ArticleBMC infectious diseases2026
Retrospective analysis of the epidemiological characteristics, risk factors, survival rate, and drug resistance of candidemia and construction of a logistic regression prediction model (2013-2024).
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo compare differences between candidemia and non-candidemia in the past 12 years and to construct a predictive model of candidemia to enrich clinical data and improve the diagnosis and treatment of candidemia.
methodsA matched case-control study design was used to collect the clinical data of inpatients in a tertiary hospital in Yunnan from 2013 to 2024. The patients were divided into candidemia group and non-candidemia group (control group), and accurately matched at 1:1. SPSS was used to compare the differences in epidemiological characteristics, risk factors and survival. Drug sensitivity analysis of candidemia was performed by broth dilution method. Logistic regression analysis was performed using R language and a clinical prediction model was constructed.
resultsA total of 134 cases were collected, including 67 with candidemia and 67 without candidemia. Elderly men were significantly more susceptible to candidemia and non-candidemia. The time to positivity of blood cultures, hospitalization duration, urolithiasis distribution rate, and mortality rate of patients with candidemia were significantly higher than those of patients without candidemia (all P < 0.05). Five types of Candida were isolated from patients with candidemia, and the antifungal drug sensitivities of amphotericin B, anidulafungin, caspofungin, and micafungin to all detected Candida strains were 100%. Chronic kidney disease, hepatorenal syndrome, tigecycline and amikacin use, abdominal infection, and invasive pulmonary fungal infection may be potential risk factors for candidemia. Logistic regression analysis showed that the time to positivity of blood cultures (≥ 2 days) (odds ratio [OR] = 121.03, P < 0.001), number of concurrent infections during hospitalization (OR = 3.9, P < 0.01), and blood transfusion treatment (OR = 6.91, P < 0.05) were risk factors, whereas fibrinogen levels (OR = 0.77, P < 0.01) and C-reactive protein levels (OR = 0.98, P < 0.01) were protective factors. The receiver operating characteristic curve, calibration curve, and clinical decision curve showed that the model was meaningful.
conclusionThis study found that there were differences in multiple outcomes between candidemia and non-candidemia. The five potential risk factors analyzed can be used as predictors of candidemia and may provide a reference for the diagnosis and treatment of clinical candidemia.
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