The Influence of the Seasonal Variability of Candida spp. Bloodstream Infections and Antifungal Treatment: A Mediterranean Pilot Study
by
, , , , , , and
Paola Di Carlo
1
,
Nicola Serra
2,*
,
Ornella Collotta
3,
Claudia Colomba
1
,
Alberto Firenze
1,
Luigi Aprea
4,
Salvatore Antonino Distefano
4,
Andrea Cortegiani
5
,
Giovanni Giammanco
1
,
Teresa Maria Assunta Fasciana
1,
Roberta Virruso
4,
Angela Capuano
6,
Consolato M. Sergi
7,8
and
Antonio Cascio
1
1
Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy
2
Department of Neuroscience, Reproductive Sciences and Dentistry Department, Audiology Section “Federico II” University of Naples Federico II, 80131 Naples, Italy
3
Degree Course in Medicine and Surgery, Medical School of Hypatia, University of Palermo, 93100 Caltanissetta, Italy
4
Azienda Ospedaliera Universitaria Policlinico “Paolo Giaccone”, 90127 Palermo, Italy
5
Department of Precision Medicine in Medical, Surgical and Critical Care Area (Me.Pre.C.C.), University of Palermo, 90127 Palermo, Italy
6
Department of Emergency, AORN Santobono-Pausilipon, 80122 Naples, Italy
7
Anatomic Pathology Division, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON K1H 8M5, Canada
8
Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2R3, Canada
*
Author to whom correspondence should be addressed.
Antibiotics 2025, 14(5), 452; https://doi.org/10.3390/antibiotics14050452
Submission received: 3 April 2025
/
Revised: 23 April 2025
/
Accepted: 28 April 2025
/
Published: 29 April 2025
(This article belongs to the Special Issue Climate Change and Antibiotic Resistance)
Abstract
:Background/Objectives: Various factors associated with seasonality, including temperature, humidity, geographical composition, and seasonal fluctuations, can influence the trends of microbes responsible for hospital infections, such as Candida spp. This study evaluates the seasonal variability of Candida spp. bloodstream infections and antifungal resistance in hospitalized patients in Sicily. Methods: We retrospectively analyzed the demographic and epidemiological characteristics of 175 patients with blood cultures positive for Candida spp. Who were hospitalized at University Hospital Paolo Giaccone (A.U.O.P.), University of Palermo, Italy, from 1 January 2022 to 31 December 2024. Data on Candida species and antifungal resistance were also collected from the hospital’s database system to prevent and control hospital infections in A.U.O.P. Results: A total of 175 patients, 57.7% males, with a mean age of 68.3 years, were included in this study. Candida parapsilosis, Candida albicans, and Candida glabrata were more frequent in ICU (54.5%, p = 0.0001), medical (72.5%, p = 0.0003), and surgical settings (24%, p = 0.0161), respectively. C. parapsilosis was more frequent in dead patients (53.2%, p = 0.005). Among the seasons, we observed a significantly higher presence of C. glabrata in Autumn (20%, p = 0.0436). From the analysis of the seasons, C. parapsilosis and C. albicans were more frequent for each season, except in Spring, where the most frequent isolates were C. glabrata (5.1%, p = 0.0237) and C. parapsilosis (9.7%, p < 0.0001). The antifungal with the most resistance to Candida spp. was fluconazole in all seasons. Conclusions: Our study highlights the seasonal trends in Candida spp. and antifungal resistance, emphasizing climate change’s challenges on fungal diseases. These findings may contribute to improving prevention and treatment strategies for candidemia.
1. Introduction
In 2022, the World Health Organization (WHO) identified Candida species such as Candida auris and C. albicans as critical-priority pathogens and C. tropicalis and C. parapsilosis as high-priority pathogens. Despite better diagnostics and available antifungal treatments, hospitalized patients with invasive candidiasis (IC) infections, including candidemia, still face high morbidity and mortality rates. IC is also a common bloodstream infection in Europe, with increased incidence in Italy over the past two decades [1,2,3,4].
Trends in candidemia have changed significantly in recent decades. The species distribution varies among countries. Currently, Candida albicans represents the most common Candida species in Europe (40% to 60% of cases). Candida glabrata is more prevalent in Northern Europe, the most common species in four geographical areas, such as the United Kingdom, France, Slovenia, and Belgium, ranging from 25% to 33%. In contrast, Candida parapsilosis has become increasingly common in Southern Europe, such as in Italy and Turkey, with rates between 24% and 26% [5].
This infection occurs when the yeast enters the bloodstream and has the potential to spread to various organs, including the eyes, heart, brain, and kidneys. Hospitalized patients, particularly those who are critically ill or frail, are at risk for invasive candidiasis. As a common healthcare-associated infection, it is essential to recognize the potential for serious complications and to take preventive measures to protect at-risk patients [3,6,7]. Surgical wounds and invasive medical devices, such as catheters, central venous lines, and drainages, allow Candida to enter the body [8,9]. C. albicans is recognized as the most prevalent species associated with candidemia, yet the growing list of Candida species implicated in this condition reflects the advancements in identification techniques. Moreover, their distribution varies in population-based studies conducted in different geographical areas. The ARTEMIS DISK Global Antifungal Surveillance Study has made significant strides by establishing an extensive registry of invasive Candida isolates gathered from 127 medical centers across 39 countries [9]. The trend is variable; traditional key species like C. albicans, C. glabrata, C. tropicalis, C. parapsilosis, and C. krusei are now accompanied by emerging species, including C. auris [10,11].
Seasonal variations are tied to climate factors such as environmental humidity, temperature, atmospheric pressure, and wind, which can impact fungal community composition. As global temperatures rise, fungi adapt, affecting their interactions with other organisms and often leading to diseases. Warmer, wetter conditions allow fungal pathogens to thrive, causing crop failures and increasing human infections, especially in regions with health disparities. These changes demand immediate attention [12,13].
Recent studies such as that of Arendrup, M. C. [5] show the impact of regional climate, reporting different Candida species in various European and non-European countries characterized by different geographical climes.
Research indicates that climate change is associated with new fungal pathogens and existing ones appearing on different hosts, some of which exhibit high drug resistance. Studies suggest these pathogens may have recently adapted to endure higher environmental temperatures [14,15,16].
Traditionally, Candida spp. sessile cells exhibit a variable response to temperature, with significant differences between strains [17,18]. Its yeast-to-hypha shape conversion also promotes the formation of a biofilm on inert (medical devices like catheters, shunts, and stents) or biological (skin or mucosa) surfaces, making it more harmful [19]. The surrounding environment influences the growth of the biofilms, and although temperature is a known factor that alters the yeast-to-hypha transition in C. albicans, its impact on biofilm formation is not yet fully understood [19,20,21]. All persistent Candida spp. infections can be successfully treated with standard fungicides like fluconazole, Amphotericin-B, and echinocandins; however, biofilm-related infections are hard to control by standard treatment.
Like many other European countries, Italy has been affected by climate change. Various factors associated with climate change, including temperature, humidity, geographical composition, and seasonal fluctuations, can influence the trends of microbes responsible for hospital infections, such as Candida spp. [22,23].
This study evaluates the seasonal variability of Candida spp. bloodstream infections and antifungal resistance in hospitalized patients in Sicily.
2. Results
Positive blood samples of Candida spp. were obtained from 175 consecutive patients with a male/female ratio of 1.33:1 (100 M and 75 F), ages 18–92 years old, a mean age of 68.3 years, and a standard deviation (SD) of 14.2. In Table 1, we report the general characteristics of the 175 study participants.
