Candida glabrata fungaemia at an Australian cancer centre: epidemiology, risk factors and therapy

A novel model for predicting deep-seated candidiasis due to Candida glabrata among cancer patients: A 6-year study in a cancer center of China

Followed by Candida albicans, Candida glabrata ranks as the second major species contributing to invasive candidiasis. Given the higher medical burden and lower susceptibility to azoles in C. glabrata infections, identifying these infections is critical. From 2016 to 2021, patients with deep-seated candidiasis due to C. glabrata and non-glabrata Candida met the criteria to be enrolled in the study. Clinical data were randomly divided into training and validation cohorts. A predictive model and nomogram were constructed using R software based on the stepwise algorithm and logistic regression. The performance of the model was assessed by the area under the receiver operating characteristic curve and decision curve analysis (DCA). A total of 197 patients were included in the study, 134 of them infected with non-glabrata Candida and 63 with C. glabrata. The predictive model for C. glabrata infection consisted of gastrointestinal cancer, co-infected with bacteria, diabetes mellitus, and kidney dysfunction. The specificity was 84.1% and the sensitivity was 61.5% in the validation cohort when the cutoff value was set to the same as the training cohort. Based on the model, treatment for patients with a high-risk threshold was better than 'treatment for all' in DCA, while opting low-risk patients out of treatment was also better than 'treatment for none' in opt-out DCA. The predictive model provides a rapid method for judging the probability of infections due to C. glabrata and will be of benefit to clinicians making decisions about therapy strategies.

Consensus guidelines for antifungal prophylaxis in haematological malignancy and haemopoietic stem cell transplantation, 2021

Antifungal prophylaxis can reduce morbidity and mortality from invasive fungal disease (IFD). However, its use needs to be optimised and appropriately targeted to patients at highest risk to derive the most benefit. In addition to established risks for IFD, considerable recent progress in the treatment of malignancies has resulted in the development of new 'at-risk' groups. The changing epidemiology of IFD and emergence of drug resistance continue to impact choice of prophylaxis, highlighting the importance of active surveillance and knowledge of local epidemiology. These guidelines aim to highlight emerging risk groups and review the evidence and limitations around new formulations of established agents and new antifungal drugs. It provides recommendations around use and choice of antifungal prophylaxis, discusses the potential impact of the changing epidemiology of IFD and emergence of drug resistance, and future directions for risk stratification to assist optimal management of highly vulnerable patients.

Risk factors for candidaemia: A prospective multi-centre case-control study

OBJECTIVES

Candidaemia carries a mortality of up to 40% and may be related to increasing complexity of medical care. Here, we determined risk factors for the development of candidaemia.

METHODS

We conducted a prospective, multi-centre, case-control study over 12 months. Cases were aged ≥18 years with at least one blood culture positive for Candida spp. Each case was matched with two controls, by age within 10 years, admission within 6 months, admitting unit, and admission duration at least as long as the time between admission and onset of candidaemia.

RESULTS

A total of 118 incident cases and 236 matched controls were compared. By multivariate analysis, risk factors for candidaemia included neutropenia, solid organ transplant, significant liver, respiratory or cardiovascular disease, recent gastrointestinal, biliary or urological surgery, central venous access device, intravenous drug use, urinary catheter and carbapenem receipt.

CONCLUSIONS

Risk factors for candidaemia derive from the infection source, carbapenem use, host immune function and organ-based co-morbidities. Preventive strategies should target iatrogenic disruption of mucocutaneous barriers and intravenous drug use.

Candidaemia and a risk predictive model for overall mortality: a prospective multicentre study

Background

Candidaemia is associated with high mortality. Variables associated with mortality have been published previously, but not developed into a risk predictive model for mortality. We sought to describe the current epidemiology of candidaemia in Australia, analyse predictors of 30-day all-cause mortality, and develop and validate a mortality risk predictive model.

Methods

Adults with candidaemia were studied prospectively over 12 months at eight institutions. Clinical and laboratory variables at time of blood culture-positivity were subject to multivariate analysis for association with 30-day all-cause mortality. A predictive score for mortality was examined by area under receiver operator characteristic curves and a historical data set was used for validation.

Results

The median age of 133 patients with candidaemia was 62 years; 76 (57%) were male and 57 (43%) were female. Co-morbidities included underlying haematologic malignancy (n = 20; 15%), and solid organ malignancy in (n = 25; 19%); 55 (41%) were in an intensive care unit (ICU). Non-albicans Candida spp. accounted for 61% of cases (81/133). All-cause 30-day mortality was 31%. A gastrointestinal or unknown source was associated with higher overall mortality than an intravascular or urologic source (p < 0.01). A risk predictive score based on age > 65 years, ICU admission, chronic organ dysfunction, preceding surgery within 30 days, haematological malignancy, source of candidaemia and antibiotic therapy for ≥10 days stratified patients into < 20% or ≥ 20% predicted mortality. The model retained accuracy when validated against a historical dataset (n = 741).

Conclusions

Mortality in patients with candidaemia remains high. A simple mortality risk predictive score stratifying patients with candidaemia into < 20% and ≥ 20% 30-day mortality is presented. This model uses information available at time of candidaemia diagnosis is easy to incorporate into decision support systems. Further validation of this model is warranted.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4065-5) contains supplementary material, which is available to authorized users.