Multifaceted Evaluation of Antibiotic Therapy as a Factor Associated with Candidemia in Non-Neutropenic Patients
by
, , , ,
Si-Ho Kim
1,
Seok Jun Mun
2,*,
Jin Suk Kang
2,
Chisook Moon
2,
Hyoung-Tae Kim
3 and
Ho Young Lee
4 1
Division of Infectious Diseases, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Republic of Korea
2
Division of Infectious Diseases, Department of Internal Medicine, Inje University Busan Paik Hospital, Inje University College of Medicine, 75 Bokji-ro, Busanjin-gu, Busan 47392, Republic of Korea
3
Department of Laboratory Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Republic of Korea
4
Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Inje University Busan Paik Hospital, Inje University College of Medicine, Busan 47392, Republic of Korea
*
Author to whom correspondence should be addressed.
J. Fungi 2023, 9(2), 270; https://doi.org/10.3390/jof9020270
Submission received: 14 December 2022
/
Revised: 4 February 2023
/
Accepted: 12 February 2023
/
Published: 18 February 2023
(This article belongs to the Special Issue New Perspectives for Candidiasis 2.0)
Abstract
:We aimed to evaluate various aspects of antibiotic therapy as factors associated with candidemia in non-neutropenic patients. A retrospective, matched, case-control study was conducted in two teaching hospitals. Patients with candidemia (cases) were compared to patients without candidemia (controls), matched by age, intensive care unit admission, duration of hospitalization, and type of surgery. Logistic regression analyses were performed to identify factors associated with candidemia. A total of 246 patients were included in the study. Of 123 candidemia patients, 36% had catheter-related bloodstream infections (CRBSIs). Independent factors in the whole population included immunosuppression (adjusted odds ratio [aOR] = 2.195; p = 0.036), total parenteral nutrition (aOR = 3.642; p < 0.001), and anti-methicillin-resistant S. aureus (MRSA) therapy for ≥11 days (aOR = 5.151; p = 0.004). The antibiotic factor in the non-CRBSI population was anti-pseudomonal beta-lactam treatment duration of ≥3 days (aOR = 5.260; p = 0.008). The antibiotic factors in the CRBSI population included anti-MRSA therapy for ≥11 days (aOR = 10.031; p = 0.019). Antimicrobial stewardship that reduces exposure to these antibacterial spectra could help prevent the development of candidemia.
1. Introduction
Candidemia is one of the most common nosocomial bloodstream infections [1]. Incidence rates of candidemia have been estimated to range from 2 to 14 cases per 100,000 persons in population-based studies, and are increasing because of an increase in the growth of those populations at high risk of candidemia [2,3,4]. Furthermore, candidemia is associated with a high crude mortality rate, despite antifungal therapy [2,3]. Mortality due to candidemia depends on the specific patient population and geographical region, and is difficult to distinguish from all-cause mortality which takes into account underlying medical conditions, but has been reported as high as 70% [4]. Given the high mortality rate associated with candidemia, preventive strategies are required, including antifungal prophylaxis and the identification of risk factors [2,5].
Previous studies have identified various risk factors for candidemia in both neutropenic and non-neutropenic patients. These include previous surgery, colonization by Candida species, presence of a central venous catheter (CVC), pancreatitis, immunosuppression, and total parenteral nutrition (TPN) [2,5,6,7,8,9,10,11,12]. Antibiotic therapy has also been frequently reported as a risk factor. Antibiotic factors found to be associated with candidemia in previous studies include the number of different antibiotics administered [6], cephalosporins [6,10], drugs with anti-anaerobic activity [6], glycopeptides [9,11,12], carbapenem or tigecycline antibiotics [10], nitroimidazoles [12], aminoglycosides [12], and any systemic antibiotic use [7,11]. However, the use of these antibiotics is inevitable for clinicians who treat various bacterial infections. Therefore, it is necessary to present antibiotic risk factors in a way that can practically intervene in antibiotic therapy. We aimed to evaluate whether various aspects of antibiotic therapy, including the duration of treatment and antibacterial spectrum, are factors associated with candidemia in non-neutropenic patients.
2. Methods
2.1. Study Design and Patients
We performed a retrospective, matched, case-control study in two university-affiliated tertiary hospitals in South Korea from January 2019 to August 2020 (Samsung Changwon Hospital, 760-bed; Inje University Busan Paik Hospital, 818-bed). Patients (age ≥ 18 years) with candidemia were included if they had at least one positive blood culture for Candida species. For each case, one control matched for age (±5 years), duration of hospitalization, hospital ward (intensive care unit (ICU)/non-ICU), and type of surgery was identified within the same hospital [12]. Duration of hospitalization in the cases was calculated as the time from the day of admission to the day of collection of the first positive blood culture with Candida species. Matched controls remained hospitalized for the equivalent time and did not develop candidemia during hospitalization. The hospital ward was assessed based on the index date (day of occurrence of candidemia in the cases or matched day in the controls). Surgical procedures within the four weeks before the index date was identified and classified as no surgery, hepatobiliary/gastrointestinal surgery, genitourinary surgery, other abdominal/pelvic surgery, cardiothoracic surgery, or other major surgery [10]. Patients who had neutropenia (absolute neutrophil count of <500 cells/mm3) or for whom we could not identify previous antibiotic therapy were excluded. Moreover, we excluded cases for which matched control patients could not be identified. The primary objective was to identify antibiotic factors associated with candidemia. The secondary objective was to identify differences in antibiotic factors according to the origins of candidemia, and the origins of candidemia were classified into catheter-related bloodstream infection (CRBSI) and non-CRBSI. The study was approved by the Samsung Changwon Hospital and Busan Paik Institutional Review Boards (IRB numbers: SCMC 2021-08-005 and BPIRB 2021-08-051).
2.2. Data Collection and Definitions
Demographic characteristics and clinical data were collected from electronic medical records. The following clinical data were collected: underlying diseases, immunosuppression, presence of a CVC or foley catheter, mechanical ventilation, continuous renal replacement therapy, TPN, antifungal prophylaxis, previous septic shock, intraabdominal infections, acute pancreatitis, and Candida colonization. Candida colonization was defined as the isolation of Candida species in the urine or respiratory specimens, because samples obtained from the stomach were not identified in the hospitals [13]. Candida CRBSI was defined as the growth of >15 colony-forming units from a catheter tip by a semiquantitative culture and/or differential time to positivity, meeting the CRBSI criteria [14]. We reviewed antibiotic therapy within the four weeks before the index date. The antibacterial spectra were classified as anti-methicillin-resistant Staphylococcus aureus (MRSA) agents, anti-pseudomonal beta-lactams (BLs), carbapenems, and anti-anaerobic agents. Anti-MRSA agents included intravenous (IV) vancomycin, IV teicoplanin, and IV or oral linezolid. Anti-pseudomonal BLs included meropenem, imipenem, piperacillin/tazobactam, cefepime, ceftazidime, cefoperazone/sulbactam and aztreonam. Anti-anaerobic agents included carbapenems, BL/beta-lactamase inhibitors, cephamycins, metronidazole, clindamycin, moxifloxacin, and tigecycline [15]. Combination therapy was defined as two or more relevant antibiotics being administered together for ≥2 days. The combination therapies evaluated were as follows: anti-MRSA agents and carbapenems, anti-MRSA agents and anti-pseudomonal BLs, double coverage for Pseudomonas, and double coverage for anaerobes. Double coverage for Pseudomonas was defined as anti-pseudomonal BLs plus any of the other agents, including aminoglycosides, fluoroquinolones, or colistin.