In Table 1, we also report the comorbidities related to candidemia. Notably, the more frequent comorbidities were chronic heart failure (34.3%, p < 0.0001), chronic obstructive pulmonary disease (22.3%, p = 0.0018), chronic renal failure (20.0%, p = 0.025), diabetes (20.6%, p = 0.0138), and organ solid tumors (20.6%, p = 0.0138).
Table 2 shows the percentages of Candida spp. considering both setting and season.
From Table 2, about Candida distribution, no significant relationship between season and hospital setting was observed (p = 0.19, by Fisher’s exact test).
Table 3 shows the percentages of Candida spp. resistance, considering antifungal and season. In the table, all percentages refer to the 175 patients. Additionally, in the table, we report two statistical analyses. The first analysis is reported in the last column, where we show the analysis among seasons for each antifungal. In contrast, the second analysis is reported in the last row, where we tested among the antifungals used the one with the highest resistance to Candida spp. in each season. Particularly, the effect size was calculated for each significant analysis.
From Table 3, we found significantly more resistance to Candida spp. in Summer (32%, p = 0.0243, post hoc Z-test) than in Autumn (28.6%), Spring (21.1%), or Winter (18.3%). Additionally, for each antifungal, no significant differences in resistance to Candida spp. were observed; while considering the antifungals with more resistance in each season, we found that fluconazole was the antifungal with the highest percentage of resistance to Candida spp.
From our power analysis, we observed a minimum bias in the statistical analysis due to the small sample size, i.e., the significant results had a low probability of bias effect due to the small sample.
Table 4 shows the distribution of Candida spp. according to the socio-demographic and setting variables of Table 1, such as age, gender, patients’ status (dead), and hospital setting (ICU, medical, and surgical).
In Table 4, no significant differences about age and gender for patients with different Candida spp. can be observed (p = 0.71 and p = 0.88, respectively). In contrast, for the status variable, among Candida spp., we found more patients died with C. parapsilosis (53.2%, p = 0.005). Furthermore, among Candida spp., patients with C. parapsilosis, C. albicans, and C. glabrata were more frequent in the ICU (54.5%, p = 0.0001), medical settings (72.5%, p = 0.0003), and surgical settings (24%, 0.0161), respectively.
The minimum biases for all the significance tests in Table 4 were observed via power analysis.
Table 5 shows the Candida species found in our sample stratified by season. In Table 5, two analyses are described. The first analysis is reported in the last column, where we show the analysis among seasons for each Candida spp., while the second analysis is reported in the last row, showing the Candida found more frequently among the Candida spp. for each season.
Table 5 shows a significantly higher presence of C. glabrata in Autumn (5.7%, p = 0.0481) among the seasons. Additionally, from the analysis of every season among Candida spp., we found that C. parapsilosis and C. albicans were the most frequent in each season, apart from Spring, where the most frequent Candida spp. were C. glabrata (5.1%, p = 0.0237) and C. parapsilosis (9.7%, p < 0.0001). From our power analysis, we observed a minimum bias in the statistical analysis due to the small sample size; i.e., the significant results had a low probability of bias effect due to the small sample.
In Table 6, we report some characteristics of the patients with positive blood samples for Candida spp. with central venous catheters.
From Table 6, we observed in patients with CVCs no significant difference among hospital settings (p = 0.062) and comorbidities (p = 0.52), while among Candida spp., Candida parapsilosis was the most frequent (50.0%, p < 0.0001).
Finally, in Figure 1, we show the resistance to all the antifungal drugs used in this study, considering all the seasons and only the Candida spp. more frequent in Table 2, such as C. albicans, C. glabrata, C. parapsilosis, and C. tropicalis. Notably, the table associated with the figure shows the resistance to all the specific antifungals used for each season.
3. Discussion
This is the first study that has assessed the seasonal distribution of Candida species and antifungal susceptibility in the bloodstream in Southern Italy after the COVID-19 pandemic era. We found a significantly higher prevalence of candidemia from Candida spp. in Summer (32%, p = 0.0243) than in Autumn (28.6%), Spring (21.1%), and Winter (18.3%). This result follows other studies performed on neonates and women, where Candida infection was more common in Summer [24,25].
The significant presence of Candida spp. in Summer could be due to the higher temperature and humidity, which create the perfect conditions for yeast, especially for Candida. Some species of Candida spp. are thermotolerant, meaning they can grow and survive at higher temperatures, such as 37 °C. Environmental factors, including seasonal climate changes and geographical distribution, can influence Candida’s ability to produce biofilms on synthetic materials. This characteristic enhances the organisms’ adhesion to medical devices, making infections more resistant to treatment [26,27]. Additionally, favorable climatic conditions increase sweating, leading to moist skin and allowing yeast to thrive. This combination raises the likelihood of infections, resulting in discomfort and irritation. Candida spp. are commonly found in the soil, hospital environments, on inanimate objects, in food, and as a typical inhabitant of the human body. They can be isolated from diseased skin, the gastrointestinal tract, female genitalia, and indwelling medical devices. Particularly, poorly air-conditioned hospital environments and fragile skin favor the penetration of Candida spp. into the body through vascular and urinary devices, favoring bacteremia caused by Candida species [28,29].
Our analysis identified several risk factors for candidemia. These include compromised immune systems resulting from conditions such as diabetes (20.6%) and solid organ tumors (20.6%) [30]. Additionally, other significant risk factors were chronic heart failure (34.3%), chronic obstructive pulmonary disease (22.3%), and chronic renal failure (20.0%). The prevalence of these conditions was probably due to the older age of our sample population [31].
Our study found no significant seasonal distribution of Candida spp. in hospital settings, as reported in Table 2.
We found a link between death during hospitalization and C. parapsilosis. This finding is confirmed by its presence in the WHO priority pathogens list [1]. C. parapsilosis is globally distributed and recognized for causing an increasing proportion of invasive Candida spp. infections. It is associated with high mortality across all fragile populations and linked to nosocomial outbreaks, especially involving the use of invasive medical devices such as central venous catheters [1,32,33].
In our study, the distribution of Candida spp. showed a prevalence of C. parapsilosis in ICUs (54.5%), C. albicans in medical settings (72.5%), and C. glabrata in surgical settings (24.0%).
Emerging studies have confirmed these findings, highlighting the prevalence of C. parapsilosis infections among ICU patients, especially for fluconazole-resistant isolates [32,33,34,35].
C. glabrata was more frequently isolated after abdominal surgery, and the likelihood of a positive culture for C. glabrata increased with patient age. This follows our findings that the patients with C. glabrata isolated in their blood had a median age of 73 [36]. Notably, C. glabrata is Turkey’s third leading cause of candidemia, as reported by Arastehfar, A. et al. [37].
As reported above, we found a significant presence of C. albicans in medical settings. This result could be due to the choice to include in the medical settings category several medical units, such as Geriatric, Infectious Disease, Hematology, and Respiratory Units [38,39,40,41].
The analysis of every season showed a similar prevalence of Candida species in patient blood samples. Particularly, C. albicans and C. parapsilosis were the most common isolates for each season, except for Spring, where the most frequent isolates were C. parapsilosis and C. glabrata.
Recent investigations have identified a significant prevalence of vulvovaginitis caused by non-albicans Candida spp., particularly C. parapsilosis and C. glabrata. In Europe, vulvovaginitis is more common in Spring and Summer. Our findings show a similar trend, with higher isolation rates for these species in Spring [25,42]. In Italy, increased temperatures and humidity during Spring may contribute to skin and mucous membrane infections, especially in the elderly, facilitating the spread of these pathogens via urinary catheters [40].
