Abstract
Background
Cancers of the gastrointestinal (GI) tract and its associated excretory glands are one of the most common causes of cancer-related death worldwide, and these patients are more likely to developing nosocomial infections due to immunodeficiency.
Objective
To explore the bacterial profile, antibiotic resistance pattern, and prognostic factors of nosocomial infections in hospitalized GI cancer patients.
Methods
All electronic medical records of nosocomial infection episodes in hospitalized GI cancer patients were retrospectively reviewed. In-hospital mortality was used to evaluate the prognosis of patients. Mann–Whitney test, Chi-square test, and binary logistic regression analysis were used to identify potential risk factors for in-hospital mortality. P-values <0.05 were considered statistically significant.
Results
A total of 428 GI cancer patients developed nosocomial infections during hospitalization. Respiratory tract infections (44.2%), bloodstream infections (BSIs) (11.7%), and abdominal cavity infections (11.4%) were the most common infection sites. The predominant causative pathogens were extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (13.6%), ESBL-negative E. coli (11.9%), and Klebsiella pneumoniae (10.0%). Multidrug-resistant (MDR) strains were detected in 27.6% of isolates. Antimicrobial susceptibility analysis showed that the isolated Gram-negative bacteria (GNB) exhibited high sensitivity to amikacin, meropenem, imipenem, and piperacillin/tazobactam, while the isolated Gram-positive bacteria exhibited high sensitivity to tigecycline, linezolid, and vancomycin. The overall in-hospital mortality of all patients was 11.2% in the study. Multivariate analysis showed that ECOG performance status ≥two scores, length of antibiotic treatment <9.0 days, existence of septic shock, and hypoproteinemia were independent risk factors for in-hospital mortality.
Conclusion
The burden of nosocomial infections in GI cancer patients is considerably high, with GNB being predominantly isolated causative pathogens. Surveillance on serum albumin level, adequate antibiotic treatment, early identification, and prompt treatment of septic shock could benefit the prognosis.
Keywords: nosocomial infections, gastrointestinal cancer, bacterial profile, antibiotic resistance, prognostic factors
Introduction
Cancers of the gastrointestinal (GI) tract and its associated excretory glands, also known as digestive cancer, are one of the most common causes of cancer-related death worldwide, and it remains a major public health concern for past decades.1 Although surgery, chemotherapy, radiotherapy, molecular targeted therapy, and immunotherapy have significantly improved the survival of GI cancer patients in recent years, it remains a bleak prognosis. Furthermore, these patients are predisposed to developing nosocomial infections due to immunodeficiency caused by malignancy itself and its treatments.2 Even though most infections are temporary, the consequences can last longer.
Nosocomial infections in cancer patients delay the initiation of chemotherapy and reduce the routine dosage.2,3 In addition, it prolongs hospitalization, raises morbidity and mortality, and increases the financial burden of patients and their families.3–5 Therefore, early identification of infection episodes and swift initiation of appropriate antibiotic treatment is pivotal for cancer patients, and it plays an important role in reducing infection-associated mortality.2 As far as we know, previously published studies have merely focused on bloodstream infections (BSIs) in cancer patients. In fact, except for BSIs, urinary tract infections, respiratory infections, and gastrointestinal tract infections are more common in nosocomial infections.6 Herein, we conducted the present study to extensively describe the microbiological distribution, antibiotic resistance pattern, and clinical outcomes of nosocomial infections in hospitalized GI cancer patients.
Materials and Methods
Study Design
This retrospective observational study enrolled all hospitalized GI cancer patients with nosocomial infections at the First Affiliated Hospital of Xi’an Jiaotong University, cancer treatment center from August 2013 to June 2019. The hospital is a 2560-bed medical center in northwestern China. Surgery, chemotherapy, radiotherapy, and immunotherapy are the most common treatment for cancer patients in the cancer treatment center. The electronic medical records of all included GI cancer patients were reviewed during the study period. All cancer patients diagnosed with nosocomial infection below the age of 18 or without complete medical records were excluded. This study was conducted under the requirements of the Declaration of Helsinki.
Data Collection
All data were extracted from electronic medical records and recorded in a standardized form in Excel. Demographic and clinical data included gender, age, tobacco use, underlying cancer type, the existence of distant metastasis, disease stage, Eastern Cooperative Oncology Group (ECOG) performance status, comorbidities, the Charlson comorbidity index (CCI),7 the degree of fever, methods of cancer treatment (surgery, chemotherapy, radiotherapy, or concurrent chemoradiotherapy), recent corticosteroid treatment, Granulocyte colony-stimulating factor (G-CSF) usage, and the type of indwelling catheters. Infection-related data included primary sites of infection, empirical antibiotic treatment, effective antibiotic treatment, the existence of septic shock, the use of ventilator, intensive care unit (ICU) admission, length of antibiotic treatment, length of hospitalization, and clinical outcomes of the analyzed infection episodes (death or discharged during hospitalization). Laboratory examination parameters mainly included blood routine test (haemoglobin, platelet count, white-cell count, neutrophils count, and lymphocytes count), serum albumin level, serum procalcitonin (PCT) concentration, and drug susceptibility tests of the isolated pathogens.
