Abstract
BackgroundInvasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality. Risk factors for mortality have been studied previously but rarely developed into a predictive nomogram, especially for cancer patients. We constructed a nomogram for mortality prediction based on a retrospective review of 10 years of data for cancer patients with invasive candidiasis.MethodsClinical data for cancer patients with invasive candidiasis during the period of 2010–2019 were studied; the cases were randomly divided into training and validation cohorts. Variables in the training cohort were subjected to a predictive nomogram based on multivariate logistic regression analysis and a stepwise algorithm. We assessed the performance of the nomogram through the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) in both the training and validation cohorts.ResultsA total of 207 cases of invasive candidiasis were examined, and the crude 30-day mortality was 28.0%. Candida albicans (48.3%) was the predominant species responsible for infection, followed by the Candida glabrata complex (24.2%) and Candida tropicalis (10.1%). The training and validation cohorts contained 147 and 60 cases, respectively. The predictive nomogram consisted of bloodstream infections, intensive care unit (ICU) admitted > 3 days, no prior surgery, metastasis and no source control. The AUCs of the training and validation cohorts were 0.895 (95% confidence interval [CI], 0.846–0.945) and 0.862 (95% CI, 0.770–0.955), respectively. The net benefit of the model performed better than “treatment for all” in DCA and was also better for opting low-risk patients out of treatment than “treatment for none” in opt-out DCA.ConclusionCancer patients with invasive candidiasis exhibit high crude mortality. The predictive nomogram established in this study can provide a probability of mortality for a given patient, which will be beneficial for therapeutic strategies and outcome improvement.
Highlights
Invasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality
A number of studies have found that mortality independently increases with elderly age, renal failure, malignant diseases, central venous catheterization (CVC), steroid therapy, admission to an intensive care unit (ICU), use of total parenteral nutrition (TPN), low lymphocyte count, gastrointestinal source of candidemia, or previous exposure to antibiotics [1, 7, 8]
The predictive nomogram model was constructed according to the data in the training cohort, and the data in the validation cohort were verified by using the same regression equations that were constructed for the training cohort
Summary
Invasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality. A number of studies have found that mortality independently increases with elderly age, renal failure, malignant diseases, central venous catheterization (CVC), steroid therapy, admission to an intensive care unit (ICU), use of total parenteral nutrition (TPN), low lymphocyte count, gastrointestinal source of candidemia, or previous exposure to antibiotics [1, 7, 8]. These predictors contribute little to obtaining a better prognosis and always vary among populations. We speculated that a model combining different risk factors (cumulative number) might provide a better prediction for the outcome of invasive candidiasis than a single factor
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