Moreover, the comparison among seasons of our blood-positive cultures for Candida spp. showed seasonal variability among Candida spp., especially for C. glabrata, with increased rates in Autumn when the temperature decreased. To our knowledge, this is the first report on this association in Mediterranean countries. In the Asia region, Hwang, Seo Young et al. [43] examined the distribution trends of Candida spp. in a Korean hospital, their antifungal susceptibilities, and seasonal variations. Potential pathophysiological mechanisms that may explain the variation in Candida distribution in relationship to season include geographical area, changes in temperature, dietary habits, and bodily adaptations due to heightened exposure to sunlight [43,44,45].
Our study found no statistically significant differences in antifungal resistance among the seasons regarding the distribution of Candida spp., while in every season, among the tested antifungal drugs, a significant resistance to fluconazole was shown (10.9% in Summer, 6.3% in Winter, 9.7% in Spring, and 12.6% in Autumn, p < 0.05) (see Table 3).
Considering the most frequent Candida spp., we found a higher frequency of resistance to fluconazole and amphotericin in the Summer for the C. parapsilosis species, with rates of 20% and 23%, respectively (Figure 1). This follows European and Italian epidemiological trends that showed an increase in fluconazole resistance in Candida spp. [34,35,36,37,38].
Antifungal resistance in Candida species is a serious issue that needs immediate attention. The spread and severity of these infections are affected by the seasons. Rising temperatures from global warming increase resistance and help Candida form biofilms, making infections harder to treat. Certain types of Candida, especially C. tropicalis, are linked to specific seasons and regions where antifungal resistance is extreme, as reported Lima, R. et al. [46].
Antifungal stewardship strategies have been implemented to optimize medication use, including the selection of drugs, dosages, and treatment durations according to international guidelines [47]. Finally, there were no changes in prescription patterns during the study period for fluconazole that could have influenced resistance patterns.
4. Limitations
This is a retrospective pilot study conducted at a single center. The study provides preliminary results that need to be confirmed by a multicenter study across different geographic areas and a large sample size, since our statistical analyses may be impacted due to the small sample, and some nonsignificant results were obtained, and some statistical tests not performed.
This preliminary study was designed to reduce costs and was conducted to identify possible correlation between candidemia and seasonality, before conducting a study on a very large sample of patients. Nevertheless, this study focused on some remarkable data associated with seasonality. Since statistical biases could influence the obtained result due to the small sample size, the authors performed a power analysis using effect size. We observed that all the significant tests showed a low probability of statistical biases due to their small sample size.
Finally, regarding any bias introduced during the selection phase of the collection and microbiological analysis of the blood samples, as described in the Section 5, the procedures were carefully conducted.
5. Materials and Methods
We performed a retrospective observational cohort study on the inpatient population admitted to wards or the Intensive Care Unit for at least 48 h to the public academic Policlinico University Hospital “Paolo Giaccone (A.U.O.P.)”, Palermo, Italy, a tertiary-level hospital with 600 inpatient beds and nearly 14,000 hospital admissions per year, between 1 January 2022 and 31 December 2024 [48]. We included only adult patients (18 years or older) with Candida species in the blood according to previous recommendations [49].
Blood culture samples were collected aseptically via peripheral venipuncture, intravenous catheters (Peripherally Inserted Central Catheter, midline), or central venous catheters (CVCs), and the positive BC results were evaluated for contamination (i.e., false positives). Particularly, the patients with intravenous catheters or central venous catheters positive for Candida spp. (28.6% = 50/175) had at least one positive venous peripheral positive for the same Candida spp.
All blood samples excluded had a high risk of contamination by nursing practices, such as the reuse of retrograde syringes, total parenteral nutrition, or retrograde medication administration, as reported by Sherertz RJ et al. [50].
Patients’ records included age, sex, isolated candida species, hospital wards, and antifungal susceptibility patterns. The data records were obtained from a database using the microbiological institutional electronic information system of the Hospital Unit for the prevention and control of hospital infection at (A.U.O.P.).
The risk factors for candidemia vary by healthcare setting. The authors classify wards into three categories—medical, surgical, and ICU—reflecting differences in procedures and patient populations. Particularly, the Medical Area included Cardiology, Endocrinology, Gastroenterology, Geriatrics, Hematology, Infectious Diseases, Internal Medicine, Nephrology, Neurology, Oncology, and Pulmonology wards. The Surgical Area included Cardiovascular Surgery, General Surgery, Neurosurgery, Oncological Surgery, Otolaryngology, Orthopedic Surgery, Plastic Surgery, Urology, Vascular Surgery, and Obstetrics and Gynecology. Finally, the Intensive Care Unit included General Intensive Care, the Post-Operative Intensive Care Unit, and Specialized Intensive Care.
Candida spp. isolates from blood cultures were analyzed across Winter, Spring, Summer, and Autumn to observe variations in species distribution according to the studied variables. For the seasonal analysis, only the first day of positive candidemia was considered in patients with recurrent or persistent blood candidemia.
The procedures performed in this study followed the ethical standards of the Institutional and/or National Research Committee and the Helsinki Declaration. The study protocol was approved by the Ethics Committee of the Azienda Ospedaliero–Universitaria Policlinico “P. Giaccone”, University of Palermo, Palermo, Italy (IRB n. 28-12/2024).
5.1. Microbiological Methods
The laboratory-based microbiological investigation was performed in the Microbiology and Virology Unit of University Hospital Policlinico “P. Giaccone” in Palermo (Italy) [51,52]. Candida spp. were isolated and identified with a MALDI-TOF-MS system (MALDI Biotyper CA System, Bruker Daltonics Inc., Billerica, MA, USA) according to standard procedures [53,54]. The susceptibility testing results considered the materials and methods used for the MIC data and used an interpretive approach with the EUCAST breakpoints and guidelines for MALDI-TOF MS [55,56].
5.2. Statistical Analysis
Categorical variables were described with numbers or percentages, while continuous variables were expressed as mean ± standard deviation (SD), and/or median and interquartile interval (IRQ = [Q1; Q3]).
Multiple comparison chi-square tests were used to individualize significant differences among three or more independent variables. If the chi-square test was significant (p < 0.05), a post hoc Z-test was performed to identify the highest or lowest significant frequency. Fisher’s exact test was used if the chi-square test was not appropriate. The chi-square goodness of fit was used to evaluate significant differences among three or more modalities of a variable. The Shapiro–Wilk test was used to check whether the data were normally distributed. The Kruskal–Wallis test, followed by a post hoc test with the Conover test for pairwise comparison, was performed for multi-comparison among three or more independent variables in the case of no normal distribution condition. Cochran’s Q-test was used to evaluate the differences between three or more dependent variables. When the Cochran’s Q-test was positive (p < 0.05), a post hoc Bonferroni–Dunn test was applied to individualize significant differences between all possible variables. Additionally, since the sample size was smaller in some analyses, we used effect size to perform power analyses for each significant statistical test. Particularly, the effect sizes were computed by the phi coefficient for categorical variables, by h2 and r for non-parametric tests (Mann–Whitney test and Wilcoxon signed-rank test, respectively), and by Cohen’s d index (paired and unpaired t-tests).
MATLAB statistical toolbox version 2008 (MathWorks, Natick, MA, USA) was used for data analysis.
