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Wang et al. BMC Infectious Diseases (2023) 23:472
https://doi.org/10.1186/s12879-023-08447-x BMC Infectious Diseases
†Ruoxuan Wang and Aimin Jiang contributed equally to this work.
*Correspondence:
Yu Yao
13572101611@163.com
Tao Tian
tiantao0607@163.com
Full list of author information is available at the end of the article
Abstract
Background Patients with malignancy are at a higher risk of developing nosocomial infections. However, limited
studies investigated the clinical features and prognostic factors of nosocomial infections due to fungi in cancer
patients. Herein, this study aims to investigate the clinical characteristics of in-hospital fungal infections and develop a
nomogram to predict the risk of in-hospital death during fungal infection of hospitalized cancer patients.
Methods This retrospective observational study enrolled cancer patients who experienced in-hospital fungal
infections between September 2013 and September 2021. Univariate and multivariate logistic regression analyses
were performed to identify independent predictors of in-hospital mortality. Variables demonstrating significant
statistical differences in the multivariate analysis were utilized to construct a nomogram for personalized prediction
of in-hospital death risk associated with nosocomial fungal infections. The predictive performance of the nomogram
was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.
Results A total of 216 participants were included in the study, of which 57 experienced in-hospital death. C.albicans
was identified as the most prevalent fungal species (68.0%). Respiratory infection accounted for the highest
proportion of fungal infections (59.0%), followed by intra-abdominal infection (8.8%). The multivariate regression
analysis revealed that Eastern Cooperative Oncology Group Performance Status (ECOG-PS) 3–4 (odds ratio [OR] = 6.08,
95% confidence interval [CI]: 2.04–18.12), pulmonary metastases (OR = 2.76, 95%CI: 1.11–6.85), thrombocytopenia
(OR = 2.58, 95%CI: 1.21–5.47), hypoalbuminemia (OR = 2.44, 95%CI: 1.22–4.90), and mechanical ventilation (OR = 2.64,
95%CI: 1.03–6.73) were independent risk factors of in-hospital death. A nomogram based on the identified risk factors
was developed to predict the individual probability of in-hospital mortality. The nomogram demonstrated satisfactory
performance in terms of classification ability (area under the curve [AUC]: 0.759), calibration ability, and net clinical
benefit.
Establishment of a risk classier to predict the
in-hospital death risk of nosocomial fungal
infections in cancer patients
Ruoxuan Wang1†, Aimin Jiang1†, Rui Zhang2, Chuchu Shi1, Qianqian Ding1, Shihan Liu1, Fumei Zhao1, Yuyan Ma1,
Junhui Liu3, Xiao Fu1, Xuan Liang1, Zhiping Ruan1, Yu Yao1* and Tao Tian1*
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Page 2 of 11
Wang et al. BMC Infectious Diseases (2023) 23:472
Introduction
Currently, cancer has emerged as a global public health
concern that demands signicant attention. Due to the
presence of malignancy and frequent anti-tumor therapy,
cancer patients are more susceptible to acquiring noso-
comial infections [1, 2]. Additionally, this population
often undergoes invasive procedures, including surgery,
tissue biopsy, and catheter placement, which signicantly
increase their risk of acquiring nosocomial infections
[3, 4]. erefore, nosocomial infections have become
one of the most common complications in oncological
patients. Once a severe infection occurs, it undoubtedly
hampers the initiation of anti-tumor treatment, pro-
longs hospitalization, increases healthcare-related bur-
dens, and, in severe cases, can result in patient mortality.
Consequently, infections have emerged as the primary
non-cancer cause of death among cancer patients [5, 6].
erefore, it is imperative for clinicians to comprehen-
sively comprehend the clinical characteristics and prog-
nostic factors associated with nosocomial infections in
cancer patients.
In recent decades, the clinical features, microbiological
distribution, and prognostic factors of in-hospital bacte-
rial infections have been well documented [1, 2, 7–11].
Furthermore, pertinent guidelines have been published
to provide guidance on managing bacterial infections
acquired during hospitalization in this particular popula-
tion [12–14]. It is worth noting that recent studies have
highlighted the signicant role of fungi as the primary
causative pathogens of nosocomial infections in cancer
patients [15–17]. In our previous study, we conducted a
thorough investigation into the microbiological distri-
bution of nosocomial infections in cancer patients. Our
ndings revealed that fungi constituted 11.4% of the
identied causative pathogens [16]. Fungi are unique in
that they do not produce endotoxins and exotoxins [18].
