Dataset Information

Epidemiology and prognostic factors of candidemia in cancer patients.

ABSTRACT: AIM: The study of candidemia in cancer patients has been limited. This retrospective study aims to investigate the epidemiologic characteristics and prognostic factors of candidemia among cancer patients. MATERIALS AND METHODS: From 2009 to 2012, cancer patients with candidemia were identified at a hospital in Taiwan. The medical records of all patients with bloodstream infections due to Candida species were retrospectively reviewed. RESULTS: During the four-year period, a total of 242 episodes of candidemia were identified among cancer patients. Half of these patients were classified as elderly (?65 years old), and more than 95% of the candidemia episodes were classified as healthcare-associated infections. Among the 242 cancer patients with candidemia, head and neck cancer was the most common, followed by gastrointestinal tract and lung cancer. Additionally, most of the patients had variable underlying conditions, such as the presence of CVC (99%) or prior exposure to broad-spectrum antibiotics (93%) and were receiving an immunosuppressant (86%). Overall, C. albicans (n?=?132, 54.5%) was the most common pathogen, followed by C. tropicalis (n?=?52, 21.5%), C. parapsilosis (n?=?38, 15.7%), and C. glabrata (n?=?29, 12.0%). Seventeen patients had polycandidal candidemia, and 77 patients had concomitant bacteremia. Approximately one-third of the patients required admission to the intensive care unit (ICU) or mechanical ventilation, and the overall in-hospital mortality was 50.8%. Multivariable analysis showed that the in-hospital mortality was significantly associated with only the non-use of antifungal agents and acute respiratory failure (P<.001). CONCLUSIONS: Candidemia can develop in patients with both solid cancer and hematological malignancy, especially for patients with underlying conditions. Overall, the associated morbidity and mortality due to Candidemia remain high. It was also determined that the non-use of antifungal agents and acute respiratory failure conditions were associated with in-hospital mortality.

SUBMITTER: Tang HJ

PROVIDER: S-EPMC4047064 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Epidemiology and prognostic factors of candidemia in cancer patients.

Tang Hung-Jen HJ Liu Wei-Lun WL Lin Hsin-Lan HL Lai Chih-Cheng CC

PloS one 20140605 6

<h4>Aim</h4>The study of candidemia in cancer patients has been limited. This retrospective study aims to investigate the epidemiologic characteristics and prognostic factors of candidemia among cancer patients.<h4>Materials and methods</h4>From 2009 to 2012, cancer patients with candidemia were identified at a hospital in Taiwan. The medical records of all patients with bloodstream infections due to Candida species were retrospectively reviewed.<h4>Results</h4>During the four-year period, a tot ...[more]

PMID: 24901336

Similar Datasets

Project description:Candidemia has been considered a persistent public health problem with great impact on hospital costs and high mortality. We aimed to evaluate the epidemiology and prognostic factors of candidemia in a tertiary hospital in Northeast Brazil from January 2011 to December 2016. Demographic and clinical data of patients were retrospectively obtained from medical records and antifungal susceptibility profiling was performed using the broth microdilution method. A total of 68 episodes of candidemia were evaluated. We found an average incidence of 2.23 episodes /1000 admissions and a 30-day mortality rate of 55.9%. The most prevalent species were Candida albicans (35.3%), Candida tropicalis (27.4%), Candida parapsilosis (21.6%) and Candida glabrata (11.8%). Higher mortality rates were observed in cases of candidemia due to C. albicans (61.1%) and C. glabrata (100%), especially when compared to C. parapsilosis (27.3%). Univariate analysis revealed some variables which significantly increased the probability of death: older age (P = 0.022; odds ratio [OR] = 1.041), severe sepsis (P < 0.001; OR = 8.571), septic shock (P = 0.035; OR = 3.792), hypotension (P = 0.003; OR = 9.120), neutrophilia (P = 0.046; OR = 3.080), thrombocytopenia (P = 0.002; OR = 6.800), mechanical ventilation (P = 0.009; OR = 8.167) and greater number of surgeries (P = 0.037; OR = 1.920). Multivariate analysis showed that older age (P = 0.040; OR = 1.055), severe sepsis (P = 0.009; OR = 9.872) and hypotension (P = 0.031; OR = 21.042) were independently associated with worse prognosis. There was no resistance to amphotericin B, micafungin or itraconazole and a low rate of resistance to fluconazole (5.1%). However, 20.5% of the Candida isolates were susceptible dose-dependent (SDD) to fluconazole and 7.7% to itraconazole. In conclusion, our results could assist in the adoption of strategies to stratify patients at higher risk for developing candidemia and worse prognosis, in addition to improve antifungal management.

Project description:ObjectiveUsing the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients.MethodsPatients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index.ResultsThere was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model.ConclusionsAlthough there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

Project description:Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia.

Dataset Information

Epidemiology and prognostic factors of candidemia in cancer patients.

Publications

Epidemiology and prognostic factors of candidemia in cancer patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets