Biomarker-guided antifungal therapy in patients with suspected invasive candidiasis: Ready for prime time?

Paiva, José Artur; Charles, Pierre‐Emmanuel

doi:10.1007/s00134-017-4990-y

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…The recognition of C. glabrata candidemia is frequently delayed, resulting in dramatic clinical deterioration and death of critically ill ICU patients [37], which might explain the higher 7-day mortality rate of C. glabrata ICUAC. Therefore, these results highlight the importance of the exploration studies of early risk management of ICUAC, such as prophylactic antifungal therapy, biomarker-based preemptive therapy and risk-based empirical therapy in patients with ICUAC, especially C. glabrata ICUAC [38,39].…”

Section: Discussionmentioning

confidence: 86%

Dynamics and Predictors of Mortality Due to Candidemia Caused by Different Candida Species: Comparison of Intensive Care Unit-Associated Candidemia (ICUAC) and Non-ICUAC

Kwon

Won

Jeong

et al. 2021

JoF

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We investigated mortality and predictors of mortality due to intensive care unit-associated candidemia (ICUAC) versus non-ICUAC by Candida species. This study included all candidemia cases in 11 hospitals from 2017 to 2018 in South Korea. The all-cause mortality rates in all 370 patients with ICUAC were approximately twofold higher than those in all 437 patients with non-ICUAC at 7 days (2.3-fold, 31.1%/13.3%), 30 days (1.9-fold, 49.5%/25.4%), and 90 days (1.9-fold, 57.8%/30.9%). Significant species-specific associations with 7- and 30-day ICUAC-associated mortality were not observed. Multivariate analysis revealed that ICU admission was an independent predictor of Candida glabrata (OR, 2.07–2.48) and Candida parapsilosis-associated mortality (OR, 6.06–11.54). Fluconazole resistance was a predictor of C. glabrata-associated mortality (OR, 2.80–5.14). Lack (less than 3 days) of antifungal therapy was the strongest predictor of 7-day mortality due to ICUAC caused by Candida albicans (OR, 18.33), Candida tropicalis (OR, 10.52), and C. glabrata (OR, 21.30) compared with 30- and 90-day mortality (OR, 2.72–6.90). C. glabrata ICUAC had a stronger association with lack of antifungal therapy (55.2%) than ICUAC caused by other species (30.6–36.7%, all p < 0.05). Most predictors of mortality associated with ICUAC were distinct from those associated with non-ICUAC and were mediated by Candida species.

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Section: Discussionmentioning

confidence: 86%

Dynamics and Predictors of Mortality Due to Candidemia Caused by Different Candida Species: Comparison of Intensive Care Unit-Associated Candidemia (ICUAC) and Non-ICUAC

Kwon

Won

Jeong

et al. 2021

JoF

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“…Early diagnosis of systemic candidiasis with the aid of novel, sensitive biomarkers helps other members of the team to properly design the following procedures of ASFP targeted toward the optimization of antifungal use and prevention of antifungal resistance emergence without the loss of the desired clinical outcome [7, 62, 63]. Use of a preauthorization-based ASFP strategy (i.e., front-end strategy) facilitates the CMLS to direct the whole ASFP team towards a successful clinical outcome and an optimized antifungal therapy by the proper selection of a biomarker with a high sensitivity and specificity [62].…”

Section: Antifungal Stewardship Policy Structurementioning

confidence: 99%

“…The biomarker-guided preemptive therapy of IFI (e.g., IC) seems to be a promising approach having such benefits as the optimized use of antifungals, lower chance of antifungal resistance development, lower healthcare costs, shorter length of hospital stay, and higher survival rate [63].…”

Section: Antifungal Stewardship Policy Structurementioning

confidence: 99%

Biomarker-guided antifungal stewardship policies for patients with invasive candidiasis