However, patients with malignancies face a heightened
risk of fungal infections due to compromised immune
function, which is associated with unfavorable clinical
outcomes [19, 20]. In this context, invasive fungal disease
(IFD) will occur in severe cases [21]. Furthermore, the
prolonged administration of antifungal therapy, coupled
with malnutrition and secondary infections, will heighten
the risk of in-hospital mortality among these patients [4].
It is well known that there is a lack of comprehensive
studies focusing on the clinical characteristics and prog-
nostic factors of nosocomial fungal infections in cancer
patients. Most importantly, no risk stratication system
was developed to predict the in-hospital mortality rate of
nosocomial fungal infections in cancer patients. In this
premier, we conducted this retrospective study to explore
the clinical features and prognostic factors of nosocomial
fungal infections in this vulnerable population. Besides,
we also aimed to construct a novel predictive model to
robustly predict their risk of in-hospital death during
nosocomial fungal infections, thus providing valuable
guidance for clinical decision-making.
Methods
Study design
is retrospective observational study was conducted at
the First Aliated Hospital of Xi’an Jiaotong University
in China from September 2013 to September 2021. is
hospital, located in northwest China, is aliated with
a university and serves as a regional medical center. It
houses a dedicated cancer treatment center that oers a
comprehensive range of anti-tumor treatments, includ-
ing surgery, chemotherapy, radiotherapy, and immuno-
therapy. is study enrolled patients who fullled the
following criteria: (1) age 18 years and above; (2) labo-
ratory test results indicative of fungal infection diag-
nosis; (3) conrmed presence of solid tumors through
histological or cytological pathology; (4) diagnosis of
nosocomial fungal infections during hospitalization; and
(5) availability of complete electronic medical records
(EMR) for the patients. Patients younger than 18 years
old with incomplete EMR were excluded from the study.
is study was approved by the Ethics Committee of
the First Aliated Hospital of Xi’an Jiaotong University
(No: XJTU1AF2020LSK-049) and conducted in accor-
dance with the principles outlined in the Declaration of
Helsinki.
Data collection
All data were extracted from the EMR and recorded in
Microsoft Excel. e demographic data collected in this
study encompassed age, gender, and smoking history.
Cancer related variables included Eastern Coopera-
tive Oncology Group-Performance Status (ECOG-PS),
tumor type, TNM staging, sites of distant metastases,
Charlson comorbidity index (CCI), anti-tumor therapy
(including but not limited to surgery, chemotherapy,
immune checkpoint inhibitor therapy, and radiother-
apy) within 30 days, corticosteroid therapy in the past
30 days, granulocyte colony-stimulating factor (G-CSF)
usage in the past 30 days and invasive procedures in the
Conclusions Fungi-related nosocomial infections are prevalent among cancer patients and are associated with poor
prognosis. The constructed nomogram provides an invaluable tool for oncologists, enabling them to make timely and
informed clinical decisions that offer substantial net clinical benefit to patients.
Keywords Cancer patients, Nosocomial infections, Fungal infections, Risk factors, In-hospital mortality, Nomograms
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Page 3 of 11
Wang et al. BMC Infectious Diseases (2023) 23:472
last one month. Simultaneously, we collected informa-
tion pertaining to the infection, which encompassed the
primary site of infection, fungal species, the coexistence
of bacterial infection, initiation time and types of intra-
venous antifungal drugs, presence of fever, and antibiotic
therapy received within the preceding 30 days. Addi-
tional variables, including admission to the intensive care
unit (ICU), mechanical ventilation, and clinical outcomes
following fungal infection (in-hospital mortality or dis-
charge), were also documented. Furthermore, the most
unfavorable outcomes from laboratory tests conducted
prior to the diagnosis of fungal infection were recorded,
encompassing blood routine tests, serum albumin levels,
and serum electrolyte levels.
Denition Nosocomial fungal infections were dened
based on the following criteria: (a) positive culture of one
or more fungal pathogens from clinical specimens (> 48h
after hospital admission), excluding cases of specimen
contamination; and (b) conrmed diagnosis of fungal
infections in the EMR by qualied physicians. Otherwise,
the case was considered community-onset [22–24]. Once
patients were suspected of having a fungal infection, vari-
ous clinical samples, such as sputum, urine, and blood
cultures, were collected. Fever was dened as either a sin-
gle axillary temperature ≥ 38.3 ℃ or two or more temper-
atures ≥ 38.0 ℃within a 12-hour period [25]. Shock was
determined by a systolic blood pressure below 90 mmHg,
which did not improve with uid therapy and/or vasoac-
tive drugs [24].
Study outcome
In the present study, we mainly focused on in-hospital
fatality caused by nosocomial fungal infections. In-hospi-
tal mortality rate estimation did not include other causes
of death, such as malignant tumors or unrelated factors.
