Clinical predictors of candidemia in medical non-neutropenic, non-ICU patients. The CaMed score

Ruiz-Ruigómez, María; Dueñas, Carlos; Hernández, Cristina; Vinuesa, David; Coronado-Álvarez, Nieves M.; Portillo-Tuñón, Vera; Cardozo, Carlos Julio Márquez; Muñoz-Medina, Leopoldo; Cabo-Magadán, Rebeca; Luna, Juan de Dios; Parra-Ruiz, Jorge

doi:10.1111/ijcp.13275

Cited by 10 publications

(9 citation statements)

References 45 publications

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“…The leukocyte count was associated with mortality in patients with Candida infections including candidemia ( Riley & Rupert, 2015 ). In addition, total parenteral nutrition could increase the risk of complications and death ( Ruiz-Ruigómez et al, 2018 ; Logan, Martin-Loeches & Bicanic, 2020 ; Poissy et al, 2020 ). The total bilirubin level was associated with mortality and factors such as age and primary diseases ( Yang et al, 2017 ; Novák et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Gao

Tang

et al. 2022

PeerJ

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Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia.

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Section: Discussionmentioning

confidence: 99%

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Gao

Tang

et al. 2022

PeerJ

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“…As the risk factors currently known to be associated with Candidemia are also related to drug-resistant bacterial infection, they do not help differentiate candidemia from a bacterial infection in patients manifesting SIRS [16]. Various scoring systems and markers have been developed to distinguish candidemia, such as the CS or Candida colonization index [3][4][5]. However, their sensitivity for differentiating invasive Candida infection and colonization is only around 60% [6].…”

Section: Discussionmentioning

confidence: 99%

“…Delayed AAT results in increased mortality. Thus, various markers and testing methods have been developed to predict candidemia to enable early-stage antifungal therapy [3][4][5]. However, there are cases in which these testing methods cannot be easily applied in actual treatment or are difficult to be commonly used.…”

Section: Introductionmentioning

confidence: 99%

Delta neutrophil index as a predictive and prognostic factor for Candidemia patients: a matched case-control study

Park

Lee

et al. 2020

BMC Infect Dis

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Background Delayed antifungal therapy for candidemia leads to increased mortality. Differentiating bacterial infection from candidemia in systemic inflammatory response syndrome (SIRS) patients is complex and difficult. The Delta Neutrophil Index (DNI) has recently been considered a new factor to distinguish infections from non-infections and predict the severity of sepsis. We aimed to assess if the DNI can predict and provide a prognosis for candidemia in SIRS patients. Methods A matched case-control study was conducted from July 2016 to June 2017 at Kangdong Sacred Heart Hospital. Among patients with a comorbidity of SIRS, those with candidemia were classified as the case group, whereas those with negative blood culture results were classified as the control group. The matching conditions included age, blood culture date, and SIRS onset location. Multivariate logistic regression was performed to evaluate DNI as a predictive and prognostic factor for candidemia. Results The 140 included patients were assigned to each group in a 1:1 ratio. The DNI_D1 values measured on the blood culture date were higher in the case group than in the control group (p < 0.001). The results of multivariate analyses confirmed DNI_D1 (odds ratio [ORs] 2.138, 95% confidential interval [CI] 1.421–3.217, p < 0.001) and Candida colonization as predictive factors for candidemia. The cutoff value of DNI for predicting candidemia was 2.75%. The area under the curve for the DNI value was 0.804 (95% CI, 0.719–0.890, p < 0.001), with a sensitivity and specificity of 72.9 and 78.6%, respectively. Analysis of 14-day mortality in patients with candidemia showed significantly higher DNI_D1 and DNI_48 in the non-survivor group than in the survivor group. Conclusions DNI was identified as a predictive factor for candidemia in patients with SIRS and a prognostic factor in predicting 14-day mortality in candidemia patients. DNI, along with clinical patient characteristics, was useful in determining the occurrence of candidemia in patients with SIRS.

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“…Various scoring systems and markers have been developed to distinguish candidemia, such as the CS or candida colonization index. [3][4][5]. However, their sensitivity for differentiating invasive Candida infection and colonization is only around 60% [6].…”

Section: Discussionmentioning

confidence: 99%

“…If the administration of AAT is delayed, the mortality rate increases. For this reason, various markers and testing methods have been developed to predict candidemia to enable early-stage antifungal therapy [3][4][5]. However, there are cases in which these testing methods cannot be easily applied in actual treatment or are difficult to be commonly used.…”

mentioning

confidence: 99%

Delta Neutrophil Index (DNI) as a Predictive and Prognostic factor for Candidemia patients: Matched Case-Control study

Park

Lee

et al. 2020

Preprint

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Background: Delayed antifungal therapy for candidemia leads to increased mortality. Discriminating bacterial infection from candidemia in systemic inflammatory response syndrome (SIRS) patients is very complex and difficult. Delta Neutrophil Index (DNI) is recently considered as a new factor which can distinguish infections from non-infections and reflect the severity of sepsis. We aimed to assess whether DNI can predict and provide a prognosis for candidemia in SIRS patients.Methods: A matched case-control study was conducted from July 2016 to June 2017 at Kangdong Sacred Heart Hospital. Among patients with comorbidity of SIRS, those with candidemia were classified as the case group, while those with negative blood culture results were classified as the control group. The matching conditions included age, blood culture date, and SIRS onset location. To evaluate DNI as a predictive and prognostic factor for candidemia, multivariate logistic regression was performed.Results: The 140 included patients were assigned to each group in a 1:1 ratio. DNI-D1 values measured on the blood culture date were higher in the case group ( p <0.001). In the multivariate analyses, DNI_D1 (Odds ration ORs 2.138, 95% confidential interval CI 1.421-3.217, P <0.001) andCandida colonization were confirmed as predictive factors for candidemia. The cutoff value of DNI for predicting candidemia was 2.75%. The area under the curve for DNI value was 0.804 (95% CI, 0.719-0.890, p<0.001), with a sensitivity and specificity of 72.9% and 78.6%, respectively. Analysis of 14day mortality was conducted for patients with candidemia. DNI_D1 and DNI_48, measured 2 days after the onset of candidemia, were both significantly high in the non-survivor group.Conclusion: DNI was identified to be a predictive factor for candidemia in patients wit SIRS and a prognostic factor that predicts 14-day mortality in candidemia patients. DNI, along with clinical characteristics of patients, were useful in determining the occurrence of candidemia in patients with SIRS.

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Clinical predictors of candidemia in medical non-neutropenic, non-ICU patients. The CaMed score

Cited by 10 publications

References 45 publications

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Delta neutrophil index as a predictive and prognostic factor for Candidemia patients: a matched case-control study

Delta Neutrophil Index (DNI) as a Predictive and Prognostic factor for Candidemia patients: Matched Case-Control study

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