Definitions
GI cancer patients were defined as infection episodes in accordance with the following conditions: (a) at least one positive for pathogen was found in the clinical sample during hospitalization; (b) according to the criteria described previously,8 there were corresponding symptoms and signs records, and the laboratory examination results or radiology results indicated the presence of nosocomial infection; or (c) diagnosis of infection was clearly recorded in electronic medical records.9 Polymicrobial infection was defined as two or more different pathogens were isolated from clinical samples during hospitalization.
Fever was defined as an axillary temperature above 38.3 ◦C or two consecutive readings of temperature was above 38 ◦C.10 Shock was considered if systolic blood pressure lower than 90 mmHg, and there was no response after administration of fluid therapy and/or vasoactive medications.11
According to the drug susceptibility tests of the isolated pathogen, the empirical antibiotic treatment regimen was considered effective if it included at least one antibiotic that was in vitro active against the microorganism.9 Otherwise, it was considered ineffective.9
Multidrug-resistant (MDR) strains were classified into multidrug-resistant Gram-negative bacteria (MDRGNB) and MDR Gram-positive bacteria. The isolated pathogen was defined as MDRGNB if it fulfilled any of the conditions: (a) extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae; (b) AmpC cephalosporinase hyper-producing Enterobacteriaceae; (c) carbapenem-resistant Enterobacteriaceae (CRE); (d) Stenotrophomonas maltophilia; and (e) MDR Pseudomonas aeruginosa and Acinetobacter baumannii.12 MDR Gram-positive bacteria included vancomycin-resistance Enterococcus faecium, and methicillin-resistant Staphylococcus aureus (MRSA).12
Antimicrobial Susceptibility Test and PCT Measurement
The commercial panels CN1S from the MicroScan automated system (Siemens Healthcare Diagnostics Ltd) or Vitek 2 Compact automated microbiology system (BioMerieux, Craponne, France) were adopted to identify microorganisms and drug susceptibility test. In accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines, the double-disc synergy test with EDTA discs was adopted to screen metallo-β-lactamase producers, and the modified Hodge method was applied to identify carbapenemase producers.13 ESBL producers were considered if the isolated pathogens were resistant to one or more extended-spectrum cephalosporins (ceftriaxone, ceftazidime, cefepime), and the double-disk synergy test was applied to identify ESBL producers.14 The time-resolved amplified cryptate emission (TRACE) technology assay (Kryptor PCT; Brahms AG; Hennigsdorf, Germany) was adopted to measure serum PCT concentrations.10
Study Outcomes
The primary endpoint of this study was in-hospital mortality. Death episodes during hospitalization that were not related to infection, such as those caused by the underlying malignancy itself or other morbidities, were excluded from the calculations of mortality rate.
Statistical Analysis
Continuous variables were described as either means and standard deviations or medians and interquartile ranges (IQR) as appropriate. Categorical variables were summarized as counts and percentages. Independent samples t-test or Mann–Whitney test were used to compare the continuous variables for data of different groups as appropriate. To compare the categorical variables, chi-square or Fisher’s exact tests were used as appropriate means of comparability. The binary logistic regression analysis was adopted to explore the independent prognostic factors for in-hospital mortality of nosocomial infections in GI cancer patients. All variables with a P-value <0.10 in univariate analysis were further included in multivariate logistic regression analysis using stepwise selection. The tests with a P-value <0.05 were considered statistically significant. SPSS software version 22.0 for Windows was used to perform all analyses.
Results
Demographic and Clinical Characteristics
We screened a total of 13,695 cancer patients who admitted to the cancer treatment center from August 2013 to June 2019. In total, 428 eligible GI cancer patients were included in this retrospective study. Among them, a total of 302 (70.6%) patients were male. The median age of all patients was 62.5 years (range, 57–69 years old). There were 224 (52.3%) patients who had a history of tobacco use. Esophageal cancer was the most common diagnosis among the overall population, accounting for 48.4%, followed by rectal cancer (11.9%), gastric cancer (10.0%), and colon cancer (9.3%). A total of 319 (74.5%) patients had an ECOG performance status lower than two scores. There were 252 (58.9%) patients who had advanced tumors (stage III–IV), and 105 patients had distant metastases. The most common underlying morbidities were diabetes mellitus identified in 37 (8.6%) patients, followed by chronic obstructive pulmonary disease (COPD) in 21 (4.9%) patients, and liver disease in 21 (4.9%) patients. Among the 428 patients, 248 (58.0%) had undergone surgery, 103 (24.1%) had received chemotherapy, 33 (7.7%) had received radiotherapy, and 14 (3.3%) had received concurrent chemoradiotherapy. The majority (74.3%) of patients had received invasive procedures within 30 days. A total of 232 (54.2%) patients had drains post-operation, followed by indwelling urinary catheters in 217 (50.7%) cases, nasogastric tube in 181 (42.3%) cases, and percutaneous pleural drainage tube in 146 (34.1%) cases. The detailed demographic and clinical characteristics of the enrolled patients were summarized in Table 1.