Statistical analysis
Fisher exact test or Chi-square test was used to compare
the proportional dierences of categorical variables. We
used an independent sample t-test or non-parametric
rank sum test to compare the dierences of continu-
ous variables. e independent inuencing factors of
in-hospital mortality were determined using univariate
and multivariate logistic regression analyses. In the mul-
tivariate analysis, variables with a signicant association
(P value < 0.05) were selected to construct a nomogram
for predicting the probability of in-hospital mortality due
to fungal infection. e predictive performance of the
nomogram was evaluated using receiver operating char-
acteristics (ROC) curve analysis, calibration curve anal-
ysis, and decision curve analysis (DCA). All statistical
analyses were performed using R software (version 4.1.3)
for Windows 64.0.
Results
The essential characteristics of the participants
During the study period, a total of 216 cancer patients
with nosocomial fungal infections were included in this
study (Fig. 1). One hundred thirty-eight were males
(64%), and 78 were females (36%). e median age was 65
years old. Among them, 90% of patients had an ECOG-
PS of 0–2, and 74% had a TNM stage of III-IV. e com-
mon malignancy diagnoses were respiratory tumors
(34%), gastrointestinal tumors (24%), and hepatobiliary
and pancreatic tumors (24%). Regarding the detailed
anti-tumor therapy, 72 patients (33.3%) underwent sur-
gery, 62 patients (29%) received chemotherapy, and 13
patients (6%) received immune checkpoint inhibitors,
respectively. Sixty-nine patients (32%) received glucocor-
ticoids within 30 days (Table1).
Infection-related features of the patients in the study
In this study, the respiratory tract was the most pre-
dominant primary infection site (59.3%), followed by the
abdominal cavity (8.8%). C. Albicans was the predomi-
nant pathogen (68%), followed by other Candida species
(19%). Two patients (0.8%) were complicated with two
or more fungal infections. During hospitalization, 140
patients (65%) received intravenous antifungal therapy.
Of these people, 122 patients (56.5%) received triazole
antifungal drugs, followed by echinocandin antifungal
drugs (5.1%). At the same time, 3.2% of patients received
two or more intravenous antifungal drugs. irty-four
patients (16%) had a history of previously known infec-
tion within 30 days before they were diagnosed with a
fungal infection. 78 patients (36%) received antibacterial
therapy (including empiric antimicrobial therapy) within
30 days before they were diagnosed with a fungal infec-
tion. Of all patients, 135 had undergone invasive proce-
dures in the past 30 days before being diagnosed with a
fungal infection, with indwelling catheterization being
the most common (28%). Furthermore, out of the 216
study participants, 43 (20%) were admitted to the inten-
sive care unit (ICU), and 29 (13%) required mechanical
ventilation during their hospital stay. Notably, the over-
all mortality rate among the study participants was 26.4%
(57/216).
Identication of risk factors for in-hospital death of
nosocomial fungal infections
We examined the correlation between the patients’ prog-
noses and clinical characteristics. e ndings revealed
signicant variability in factors such as ECOG-PS, TNM
stage, presence of pulmonary and liver metastases, CCI
score, receipt of surgery or chemotherapy within 30 days,
as well as laboratory results including platelet count,
serum albumin level, serum calcium level, and serum
sodium level (P < 0.05; Table 1). Meanwhile, there were
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Wang et al. BMC Infectious Diseases (2023) 23:472
variations between the two groups in terms of body tem-
perature, antifungal therapy, immunoglobulin therapy,
admission to the ICU, mechanical ventilation, and type
of sepsis (P < 0.05; Table 2). e results of the univari-
ate logistic analysis revealed that several factors were
associated with in-hospital death, including ECOG-PS
3–4, TNM stage III-IV, lung metastasis, bone metasta-
sis, radical surgery within 30 days, CCI, admission to the
ICU, mechanical ventilation, hypoproteinemia, throm-
bocytopenia, and hyponatremia. Subsequently, the mul-
tivariate analysis identied ECOG-PS 3–4 (OR = 6.08,
95% CI: 2.04–18.12, P = 0.001), pulmonary metastases
(odds ratio [OR] = 2.76, 95% condence interval (CI):
1.11–6.85, P = 0.029), thrombocytopenia (OR = 2.58, 95%
CI: 1.21–5.47, P = 0.014), hypoalbuminemia (OR = 2.44,
95% CI: 1.22–4.90, P = 0.012), and mechanical ventila-
tion (OR = 2.64, 95% CI: 1.03–6.73, P = 0.042) as indepen-
dent factors inuencing in-hospital death in solid-tumor
patients with in-hospital fungal infections (Table3).
