Need for expanded Candida Score for empiric antifungal use in medically critically ill patients?

Laine, Melanie; Flannery, Alexander H.; Moody, Breanna; Bastin, Melissa L. Thompson

doi:10.1186/s13054-019-2525-3

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Current tools for predicting candidemia have major limitations. Although the Candida Score is the most widely used tool to predict invasive candidemia, its sensitivity was reported to be only 37% in a recent external validation study [ 16 ], which was lower than the 81% in the original study [ 13 ]. This might have been a result of differences in the patient population.…”

Section: Discussionmentioning

confidence: 99%

Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study

Yoo¹,

Kim²,

Hur³

et al. 2021

JMIR Med Inform

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Background Appropriate empirical treatment for candidemia is associated with reduced mortality; however, the timely diagnosis of candidemia in patients with sepsis remains poor. Objective We aimed to use machine learning algorithms to develop and validate a candidemia prediction model for patients with cancer. Methods We conducted a single-center retrospective study using the cancer registry of a tertiary academic hospital. Adult patients diagnosed with malignancies between January 2010 and December 2018 were included. Our study outcome was the prediction of candidemia events. A stratified undersampling method was used to extract control data for algorithm learning. Multiple models were developed—a combination of 4 variable groups and 5 algorithms (auto-machine learning, deep neural network, gradient boosting, logistic regression, and random forest). The model with the largest area under the receiver operating characteristic curve (AUROC) was selected as the Candida detection (CanDETEC) model after comparing its performance indexes with those of the Candida Score Model. Results From a total of 273,380 blood cultures from 186,404 registered patients with cancer, we extracted 501 records of candidemia events and 2000 records as control data. Performance among the different models varied (AUROC 0.771- 0.889), with all models demonstrating superior performance to that of the Candida Score (AUROC 0.677). The random forest model performed the best (AUROC 0.889, 95% CI 0.888-0.889); therefore, it was selected as the CanDETEC model. Conclusions The CanDETEC model predicted candidemia in patients with cancer with high discriminative power. This algorithm could be used for the timely diagnosis and appropriate empirical treatment of candidemia.

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Section: Discussionmentioning

confidence: 99%

Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study

Yoo¹,

Kim²,

Hur³

et al. 2021

JMIR Med Inform

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“…According to the study that used "Candida score," surgery, multifocal colonization, PN treatment, and severe sepsis were independent predictors of proven Candida infection 9 . In contrast, the sensitivity of this scoring system may differ in each patient group 13 . In this study, a significant relationship was found between Candida score and candidemia using the Poisson regression analysis.…”

Section: Discussionmentioning

confidence: 96%

“…Although LOS and piperacillin-tazobactam administration were found as risk factors in this study, it is known that the factors affecting the development of candidemia may differ between patient groups depending on the chosen study design. Recent guidelines recommend the use of risk prediction tools to facilitate earlier recognition and initiation of antifungal treatment 12,13 . According to the study that used "Candida score," surgery, multifocal colonization, PN treatment, and severe sepsis were independent predictors of proven Candida infection 9 .…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the effect of antibiotics used during parenteral nutrition treatment on Candidemia

Yalçın

Sürmelioğlu

Özkan-Kuşcu

et al. 2021

Rev. Assoc. Med. Bras.

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OBJECTIVE: Parenteral nutrition is an important risk factor for candidemia. In this risk analysis study, the effect of previous antibiotic administration apart from the length of hospital stay, duration of Parenteral nutrition treatment, and Candida score parameters on developing candidemia was evaluated in the non-neutropenic patients receiving Parenteral nutrition treatment. METHODS: In this double center, retrospective, and cross-sectional study, the data of patients who received Parenteral nutrition treatment were collected. Patients with or without candidemia after the initiation of Parenteral nutrition treatment were compared in terms of demographic features, Candida score, length of hospital stay, duration of Parenteral nutrition treatment, and previous use of antibiotics.Then, predictor factors affecting the probability of candidemia during Candida growth time were determined by the Cox regression analysis.RESULTS: A total of 148 patients (59.5% males) were included and 16 (10.81%) of these had candidemia after initiation of parenteral nutrition treatment. The median (min-max) duration of parenteral nutrition treatment was 11 (4-72) days and the Candida growth time was 13 (7-29) days. Statistically significant differences were found between patients with or without candidemia groups in terms of length of hospital stay (p<0.001), duration of parenteral nutrition treatment (p<0.001), and Candida score (p<0.001). To determine the effect of these variables and antibiotics on candidemia, length of hospital stay [Hazard Ratio 1.030; p=0.021] and piperacillin-tazobactam (Hazard Ratio 5.626; p=0.030) were found significant and independent risk factors on the development of candidemia.CONCLUSION: There are some well-known risk factors including length of hospital stay, duration of Parenteral nutrition treatment, and Candida score; the potential impact of piperacillin-tazobactam administration should also be considered since they may be effective on the development of candidemia.

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“…Candida score) could be combined with diagnostic tests with short turnaround, such as BDG detection, for a more accurate and time-efficient prediction of IC. 75 …”

Section: Diagnosticsmentioning

confidence: 99%

Invasive candidiasis: current clinical challenges and unmet needs in adult populations

Soriano

Honoré

Puerta‐Alcalde

et al. 2023

Journal of Antimicrobial Chemotherapy

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Invasive candidiasis (IC) is a serious infection caused by several Candida species, and the most common fungal disease in hospitals in high-income countries. Despite overall improvements in health systems and ICU care in the last few decades, as well as the development of different antifungals and microbiological techniques, mortality rates in IC have not substantially improved. The aim of this review is to summarize the main issues underlying the management of adults affected by IC, focusing on specific forms of the infection: IC developed by ICU patients, IC observed in haematological patients, breakthrough candidaemia, sanctuary site candidiasis, intra-abdominal infections and other challenging infections. Several key challenges need to be tackled to improve the clinical management and outcomes of IC patients. These include the lack of global epidemiological data for IC, the limitations of the diagnostic tests and risk scoring tools currently available, the absence of standardized effectiveness outcomes and long-term data for IC, the timing for the initiation of antifungal therapy and the limited recommendations on the optimal step-down therapy from echinocandins to azoles or the total duration of therapy. The availability of new compounds may overcome some of the challenges identified and increase the existing options for management of chronic Candida infections and ambulant patient treatments. However, early identification of patients that require antifungal therapy and treatment of sanctuary site infections remain a challenge and will require further innovations.

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Need for expanded Candida Score for empiric antifungal use in medically critically ill patients?

Cited by 4 publications

References 5 publications

Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study

Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study

Evaluation of the effect of antibiotics used during parenteral nutrition treatment on Candidemia

Invasive candidiasis: current clinical challenges and unmet needs in adult populations

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