Honarvar¹,

Lankarani²,

Taghavi³

et al. 2019

CMM

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Invasive fungal infections (IFIs) are among the life-threatening issues in patients with impaired immune system. High administration of antifungals in these patients imposes a heavy economic burden on the national health system. In addition, despite the usually expensive antifungal regimens, the mortality rate due to fungal infections is still high, resulting in the loss of hundreds of lives per year.Survival rate is an indicator of the success of national healthcare policies. Early diagnosis of IFI is critical because any delays may be fatal. The weakness of the old-fashioned culture-based diagnostic methods lies in their time-consuming laboratory procedures. To overcome this problem, several diagnostic approaches have been developed to facilitate the early diagnosis of invasive candidiasis as the most prevalent IFI.These methods are based on the detection of serologic and molecular footprints. However, nowadays, antibiotic resistance and proper and cost-effective use of antibiotics are given special attention in national healthcare policies. The instructions for controlling these indices have been collected under the name of antibiotic stewardship. The present review study was targeted toward providing insight into novel diagnostic biomarkers and antifungal stewardship programs. The simultaneous investigation of these two issues facilitates the achievement of a novel health policy for the treatment of systemic candidiasis in immunocompromised patients.

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“…Furthermore, biomarkers could not accurately predict candidemia in many conditions. BGD performed high percentage of false-positive results due to transfusion of human blood products, hemodialysis, infection by some Gram-positive bacteria, use of certain beta-lactam antibiotics, cellulose dressings or enteral nutrition, and disruptions of gastrointestinal (GI) tract integrity [13,14].Therefore, the demand for the development of more rapid and accurate approaches to differentiate candidemia from bacterial infections is not only evident but also urgent, such approaches could aid ICU physicians in making informed decision regarding the initiation of antifungal therapy [15,16].…”

Section: Introductionmentioning

confidence: 99%

A machine learning model for early candidemia prediction in the intensive care unit: Clinical application

Meng,

Chen,

et al. 2024

Preprint

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Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and delayed antifungal therapy can significantly increase mortality rates, particularly in the intensive care unit (ICU). This study aims to develop a machine learning predictive model for early candidemia diagnosis in ICU patients, leveraging their clinical information and findings. We conducted this study with a cohort of 334 patients admitted to the ICU unit at Ji Ning NO.1 people’s hospital in China from Jan. 2015 to Dec. 2022. To ensure the model’s reliability, we validated this model with an external group consisting of 77 patients from other sources. The candidemia to bacteremia ratio is 1:1. We collected relevant clinical procedures and eighteen key examinations or tests features to support the recursive feature elimination (RFE) algorithm. These features included total bilirubin, age, platelet count, hemoglobin, CVC, lymphocyte, Duration of stay in ICU, neutrophils, monocyte, antibiotic therapy, PCT, CRP, WBC, immunosuppressive drugs, TPN, abdominal surgery, chemotherapy and solid cancer. To construct the candidemia diagnosis model, we employed random forest (RF) algorithm alongside other machine learning methods and conducted internal and external validation with training and testing sets allocated in a 7:3 ratio. The RF model demonstrated the highest area under the receiver operating characteristic (AUROC) with values of 0.87 and 0.83 for internal and external validation, respectively. To evaluate the importance of features in predicting candidemia, Shapley additive explanation (SHAP) values were calculated and results revealed that total bilirubin and age were the most important factors in the prediction model. This advancement in candidemia prediction holds significant promise for early intervention and improved patient outcomes in the ICU setting, where timely diagnosis is of paramount crucial.

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Biomarker-guided antifungal therapy in patients with suspected invasive candidiasis: Ready for prime time?

Cited by 5 publications

References 18 publications

Dynamics and Predictors of Mortality Due to Candidemia Caused by Different Candida Species: Comparison of Intensive Care Unit-Associated Candidemia (ICUAC) and Non-ICUAC

Dynamics and Predictors of Mortality Due to Candidemia Caused by Different Candida Species: Comparison of Intensive Care Unit-Associated Candidemia (ICUAC) and Non-ICUAC

Biomarker-guided antifungal stewardship policies for patients with invasive candidiasis

A machine learning model for early candidemia prediction in the intensive care unit: Clinical application

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