Nomogram establishment and evaluation
Nowadays, nomograms are widely used in clinical medi-
cine research to convert regression models into eas-
ily interpretable risk score systems. In this study, we
employed a multivariate logistic regression analysis to
identify independent predictive factors. Based on these
factors, we developed a nomogram (Fig.2) to predict the
risk of in-hospital death due to nosocomial fungal infec-
tion in cancer patients. Based on the contribution of each
independent factor to the outcome in the nomogram,
clinicians can readily assess the personalized risk of in-
hospital death during fungal infection. We used multiple
methods to assess the performance of this nomogram,
including ROC curve, calibration curve, and DCA. e
area under the ROC curve (AUC) of the nomogram was
0.759 (95%CI: 0.682–0.835) (Fig.3), suggesting an excel-
lent discrimination ability in predicting the nosocomial
death risk of patients. Besides, the calibration curve
showed a high consistency between the actual in-hospi-
tal mortality and the estimated probability through the
nomogram (Fig.4). As it is well-known, ROC curve and
calibration curve rely on sensitivity and specicity and
may not accurately identify “false positive” and “false
negative” events. Hence, we conducted DCA to assess
the net clinical benet of the nomogram. e ndings
demonstrated that the nomogram consistently provided
greater net clinical benet across the entire range of risk
thresholds compared to individual factors alone (Fig.5).
In summary, the developed nomogram serves as a reli-
able risk classier for predicting the risk of in-hospital
death due to nosocomial fungal infections in patients
with solid tumors.
Fig. 1 Flow chart of the study
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Wang et al. BMC Infectious Diseases (2023) 23:472
Discussion
In our study, the prevalence of fungal infections during
hospitalization among cancer patients over the 8-year
study period was 1.3%, which was found to be lower than
that reported in previous studies [26]. It could be attrib-
uted by the fact that the incidence of healthcare-related
infections varies among dierent regions. We observed
that respiratory tumors accounted for the most common
Table 1 The general characteristics of cancer patients with nosocomial fungal infection
Variable Overall, N = 2161Survival, N = 1591Death, N = 571P-value2
Demographic data
Age (years) 65 (58, 71) 65 (59, 71) 65 (58, 71) > 0.900
Gender 0.200
male 138 (64%) 98 (62%) 40 (70%)
female 78 (36%) 61 (38%) 17 (30%)
Smoking history (Yes) 97 (45%) 66 (42%) 31 (54%) 0.094
Days of hospitalization(days) 17 (9, 27) 17 (10, 27) 16 (8, 26) 0.400
ECOG-performance status < 0.001
0,1,2 194 (90%) 153 (96%) 41 (72%)
3,4 22 (10%) 6 (4.0%) 16 (28%)
TNM stage 0.014
Stage I-II 57 (26%) 49 (31%) 8 (14%)
Stage III-IV 159 (74%) 110 (69%) 49 (86%)
Underlying cancer type 0.400
Head and neck cancer 7 (3.2%) 6 (3.8%) 1 (1.75%)
Lung cancer 73 (34%) 50 (31.4%) 23 (40%)
Esophago-gastrointestinal cancer 35 (16%) 26 (16.3%) 9 (16%)
Colon and rectal cancer 17 (8.0%) 10 (6.3%) 7 (12%)
Hepatobiliary and pancreatic cancer 52 (24%) 42 (26.4%) 10 (18%)
Breast cancer 6 (2.8%) 5 (3.1%) 1 (1.75%)
Genitourinary cancer 8 (3.6%) 4 (2.5%) 4 (7.0%)
Gynecological cancer 10 (4.6%) 9 (5.7%) 1 (1.75%)
Lymphoma 4 (1.9%) 4 (2.5%) 0 (0%)
Others 4 (1.9%) 3 (2.0%) 1 (1.75%)
Distant metastasis
Liver metastasis 32 (15%) 26 (16%) 6 (11%) 0.300
Lung metastasis 31 (14%) 18 (11%) 13 (23%) 0.034
Brain metastasis 10 (4.6%) 5 (3.1%) 5 (8.8%) 0.130
Bone metastasis 40 (19%) 24 (15%) 16 (28%) 0.030