2.3. Statistical Analysis
Continuous variables are presented as medians (interquartile range (IQR)). Categorical variables are presented as frequency counts (percentages). Continuous variables were compared using a Student’s t test, or a Mann–Whitney U test as the results of the normality test. A Fisher’s exact test or chi-square test was used to compare categorical variables. The cut-off value of continuous variables was determined using a receiver operating characteristic (ROC) curve with the Youden Index. Univariable logistic regression analysis was performed to identify possible factors associated with candidemia. Variables with a p value < 0.1 in univariable analysis and those that were clinically relevant were included in the multivariable logistic regression model, and backward conditional selection was used to identify significant variables. Subgroup analysis was conducted comparing antibiotic factors between patients with CRBSI and those without CRBSI. A variance inflation factor of ≥10 was considered to indicate significant multicollinearity. The Hosmer–Lemeshow statistic was used to assess the goodness-of-fit of the model. p values were two-tailed, and p values < 0.05 were considered statistically significant. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 25.0 (IBM, Armonk, NY, USA).
3. Results
3.1. Study Population and Clinical Characteristics
Of 132 patients with candidemia, six patients for whom we could not identify previous antibiotic therapy and three patients for whom matched control patients were not identified were excluded. Overall, 246 patients (123 cases and 123 controls; 74 pairs and 49 pairs in Samsung Changwon Hospital and Inje University Busan Paik Hospital, respectively) were included in the study. C. albicans (53.7%) was the most common species identified in those patients with candidemia, followed by C. glabrata (20.3%), C. parapsilosis (11.4%), and C. tropicalis (8.9%). The median length of hospital stay before the index date was 14 days (IQR 5–28). Of the study population, 21.9% underwent surgery in the four weeks before the index date (hepatobiliary/gastrointestinal surgery 8.1%; genitourinary surgery 1.6%; other surgeries 12.2%). The median age was 73 years (IQR 63–80), and 39% of all patients were hospitalized in the ICU (Table 1). Forty-two (36%) patients with candidemia had CRBSI, and C. albicans (22, 52.4%) was also the most common pathogen in these patients. Metastatic cancer, an immunosuppressed state, presence of a CVC, and TPN were more frequent in case than in control patients. One patient with candidemia had acute pancreatitis.
The median number of antibiotics to which the entire population was exposed was two (IQR 1–4), and case patients were treated with more antibiotics than control patients (Table 2). In the whole population, patients with candidemia were treated more with tigecycline, glycopeptides and fluoroquinolones than control patients. Furthermore, patients with candidemia had longer anti-MRSA, anti-pseudomonal BL and anti-anaerobic treatment durations than control patients. In the non-CRBSI population, patients with candidemia were treated more with piperacillin/tazobactam. In the CRBSI population, patients with candidemia had longer carbapenem and anti-anaerobic treatment durations.
3.2. Factors Associated with Candidemia
Univariable logistic regression analysis in the whole population identified 19 possible factors, including 12 antibiotic factors (Table 3). Independent factors in the whole population were immunosuppression (adjusted odds ratio (aOR) = 2.195; 95% confidence interval (CI) = 1.053–4.577; p = 0.036), TPN (aOR = 3.642; 95% CI = 1.972–6.725; p < 0.001), and anti-MRSA therapy for ≥11 days (aOR = 5.151; 95% CI = 1.669–15.900; p = 0.004).
Univariable logistic regression analysis in the non-CRBSI population identified the 13 possible factors, including 10 antibiotic factors (Table 4). Independent factors in the non-CRBSI population were TPN (aOR = 4.745; 95% CI = 2.306–9.763; p < 0.001) and anti-pseudomonal BL treatment duration of ≥3 days (aOR = 5.260; 95% CI = 1.530–18.084; p = 0.008).
Univariable logistic regression analysis in the CRBSI population identified the 14 possible factors, including seven antibiotic factors (Table 5). Independent factors in the CRBSI population were immunosuppression (aOR = 5.745; 95% CI = 1.609–20.513; p = 0.007), CVC (aOR= 23.270; 95% CI = 2.496–216.931; p = 0.006), and anti-MRSA therapy for ≥11 days (aOR = 10.031; 95% CI = 1.460–68.889; p = 0.019).
4. Discussion
We evaluated whether various aspects of antibiotic therapy were factors associated with candidemia, including the duration of treatment and antibacterial spectrum. Among various antibiotic factors, anti-MRSA therapy for ≥11 days was significantly associated with candidemia in the whole and CRBSI populations. Furthermore, anti-pseudomonal BL treatment for a duration of ≥3 days was significantly associated with candidemia in the non-CRBSI population. Along with antibiotic factors, immunosuppression, CVC, and TPN were independent factors for candidemia, consistent with previous studies [7,8,9,10,11,12,16]. Further detailed investigation of these factors could provide insights into the origin of candidemia and point the way to preventive measures.
