[Retracted] Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single‐Centre Retrospective Study

Li, Jingyi; Li, Yaling; Gao, Yali; Niu, Xueli; Tang, Mingsui; Fu, Chang; Wang, Zihan; Liu, Jiayi; Song, Bing; Chen, Hongduo; Gao, Xing‐Hua; Guan, Xiu-Hao

doi:10.1155/2022/7896218

Cited by 4 publications

(6 citation statements)

References 39 publications

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“…When compared our study with Li, et al's predictive model for candidemia in cancer patients [31], it is worth noting that while they achieved a slightly higher AUC value, variance in patient selection criteria plays a signi cant in uence. Our study exclusively focused on ICU patients, a group with widely distributed risk factors.…”

Section: Discussionmentioning

confidence: 73%

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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Section: Discussionmentioning

confidence: 73%

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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“…The biofilm development follows sequential steps over a period of 24–48 h ( 15 ). Interestingly, our findings demonstrate that quinalizarin applied to the biofilm at the concentration corresponding to the MIC value (8 µg/mL) reduced the metabolic activity of the cells to 79%, compared to the control (biofilm without quinalizarin).…”

Section: Resultsmentioning

confidence: 99%

Quinalizarin as a potential antifungal drug for the treatment of Candida albicans fungal infection in cancer patients

Janeczko,

Kochanowicz,

Górka

et al. 2024

Microbiol Spectr

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This study aims to analyze the antifungal properties of quinalizarin, a plant-derived compound with proven anticancer effects. Quinalizarin exhibited antifungal activity against opportunistic pathogenic Candida species and Geotrichum capitatum . The treatment with this anthraquinone reduced hyphal growth, inhibited biofilm formation, and damaged mature Candida albicans biofilms. Real-time RT-PCR revealed that quinalizarin downregulated the expression of hyphae-related and biofilm-specific genes. The flow cytometry method used in the study showed that both apoptosis and necrosis were the physiological mechanisms of quinalizarin-induced C. albicans cell death, depending on the dose of the antifungal agent. A further study revealed an increase in the levels of intracellular reactive oxygen species and alterations in mitochondrial membrane potential after treatment with quinalizarin. Finally, quinalizarin was found to have low toxicity in a hemolytic test using human erythrocytes. In conclusion, we have identified quinalizarin as a potential antifungal compound. IMPORTANCE This article is a study to determine the antifungal activity of quinalizarin (1,2,5,8-tetrahydroxyanthraquinone). Quinalizarin has potential antitumor properties and is effective in different types of tumor cells. The aim of the present study was to prove that quinalizarin can be used simultaneously in the treatment of cancer and in the treatment of intercurrent fungal infections. Quinalizarin was identified as a novel antifungal compound with low toxicity. These results may contribute to the development of a new drug with dual activity in the treatment of cancer-associated candidiasis.

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“…These findings are consistent with those of recent studies conducted in Italy and the United States. 14–16 …”

Section: Discussionmentioning

confidence: 99%

Invasive Candidiasis in Patients with Solid Tumors: A Single-Center Retrospective Study

Bai¹,

Luo²

2023

IJGM

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Background Invasive candidiasis (ICs) is one of the common causes of death in patients with solid tumors. However, studies on the clinical characteristics of ICs with solid tumors are limited. Methods The purpose of this study was to retrospectively analyse the clinical characteristics, laboratory results and risk factor prediction of inpatients with ICs and solid tumors. We reviewed the clinical data and candida specimen information of hospitalized patients diagnosed with solid tumors combined with ICs at the First Hospital of China Medical University from January 2016 to December 2020. Multivariate logistic regression analysis was used to assess the prognostic factors associated with mortality in these patients. Results A total of 243 ICs patients with solid tumors were included in this study. The average ± SD age was 62.8 ± 11.7 (range: 27–93 years old), of which nearly 41% were ≥ 65 years old (99/243, 40.7%), and most were male (162/243, 66.6%). Most patients had malignant tumors of the digestive system. The most common candida was Candida parapsilosis (101/243, 41.5%), followed by Candida guilliermondii (83/243, 34.1%), Candida albicans (32/243, 13.1%), Candida glabrata (17/243, 6.9%), Candida tropicalis (7/243, 2.8%) and Candida krusei (3/243, 1.2%). Multivariate logistic regression analysis showed that the length of stay in the ICU, urinary catheter, total parenteral nutrition, stay in the ICU, renal failure and neutrophil count were prognostic factors related to death. Conclusion In this study, based on the clinical data of solid tumor patients with ICs in the past 5 years, the results showed that the length of stay in the ICU, urinary catheter, total parenteral nutrition, stay in the ICU, renal failure and neutrophil count were identified as the main prognostic factors. This study can be used to help clinicians carry out early intervention for high-risk patients.

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[Retracted] Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single‐Centre Retrospective Study

Cited by 4 publications

References 39 publications

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

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

Quinalizarin as a potential antifungal drug for the treatment of Candida albicans fungal infection in cancer patients

Invasive Candidiasis in Patients with Solid Tumors: A Single-Center Retrospective Study

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