Other metastasis 41 (19%) 29 (18%) 12 (21%) 0.600
CCI score 0.012
0–3 207 (96%) 156 (98%) 51 (89%)
> 3 9 (4.0%) 3 (2.0%) 6 (11%)
Operation type (within 30 days) 0.017
Unoperated 144 (67%) 99 (62%) 45 (79%)
Curative operation 60 (27.7%) 52 (33%) 8 (14%)
Palliative operation 12 (5.6%) 8 (5.0%) 4 (7.0%)
Prior treatment (within 30 days)
Chemotherapy 62 (29%) 47 (30%) 15 (26%) 0.600
Radiotherapy 20 (9.3%) 14 (8.8%) 6 (11%) 0.700
Concurrent chemoradiotherapy 11 (5.1%) 8 (5.0%) 3 (5.3%) > 0.900
Perfusion therapy 11 (5.1%) 10 (6.3%) 1 (1.8%) 0.300
Immunotherapy 13 (6.0%) 7 (4.4%) 6 (11%) 0.110
Targeted therapy 15 (6.9%) 8 (5.0%) 7 (12%) 0.075
Glucocorticoid therapy 69 (32%) 53 (33%) 16 (28%) 0.500
G-CSF usage 47 (22%) 35 (22%) 12 (21%) 0.900
Abbreviations: ECOG Eastern Cooperative Oncology Group, CCI Charlson Co-morbidity Index score, G-CSF granulocyte colony-stimulating factor
1n (%); Median (IQR),2Pear son’s Chi-squared test; W ilcoxon rank sum test; Fishe r’s exact test
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Wang et al. BMC Infectious Diseases (2023) 23:472
Variable Overall, N = 2161Survival, N = 1591Death, N = 571P-value2
Primary sites of infection 0.500
Respiratory tract 128 (59.3%) 93 (58.5%) 35 (61.4%)
Digestive tract 15 (6.9%) 11 (6.9%) 4 (7.0%)
Urinary tract 11 (5.1%) 7 (4.4%) 4 (7.0%)
Thoracic cavity 5 (2.3%) 5 (3.1%) 0 (0%)
Abdominal cavity 19 (8.8%) 16 (10.1%) 3 (5.3%)
Fungi types 0.200
Candida albicans 146 (68%) 114 (71.7%) 32 (56.1%)
Mycotoruloides 41 (19%) 26 (16.4%) 15 (26.3%)
Aspergillus flavus 6 (2.6%) 4 (2.5%) 2 (3.5%)
Aspergillus 18 (8.3%) 11 (6.9%) 7 (12.3%)
Penicillium 1 (0.5%) 1 (0.6%) 0 (0%)
Coinfection 2 (0.8%) 1 (0.6%) 1 (1.8%)
Others 2 (0.8%) 2 (1.3%) 0 (0%)
Types of antifungal drugs 0.005
Unantifungal treatment 76 (35.2%) 63 (39.6%) 13 (22.9%)
Triazole antifungal agent 122 (56.5%) 88 (55.3%) 34 (59.5%)
Echinocandin antifungal agent 11 (5.1%) 6 (3.8%) 5 (8.8%)
Combination therapy 7 (3.2%) 2 (1.3%) 5 (8.8%)
Length of antifungal treatment (days) 4 (0, 8) 3 (0, 8) 5 (1, 9) 0.110
Temperature(≥ 38°C) 68 (31%) 43 (27%) 25 (44%) 0.019
Infection history (within 30 days) 34 (16%) 23 (14%) 11 (19%) 0.400
Antibiotic usage(within 30 days) 78 (36%) 52 (33%) 26 (46%) 0.082
FN history (within 30 days) 3 (1.4%) 2 (1.3%) 1 (1.8%) > 0.900
Invasive procedure (within 30 days) 135 (62%) 104 (65%) 31 (54%) 0.140
Biliary stent implantation 7 (3.2%) 7 (4.4%) 0 (0%) 0.200
Ureteral stent implantation 3 (1.4%) 2 (1.3%) 1 (1.8%) > 0.900
Indwelling urinary catheter 60 (28%) 46 (29%) 14 (25%) 0.500
PICC 16 (7.4%) 12 (7.5%) 4 (7.0%) > 0.900
Infusion port implantation 3 (1.4%) 3 (1.9%) 0 (0%) 0.600
Thoracic puncture catheter drainage 34 (16%) 25 (16%) 9 (16%) > 0.900
Abdominal catheterization 19 (8.8%) 15 (9.4%) 4 (7.0%) 0.600
Arterial catheterization 5 (2.3%) 1 (0.6%) 4 (7.0%) 0.018
Central venous pressure apparatus 11 (5.1%) 8 (5.0%) 3 (5.3%) > 0.900
Postoperative drainage 59 (27%) 50 (31%) 9 (16%) 0.023
Indwelling gastric tube 49 (23%) 37 (23%) 12 (21%) 0.700
Combined with bacterial infection 54 (25%) 37 (23%) 17 (30%) 0.300
Immunoglobulin use 40 (19%) 23 (14%) 17 (30%) 0.010
ICU admission 43 (20%) 26 (16%) 17 (30%) 0.029
Mechanical ventilation 29 (13%) 14 (8.8%) 15 (26%) < 0.001
Cardiac arrest 12 (5.6%) 0 (0%) 12 (21%) < 0.001
Sepsis classication < 0.001
None 176 (81.5%) 140 (88.1%) 36 (63.2%)
Sepsis 21 (9.7%) 15 (9.4%) 6 (10.5%)
Severe sepsis 5 (2.3%) 3 (1.9%) 2 (3.5%)
Septic shock 14 (6.5%) 1 (0.6%) 13 (22.8%)
Laboratory indexes
Hemoglobin(g/L) 103 (90, 120) 106 (93, 120) 98 (85, 115) 0.041
