications
• Pneumonia, Bacterial/diagnosis
• Pneumonia, Bacterial/epidemiology
• Prognosis
• Republic of Korea/epidemiology
• Retrospective Studies
• Severity of Illness Index

(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.

Multicenter retrospective cohort study.

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.

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).

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.

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.

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.

• Aged
• Bacteremia/diagnosis
• Bacteremia/epidemiology
• Demography
• Emergency Service, Hospital/statistics & numerical data
• Female
• Hospitals, Community/statistics & numerical data
• Humans
• Japan/epidemiology
• Male
• Models, Theoretical
• Multivariate Analysis
• ROC Curve
• Reproducibility of Results
• Retrospective Studies

• Cirrhosis
• FXR agonist
• GCSF
• bacterial infections
• bacterial translocation
• beta-blockers
• bile acids
• fecal microbiota transplant
• probiotics
• prokinetics
• rifaximin
• selective intestinal decontamination
• statins

• Animals
• Anti-Bacterial Agents/therapeutic use
• Bacterial Infections/complications
• Bacterial Infections/prevention & control
• Bacterial Translocation
• Gastrointestinal Agents/therapeutic use
• Gastrointestinal Motility
• Humans
• Intestines/microbiology
• Intestines/physiopathology
• Liver Cirrhosis/complications
• Probiotics/therapeutic use
• Rifamycins/therapeutic use
• Rifaximin

• P30 DK34989 NIDDK NIH HHS
• U01 AA021908 NIAAA NIH HHS
• T35 DK104689 NIDDK NIH HHS
• T35DK104689 NIDDK NIH HHS
• P30 DK034989 NIDDK NIH HHS

• Contraindications
• End Stage Liver Disease/diagnosis
• End Stage Liver Disease/etiology
• End Stage Liver Disease/surgery
• Europe
• Humans
• Liver Transplantation/methods
• Patient Selection
• Prognosis
• Risk Adjustment/methods

• C-reactive protein
• blood culture
• community-acquired infection
• kidney transplantation
• white blood cell count

• Adult
• Anti-Bacterial Agents/therapeutic use
• Bacteremia/microbiology
• Bacteria/isolation & purification
• Blood Culture/methods
• C-Reactive Protein/analysis
• Community-Acquired Infections/drug therapy
• Female
• Fever/blood
• Humans
• Immunocompromised Host
• Kidney Transplantation
• Leukocyte Count/methods
• Male
• Microbial Sensitivity Tests
• Middle Aged
• Outpatients
• Retrospective Studies
• Sensitivity and Specificity
• Urinary Tract Infections/microbiology

The diagnostic yield of blood cultures is limited in patients with community-acquired pneumonia (CAP). Yet, positive blood culture results provide important information for antibiotic treatment and for monitoring epidemiologic trends. We investigated the potential of clinical predictors to improve the cost-benefit ratio of obtaining blood cultures.

Data from two prospective cohort studies of adults with suspected CAP, admitted to non-ICU wards, were combined. Two models were created, one using readily available parameters and one additionally including laboratory parameters.

3,786 patients were included (2,626 (69%) with X-ray confirmed CAP). Blood cultures were obtained from 2,977 (79%) patients (and from 2,107 (80%) with X-ray confirmed CAP). 266 (8.9%) of the patients with a blood culture had bacteraemia. Clinical predictors of bacteraemia were absence of pre-admission antibiotic treatment, pleuritic pain, gastro-intestinal symptoms, tachycardia, tachypnea, hypotension and absence of hypoxia. After including laboratory results in the model, younger age, C-reactive protein, leukocytosis or leukopenia, low thrombocyte count, low sodium level, elevated urea and elevated arterial pH were added, while gastro-intestinal symptoms and hypotension were no longer significant. The area under the receiver operating characteristics curve was 0.66 (95% confidence interval 0.63–0.70) for the first model and 0.76 (95% confidence interval 0.73–0.79) for the second model.

In conclusion, in patients hospitalized with CAP, bacteraemia was moderately predictable using clinical parameters only. We recommend against the use of a risk prediction model for the decision to obtain blood cultures.

• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Antigens, Bacterial/analysis
• Bacteremia/diagnosis
• Bacteriological Techniques/methods
• Diagnostic Tests, Routine/methods
• False Positive Reactions
• Female
• Humans
• Male
• Middle Aged
• Pneumococcal Infections/diagnosis
• Prospective Studies
• Sensitivity and Specificity
• Urine/chemistry
• Young Adult

• Bacterial bloodstream infections
• blood cultures
• prediction models
• sepsis
• validation

• Bacteremia/diagnosis
• Blood/microbiology
• Decision Support Techniques
• Humans
• Microbiological Techniques/methods