Candidemia primarily originates from the gastrointestinal tract and intravascular catheters or the skin, but it is difficult to distinguish among these sources of candidemia [2]. This study showed that anti-MRSA therapy for ≥11 days was an independent factor for candidemia in the whole and CRBSI populations. This antibiotic factor may be associated more with candidemia originating from the skin or intravascular catheters, rather than the gastrointestinal tract for the following reasons: first, antibiotic therapy promotes Candida colonization by dysbiosis of body sites [5], and the bacterial composition of the skin microbiota differs from that of the gastrointestinal microbiota. The gut microbiota is comprised mainly of obligate anaerobes belonging to Firmicutes (mostly Gram-positive) and Bacteroidetes (mostly Gram-negative), and includes at least 160 prevalent bacterial species [17]. By contrast, the skin microbiota comprises primarily Gram-positive bacteria, including Propionibacterium, Corynebacterium, and Staphylococcus species [18]. These Gram-positive bacteria are highly susceptible to the agents used to treat MRSA infections, such as vancomycin and linezolid [19,20]. Second, in contrast with the skin, in the gastrointestinal tract, even short-term antibiotic therapy can cause significant dysbiosis. Murine and human studies have shown that antibiotic exposure of 4 or 5 days dramatically reduces the microbial diversity and richness of the gut microbiota [21,22]. These results correspond to the risk factors associated with C. difficile infection and candidemia, both of which are associated with gut dysbiosis. The use of parenteral or oral vancomycin and anti-anaerobic agents for >3 days has been shown to be associated with candidemia in pediatric ICU patients [8]. Another study showed that empirical use of anti-pseudomonal BLs for >48 h was an independent risk factor of C. difficile infection [23]. Furthermore, these results are consistent with our finding that anti-pseudomonal BL treatment for a duration of ≥3 days was an independent factor in the non-CRBSI population. By contrast, exposure of the skin microbiota to antibiotics has been reported to have limited effects. Two mouse model studies showed that oral antibiotic treatment for four weeks did not significantly affect the bacterial density or composition of the skin microbiota in contrast with the gut microbiota [24,25]. Two human studies evaluated the effect of four- to six-week tetracycline treatment on the skin microbiota [26,27]. Both studies showed changes in the relative abundance of bacterial taxa, but no significant changes in microbial diversity. Another human study showed no significant differences in the microbiome comparing skin sites before and after oral antibiotic therapy for skin and soft tissue infections [28]. Given these results, systemic antibiotic exposure may affect the skin microbiota less than the gut microbiota, and prolonged treatment duration may be necessary to cause candidemia originating from the skin or intravascular catheters.
In the non-CRBSI population, TPN and anti-pseudomonal BL treatment for a duration of ≥3 days were independent factors. These factors are known to be associated with candidemia originating from the gastrointestinal tract. TPN can alter the gut barrier function and contribute to Candida translocation from the gut, although lipid emulsions and glucose contained in TPN can promote Candida CRBSIs [9,29,30,31]. Most anti-pseudomonal BLs have an anti-anaerobic activity, which primarily affects the gut microbiota and can induce dysbiosis on gut microbiota [32]. Because these antibiotics also have broad-spectrum antibacterial activity, they may inhibit the subsequent bloom of low-abundance commensals, including Enterobacterales, providing additional spatial liberation of ecological niches for Candida species [21].
We identified that immunosuppression is significantly associated with candidemia in the CRBSI population, rather than in the non-CRBSI population. This result may be because the study was conducted on patients without neutropenia. Previous study in a murine model showed that both neutropenia and mucosal disruption were required for C. albicans dissemination from the gastrointestinal tract after immunosuppression with anti-cancer drugs [33]. In contrast, in Candida CRBSI, fungal cells directly invade the bloodstream and then adhere to the CVC surfaces and form biofilms [34]. Furthermore, formed biofilms protect fungal cells from the host immune system defenses, as well as antifungal drugs [35]. Because of these differences in mechanisms, moderate immunosuppression without neutropenia may be more vulnerable to candidemia originating from intravascular catheters than candidemia originating from the gastrointestinal tract.
In univariable analysis, various antibiotic factors, including the number of antibiotics received, duration of antibiotic therapy, exposure to fluoroquinolones, tigecycline, piperacillin/tazobactam, carbapenem treatment and anti-anaerobic therapy, were also identified as possible factors associated with candidemia. These findings may indirectly reflect the close association between antibiotic therapy and candidemia. Furthermore, among these factors, anti-anaerobic therapy is well known to be associated with candidemia originating from the gastrointestinal tract [16,32]. However, anti-anaerobic therapy was not an independent factor for candidemia in this study. This result might be due to correlations with other antibiotic factors, especially anti-pseudomonal BLs. Furthermore, evaluating the impact of antibiotic therapy on the microbiota may require consideration not only of the antibacterial spectrum, but also the pharmacokinetics of the antibiotic, including routes of administration and excretion [32].
There are several limitations to this study. First, because it was a retrospective study, unidentified factors might have affected the occurrence of candidemia. Second, the antibacterial spectrum and combination therapy categories used in this study were somewhat arbitrary, as antibiotics classified into the same category have heterogeneous pharmacokinetic properties. However, we think that these categories used in this study can reflect real-world approaches to antimicrobial de-escalation, including narrowing the spectrum of the pivotal antimicrobial and early discontinuation of unnecessary companion antibiotics [36]. Third, because the study population was relatively small, other factors might not have been identified as significant in the multivariable analysis, given the many possible factors included in the univariable analysis. Fourth, we evaluated the origins of candidemia by distinguishing them as being from the gastrointestinal tract and intravascular catheters. However, candidemia often arises from the urinary tract, and candidemia from other origins can lead to the colonization of the catheter and biofilm formation [2]. In addition, the non-CRBSI population may include misdiagnosed CRBSI patients. These factors could make it difficult to clearly distinguish the origins of candidemia. Fifth, because age, ICU admission, and surgery were used as matching criteria, these factors could not be evaluated.
In conclusion, we identified specific antibiotic exposures and durations as independent factors of candidemia. The most influential antibiotic factors differed depending on the presence of CRBSI, which may suggest that the antibiotics that affect candidemia differ depending on the origin of candidemia. Furthermore, the associations with specific antibacterial spectrums and treatment durations identified in this study suggest that active antimicrobial stewardship, especially de-escalation or cessation of anti-MRSA and anti-pseudomonal BL treatment, could help reduce the incidence of candidemia.
Author Contributions
Conceptualization, S.J.M.; Methodology, S.J.M. and S.-H.K.; Formal Analysis: S.-H.K. and S.J.M.; Investigation, J.S.K., C.M., H.-T.K., H.Y.L., S.-H.K. and S.J.M.; Writing—Original Draft Preparation, S.-H.K. and S.J.M.; Writing—Review & Editing, J.S.K., C.M., H.-T.K. and H.Y.L.; Funding Acquisition, S.J.M. and S.-H.K. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the 2021 research grant from Inje University Busan Paik Hospital, and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR21C1003).
Institutional Review Board Statement
The study was approved by the Samsung Changwon Hospital and Busan Paik Institutional Review Boards (IRB numbers: SCMC 2021-08-005 and BPIRB 2021-08-051).