< 110 131 (61%) 94 (59%) 37 (65%) 0.400
Platelet count (×109/L) 176 (111, 252) 197 (132, 266) 117 (58, 210) < 0.001
< 100 50 (23%) 26 (16%) 24 (42%) < 0.001
Leucocyte count (×109/L) 8.0 (5.4, 11.2) 8.1 (5.4, 11.1) 7.9 (5.1, 11.9) 0.700
< 4.0 33 (15%) 26 (16%) 7 (12%) 0.500
Table 2 The infection-related characteristics of cancer patients with nosocomial fungal infection
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Wang et al. BMC Infectious Diseases (2023) 23:472
malignancy type that occurs nosocomial fungal infec-
tions. On the one hand, according to the newest cancer
statistics, respiratory tumors remain the most common
malignancies all over the world[27]. On the other hand,
emerging evidence has shown that respiratory tumor
cells could secrete immunosuppressive factors, which
will inhibit the normal natural barrier function of the
respiratory tract. Ultimately, lung cancer patients are
more susceptible to co-infection compared to other
tumor patients due to increased alveolar and bronchial
secretions, as well as bronchial mass obstruction [28, 29].
In this study, we found that C. Albicans was the pre-
dominant causative pathogen, accounting for 68% of the
isolates, followed by other Candida genera (19%). ese
ndings are in line with previous studies [11, 30]. In this
retrospective study, 76 patients (36%) received antibacte-
rial therapy within 30 days before the diagnosis of fungal
infection, which is the most crucial treatment received
in the previous 30 days for this study population. is
nding aligns with our standard perspective, suggest-
ing that the use of antibiotics may disrupt the microbial
balance and promote fungal overgrowth [31]. Further-
more, within this subpopulation, we observed that 26
participants succumbed to nosocomial fungal infections,
representing 46% of all recorded deaths. is highlights
the importance of vigilant monitoring by clinicians
for patients who experience nosocomial fungal infec-
tions, as these individuals are more likely to experience
adverse clinical outcomes compared to other subpopu-
lations. A total of 135 patients, representing 62% of the
total population, underwent invasive procedures prior
to the diagnosis of fungal infection. ese procedures,
including thoracic or abdominal puncture and central
venous catheter (CVC) placement, can result in damage
to the mucous membranes of body cavities and the inner
walls of blood vessels. is compromised physiologi-
cal immune barrier renders patients more vulnerable to
Table 3 Logistic regression analysis to identify the influencing
factors of in-hospital death in cancer patients with nosocomial
fungal infection
Variable OR
(univariable)
OR (multi-
variable)
ECOG-PS 0,1,2 REF (1.00) REF (1.00)
3,4 9.95 (3.66–27.04,
P < 0.001)
6.08 (2.04–
18.12,
P = 0.001)
TNM stage I-II REF (1.00)
III-IV 2.73 (1.20–6.19,
P = 0.016)
Pulmonary
metastasis
Yes 2.31 (1.05–5.10,
P = 0.037)
2.76 (1.11–
6.848,
P = 0.029)
Bone metastasis Yes 2.20 (1.07–4.52,
P = 0.033)
Operation type Unoperated REF (1.00)
Radical operation 0.34 (0.15–0.77,
P = 0.010)
Palliative
operation
1.10 (0.31–3.84,
P = 0.881)
ICU admission Yes 2.17 (1.07–4.40,
P = 0.031)
CCI ≤ 3 REF (1.00)
> 3 2.69 (1.13–6.40,
P = 0.026)
Platelet count
(×109/L)
< 100 3.72 (1.90–7.29,
P < 0.001)
2.58
(1.21–5.47,
P = 0.014)
Albumin(g/L) < 30 3.01 (1.61–5.62,
P < 0.001)
2.44
(1.22–4.90,
P = 0.012)
Serum
sodium(mmol/L)
< 130 2.67 (1.41–5.07,
P = 0.003)
Mechanical
ventilation
Yes 3.70 (1.65–8.28,
P = 0.002)
2.64
(1.03–6.73,
P = 0.042)
Abbreviations: OR odds ratio, ECOG- PS Eastern Cooperative Oncology Group
