Mobiluncus curtisii Bacteremia: Case Study and Literature Review

Background: There are few reports of bacteremia caused by Mobiluncus curtisii in the literature. We present a review of the literature in addition to a case study. Method: We describe the case of an 82-year-old patient who underwent gastrointestinal surgery and subsequently presented with dehydration, nausea, and hyperkalemia secondary to diarrhea. Further clinical work included blood cultures, and the patient was started empirically on piperacillin/tazobactam. Results: After five days, the blood culture bottle showed growth of a gram-variable, curved rod-shaped organism. After culture under anaerobic conditions on sheep blood agar, the organism was identified as Mobiluncus curtisii by MALDI-TOF mass spectrometry and enzymatic technology. A review of the literature reveals five additional cases of Mobiluncus curtisii bacteremia. Conclusions: This is the sixth case in the literature describing Mobiluncus species bacteremia. This organism is rarely identified in blood culture and is most often thought of in the context of bacterial vaginosis. However, the reported cases of bacteremia show gastrointestinal symptoms and presumed gastrointestinal source of infection. The pathogenesis of infection of this organism requires further investigation.

C-Reactive Protein Monitoring and Clinical Presentation of Fever as Predictive Factors of Prolonged Febrile Neutropenia and Blood Culture Positivity after Autologous Hematopoietic Stem Cell Transplantation—Single-Center Real-Life Experience

Background: Febrile neutropenia (FN) is a medical emergency that requires urgent evaluation, timely administration of empiric broad-spectrum antibiotics and careful monitoring in order to optimize the patient’s outcome, especially in the setting of both allogeneic and autologous hematopoietic stem cell transplant (ASCT). Methods: In this real-life retrospective study, a total of 49 consecutive episodes of FN were evaluated in 40 adult patients affected by either multiple myeloma (thirty-eight) or lymphoma (eleven), following ASCT, with nine patients having fever in both of the tandem transplantations. Results: Febrile neutropenia occurred a median of 7 days from ASCT. Median duration of FN was 2 days, with 25% of population that had fever for at least four days. Ten patients had at least one fever spike superior to 39 °C, while the median number of daily fever spikes was two. Twenty patients had positive blood cultures with XDR germs, namely Pseudomonas aeruginosa and Klebsiella pneumoniae, present in seven cases. ROC analysis of peak C-reactive protein (CRP) values was conducted based on blood culture positivity and a value of 12 mg/dL resulted significant. Onset of prolonged fever with a duration greater than 3 days was associated with the presence of both a peak number of three or more daily fever spikes (p = 0.02) and a body temperature greater than 39 °C (p = 0.04) based on odds ratio (OR). Blood culture positivity and peak CRP values greater than 12 mg/dL were also associated with prolonged fever duration, p = 0.04, and p = 0.03, respectively. The probability of blood culture positivity was also greater in association with fever greater than 39 °C (p = 0.04). Furthermore, peak CRP values below the cut-off showed less probability of positive blood culture (p = 0.02). Conclusions: In our study, clinical characteristics of fever along with peak CRP levels were associated with a higher probability of both prolonged fever duration and positive blood culture, needing extended antibiotic therapy.

Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time

Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617–0.7623) and 0.753 (95% CI; 0.7520–0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388–0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.

Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model

Objective This study was performed to identify predictive factors for bacteremia among patients with pyelonephritis using a chi-square automatic interaction detector (CHAID) decision tree analysis model. Methods This retrospective cross-sectional survey was performed at Juntendo University Nerima Hospital, Tokyo, Japan and included all patients with pyelonephritis from whom blood cultures were taken. At the time of blood culture sample collection, clinical information was extracted from the patients’ medical charts, including vital signs, symptoms, laboratory data, and culture results. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using Student’s t-test or the chi-square test and the CHAID decision tree analysis model. Results In total, 198 patients (60 (30.3%) men, 138 (69.7%) women; mean age, 74.69 ± 15.27 years) were included in this study, of whom 92 (46.4%) had positive blood culture results. The CHAID decision tree analysis revealed that patients with a white blood cell count of >21,000/μL had a very high risk (89.5%) of developing bacteremia. Patients with a white blood cell count of ≤21,000/μL plus chills plus an aspartate aminotransferase concentration of >19 IU/L constituted the high-risk group (69.0%). Conclusion The present results are extremely useful for predicting the results of bacteremia among patients with pyelonephritis.

Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study

ObjectivesTo develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting.DesignRetrospective observational study.SettingED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020.ParticipantsAdult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits.Main outcome measuresThe primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED.ResultsIn 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%.ConclusionsBoth models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.