Informed Consent Statement
Patient consent was waived due to the retrospective nature of this study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions privacy.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Wisplinghoff, H.; Bischoff, T.; Tallent, S.M.; Seifert, H.; Wenzel, R.P.; Edmond, M.B. Nosocomial bloodstream infections in US hospitals: Analysis of 24,179 cases from a prospective nationwide surveillance study. Clin. Infect. Dis. 2004, 39, 309–317. [Google Scholar] [CrossRef] [Green Version]
- Kullberg, B.J.; Arendrup, M.C. Invasive Candidiasis. N. Engl. J. Med. 2015, 373, 1445–1456. [Google Scholar] [CrossRef] [Green Version]
- Antinori, S.; Milazzo, L.; Sollima, S.; Galli, M.; Corbellino, M. Candidemia and invasive candidiasis in adults: A narrative review. Eur. J. Intern. Med. 2016, 34, 21–28. [Google Scholar] [CrossRef]
- Lamoth, F.; Lockhart, S.R.; Berkow, E.L.; Calandra, T. Changes in the epidemiological landscape of invasive candidiasis. J. Antimicrob. Chemother. 2018, 73, i4–i13. [Google Scholar] [CrossRef] [Green Version]
- Pfaller, M.A.; Diekema, D.J. Epidemiology of invasive candidiasis: A persistent public health problem. Clin. Microbiol. Rev. 2007, 20, 133–163. [Google Scholar] [CrossRef] [Green Version]
- Eggimann, P.; Garbino, J.; Pittet, D. Epidemiology of Candida species infections in critically ill non-immunosuppressed patients. Lancet Infect. Dis. 2003, 3, 685–702. [Google Scholar] [CrossRef]
- Ostrosky-Zeichner, L.; Sable, C.; Sobel, J.; Alexander, B.D.; Donowitz, G.; Kan, V.; Kauffman, C.A.; Kett, D.; Larsen, R.A.; Morrison, V.; et al. Multicenter retrospective development and validation of a clinical prediction rule for nosocomial invasive candidiasis in the intensive care setting. Eur. J. Clin. Microbiol. Infect. Dis. 2007, 26, 271–276. [Google Scholar] [CrossRef]
- Zaoutis, T.E.; Prasad, P.A.; Localio, A.R.; Coffin, S.E.; Bell, L.M.; Walsh, T.J.; Gross, R. Risk factors and predictors for candidemia in pediatric intensive care unit patients: Implications for prevention. Clin. Infect. Dis. 2010, 51, e38–e45. [Google Scholar] [CrossRef] [Green Version]
- Luzzati, R.; Cavinato, S.; Giangreco, M.; Grana, G.; Centonze, S.; Deiana, M.L.; Biolo, G.; Barbone, F. Peripheral and total parenteral nutrition as the strongest risk factors for nosocomial candidemia in elderly patients: A matched case-control study. Mycoses 2013, 56, 664–671. [Google Scholar] [CrossRef] [PubMed]
- Playford, E.G.; Lipman, J.; Jones, M.; Lau, A.F.; Kabir, M.; Chen, S.C.; Marriott, D.J.; Seppelt, I.; Gottlieb, T.; Cheung, W.; et al. Problematic Dichotomization of Risk for Intensive Care Unit (ICU)-Acquired Invasive Candidiasis: Results Using a Risk-Predictive Model to Categorize 3 Levels of Risk From a Multicenter Prospective Cohort of Australian ICU Patients. Clin. Infect. Dis. 2016, 63, 1463–1469. [Google Scholar] [CrossRef] [Green Version]
- Falcone, M.; Tiseo, G.; Tascini, C.; Russo, A.; Sozio, E.; Raponi, G.; Rosin, C.; Pignatelli, P.; Carfagna, P.; Farcomeni, A.; et al. Assessment of risk factors for candidemia in non-neutropenic patients hospitalized in Internal Medicine wards: A multicenter study. Eur. J. Intern. Med. 2017, 41, 33–38. [Google Scholar] [CrossRef] [PubMed]
- Poissy, J.; Damonti, L.; Bignon, A.; Khanna, N.; Von Kietzell, M.; Boggian, K.; Neofytos, D.; Vuotto, F.; Coiteux, V.; Artru, F.; et al. Risk factors for candidemia: A prospective matched case-control study. Crit. Care 2020, 24, 109. [Google Scholar] [CrossRef]
- Leon, C.; Ruiz-Santana, S.; Saavedra, P.; Almirante, B.; Nolla-Salas, J.; Alvarez-Lerma, F.; Garnacho-Montero, J.; Leon, M.A.; Group, E.S. A bedside scoring system (“Candida score”) for early antifungal treatment in nonneutropenic critically ill patients with Candida colonization. Crit. Care Med. 2006, 34, 730–737. [Google Scholar] [CrossRef]
- Mermel, L.A.; Allon, M.; Bouza, E.; Craven, D.E.; Flynn, P.; O’Grady, N.P.; Raad, I.I.; Rijnders, B.J.; Sherertz, R.J.; Warren, D.K. Clinical practice guidelines for the diagnosis and management of intravascular catheter-related infection: 2009 Update by the Infectious Diseases Society of America. Clin. Infect. Dis. 2009, 49, 1–45. [Google Scholar] [CrossRef]
- Brook, I. Spectrum and treatment of anaerobic infections. J. Infect. Chemother. 2016, 22, 1–13. [Google Scholar] [CrossRef] [Green Version]
- Yoo, J.; Kim, S.H.; Hur, S.; Ha, J.; Huh, K.; Cha, W.C. Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study. JMIR Med. Inform. 2021, 9, e24651. [Google Scholar] [CrossRef]
- Qin, J.; Li, R.; Raes, J.; Arumugam, M.; Burgdorf, K.S.; Manichanh, C.; Nielsen, T.; Pons, N.; Levenez, F.; Yamada, T.; et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010, 464, 59–65. [Google Scholar] [CrossRef] [Green Version]
- Byrd, A.L.; Belkaid, Y.; Segre, J.A. The human skin microbiome. Nat. Rev. Microbiol. 2018, 16, 143–155. [Google Scholar] [CrossRef] [PubMed]
