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

Candida spp. Deep Sternal Wound Infections: A Consequence of Antibiotic use?

A cluster of deep sternal wound infections caused by Candida spp. occurred at our institution. Investigation did not disclose a common environmental source. We postulate that broad-spectrum antibiotic surgical prophylaxis and liberal use of antibiotics contributed to these infections.

Fungal Spectrum and Susceptibility Against Nine Antifungal Agents in 525 Deep Fungal Infected Cases

Background

Deep fungal infection has become an important cause of infection and death in hospitalized patients, and this has worsened with increasing antifungal drug resistance.

Objective

A 3-year retrospective study was conducted to investigate the clinical characteristics, pathogen spectrum, and drug resistance of deep fungal infection in a regional hospital of Guangzhou, China.

Methods

Non-duplicate fungi isolates recovered from blood and other sterile body fluids of in-patients of the clinical department were identified using biochemical tests of pure culture with the API20C AUX and CHROMagar medium. Antifungal susceptibilities were determined by Sensititre YeastOne^® panel trays.

Results

In this study, 525 patients (283 female, 242 male) with deep fungal infection were included, half of them were elderly patients (≥60 years) (54.67%, n=286). A total of 605 non-repetitive fungi were finally isolated from sterile samples, of which urine specimens accounted for 66.12% (n=400). Surgery, ICU, and internal medicine were the top three departments that fungi were frequently detected. The mainly isolated fungal species were Candida albicans (43.97%, n=266), Candida glabrata (20.00%, n=121), and Candida tropicalis (17.02%, n=103), which contributed to over 80% of fungal infection. The susceptibility of the Candida spp. to echinocandins, 5-fluorocytosine, and amphotericin B remained above 95%, while C. glabrata and C. tropicalis to itraconazole were about 95%, and the dose-dependent susceptibility of C. glabrata to fluconazole was more than 90%. The echinocandins had no antifungal activity against Trichosporon asahi in vitro (MIC₉₀>8 μg/mL), but azole drugs were good, especially voriconazole and itraconazole (MIC₉₀ = 0.25 μg/mL).

Conclusion

The main causative agents of fungal infection were still the genus of Candida. Echinocandins were the first choice for clinical therapy of Candida infection, followed with 5-fluorocytosine and amphotericin B. Azole antifungal agents should be used with caution in Candida glabrata and Candida tropicalis infections.

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

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.

Clinical predictive models of invasive Candida infection: a systematic literature review

Clinical predictive models (CPM) serve to identify and categorize patients into risk categories to assist in treatment and intervention recommendations. Predictive accuracy and practicality of models varies depending on methods used for their development, and should be evaluated.The aim of this study was to summarize currently available CPM for invasive candidiasis, analyze their performance, and assess their suitability for use in clinical decision making.We identified studies that described the construction of a CPM for invasive candidiasis from PubMed/MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library databases and Clinicaltrials.gov. Data extracted included: author, data source, study design, recruitment period, characteristics of study population, outcome types, predictor types, number of study participants and outcome events, modelling method and list of predictors used in the final model. Calibration and discrimination in the derivative datasets were used to assess the performance of each model.Ten articles were identified in our search and included for full text review. Five models were developed using data from ICUs, and five models included all hospitalized patients. The findings of this review highlight the limitations of currently available models to predict invasive candidiasis, including lack of generalizability, difficulty in everyday clinical use, and overly optimistic performance.There are significant concerns regarding predictive performance and usability in every day practice of existing CPM to predict invasive candidiasis.

LAY SUMMARY

Clinical predictive models may assist in early identification of patients at risk for invasive candidiasis to initiate appropriate treatment. The findings of this systematic review highlight the limitations of currently available models to predict invasive candidiasis.

Personalized machine learning approach to predict candidemia in medical wards

PURPOSE

Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs).

METHODS

A set of 42 potential predictors was acquired in a sample of 295 patients (male: 142, age: 72 ± 15 years; candidemia: 157/295; bacteremia: 138/295). Using tenfold cross-validation, a RF algorithm was compared with a classic stepwise multivariable logistic regression model; discriminative performance was assessed by C-statistics, sensitivity and specificity, while calibration was evaluated by Hosmer-Lemeshow test.

RESULTS

The best tuned RF algorithm demonstrated excellent discrimination (C-statistics = 0.874 ± 0.003, sensitivity = 84.24% ± 0.67%, specificity = 91% ± 2.63%) and calibration (Hosmer-Lemeshow statistics = 12.779 ± 1.369, p = 0.120), markedly greater than the ones guaranteed by the classic stepwise logistic regression (C-statistics = 0.829 ± 0.011, sensitivity = 80.21% ± 1.67%, specificity = 84.81% ± 2.68%; Hosmer-Lemeshow statistics = 38.182 ± 15.983, p < 0.001). In addition, RF suggests a major role of in-hospital antibiotic treatment with microbioma highly impacting antimicrobials (MHIA) that are found as a fundamental risk of candidemia, further enhanced by TPN. When in-hospital MHIA therapy is not performed, PICC is the dominant risk factor for candidemia, again enhanced by TPN. When PICC is not used and MHIA therapy is not performed, the risk of candidemia is minimum, slightly increased by in-hospital antibiotic therapy.

CONCLUSION

RF accurately estimates the risk of candidemia in patients admitted to IMWs. Machine learning technique might help to identify patients at high risk of candidemia, reduce the delay in empirical treatment and improve appropriateness in antifungal prescription.

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

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.