Table 1.
Demographic and Clinical Characteristics of Nosocomial Infections in Gastrointestinal Cancer Patients
| Characteristics | All N =428 | Univariate Analysis | Multivariate Analysis | |||
|---|---|---|---|---|---|---|
| Survivor N =380, n (%) | Non-Survivor N = 48, n (%) | P-value | OR (95% CI) | P-value | ||
| Demographic data | ||||||
| Sex (male) | 302 (70.6) | 272 (71.6) | 30 (62.5) | 0.193 | ||
| Age (years) | 62.5 (57.0–69.0) | 62.0 (57.0–69.0) | 64.0 (59.0–72.0) | 0.444 | ||
| Tobacco use | 224 (52.3) | 200 (52.6) | 24 (50.0) | 0.731 | ||
| ECOG performance status | <0.001 | |||||
| 0,1 | 319 (74.5) | 303 (79.7) | 16 (33.3) | REF (1.00) | ||
| 2,3,4 | 109 (25.5) | 77 (20.3) | 32 (66.7) | 9.99 (3.57–27.92) | <0.001 | |
| Underlying cancer type | ||||||
| Esophageal cancer | 207 (48.4) | 188 (49.5) | 19 (39.6) | 0.196 | ||
| Gastroesophageal junction cancer | 35 (8.2) | 33 (8.7) | 2 (4.2) | 0.426 | ||
| Gastric cancer | 43 (10.0) | 39 (10.3) | 4 (8.3) | 0.869 | ||
| Hepatobiliary cancer | 31 (7.2) | 24 (6.3) | 7 (14.6) | 0.074 | ||
| Pancreatic cancer | 21 (4.9) | 17 (4.5) | 4 (8.3) | 0.417 | ||
| Colon cancer | 40 (9.3) | 31 (8.2) | 9 (18.8) | 0.035 | 3.08 (0.85–11.17) | 0.086 |
| Rectal cancer | 51 (11.9) | 48 (12.6) | 3 (6.3) | 0.198 | ||
| Existence of distant metastasis | 105 (24.5) | 82 (21.6) | 23 (47.9) | <0.001 | 0.44 (0.15–1.30) | 0.137 |
| Stage of cancer | 0.140 | |||||
| I–II | 176 (41.1) | 161 (42.4) | 15 (31.3) | |||
| III–IV | 252 (58.9) | 219 (57.6) | 33 (68.8) | |||
| Comorbidities | ||||||
| COPD | 21 (4.9) | 13 (3.4) | 8 (16.7) | <0.001 | 4.37 (0.41–47.17) | 0.225 |
| Cerebrovascular disease | 8 (1.9) | 5 (1.3) | 3 (6.3) | 0.050 | 5.01 (0.27–94.27) | 0.282 |
| Liver disease | 21 (4.9) | 13 (3.4) | 8 (16.7) | <0.001 | 3.01 (0.37–24.33) | 0.301 |
| Diabetes | 37 (8.6) | 31 (8.2) | 6 (12.5) | 0.462 | ||
| Renal disease | 12 (2.8) | 9 (2.4) | 3 (6.3) | 0.284 | ||
| CCI | <0.001 | 0.638 | ||||
| 0 | 344 (80.4) | 316 (83.2) | 28 (58.3) | REF (1.00) | ||
| 1–2 | 75 (17.5) | 60 (15.8) | 15 (31.3) | 0.51 (0.11–2.39) | 0.391 | |
| ≥3 | 9 (2.1) | 4 (1.1) | 5 (10.4) | 0.96 (0.05–18.22) | 0.976 | |
| Existence of fever | 188 (43.9) | 163 (42.9) | 25 (52.1) | 0.227 | ||
| Surgery (within 30 days) | <0.001 | 0.168 | ||||
| None | 180 (42.1) | 139 (36.6) | 41 (85.4) | REF (1.00) | ||
| Curative surgery | 225 (52.6) | 221 (58.2) | 4 (8.3) | 0.09 (0.01–1.19) | 0.067 | |
| Palliative surgery | 23 (5.4) | 20 (5.3) | 3 (6.3) | 0.20 (0.02–1.69) | 0.140 | |
| Chemotherapy (within 30 days) | 0.613 | |||||
| None | 325 (75.9) | 285 (75.0) | 40 (83.3) | |||
| Neoadjuvant | 8 (1.9) | 7 (1.8) | 1 (2.1) | |||
| Adjuvant | 43 (10.0) | 40 (10.5) | 3 (6.3) | |||
| 1st line | 40 (9.3) | 38 (10.0) | 2 (4.2) | |||
| 2nd line | 7 (1.6) | 6 (1.6) | 1 (2.1) | |||
| ≥3rd line | 5 (1.2) | 4 (1.1) | 1 (2.1) | |||
| Radiotherapy (within 30 days) | 33 (7.7) | 23 (6.1) | 10 (20.8) | 0.001 | 2.66 (0.53–13.32) | 0.234 |
|
Concurrent chemoradiotherapy (within 30 days) |
14 (3.3) | 13 (3.4) | 1 (2.1) | 0.952 | ||
| Corticosteroid therapy (within 30 days) | 128 (29.9) | 111 (29.2) | 17 (35.4) | 0.376 | ||