- Oprica, C.; Nord, C.E.; ESCMID Study Group on Antimicrobial Resistance in Anaerobic Bacteria. European surveillance study on the antibiotic susceptibility of Propionibacterium acnes. Clin. Microbiol. Infect. 2005, 11, 204–213. [Google Scholar] [CrossRef] [Green Version]
- Neemuchwala, A.; Soares, D.; Ravirajan, V.; Marchand-Austin, A.; Kus, J.V.; Patel, S.N. In Vitro Antibiotic Susceptibility Pattern of Non-diphtheriae Corynebacterium Isolates in Ontario, Canada, from 2011 to 2016. Antimicrob. Agents Chemother. 2018, 62, e01776-17. [Google Scholar] [CrossRef] [Green Version]
- Palleja, A.; Mikkelsen, K.H.; Forslund, S.K.; Kashani, A.; Allin, K.H.; Nielsen, T.; Hansen, T.H.; Liang, S.; Feng, Q.; Zhang, C.; et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 2018, 3, 1255–1265. [Google Scholar] [CrossRef]
- Venturini, C.; Bowring, B.; Fajardo-Lubian, A.; Devine, C.; Iredell, J. Effects of Antibiotic Treatment with Piperacillin/Tazobactam versus Ceftriaxone on the Composition of the Murine Gut Microbiota. Antimicrob. Agents Chemother. 2021, 65, e01504-20. [Google Scholar] [CrossRef] [PubMed]
- Seddon, M.M.; Bookstaver, P.B.; Justo, J.A.; Kohn, J.; Rac, H.; Haggard, E.; Mediwala, K.N.; Dash, S.; Al-Hasan, M.N. Role of Early De-escalation of Antimicrobial Therapy on Risk of Clostridioides difficile Infection Following Enterobacteriaceae Bloodstream Infections. Clin. Infect. Dis. 2019, 69, 414–420. [Google Scholar] [CrossRef] [PubMed]
- Naik, S.; Bouladoux, N.; Wilhelm, C.; Molloy, M.J.; Salcedo, R.; Kastenmuller, W.; Deming, C.; Quinones, M.; Koo, L.; Conlan, S.; et al. Compartmentalized control of skin immunity by resident commensals. Science 2012, 337, 1115–1119. [Google Scholar] [CrossRef] [Green Version]
- Dellacecca, E.R.; Cosgrove, C.; Mukhatayev, Z.; Akhtar, S.; Engelhard, V.H.; Rademaker, A.W.; Knight, K.L.; Le Poole, I.C. Antibiotics Drive Microbial Imbalance and Vitiligo Development in Mice. J. Invest. Dermatol. 2020, 140, 676–687.e676. [Google Scholar] [CrossRef]
- Chien, A.L.; Tsai, J.; Leung, S.; Mongodin, E.F.; Nelson, A.M.; Kang, S.; Garza, L.A. Association of Systemic Antibiotic Treatment of Acne With Skin Microbiota Characteristics. JAMA Dermatol. 2019, 155, 425–434. [Google Scholar] [CrossRef]
- Woo, Y.R.; Lee, S.H.; Cho, S.H.; Lee, J.D.; Kim, H.S. Characterization and Analysis of the Skin Microbiota in Rosacea: Impact of Systemic Antibiotics. J. Clin. Med. 2020, 9, 185. [Google Scholar] [CrossRef] [Green Version]
- Chan, A.A.; Flores, E.A.; Navarrete, M.; Phan Tran, D.; Lee, D.J.; Miller, L.G. The Effect of Systemic Antibiotics for Suppurative Skin and Soft Tissue Infections on the Skin Microbiome. Open Forum Infect. Dis. 2022, 9, ofac141. [Google Scholar] [CrossRef]
- Pappo, I.; Polacheck, I.; Zmora, O.; Feigin, E.; Freund, H.R. Altered gut barrier function to Candida during parenteral nutrition. Nutrition 1994, 10, 151–154. [Google Scholar]
- Shin, J.H.; Kee, S.J.; Shin, M.G.; Kim, S.H.; Shin, D.H.; Lee, S.K.; Suh, S.P.; Ryang, D.W. Biofilm production by isolates of Candida species recovered from nonneutropenic patients: Comparison of bloodstream isolates with isolates from other sources. J. Clin. Microbiol. 2002, 40, 1244–1248. [Google Scholar] [CrossRef] [Green Version]
- Swindell, K.; Lattif, A.A.; Chandra, J.; Mukherjee, P.K.; Ghannoum, M.A. Parenteral lipid emulsion induces germination of Candida albicans and increases biofilm formation on medical catheter surfaces. J. Infect. Dis. 2009, 200, 473–480. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Saliba, R.; Mizrahi, A.; Gauthier, P.P.; Alban, L.M.; Zahar, J.R.; Pilmis, B. Antimicrobial Stewardship Program: Reducing Antibiotic’s Spectrum of Activity Is not the Solution to Limit the Emergence of Multidrug-Resistant Bacteria. Antibiotics 2022, 11, 70. [Google Scholar] [CrossRef] [PubMed]
- Koh, A.Y.; Köhler, J.R.; Coggshall, K.T.; Van Rooijen, N.; Pier, G.B. Mucosal damage and neutropenia are required for Candida albicans dissemination. PLoS Pathog. 2008, 4, e35. [Google Scholar] [CrossRef] [Green Version]
- Jung, P.; Mischo, C.E.; Gunaratnam, G.; Spengler, C.; Becker, S.L.; Hube, B.; Jacobs, K.; Bischoff, M. Candida albicans adhesion to central venous catheters: Impact of blood plasma-driven germ tube formation and pathogen-derived adhesins. Virulence 2020, 11, 1453–1465. [Google Scholar] [CrossRef] [PubMed]
- Cavalheiro, M.; Teixeira, M.C. Candida Biofilms: Threats, Challenges, and Promising Strategies. Front. Med. 2018, 5, 28. [Google Scholar] [CrossRef] [Green Version]
- Lakbar, I.; De Waele, J.J.; Tabah, A.; Einav, S.; Martin-Loeches, I.; Leone, M. Antimicrobial De-Escalation in the ICU: From Recommendations to Level of Evidence. Adv. Ther. 2020, 37, 3083–3096. [Google Scholar] [CrossRef] [PubMed]
Table 1.
Clinical characteristics of patients with candidemia and matched controls.