Seasonal Variable
Our study aimed to evaluate the impact of climate on bloodstream infections caused by Candida spp. in Sicily. We considered seasonality as a key variable in our analysis. Environmental parameters such as temperature, atmospheric pressure, precipitation index, etc., were not considered individually, as they are parameters that do not have standard variations but are highly variable. Therefore, the use of these parameters could bring a bias to our statistical analyses. The authors overcame this bias by considering a standard measure of climate variations in Italy, represented by the seasons. This can be considered a macro parameter that involves all the others. Mathematically, this step is the basis of reducing our statistical biases, i.e., identifying a standard and dominant parameter that can include all the other sub-parameters with greater variability.
6. Conclusions
The rise in Candida spp. cases during Summer can be attributed to increased temperatures and heightened sunlight exposure. To help mitigate the risk of Candida spp. bloodstream infections, patients in hospitals who are vulnerable should be proactive about their habits during this season. Understanding the seasonality of environmental contamination is vital for addressing its impact on hospitalized populations. Therefore, exploring the seasonal patterns of these factors and their effects on patients is essential for improving care. Additionally, the growing resistance to fluconazole and the mortality associated with C. parapsilosis underscore the need for a robust surveillance system in the ICU. By prioritizing further research, we can develop more effective strategies to tackle this common condition and enhance patient outcomes.
A recent study by Laura García-Gutierrez et al. [57] focused on the environmental conditions within hospitals, with a particular emphasis on air quality. The research aimed to analyze the gradients present in these environments and highlighted the prevalence of certain opportunistic pathogens. It is essential to reconsider key factors that could compromise controlled conditions in sensitive areas, such as HEPA-protected rooms. These factors may include the introduction of organic matter by healthcare personnel via contaminated shoes and clothing, irregular functioning of ventilation systems, and insufficient monitoring of personnel access.
Finally, we suggest the implementation and development of new environmental monitoring programs in hospitals that include air conditioning capable of adapting the internal temperature of the structure to sudden changes in external environmental temperatures to guarantee a reduction in at-risk biological surfaces (skin or mucous membranes) and consequently a decrease in Candida spp. bloodstream infections.
Author Contributions
Conceptualization: P.D.C.; Data Curation: O.C., A.F. and L.A.; Formal Analysis: N.S. and A.C. (Angela Capuano); Investigation: S.A.D., T.M.A.F., O.C. and C.C.; Software: N.S., A.C. (Angela Capuano) and C.C.; Methodology: C.M.S., A.C. (Andrea Cortegiani), R.V. and L.A.; Supervision: P.D.C., A.C. (Antonio Cascio) and G.G.; Writing—Original Draft: C.M.S., N.S. and P.D.C.; Writing—Review and Editing: P.D.C., N.S., C.M.S., A.C. (Andrea Cortegiani), T.M.A.F., G.G. and A.C. (Antonio Cascio). All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study protocol was approved by the Ethics Committee of the Azienda Ospedaliero–Universitaria Policlinic “P. Giaccone”, University of Palermo, Palermo, Italy (IRB n. 28-12/2024).
Informed Consent Statement
Not applicable.
Data Availability Statement
The dataset is available on request from the project manager, Paola Di Carlo.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- World Health Organization. WHO Fungal Priority Pathogens List to Guide Research, Development and Public Health Action 2022. Available online: https://iris.who.int/bitstream/handle/10665/363682/9789240060241-eng.pdf?sequence=1> (accessed on 8 March 2025).
- Pappas, P.G.; Lionakis, M.S.; Arendrup, M.C.; Kullberg, B.J. Invasive candidiasis. Nat. Rev. Dis. Primers 2018, 4, 18026. [Google Scholar] [CrossRef] [PubMed]
- Koehler, P.; Stecher, M.; Cornely, O.A.; Koehler, D.; Vehreschild, M.J.G.T.; Bohlius, J.; Wisplinghoff, H.; Vehreschild, J.J. Morbidity and mortality of candidaemia in Europe: An epidemiologic meta-analysis. Clin. Microbiol. Infect. 2019, 25, 1200–1212. [Google Scholar] [CrossRef] [PubMed]
- Rodolico, V.; Di Carlo, P.; Gulotta, G.; D’Arpa, F.; Salamone, G.; Cocorullo, G.; Agrusa, A.; Giammanco, A.; Sergi, C. Intra-abdominal Candida spp. infection in acute abdomen in a quality assurance (QA)-certified academic setting. J. Clin. Pathol. 2017, 70, 579–583. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Arendrup, M.C.; Arikan-Akdagli, S.; Jørgensen, K.M.; Barac, A.; Steinmann, J.; Toscano, C.; Arsenijevic, V.A.; Sartor, A.; Lass-Flörl, C.; Hamprecht, A.; et al. European candidaemia is characterised by notable differential epidemiology and susceptibility pattern: Results from the ECMM Candida III study. J. Infect. 2023, 87, 428–437. [Google Scholar] [CrossRef]
- Meneghello, S.; Bernabè, G.; Di Pietra, G.; Di Sopra, S.; Del Vecchio, C.; Cattelan, A.M.; Castagliuolo, I.; Brun, P. Prevalence, Species Distribution and Resistance of Candidemia in Pediatric and Adult Patients in a Northeast Italy University Hospital. J. Fungi 2024, 10, 707. [Google Scholar] [CrossRef]
- Bassetti, M.; Peghin, M.; Carnelutti, A.; Righi, E.; Merelli, M.; Ansaldi, F.; Trucchi, C.; Alicino, C.; Sartor, A.; Toniutto, P.; et al. Clinical characteristics and predictors of mortality in cirrhotic patients with candidemia and intra-abdominal candidiasis: A multicenter study. Intensive Care Med. 2017, 43, 509–518. [Google Scholar] [CrossRef] [PubMed]
- Kojic, E.M.; Darouiche, R.O. Candida infections of medical devices. Clin. Microbiol. Rev. 2004, 17, 255–267. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Ranđelović, M.; Ignjatović, A.; Đorđević, M.; Golubović, M.; Stalević, M.; Rančić, N.; Otašević, S. Superficial Candidiasis: Cluster Analysis of Species Distribution and Their Antifungal Susceptibility In Vitro. J. Fungi 2025, 11, 338. [Google Scholar] [CrossRef]
- Pfaller, M.A.; Diekema, D.J.; Rinaldi, M.G.; Barnes, R.; Hu, B.; Veselov, A.V.; Tiraboschi, N.; Nagy, E.; Gibbs, D.L. Results from the ARTEMIS DISK Global Antifungal Surveillance Study: A 6.5-year analysis of susceptibilities of Candida and other yeast species to fluconazole and voriconazole by standardized disk diffusion testing. J. Clin. Microbiol. 2005, 43, 5848–5859. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- De Gaetano, S.; Midiri, A.; Mancuso, G.; Avola, M.G.; Biondo, C. Candida auris Outbreaks: Current Status and Future Perspectives. Microorganisms 2024, 12, 927. [Google Scholar] [CrossRef]
- Wang, Z.; Liu, H.; Xu, P.; Nie, C.; Geng, X.; Chen, J.; Liu, S.; Wei, M. Inhalable Fungi and Opportunistic Pathogens During Haze and Haze-Dust Events from Winter to Springtime in One Typical Inland City of Northern China. J. Geophys. Res. Atmos. 2024, 129, e2024JD040792. [Google Scholar] [CrossRef]
- Case, N.T.; Gurr, S.J.; Fisher, M.C.; Blehert, D.S.; Boone, C.; Casadevall, A.; Chowdhary, A.; Cuomo, C.A.; Currie, C.R.; Denning, D.W.; et al. Fungal impacts on Earth’s ecosystems. Nature 2025, 638, 49–57. [Google Scholar] [CrossRef] [PubMed]
- Nawaz, A.; Khadija, B.; Saqib, M.A.; Tara, T.; Umar, M. Unveiling the Impact of Atmospheric Temperature on Antifungal Resistance and Virulence Factors in Candida spp. Isolated from Forest Ecosystem. SSRN 2025, 20. [Google Scholar] [CrossRef]
- High time to tackle drug-resistant fungal infections. Nature 2025, 640, 569. [CrossRef] [PubMed]
- Gadre, A.; Enbiale, W.; Andersen, L.K.; Coates, S.J. The effects of climate change on fungal diseases with cutaneous manifestations: A report from the International Society of Dermatology Climate Change Committee. J. Clim. Change Health 2022, 6, 100156. [Google Scholar] [CrossRef]
- Lemos-Carolino, M.; Madeira-Lopes, A. The temperature profile of the pathogenic yeast Candida albicans. Z. Für Allg. Mikrobiol. 1981, 22, 705–709. [Google Scholar] [CrossRef]
- Al-Fattani, M.A.; Douglas, L.J. Biofilm matrix of Candida albicans and Candida tropicalis: Chemical composition and role in drug resistance. J. Med. Microbiol. 2006, 55 Pt 8, 999–1008. [Google Scholar] [CrossRef] [PubMed]
- Ahamad, I.; Bano, F.; Anwer, R.; Srivastava, P.; Kumar, R.; Fatma, T. Antibiofilm Activities of Biogenic Silver Nanoparticles Against Candida albicans. Front. Microbiol. 2022, 12, 741493. [Google Scholar] [CrossRef]
- Alim, D.; Sircaik, S.; Panwar, S.L. The Significance of Lipids to Biofilm Formation in Candida albicans: An Emerging Perspective. J. Fungi 2018, 4, 140. [Google Scholar] [CrossRef]
- Pierantoni, D.C.; Corte, L.; Casadevall, A.; Robert, V.; Cardinali, G.; Tascini, C. How does temperature trigger biofilm adhesion and growth in Candida albicans and two non-Candida albicans Candida species? Mycoses 2021, 64, 1412–1421. [Google Scholar] [CrossRef]
- Vineis, P.; Romanello, M.; Michelozzi, P.; Martuzzi, M. Health co-benefits of climate change action in Italy. Lancet Planet. Health 2022, 6, e293–e294. [Google Scholar] [CrossRef] [PubMed]
- Seidel, D.; Wurster, S.; Jenks, J.D.; Sati, H.; Gangneux, J.P.; Egger, M.; Alastruey-Izquierdo, A.; Ford, N.P.; Chowdhary, A.; Sprute, R.; et al. Impact of climate change and natural disasters on fungal infections. Lancet Microbe. 2024, 5, e594–e605. [Google Scholar] [CrossRef] [PubMed]
- Edi-Osagie, N.E.; BEmmerson, A.J. Seasonality of invasive Candida infection in neonates. Acta PæDiatrica 2004, 94, 72–74. [Google Scholar] [CrossRef] [PubMed]
- Donders, G.G.; Ruban, K.; Donders, F.; Reybrouck, R. Lab-Based Retrospective 10-Year Analysis Shows Seasonal Variation of Vaginal Candida Infection Rates in Belgium. J. Clin. Med. 2022, 11, 574. [Google Scholar] [CrossRef]
- Actor, J.K. Mycology. In Elsevier’s Integrated Review Immunology and Microbiology, 2nd ed.; Elsevier: Amsterdam, The Netherlands, 2011; pp. 139–146. [Google Scholar] [CrossRef]
- Robert, V.; Cardinali, G.; Casadevall, A. Distribution and impact of yeast thermal tolerance permissive for mammalian infection. BMC Biol. 2015, 13, 18. [Google Scholar] [CrossRef]
- George, M.E.; Gaitor, T.T.; Cluck, D.B.; Henao-Martínez, A.F.; Sells, N.R.; Chastain, D.B. The impact of climate change on the epidemiology of fungal infections: Implications for diagnosis, treatment, and public health strategies. Ther. Adv. Infect. Dis. 2025, 12, 20499361251313841. [Google Scholar] [CrossRef]
- Kashyap, B.; Das, S.; Gupta, K.; Sagar, T. Current Scenario of Geriatric Fungal Infections: A Prevalence Study from East Delhi. Aging Med. Healthc. 2019, 10, 46–50. [Google Scholar] [CrossRef]
- Wolfgruber, S.; Sedik, S.; Klingspor, L.; Tortorano, A.; Gow, N.A.R.; Lagrou, K.; Gangneux, P.; Maertens, J.; Meis, J.F.; Lass-Flörl, C.; et al. Insights from Three Pan-European Multicentre Studies on Invasive Candida Infections and Outlook to ECMM Candida IV. Mycopathologia 2024, 189, 70. [Google Scholar] [CrossRef]
- Barchiesi, F.; Orsetti, E.; Mazzanti, S.; Trave, F.; Salvi, A.; Nitti, C.; Manso, E. Candidemia in the elderly: What does it change? PLoS ONE 2017, 12, e0176576. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Asogan, M.; Kim, H.Y.; Kidd, S.; Alastruey-Izquierdo, A.; Govender, N.P.; Dao, A.; Shin, H.; Heim, J.; Ford, N.P.; Gigante, V.; et al. Candida parapsilosis: A systematic review to inform the World Health Organization fungal priority pathogens list. Med. Mycol. 2024, 62, myad131. [Google Scholar] [CrossRef]
- Viale, P.L.; Mirandola, S.; Natalini, C.; Esposti, L.D.; Dovizio, M.; Veronesi, C.; Forcina, G.; Navalesi, P.; Boscolo, A. A retrospective Italian analysis on the characteristics of invasive fungal infections in the intensive care unit setting: CHARTER-IFI study. Mycoses 2024, 67, e13779. [Google Scholar] [CrossRef] [PubMed]
- Vena, A.; Tiseo, G.; Falcone, M.; Bartalucci, C.; Marelli, C.; Cesaretti, M.; Di Pilato, V.; Escribano, P.; Forniti, A.; Giacobbe, D.R.; et al. Impact of Fluconazole Resistance on the Outcomes of Patients with Candida parapsilosis Bloodstream Infections: A Retrospective Multicenter Study. Clin. Infect. Dis. 2025, 80, 540–550. [Google Scholar] [CrossRef] [PubMed]
- Caggiano, G.; Fioriti, S.; Morroni, G.; Apollonio, F.; Triggiano, F.; D’Achille, G.; Stefanizzi, P.; Dalfino, L.; Ronga, L.; Mosca, A.; et al. Genotypic and phenotypic characteristics of Candida parapsilosis bloodstream isolates: Health Care Associated Infections in a teaching Hospital in Italy. J. Infect. Public Health 2024, 17, 967–974. [Google Scholar] [CrossRef] [PubMed]