Performance Status, ICU intensive care u unit, CCI Charlson Co-morbidity Index
score
Variable Overall, N = 2161Survival, N = 1591Death, N = 571P-value2
>10.0 78 (36%) 37 (23.3%) 35 (61.4%) 0.874
Neutrophils(×109/L) 6.4 (3.7, 9.3) 6.3 (3.5, 9.2) 6.8 (4.3, 10.8) 0.300
Lymphocyte(×109/L) 0.82 (0.52, 1.11) 0.84 (0.52, 1.14) 0.71 (0.48, 1.06) 0.140
Monocyte(×109/L) 0.42 (0.25, 0.71) 0.42 (0.25, 0.72) 0.40 (0.23, 0.68) 0.800
Albumin(g/L) 30.9 (28.2, 35.0) 31.8 (28.8, 36.0) 29.0 (25.8, 31.2) < 0.001
< 30 90 (42%) 55 (35%) 35 (61%) < 0.001
Serum calcium(mmol/L) 2.06 (1.95, 2.20) 2.09 (1.98, 2.21) 2.01 (1.89, 2.12) 0.006
< 2.0 144 (67%) 114 (72%) 30 (53%) 0.009
Serum corrected calcium(mmol/L) 2.28 (2.20, 2.38) 2.28 (2.21, 2.38) 2.27 (2.18, 2.40) 0.800
Serum sodium(mmol/L) 138.0(134.0,140.1) 138.4(135.6, 141.0) 135.3(131.0,139.0) 0.020
< 130 61 (28%) 36 (23%) 25 (44%) 0.002
Abbreviations: PICC peripherally inserted central catheter, ICU Intensive Ca re Unit
1n (%); Median (IQR),2Pear son’s Chi-squared test; W ilcoxon rank sum test; Fishe r’s exact test
Table 2 (continued)
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Page 8 of 11
Wang et al. BMC Infectious Diseases (2023) 23:472
fungal displacement and colonization, thereby elevating
the risk of infection [32].
In the current study, we identied ECOG-PS 3–4, lung
metastases, mechanical ventilation, thrombocytopenia,
and hypoalbuminemia as independent risk factors for
in-hospital mortality due to nosocomial fungal infec-
tions in cancer patients. Generally, cancer patients with
poor ECOG-PS and distant metastases exhibit restricted
Fig. 3 The ROC curve to evaluate the discrimination ability of the nomogram. AUC = 0.759 (95%CI: 0.682–0.835). ROC, receiver operating characteristic
curve
Fig. 2 A nomogram to predict the risk of in-hospital death from fungal infections in cancer patients. This patient’s albumin level was 35 g/L, platelet
count was 88 × 109/L, without mechanical ventilation, no pulmonary metastasis and ECOG-PS 1. According to the nomogram, we can calculate that the
total point for this patient is 139 and its corresponding in-hospital death risk is 21.2%
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 9 of 11
Wang et al. BMC Infectious Diseases (2023) 23:472
physical functioning and a considerable tumor burden,
resulting in unfavorable clinical outcomes. e prog-
nostic importance of mechanical ventilation in cancer
patients with nosocomial infections during hospitaliza-
tion has been extensively reported [22, 25, 33, 34]. We
found that patients with hypoalbuminemia and thrombo-
cytopenia were associated with higher in-hospital mor-
tality. Serum albumin, a marker of ’patients’ nutritional
status, often indicates immunosuppression, malnutri-
tion, and cachexia in individuals with malignancy. e
Fig. 5 Decision curve analysis of the nomogram for predicting in-hospital death risk of nosocomial infections caused by fungi in cancer patients
Fig. 4 The calibration curve of the nomogram for predicting in-hospital death risk of nosocomial infections caused by fungi in cancer patients
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 10 of 11
Wang et al. BMC Infectious Diseases (2023) 23:472
presence of hypoproteinemia in these patients is associ-
ated with a poor prognosis and an increased risk of can-
cer-related deaths [35–37]. Moreover, a growing body of
studies have demonstrated that thrombocytopenia is cor-
related with an unfavorable prognosis in various diseases,
including cancer and infections [3, 38].