| Characteristics | Whole Population | Non-CRBSI | CRBSI | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Controls (n = 123) | Cases (n = 123) | p Value | Controls (n = 81) | Cases (n = 81) | p Value | Controls (n = 42) | Cases (n = 42) | p Value | |
| Male | 69 (56.1) | 69 (56.1) | >0.999 | 47 (58.0) | 49 (60.5) | 0.749 | 22 (52.4) | 20 (47.6) | 0.663 |
| Age, years, median (IQR) | 73 (64–81) | 73 (62–80) | 0.938 | 73 (64.5–81) | 73 (63–81.5) | 0.889 | 71 (60–78.25) | 70 (70–78) | 0.992 e |
| LOS, days, median (IQR) | 14 (5–28) | 14 (5–28) | 0.985 | 13 (4.5–26) | 13 (4.5–26) | 0.984 | 14 (5.75–33.25) | 14 (5.75–33.25) | >0.999 |
| Underlying medical condition | |||||||||
| Heart failure | 15 (12.2) | 9 (7.3) | 0.197 | 11 (13.6) | 6 (7.4) | 0.200 | 4 (9.5) | 3 (7.1) | >0.999 |
| DM | 48 (39.0) | 41 (33.3) | 0.353 | 33 (40.7) | 33 (40.7) | >0.999 | 15 (35.7) | 8 (19.0) | 0.087 |
| Chronic liver disease | 10 (8.1) | 6 (4.9) | 0.301 | 7 (8.6) | 4 (4.9) | 0.349 | 3 (7.1) | 2 (4.8) | >0.999 |
| CKD a | 20 (16.3) | 13 (10.6) | 0.190 | 15 (18.5) | 11 (13.6) | 0.392 | 5 (11.9) | 2 (4.8) | 0.433 |
| HD dependence | 11 (8.9) | 10 (8.1) | 0.820 | 9 (11.1) | 8 (9.9) | 0.798 | 2 (4.8) | 2 (4.8) | >0.999 |
| Chronic pulmonary disease | 6 (4.9) | 8 (6.5) | 0.582 | 4 (4.9) | 6 (7.4) | 0.514 | 2 (4.8) | 2 (4.8) | >0.999 |
| Solid cancer | 13 (10.6) | 13 (10.6) | >0.999 | 8 (9.9) | 10 (12.3) | 0.617 | 5 (11.9) | 3 (7.1) | 0.713 |
| Metastatic cancer | 20 (16.1) | 34 (27.4) | 0.031 | 11 (13.6) | 13 (16.0) | 0.658 | 9 (21.4) | 21 (50.0) | 0.006 |
| Hematologic malignancy | 1 (0.8) | 2 (1.6) | >0.999 | 1 (1.2) | 2 (2.5) | >0.999 | 0 (0) | 0 (0) | NA |
| Immunosuppression b | 17 (13.8) | 33 (26.8) | 0.011 | 10 (12.3) | 13 (16.0) | 0.499 | 7 (16.7) | 20 (47.6) | 0.002 |
| Clinical risk factors c | |||||||||
| ICU admission | 48 (39.0) | 48 (39.0) | >0.999 | 35 (43.2) | 35 (43.2) | >0.999 | 13 (31.0) | 13 (31.0) | >0.999 |
| CVC | 69 (56.1) | 101 (82.1) | <0.001 | 47 (58.0) | 60 (74.1) | 0.031 | 22 (52.4) | 41 (97.6) | <0.001 |
| Urinary catheter | 89 (72.4) | 77 (62.6) | 0.102 | 62 (76.5) | 55 (67.9) | 0.219 | 27 (64.3) | 22 (52.4) | 0.268 |
| MV | 42 (34.1) | 40 (32.5) | 0.787 | 29 (35.8) | 29 (35.8) | >0.999 | 13 (31.0) | 11 (26.2) | 0.629 |
| CRRT | 10 (8.1) | 12 (9.8) | 0.655 | 9 (11.1) | 7 (8.6) | 0.598 | 1 (2.4) | 5 (11.9) | 0.202 |
| TPN | 52 (42.3) | 93 (75.6) | <0.001 | 35 (43.2) | 63 (77.8) | <0.001 | 17 (40.5) | 30 (71.4) | 0.004 |
| Previous septic shock | 34 (27.6) | 48 (39.0) | 0.058 | 26 (32.1) | 33 (40.7) | 0.253 | 8 (19.0) | 15 (35.7) | 0.087 |
| IAI | 18 (14.6) | 29 (23.6) | 0.074 | 17 (21.0) | 22 (27.2) | 0.358 | 1 (2.4) | 7 (16.7) | 0.057 |
| Candida colonization d | 12 (9.8) | 21 (17.1) | 0.092 | 9 (11.1) | 13 (16.0) | 0.359 | 3 (7.1) | 8 (19.0) | 0.106 |
| Antifungal prophylaxis | 0 (0) | 2 (1.6) | 0.156 | 0 (0) | 1 (1.2) | >0.999 | 0 (0) | 1 (2.4) | >0.999 |
Data are presented as the numbers (%), unless otherwise indicated. CKD, chronic kidney disease; CRBSI, catheter-related bloodstream infection; CRRT, continuous renal replacement therapy; CVC, central venous catheter; HD, hemodialysis; IAI, intra-abdominal infection; ICU, intensive care unit; IQR, interquartile range; LOS, length of stay; MV, mechanical ventilation; TPN, total parenteral nutrition. a Defined as estimated glomerular filtration rate (GFR) < 60 mL/min/1.73 m2 for ≥3 months. b Defined as the use of steroids (prednisolone > 0.5 mg/kg/d or equivalent for >1 month), chemotherapy, or anti-tumor necrosis factor therapy within the past 3 months. c Within 2 weeks before candidemia (cases) or matched time period (controls). d Defined as the isolation of Candida species in the urine or respiratory specimens. e A Student’s t test was used to compare between the groups.
Table 2.
Antibiotic therapy in patients with candidemia and matched controls.