- Klingspor, L.; Tortorano, A.; Peman, J.; Willinger, B.; Hamal, P.; Sendid, B.; Velegraki, A.; Kibbler, C.; Meis, J.; Sabino, R.; et al. Invasive Candida infections in surgical patients in intensive care units: A prospective, multicentre survey initiated by the European Confederation of Medical Mycology (ECMM) (2006–2008). Clin. Microbiol. Infect. 2014, 21, e1–e87. [Google Scholar] [CrossRef] [PubMed]
- Arastehfar, A.; Daneshnia, F.; Salehi, M.; Yaşar, M.; Hoşbul, T.; Ilkit, M.; Pan, W.; Hagen, F.; Arslan, N.; Perlin, D.S. Low level of antifungal resistance of Candida glabrata blood isolates in Turkey: Fluconazole minimum inhibitory concentration and FKS mutations can predict therapeutic failure. Mycoses 2020, 63, 911. [Google Scholar] [CrossRef]
- De Francesco, M.A.; Piccinelli, G.; Gelmi, M.; Gargiulo, F.; Ravizzola, G.; Pinsi, G.; Peroni, L.; Bonfanti, C.; Caruso, A. Invasive Candidiasis in Brescia, Italy: Analysis of Species Distribution and Antifungal Susceptibilities During Seven Years. Mycopathologia 2017, 182, 897–905. [Google Scholar] [CrossRef]
- Dekkers, B.G.J.; Veringa, A.; EMarriott, D.J.; Boonstra, J.M.; Doukas, F.F.; McLachlan, A.J.; Alffenaar, W.C. Invasive Candidiasis in the Elderly: Considerations for Drug Therapy. Drugs Aging 2018, 35, 781. [Google Scholar] [CrossRef]
- Salmanton-García, J.; Cornely, O.A.; Stemler, J.; Barać, A.; Steinmann, J.; Siváková, A.; Akalin, E.H.; Arikan-Akdagli, S.; Loughlin, L.; Toscano, C.; et al. Attributable mortality of candidemia—Results from the ECMM Candida III multinational European Observational Cohort Study. J. Infect. 2024, 89, 106229. [Google Scholar] [CrossRef]
- Sharifi, M.; Badiee, P.; Abastabar, M.; Morovati, H.; Haghani, I.; Noorbakhsh, M.; Mohammadi, R. A 3-year study of Candida infections among patients with malignancy: Etiologic agents and antifungal susceptibility profile. Front. Cell. Infect. Microbiol. 2023, 13, 1152552. [Google Scholar] [CrossRef]
- Corsello, S.; Spinillo, A.; Osnengo, G.; Penna, C.; Guaschino, S.; Beltrame, A.; Blasi, N.; Festa, A. An epidemiological survey of vulvovaginal candidiasis in Italy. Eur. J. Obstet. Gynecol. Reprod. Biol. 2003, 110, 66–72. [Google Scholar] [CrossRef] [PubMed]
- Hwang, S.Y.; Lim, Y.K.; Choe, K.W.; ho Choi, Y.; Lee, M.K. Seasonality and epidemiological trends in species distribution and antifungal susceptibility of Candida species isolated from various clinical specimens conducted during 2011–2022, Korea: A retrospective surveillance study. Ann. Clin. Microbiol. 2024, 27, 185–196. [Google Scholar] [CrossRef]
- Hossain, M.M.; Sultana, F.; Mostafa, M.; Ferdus, H.; Kundu, M.; Adhikary, S.; Sabbir, M.A.A. Stress Responses and Mechanisms of Phytopathogens Infecting Humans: Threats, Drivers, and Recommendations. Stresses 2025, 5, 28. [Google Scholar] [CrossRef]
- Khan, S.; Cai, L.; Bilal, H.; Khan, M.N.; Fang, W.; Zhang, D.; Yao, F.; Wang, X.; Wang, Q.; Hou, B.; et al. An 11-Year retrospective analysis of candidiasis epidemiology, risk factors, and antifungal susceptibility in a tertiary care hospital in China. Sci. Rep. 2025, 15, 7240. [Google Scholar] [CrossRef] [PubMed]
- Lima, R.; Ribeiro, F.C.; Colombo, A.L.; De Almeida, J.N. The emerging threat antifungal-resistant Candida tropicalis in humans, animals, and environment. Front. Fungal Biol. 2022, 3, 957021. [Google Scholar] [CrossRef]
- Cornely, O.A.; Sprute, R.; Bassetti, M.; Chen, S.C.; Groll, A.H.; Kurzai, O.; Lass-Flörl, C.; Ostrosky-Zeichner, L.; Rautemaa-Richardson, R.; Revathi, G.; et al. Global guideline for the diagnosis and management of candidiasis: An initiative of the ECMM in cooperation with ISHAM and ASM. Lancet Infect. Dis. 2025, 25, E280–E293. [Google Scholar] [CrossRef]
- Russo Fiorino, G.; Maniglia, M.; Marchese, V.; Aprea, L.; Torregrossa, M.V.; Campisi, F.; Favaro, D.; Calamusa, G.; Amodio, E. Healthcare-associated infections over an eight year period in a large university hospital in Sicily (Italy, 2011–2018). J. Infect. Prev. 2021, 22, 220–230. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- De Pauw, B.; Walsh, T.J.; Donnelly, J.P.; Stevens, D.A.; Edwards, J.E.; Calandra, T.; Pappas, P.G.; Maertens, J.; Lortholary, O.; Kauffman, C.A.; et al. Revised Definitions of Invasive Fungal Disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) Consensus Group. Clin. Infect. Dis. 2008, 46, 1813–1821. [Google Scholar]
- Sherertz, R.J.; Gledhill, K.S.; Hampton, K.D.; Pfaller, M.A.; Givner, L.B.; Abramson, J.S.; Dillard, R.G. Outbreak of Candida bloodstream infections associated with retrograde medication administration in a neonatal intensive care unit. J. Pediatr. 1992, 120, 455–461. [Google Scholar] [CrossRef] [PubMed]
- Serra, N.; Di Carlo, P.; Andriolo, M.; Mazzola, G.; Diprima, E.; Rea, T.; Anastasia, A.; Fasciana, T.M.; Pipitò, L.; Capra, G.; et al. Staphylococcus aureus and Coagulase-Negative Staphylococci from Bloodstream Infections: Frequency of Occurrence and Antimicrobial Resistance, 2018–2021. Life 2023, 13, 1356. [Google Scholar] [CrossRef]
- Di Carlo, P.D.; Serra, N.; Assunta Fasciana, T.M.; Giammanco, A.; Rea, T.; Napolitano, M.S.; Lucchesi, A.; Cascio, A.; Sergi, C.M. Microbial profile in bile from pancreatic and extra-pancreatic biliary tract cancer. PLoS ONE 2024, 19, e0294049. [Google Scholar] [CrossRef]
- Lacroix, C.; Gicquel, A.; Sendid, B.; Meyer, J.; Accoceberry, I.; François, N.; Morio, F.; Desoubeaux, G.; Chandenier, J.; Kauffmann-Lacroix, C.; et al. Evaluation of two matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) systems for the identification of Candida species. Clin. Microbiol. Infect. 2014, 20, 153–158. [Google Scholar] [CrossRef] [PubMed]
- Wilson, D.A.; Young, S.; Timm, K.; Novak-Weekley, S.; Marlowe, E.M.; Madisen, N.; Lillie, J.L.; Ledeboer, N.A.; Smith, R.; Hyke, J.; et al. Multicenter Evaluation of the Bruker MALDI Biotyper CA System for the Identification of Clinically Important Bacteria and Yeasts. Am. J. Clin. Pathol. 2017, 147, 623–631. [Google Scholar] [CrossRef] [PubMed]
- Astvad, K.M.T.; Arikan-Akdagli, S.; Arendrup, M.C. A Pragmatic Approach to Susceptibility Classification of Yeasts without EUCAST Clinical Breakpoints. J. Fungi 2022, 8, 141. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- European Committee on Antimicrobial Susceptibility Testing Breakpoint Tables for Interpretation of MICs for Antifungal Agents. Available online: https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/AFST/Clinical_breakpoints/AFST_BP_v11.0.pdf (accessed on 3 April 2024).
