Time to and differential time to blood culture positivity for assessing catheter‐related yeast fungaemia: A longitudinal, 7‐year study in a single university hospital

Gits-Muselli, Maud; Villiers, S.; Hamane, Samia; Berçot, Béatrice; Donay, Jean-Luc; Denis, Blandine; Guigue, Nicolas; Alanio, Alexandre; Bretagne, Stéphane

doi:10.1111/myc.13024

Cited by 9 publications

(10 citation statements)

References 37 publications

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“…Following initiation of antimicrobial therapy, the sensitivity drops ( 7 ). Another challenge with BC is the long duration from sample collection until a result is received ( 10 ). However, to combat the high mortality of bloodstream infections (BSI), especially candidemia ( 11 , 12 ), the timely administration of appropriate antimicrobial agents is of vital importance.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Bacterial and Fungal Pathogens Using the T2MR versus Blood Culture in Patients with Severe COVID-19

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Rapid Detection of Bacterial and Fungal Pathogens Using the T2MR versus Blood Culture in Patients with Severe COVID-19

et al. 2022

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“…Other studies have recommended against using DTP to diagnose CRBSI from Candida spp. Due to slower growth rate of fungi, the standard cut-off of 120 min might not be optimum [ 24 – 26 ]. In one case, BSI was detected only by pooled blood sampling, but the recovered pathogen was B. pseudomallei , which rarely causes CRBSI [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

The comparative accuracy of pooled vs. individual blood culture sampling methods for diagnosis of catheter-related bloodstream infection

et al. 2022

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Background Catheter-related bloodstream infection (CRBSI) is associated with increased morbidity, mortality, and cost of treatment in critically ill patients. A differential time to positivity (DTP) of 120 min or more between blood cultures obtained through the catheter vs. peripheral vein is an indicator of CRBSI with high sensitivity and specificity. However, it is no clear whether pooled sampling would be as efficient as individual sampling in order to reduce costs, contamination, or anemia. Methods This was a prospective diagnostic study conducted at the medical ICU and semi-ICU of Khon Kaen University’s Srinagarind Hospital in Thailand from May 2020 to November 2021. Fifty patients with triple-lumen central venous catheters (CVCs) who were clinically suspected of CRBSI were enrolled. 15 mL of blood was drawn through each catheter lumen, 10 mL of which was inoculated into three blood culture bottles, and the remaining 5 mL was pooled into a single bottle. Sensitivity, specificity, accuracy, and time to positivity of the pooled blood cultures were calculated using individual blood cultures as a reference. Results Of the 50 patients enrolled, 14 (28%) were diagnosed with CRBSI, 57.9% of whom were infected with gram-negative bacteria as the causative pathogen (57.9%). Extensively drug-resistant (XDR) Klebsiella pneumoniae was the most common organism. Sensitivity and specificity of the pooled blood sampling method were 69.23% (95% CI [0.44–0.94]) and 97.3% (95% CI [0.92–1.02]), respectively. The area under the ROC curve (AUC) was 0.83 (95% CI [0.68–0.99]). A paired T-Test to compare time to positivity of the pooled blood bottle and the first positive culture from the individual bottles indicated statistical significance (14.9 and 12.4 h, respectively). The mean difference was 2.5 [0.9–4.1] h, with a 95% CI and a p-value of 0.006. Conclusion Pooled blood sampling results in a lower sensitivity and longer time to positivity for CRBSI diagnosis in patients with triple-lumen CVCs than individual lumen sampling. Trial registration Retrospectively registered at Thai Clinical Trials Registry. The study was reviewed and approved on 08/03/2022. TCTR identification number is TCTR20220308002

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“…Previous studies have proven that early optimal antifungal treatment can decrease patient mortality (8)(9)(10). A definitive diagnosis of candidaemia mainly relies on blood culture (11)(12)(13), which takes time and can thus cause a delay in timely treatment of patients. Early recognition is very difficult, and the indiscriminate use of antifungal agents can cause drug resistance and increase the patient's economic burden.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome

et al. 2021

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Background: Distinguishing ICU patients with candidaemia can help with the precise prescription of antifungal drugs to create personalized guidelines. Previous prediction models of candidaemia have primarily used traditional logistic models and had some limitations. In this study, we developed a machine learning algorithm trained to predict candidaemia in patients with new-onset systemic inflammatory response syndrome (SIRS).Methods: This retrospective, observational study used clinical information collected between January 2013 and December 2017 from three hospitals. The ICU patient data were used to train 4 machine learning algorithms–XGBoost, Support Vector Machine (SVM), Random Forest (RF), ExtraTrees (ET)–and a logistic regression (LR) model to predict patients with candidaemia.Results: Of the 8,002 cases of new-onset SIRS (in 7,932 patients) included in the analysis, 137 new-onset SIRS cases (in 137 patients) were blood culture positive for candidaemia. Risk factors, such as fungal colonization, diabetes, acute kidney injury, the total number of parenteral nutrition days and renal replacement therapy, were important predictors of candidaemia. The XGBoost machine learning model outperformed the other models in distinguishing patients with candidaemia [XGBoost vs. SVM vs. RF vs. ET vs. LR; area under the curve (AUC): 0.92 vs. 0.86 vs. 0.91 vs. 0.90 vs. 0.52, respectively]. The XGBoost model had a sensitivity of 84%, specificity of 89% and negative predictive value of 99.6% at the best cut-off value.Conclusions: Machine learning algorithms can potentially predict candidaemia in the ICU and have better efficiency than previous models. These prediction models can be used to guide antifungal treatment for ICU patients when SIRS occurs.

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Time to and differential time to blood culture positivity for assessing catheter‐related yeast fungaemia: A longitudinal, 7‐year study in a single university hospital

Cited by 9 publications

References 37 publications

Rapid Detection of Bacterial and Fungal Pathogens Using the T2MR versus Blood Culture in Patients with Severe COVID-19

Rapid Detection of Bacterial and Fungal Pathogens Using the T2MR versus Blood Culture in Patients with Severe COVID-19

The comparative accuracy of pooled vs. individual blood culture sampling methods for diagnosis of catheter-related bloodstream infection

Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome

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