| Antibiotic Therapy | Whole Population | Non-CRBSI | CRBSI | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Controls (n = 123) | Cases (n = 123) | p Value | Controls (n = 81) | Cases (n = 81) | p Value | Controls (n = 42) | Cases (n = 42) | p Value | |
| Specific antibiotic | |||||||||
| Amoxicillin/clavulanate a | 3 (2.4) | 1 (0.8) | 0.622 | 2 (2.5) | 0 (0) | 0.497 | 1 (2.4) | 1 (2.4) | >0.999 |
| Piperacillin/tazobactam | 47 (38.2) | 60 (48.8) | 0.095 | 29 (35.8) | 42 (51.9) | 0.040 | 18 (42.9) | 18 (42.9) | >0.999 |
| Cephalosporins G1/2 | 16 (13.0) | 12 (9.8) | 0.422 | 8 (9.9) | 7 (8.6) | 0.786 | 8 (19.0) | 5 (11.9) | 0.365 |
| Cephalosporins G3 | 41 (33.3) | 29 (23.6) | 0.090 | 29 (35.8) | 19 (23.5) | 0.085 | 12 (28.6) | 10 (23.8) | 0.620 |
| Cephalosporins G4 | 11 (8.9) | 19 (15.4) | 0.119 | 5 (6.2) | 12 (14.8) | 0.073 | 6 (14.3) | 7 (16.7) | 0.763 |
| Carbapenems | 40 (32.5) | 47 (38.2) | 0.351 | 29 (35.8) | 29 (35.8) | >0.999 | 11 (26.2) | 18 (42.9) | 0.108 |
| Fluoroquinolones | 29 (23.6) | 44 (35.8) | 0.036 | 21 (25.9) | 31 (38.3) | 0.092 | 8 (19.0) | 13 (31.0) | 0.208 |
| Glycopeptides | 26 (21.1) | 40 (32.5) | 0.044 | 19 (23.5) | 29 (35.8) | 0.085 | 7 (16.7) | 11 (26.2) | 0.287 |
| Metronidazole | 22 (17.9) | 21 (17.1) | 0.867 | 17 (21.0) | 13 (16.0) | 0.418 | 5 (11.9) | 8 (19.0) | 0.365 |
| Aminoglycosides | 4 (3.3) | 5 (4.1) | >0.999 | 3 (3.7) | 2 (2.5) | >0.999 | 1 (2.4) | 3 (7.1) | 0.616 |
| Tigecycline | 1 (0.8) | 8 (6.5) | 0.036 | 0 (0) | 4 (4.9) | 0.120 | 1 (2.4) | 4 (9.5) | 0.360 |
| Colistin | 9 (7.3) | 9 (7.3) | >0.999 | 5 (6.2) | 6 (7.4) | 0.755 | 4 (9.5) | 3 (7.1) | >0.999 |
| Clindamycin | 4 (3.3) | 4 (3.3) | >0.999 | 3 (3.7) | 3 (3.7) | >0.999 | 1 (2.4) | 1 (2.4) | >0.999 |
| Number of antibiotics exposed to, median (IQR) | 2 (1–4) | 3 (1–4) | 0.019 | 2 (1–4) | 3 (1–4) | 0.133 | 2 (1–3.25) | 3 (1–4) | 0.054 |
| Combination therapy (for ≥2 d) | |||||||||
| Anti-MRSA agents and carbapenems b | 18 (14.6) | 25 (20.3) | 0.240 | 15 (18.5) | 16 (19.8) | 0.842 | 3 (7.1) | 9 (21.4) | 0.061 |
| Anti-MRSA agents and anti-pseudomonal BLs b,c | 26 (21.1) | 35 (28.5) | 0.184 | 18 (22.2) | 23 (28.4) | 0.366 | 8 (19.0) | 12 (28.6) | 0.306 |
| Double coverage for Pseudomonas d | 25 (20.3) | 37 (30.1) | 0.078 | 16 (19.8) | 26 (32.1) | 0.073 | 9 (21.4) | 11 (26.2) | 0.608 |
| Double coverage for anaerobes e | 7 (5.7) | 12 (9.8) | 0.232 | 4 (4.9) | 8 (9.9) | 0.230 | 3 (7.1) | 4 (9.5) | >0.999 |
| Duration of therapy, days, median (IQR) | |||||||||
| Total duration | 10 (2–20) | 12 (4–25) | 0.096 | 9 (2–20.5) | 11 (4–21) | 0.391 | 11.5 (1.75–18.25) | 15 (4.75–28) | 0.091 |
| IV duration | 9 (1–19) | 11 (4–21) | 0.136 | 7 (1.5–20) | 10 (4–21) | 0.272 | 11 (1–17.5) | 13.5 (3–28) | 0.272 |
| Anti-MRSA therapy b | 0 (0–0) | 0 (0–5) | 0.044 | 0 (0–1) | 0 (0–4.5) | 0.107 | 0 (0–0.25) | 0 (0–7.75) | 0.228 |
| Carbapenem treatment duration | 0 (0–3) | 0 (0–5) | 0.197 | 0 (0–4) | 0 (0–4) | 0.972 | 0 (0–1) | 0 (0–12) | 0.031 |
| Anti-pseudomonal BL treatment duration c | 3 (0–14) | 6 (0–15) | 0.029 | 2 (0–14) | 5 (1.5–13.5) | 0.068 | 6.5 (0–13.25) | 8.5 (0–18) | 0.173 |
| Anti-anaerobic therapy e | 5 (0–14) | 7 (2–15) | 0.024 | 5 (0–16) | 6 (2–14.5) | 0.273 | 3 (0–12) | 9 (2.75–18.25) | 0.020 |
Data are presented as the numbers (%), unless otherwise indicated. BL, beta-lactam; CRBSI, catheter-related bloodstream infection; FQ, fluoroquinolone; IQR, interquartile range; IV, intravenous; MRSA, methicillin-resistant Staphylococcus aureus. a This category includes ampicillin/sulbactam. b Anti-MRSA agents included IV vancomycin, IV teicoplanin, and IV or oral linezolid. c Anti-pseudomonal BLs included meropenem, imipenem, piperacillin/tazobactam, cefepime, ceftazidime, cefoperazone/sulbactam, and aztreonam. d This category was defined as anti-pseudomonal BLs plus any of the other agents including aminoglycosides, fluoroquinolones, or colistin. e Anti-anaerobic agents included carbapenems, BL/beta-lactamase inhibitors, cephamycins, metronidazole, clindamycin, moxifloxacin, and tigecycline.
Table 3.
Univariable and multivariable logistic regression analyses in the whole population.
| Variable | OR | p Value | Adjusted OR a,b | p Value |
|---|---|---|---|---|
| Metastatic cancer | 1.967 (1.057–3.661) | 0.033 | ||
| Immunosuppression | 2.286 (1.195–4.375) | 0.013 | 2.195 (1.053–4.577) | 0.036 |
| CVC | 3.593 (2.006–6.434) | <0.001 | 1.989 (1–3.958) | 0.050 |
| TPN | 4.233 (2.454–7.302) | <0.001 | 3.642 (1.972–6.725) | <0.001 |
| Previous septic shock | 1.675 (0.980–2.864) | 0.059 | ||
| Intra-abdominal infection | 1.800 (0.939–3.449) | 0.077 | ||
| Candida colonization | 1.904 (0.892–4.066) | 0.096 | ||
| Piperacillin/tazobactam | 1.540 (0.927–2.557) | 0.095 | ||
| Cephalosporins G3 | 0.617 (0.352–1.080) | 0.091 | ||
| Fluoroquinolones | 1.805 (1.035–3.148) | 0.037 | ||
| Glycopeptides | 1.798 (1.013–3.193) | 0.045 | ||
| Tigecycline | 8.487 (1.045–68.919) | 0.045 | ||
| Exposed to ≥3 antibiotics c | 2.384 (1.425–3.991) | 0.001 | ||
| Double coverage for Pseudomonas | 1.687 (0.940–3.025) | 0.080 | ||
| Total duration of antibiotic therapy ≥8 d c | 1.551 (0.930–2.586) | 0.093 | ||
| Duration of IV antibiotic therapy ≥2 d c | 1.966 (1.032–3.745) | 0.040 | ||
| Anti-MRSA therapy ≥11 d c | 3.562 (1.371–9.259) | 0.009 | 5.151 (1.669–15.900) | 0.004 |
| Anti-pseudomonal BL treatment duration ≥3 d c | 2.214 (1.312–3.734) | 0.003 | ||
| Anti-anaerobic therapy ≥3 d c | 2.083 (1.215–3.569) | 0.008 |
BL, beta-lactam; CVC, central venous catheter; IV, intravenous; MRSA, methicillin-resistant Staphylococcus aureus; OR, odds ratio; TPN, total parenteral nutrition. a Variables with a variance inflation factor (VIF) ≥ 2: anti-pseudomonal BL treatment ≥3 d 4.366, anti-anaerobic therapy ≥3 d 3.315, number of antibiotics exposed to ≥3 d 2.636, duration of IV antibiotic therapy ≥2 d 2.228, total duration of antibiotic therapy ≥8 d 2.045. b Hosmer–Lemeshow test: p = 0.813. c The cut-off value was determined using a receiver operating characteristic (ROC) curve with the Youden Index.