- García-Gutiérrez, L.; Baena Rojas, B.; Ruiz, M.; Hernández Egido, S.; Ruiz-Gaitán, A.C.; Laiz, L.; Pemán, J.; Cuétara-García, M.S.; Mellado, E.; Martin-Sanchez, P.M. Fungal burden assessment in hospital zones with different protection degrees. Build. Environ. 2025, 269, 112454. [Google Scholar] [CrossRef]
Figure 1.
Antibiotic resistance considering season and Candida spp. Not including less frequent Candida spp. such as C. krusei, C. lusitaniae, C. norvegensis, and C. pulcherrima.
Figure 1.
Antibiotic resistance considering season and Candida spp. Not including less frequent Candida spp. such as C. krusei, C. lusitaniae, C. norvegensis, and C. pulcherrima.
Table 1.
General characteristics of 175 patients.
| Parameters | Sample |
|---|---|
| Patients | 175 |
| Age at hospitalization | |
| Mean ± SD | 68.3 (14.2) |
| Median (IQR) | 70.0 (61.3, 77.8) |
| Gender | |
| Male | 57.1% (100) |
| Female | 42.9% (75) |
| Deaths | 41.7% (73) |
| Hospital setting | |
| ICU | 37.7% (66) |
| Medical setting | 51.4% (90) |
| Surgical setting | 10.9% (19) |
| Comorbidities † | |
| Acute coronary syndrome (ACS) | 6.9% (12) |
| Autoimmune diseases (AD) | 2.3% (4) |
| Chronic heart failure (CHF) | 34.3% (60) |
| Chronic obstructive pulmonary disease (COPD) | 22.3% (39) |
| Chronic renal failure (CRF) | 20.0% (35) |
| Dementia (DM) | 9.1% (16) |
| Diabetes | 20.6% (36) |
| Organ solid tumors | 20.6% (36) |
| Hematological tumors | 4.6% (8) |
| Cerebrovascular (brain) insult | 13.1% (23) |
| Haemodialysis (HD) | 10.9% (19) |
| Liver cirrhosis (LC) | 4.0% (7) |
| Candida spp. by Season | |
| Autumn | 28.6% (50) |
| Summer | 32.0% (56) |
| Winter | 18.3% (32) |
| Spring | 21.1% (37) |
SD = standard deviation; IQR= interquartile range; † = some patients had two or more comorbidities.
Table 2.
Percentages of Candida spp. considering both hospital setting and season.
| Hospital Setting/Seasons | Autumn | Summer | Winter | Spring |
|---|---|---|---|---|
| ICU | 10.9% (19) | 11.4% (20) | 8.0% (14) | 7.4% (13) |
| Medical | 15.4% (27) | 17.7% (31) | 9.7% (17) | 8.6% (15) |
| Surgical | 2.3% (4) | 2.9% (5) | 0.6% (1) | 5.1% (9) |
Table 3.
Percentages of Candida spp. resistance considering both the type of antifungal and the season.
Table 3.
Percentages of Candida spp. resistance considering both the type of antifungal and the season.
| Antifungal | Total | Autumn (Au) | Summer (Su) | Winter (Wi) | Spring (Sp) | Analysis Among Seasons p-Value (Test) |
|---|---|---|---|---|---|---|
| Total | 175 | 28.6% (50) | 32.0% (56) | 18.3% (32) | 21.1% (37) | 0.0364 * (Cg) Su **, p = 0.0263(Z) Effect size: phi = 0.644, large effect |
| (1) Amphotericin | 7.4% (13) | 2.9% (5) | 2.3% (4) | 1.1% (2) | 1.1% (2) | 0.56 (Cg) |
| (2) Anidulafungin | 1.1% (2) | 0.0% (0) | 0.6% (1) | 0.6% (1) | 0.0% (0) | N/A |
| (3) Caspofungin | 2.3% (4) | 0.6% (1) | 1.1% (2) | 0.6% (1) | 0.0% (0) | N/A |
| (4) Fluconazole | 39.4% (69) | 12.6% (22) | 10.9% (19) | 6.3% (11) | 9.7% (17) | 0.29 (Cg) |
| (5) Itraconazole | 13.7% (24) | 5.7% (10) | 2.3% (4) | 2.9% (5) | 2.9% (5) | 0.30 (Cg) |
| (6) Micafungin | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | N/A |
| (7) Posaconazole | 6.9% (12) | 2.3% (4) | 1.7% (3) | 1.7% (3) | 1.1% (2) | 0.88 (Cg) |
| (8) Voriconazole | 12.0% (21) | 1.7% (3) | 5.7% (10) | 1.7% (3) | 2.9% (5) | 0.10 (Cg) |
| Analysis in season, p-value (test) | p < 0.001 * (Q) (4)>(1),(2),(3),(5),(6),(7),(8), p < 0.05 * (B) Effect size: η2 = 0.17, large effect | p < 0.001 * (Q) (4)>(1),(2),(3),(5),(6),(7),(8), p < 0.05 * (B) Effect size: η2 = 0.20, large effect | p < 0.001 * (Q) (4)>(1),(2),(3),(5),(6),(7), p < 0.05 * (B) Effect size: η2 = 0.15, large effect | p < 0.001 * (Q) (4)>(1),(2),(3),(6),(7),(8), p < 0.05 * (B) Effect size: η2 = 0.15, large effect | p < 0.001 * (Q) (4)>(1),(2),(3),(5),(6),(7),(8), p < 0.05 * (B) Effect size: η2 = 0.22, large effect |
* = significant test; ** = modality more frequent; Cg = chi-square goodness of fit; Q = Cochran’s Q-test; B = post hoc Q-test by Bonferroni–Dunn method.
Table 4.
Distribution of Candida spp. according to age, gender, patient status, and hospital setting. We excluded less frequent Candida spp., such as Candida norvegensis and Candida pulcherrima, from statistical analysis.
Table 4.
Distribution of Candida spp. according to age, gender, patient status, and hospital setting. We excluded less frequent Candida spp., such as Candida norvegensis and Candida pulcherrima, from statistical analysis.