Simply identifying risk factors for in-hospital mortal-
ity of nosocomial fungal infection is insucient to assist
clinicians in making precise and timely decisions. Conse-
quently, we have developed a dependable risk stratica-
tion system that incorporates the identied variables to
accurately predict the likelihood of in-hospital death in
this patient population. Subsequent evaluation demon-
strated that our nomogram exhibits satisfactory discrimi-
nation ability, calibration ability, and net clinical benet.
Notably, the ndings from DCA revealed its signicant
superiority in terms of net clinical benets compared to
other variables. In summary, the developed nomogram
serves as a reliable tool for predicting personalized in-
hospital death risk associated with nosocomial fungal
infections in cancer patients. To the best of our knowl-
edge, this study represents the rst comprehensive inves-
tigation of the clinical characteristics, microbiological
distribution, and clinical outcomes associated with noso-
comial fungal infections among cancer patients in China.
Notably, we have also developed a dependable nomogram
capable of accurately predicting in-hospital mortality
rates for these patients. However, our study had several
limitations that should be acknowledged. Firstly, the ret-
rospective design introduced potential biases that were
unavoidable. Secondly, we were unable to collect cer-
tain variables, such as detailed chemotherapy regimen,
radiotherapy dosage, and laboratory results of D-glucan
and galactomannan tests, which could have impacted the
clinical outcomes of the participants. irdly, although
the developed nomogram demonstrated excellent pre-
dictive power, it is essential to conduct independent
external validation in the future to conrm its generaliz-
ability. erefore, well-designed large-scale cohort stud-
ies should be undertaken to validate our ndings.
Conclusion
Nosocomial fungal infections are prevalent among can-
cer patients, with Candida albicans being the most fre-
quently isolated causative pathogen. Furthermore, these
infections have been linked to adverse clinical outcomes
in these individuals. Moreover, we constructed a robust
nomogram that could accurately forecasting the risk of
in-hospital mortality resulting from nosocomial fungal
infections in cancer patients. Implementing meticulous
patient management strategies, such as closely monitor-
ing serum albumin levels and platelet counts, adminis-
tering timely interventions, and providing precise care
for individuals with lung metastases and high ECOG-PS
scores, could signicantly enhance the prognosis of noso-
comial fungal infections in this population.
Abbreviations
EMR Electronic Medical Record
FN Febrile neutropenia
OR Odds Ratio
CI Confidence interval
ECOG-PS Eastern Cooperative Oncology Group-Performance Status
CCI Charlson Comorbidity Index
G-CSF Granulocyte Colony-stimulating Factor
ICU Intensive Care Unit
PICC Peripherally Inserted Central Catheter
PNI Prognostic Nutritional Index
NSCLC Non-small Cell Lung Cancer
ICIs Immune Checkpoint Inhibitors
Acknowledgements
We would like to sincerely appreciate all the participants involved in this study.
Author contributions
TT and YY conceived the study. RXW, AMJ, JHL and RZ were involved in data
collecting, statistical analysis, and manuscript drafting. CCS and QQD conducted
the data collection and analysis and provided the critical revision. SHL, FMZ, and
YYM were involved in data collecting. XF participated in the study design and
helped with the data collection. XL and ZPR participated in the study design and
manuscript revision. All authors read and approved the final manuscript.
Funding
This study was supported by the CSCO-Hengrui Cancer Research Fundation (NO.
Y-HR2019-0401), Medical scientific research project (Medical research project
for young and middle-aged oncologist of lung cancer), and Youth Program of
National Natural Science Foundation of China (NO. 82002437).
Data Availability
The datasets used and analyzed during the current study available from the
corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the ethics committee of the First Affiliated
Hospital of Xi’an Jiaotong University (No: XJTU1AF2020LSK-049).
Written inform consent was waived by the ethics committee of the First
Affiliated Hospital of Xi’an Jiaotong University due to the retrospective
noninterventional study design.
Statement
All methods of this study were carried out in accordance with relevant
guidelines and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1Department of Medical Oncology, The First Affiliated Hospital of Xi’an
Jiaotong University, No. 277 Yanta West Road, Xi’an, Shaanxi
710061, People’s Republic of China
2Department of Medical Oncology, Baoji Traditional Chinese Medicine
Hospital, No.43 Baofu Road, Baoji, Shaanxi
721001, People’s Republic of China
3Department of Clinical Laboratory, The First Affiliated Hospital of Xi’an
Jiaotong University, No. 277 Yanta West Road, Xi’an, Shaanxi
710061, People’s Republic of China
Received: 5 February 2023 / Accepted: 6 July 2023
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Page 11 of 11
Wang et al. BMC Infectious Diseases (2023) 23:472
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