Table 4.
Univariable and multivariable logistic regression analyses in the non-CRBSI population.
| Variable | OR | p Value | Adjusted OR a,b | p Value |
|---|---|---|---|---|
| Immunosuppression c | 1.357 (0.558–3.302) | 0.501 | ||
| CVC | 2.067 (1.063–4.017) | 0.032 | ||
| TPN | 4.600 (2.321–9.115) | <0.001 | 4.745 (2.306–9.763) | <0.001 |
| Piperacillin/tazobactam | 1.931 (1.029–3.624) | 0.040 | ||
| Cephalosporins G3 | 0.549 (0.277–1.091) | 0.087 | ||
| Cephalosporins G4 | 2.643 (0.886–7.886) | 0.081 | ||
| Fluoroquinolones | 1.771 (0.907–3.459) | 0.094 | ||
| Glycopeptides | 1.820 (0.917–3.613) | 0.087 | ||
| Exposed to ≥3 antibiotics d | 1.931 (1.029–3.624) | 0.040 | ||
| Double coverage for Pseudomonas | 1.920 (0.936–3.941) | 0.075 | ||
| Duration of IV antibiotic therapy ≥2 d d | 2.328 (1.012–5.353) | 0.047 | ||
| Anti-pseudomonal BL treatment duration ≥3 d d | 2.585 (1.349–4.953) | 0.004 | 5.260 (1.530–18.084) | 0.008 |
| Anti-anaerobic therapy ≥3 d d | 1.751 (0.903–3.395) | 0.097 |
BL, beta-lactam; CRBSI, catheter-related bloodstream infection; CVC, central venous catheter; IV, intravenous; OR, odds ratio; TPN, total parenteral nutrition. a Variables with a variance inflation factor (VIF) ≥ 2: anti-pseudomonal BL treatment duration ≥3 d 4.750, anti-anaerobic therapy ≥3 d 3.895, number of antibiotics exposed to ≥3 d 2.848, duration of IV antibiotic therapy ≥2 d 2.310, fluoroquinolones 2.112. b Hosmer–Lemeshow test: p = 0.728. c Although the p value was greater than 0.1, it was included in the multivariable model considering its clinical relevance. d The cut-off value was determined using a receiver operating characteristic (ROC) curve with the Youden Index.
Table 5.
Univariable and multivariable logistic regression analyses in the CRBSI population.
| Variable | OR | p Value | Adjusted OR a,b | p Value |
|---|---|---|---|---|
| Diabetes mellitus | 0.424 (0.156–1.146) | 0.091 | ||
| Metastatic cancer | 3.667 (1.413–9.514) | 0.008 | ||
| Immunosuppression | 4.545 (1.651–12.512) | 0.003 | 5.745 (1.609–20.513) | 0.007 |
| CVC | 37.273 (4.684–296.590) | 0.001 | 23.270 (2.496–216.931) | 0.006 |
| TPN | 3.676 (1.480–9.132) | 0.005 | ||
| Previous septic shock | 2.361 (0.872–6.391) | 0.091 | ||
| Intra-abdominal infection | 8.200 (0.962–69.925) | 0.054 | ||
| Anti-MRSA agents and carbapenems | 3.545 (0.886–14.184) | 0.074 | ||
| Exposed to ≥3 antibiotics c | 3.625 (1.469–8.945) | 0.005 | ||
| Total duration of antibiotic therapy ≥15 d c,d | 2.454 (1.006–5.984) | 0.048 | ||
| Anti-MRSA therapy ≥11 d c | 3.545 (0.886–14.184) | 0.074 | 10.031 (1.460–68.889) | 0.019 |
| Carbapenem treatment duration ≥6 d c | 4.554 (1.482–13.991) | 0.008 | 4.013 (0.997–16.148) | 0.050 |
| Anti-pseudomonal BL treatment duration ≥17 d c | 2.960 (0.938–9.339) | 0.064 | ||
| Anti-anaerobic therapy ≥3 d c | 2.909 (1.144–7.397) | 0.025 |
BL, beta-lactam; CVC, central venous catheter; IV, intravenous; MRSA, methicillin-resistant Staphylococcus aureus; OR, odds ratio; TPN, total parenteral nutrition. a Variables with a variance inflation factor (VIF) ≥ 2: number of exposed antibiotics ≥3 d 3.097, carbapenem duration ≥6 d 2.990, total duration of antibiotic therapy ≥15 d 2.329, metastatic cancer 2.243, anti-pseudomonal BL duration ≥17 d 2.139. b Hosmer–Lemeshow test: p = 0.538. c The cut-off value was determined using a receiver operating characteristic (ROC) curve with the Youden Index. d The factor “Duration of IV antibiotic therapy ≥15 d” was excluded because multicollinearity was confirmed.
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MDPI and ACS Style
Kim, S.-H.; Mun, S.J.; Kang, J.S.; Moon, C.; Kim, H.-T.; Lee, H.Y. Multifaceted Evaluation of Antibiotic Therapy as a Factor Associated with Candidemia in Non-Neutropenic Patients. J. Fungi 2023, 9, 270. https://doi.org/10.3390/jof9020270
AMA Style
Kim S-H, Mun SJ, Kang JS, Moon C, Kim H-T, Lee HY. Multifaceted Evaluation of Antibiotic Therapy as a Factor Associated with Candidemia in Non-Neutropenic Patients. Journal of Fungi. 2023; 9(2):270. https://doi.org/10.3390/jof9020270
Chicago/Turabian StyleKim, Si-Ho, Seok Jun Mun, Jin Suk Kang, Chisook Moon, Hyoung-Tae Kim, and Ho Young Lee. 2023. "Multifaceted Evaluation of Antibiotic Therapy as a Factor Associated with Candidemia in Non-Neutropenic Patients" Journal of Fungi 9, no. 2: 270. https://doi.org/10.3390/jof9020270
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