| Isolated Strains | Age % (n) Mean (SD) Median (IQR) | Gender Males/Females % (n) | Status (††) Dead (Yes) % (n) | Hospital Setting ICU; Medical; Surgical |
|---|---|---|---|---|
| Candida albicans (C.A.) (n = 51) | n = 51 69.6 (13.9) 71 (62, 78.5) | 56.9% (29) 43.1% (22) | 30.5% (18) | 17.6% (9); 72.5% (37); 9.8% (5) |
| Candida glabrata (C.G.) (n = 25) | n = 25 70.3 (11.5) 73 (62.8, 77.5) | 60% (15) 40% (10) | 28.0% (7) | 28.0% (7); 48.0% (12); 24.0% (7) |
| Candida krusei (C.K.) (n = 4) | n = 4 69.8 (16.0) 66.5 (60, 79.5) | 25% (1) 75% (3) | 0.0% (0) | 25.0% (1); 75.0% (3); 0.0% (0) |
| Candida lusitaniae (C.L.) (n = 3) | n = 3 73.7 (10.1) 79 (66.3, 79.8) | 66.7% (2) 33.3% (1) | 66.7% (2) | 66.7% (2); 0.0% (0); 33.3% (1) |
| Candida norvegensis (C.N.) † (n = 1) | n = 1 65 | 100% (1) 0.0% (0) | 100% (1) | 0.0% (0); 100% (1); 0.0% (0) |
| Candida parapsilosis (C.P.) (n = 77) | n = 77 66.1 (15.7) 68 (58.5, 77) | 57.1% (44) 42.9% (33) | 53.2% (41) | 54.5% (42); 39.0% (30); 6.5% (5) |
| Candida pulcherrima (C.Pu.) † (n = 1) | n = 1 71 | 100% (1) 0.0% (0) | 0.0% (0) | 0.0% (0); 0.0% (0); 100% (1) |
| Candida tropicalis (C.T.) (n = 13) | n = 13 70.8 ± 12.9 71 (67.5, 79) | 53.8% (7) 46.2% (6) | 30.8% (4) | 38.5% (5); 53.8% (7); 7.7% (1) |
| p-value (test) | p = 0.86 (KW) | p = 0.88 (F) | p < 0.0402 * (F) C.P. **, 53.2%, p = 0.005 (Z) Effect size: phi = 0.86, large effect | p = 0.0001 * (F) C.P./ICU **, 54.5%, p = 0.0001(Z) C.A./Medical **, 72.5%, p = 0.0003(Z) C.G./Surgical **, 24%, p = 0.0161 (Z) Effect size: phi = 2.37, large effect |
† = Candida species not included in statistical analysis; †† = died during hospitalization * = significant test; F = Fisher’s exact test; Z = post hoc Z-test; ** = more frequent; KW = Kruskal–Wallis test.
Table 5.
Candida spp. isolated from 175 patients and stratified for season.
| Isolated Strains | Total | Autumn (Au) | Summer (Su) | Winter (Wi) | Spring (Sp) | p-Value Among Seasons (Test) |
|---|---|---|---|---|---|---|
| Total | 175 | 28.6% (50) | 32.0% (56) | 18.3% (32) | 21.1% (37) | 0.053 (Cg) |
| Candida albicans (C.A.) | 29.1% (51) | 8.0% (14) | 11.4% (20) | 5.1% (9) | 4.6% (8) | 0.07 (Cg) |
| Candida glabrata (C.G.) | 14.3% (25) | 5.7% (10) | 0.6% (1) | 2.9% (5) | 5.1% (9) | 0.0436 * (Cg) Au **, p = 0.0481 (Z) Effect size: phi = 1.62, large effect |
| Candida krusei (C.K.) | 2.3% (4) | 1.7% (3) | 0.6% (1) | 0.0% (0) | 0.0% (0) | N/A |
| Candida lusitaniae (C.L.) | 1.7% (3) | 0.6% (1) | 0.0% (0) | 0.0% (0) | 1.1% (2) | N/A |
| Candida norvegensis (C.N.) | 0.6% (1) | 0.0% (0) | 0.6% (1) | 0.0% (0) | 0.0% (0) | N/A |
| Candida parapsilosis (C.P.) | 44.0% (77) | 10.3% (18) | 15.4% (27) | 8.6% (15) | 9.7% (17) | 0.22 (Cg) |
| Candida pulcherrima (C.Pu.) | 0.6% (1) | 0.0% (0) | 0.6% (1) | 0.0% (0) | 0.0% (0) | N/A |
| Candida tropicalis (C.T.) | 7.4% (13) | 2.3% (4) | 2.9% (5) | 1.7% (3) | 0.6% (1) | 0.44 (Cg) |
| Analysis into groups p-value (test) | p < 0.0001* (Cg) C.A. **, p < 0.0001(Z) C.P. **, p < 0.0001(Z) Effect size: phi = 11.5, large effect | p < 0.0001 * (Cg) C. A. **, p = 0.0002 (Z) C.P. **, p < 0.0001 (Z) Effect size: phi = 2.37, large effect | p < 0.0001 * (Cg) C. A. **, p < 0.0001 (Z) C.P. **, p < 0.0001 (Z) Effect size: phi = 3.89, large effect | p < 0.0001 * (Cg) C. A. **, p = 0.0027 (Z) C.P. **, p < 0.0001 (Z) Effect size: phi = 5.61, large effect | p < 0.0001 * (Cg) C.G. **, p = 0.0237 (Z) C.P. **, p < 0.0001 (Z) Effect size: phi = 5.62, large effect |
N/A = test not performed, n < 10; * = significant test; Cg = chi-square goodness fit; Z = post hoc Z-test; ** = more frequent.
Table 6.
Hospital setting, comorbidities, and Candida spp. related to patients with positive blood samples for Candida spp. with central venous catheters.
Table 6.
Hospital setting, comorbidities, and Candida spp. related to patients with positive blood samples for Candida spp. with central venous catheters.
| Parameters | Sample |
|---|---|
| Central venous catheters | 28.6% (50) |
| Hospital setting | |
| ICU | 38.0% (19) |
| Medical | 44.0% (22) |
| Surgical | 18.0% (9) |
| Comorbidities † | |
| Chronic heart failure | 24.0% (12) |
| Chronic obstructive pulmonary disease | 18.0% (9) |
| Chronic renal failure | 28.0% (14) |
| Diabetes | 16.0% (8) |
| Oncologic patients ††† | 30.0% (15) |
| Candida spp. †† | |
| Candida albicans | 30.0% (15) |
| Candida glabrata | 10.0% (5) |
| Candida parapsilosis | 50.0% (25) * |
| Candida tropicalis | 10.0% (5) |
† = comorbidities more frequent; †† = Candida spp. more frequent; ††† = including organ solid tumors and/or hematologic tumors; * = significance percentage obtained using chi-square goodness of fit.
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MDPI and ACS Style
Di Carlo, P.; Serra, N.; Collotta, O.; Colomba, C.; Firenze, A.; Aprea, L.; Distefano, S.A.; Cortegiani, A.; Giammanco, G.; Fasciana, T.M.A.; et al. The Influence of the Seasonal Variability of Candida spp. Bloodstream Infections and Antifungal Treatment: A Mediterranean Pilot Study. Antibiotics 2025, 14, 452. https://doi.org/10.3390/antibiotics14050452
AMA Style
Di Carlo P, Serra N, Collotta O, Colomba C, Firenze A, Aprea L, Distefano SA, Cortegiani A, Giammanco G, Fasciana TMA, et al. The Influence of the Seasonal Variability of Candida spp. Bloodstream Infections and Antifungal Treatment: A Mediterranean Pilot Study. Antibiotics. 2025; 14(5):452. https://doi.org/10.3390/antibiotics14050452
Chicago/Turabian StyleDi Carlo, Paola, Nicola Serra, Ornella Collotta, Claudia Colomba, Alberto Firenze, Luigi Aprea, Salvatore Antonino Distefano, Andrea Cortegiani, Giovanni Giammanco, Teresa Maria Assunta Fasciana, and et al. 2025. "The Influence of the Seasonal Variability of Candida spp. Bloodstream Infections and Antifungal Treatment: A Mediterranean Pilot Study" Antibiotics 14, no. 5: 452. https://doi.org/10.3390/antibiotics14050452
APA StyleDi Carlo, P., Serra, N., Collotta, O., Colomba, C., Firenze, A., Aprea, L., Distefano, S. A., Cortegiani, A., Giammanco, G., Fasciana, T. M. A., Virruso, R., Capuano, A., Sergi, C. M., & Cascio, A. (2025). The Influence of the Seasonal Variability of Candida spp. Bloodstream Infections and Antifungal Treatment: A Mediterranean Pilot Study. Antibiotics, 14(5), 452. https://doi.org/10.3390/antibiotics14050452
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