| Prior G-CSF use (within 30 days) | 79 (18.5) | 65 (17.1) | 14 (29.2) | 0.042 | 0.30 (0.07–1.28) | 0.104 |
| Invasive procedure (within 30 days) | 318 (74.3) | 292 (76.8) | 26 (54.2) | 0.001 | 0.77 (0.26–2.31) | 0.645 |
|
Presence of indwelling catheters or other devices |
||||||
| Ureteral stent | 15 (3.5) | 10 (2.6) | 5 (10.4) | 0.019 | 0.94 (0.13–6.85) | 0.949 |
| Indwelling urinary catheters | 217 (50.7) | 212 (55.8) | 5 (10.4) | <0.001 | 0.31 (0.02–4.28) | 0.380 |
| CVC (port-a-cath or PICC) | 56 (13.1) | 49 (12.9) | 7 (14.6) | 0.744 | ||
| Percutaneous pleural drainage tube | 146 (34.1) | 141 (37.1) | 5 (10.4) | <0.001 | 1.01 (0.23–4.50) | 0.988 |
| Percutaneous abdomen drainage tube | 19 (4.4) | 13 (3.4) | 6 (12.5) | 0.012 | 2.01 (0.39–10.28) | 0.403 |
| Drains post-operation | 232 (54.2) | 224 (58.9) | 8 (16.7) | <0.001 | 1.33 (0.16–11.34) | 0.794 |
| Nasogastric tube | 181 (42.3) | 175 (46.1) | 6 (12.5) | <0.001 | 2.39 (0.29–19.65) | 0.416 |
Note: Bold values indicate statistical significance.
Abbreviations: OR, odds ratio; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group; COPD, chronic obstructive pulmonary disease, CCI, Charlson comorbidity index score; G-CSF, granulocyte colony-stimulating factor; CVC, central venous catheter; PICC, peripherally inserted central catheter.
Infection-Related Characteristics
Of the 428 GI cancer patients with nosocomial infections, a total of 189 (44.2%) patients were confirmed as respiratory tract infections, followed by BSIs in 50 (11.7%) patients, abdominal cavity infections in 49 (11.4%) patients, and thoracic cavity infections in 41 (9.6%) patients. There were 398 (93.0%) patients had received empirical antibiotic treatment during hospitalization. The most common empirical antibiotic treatment regimens were combination therapy (52.3%), followed by β-lactam/β-lactamase inhibitor combinations (15.9%), and cephalosporins (11.0%). A total of 239 (55.8%) patients were confirmed as to have had received effective empirical antibiotic treatment. The median length of antibiotic treatment for all patients was 9.0 days (range, 6.0–13.0 days). During hospitalization, 73 (17.1%) patients were treated in the ICU, 44 (10.3%) received mechanical ventilation, and 49 (11.4%) presented septic shock. Finally, a total of 48 (11.2%) patients died due to nosocomial infections. The median length of hospitalization of all patients was 21.0 days (range, 11.0–28.0 days). The detailed infection-related characteristics of the included patients were summarized in Table 2.
Table 2.
Infection-Related Characteristics of Nosocomial Infections in Gastrointestinal Cancer Patients
| Characteristics | All N =428 | Univariate Analysis | Multivariate Analysis | |||
|---|---|---|---|---|---|---|
| Survivor N =380, n (%) | Non-Survivor N = 48, n (%) | P-value | OR (95% CI) | P-value | ||
| Primary sites of infection | ||||||
| Respiratory tract | 189 (44.2) | 169 (44.5) | 20 (41.7) | 0.712 | ||
| Urinary tract | 38 (8.9) | 36 (9.5) | 2 (4.2) | 0.343 | ||
| Gastrointestinal tract | 24 (5.6) | 17 (4.5) | 7 (14.6) | 0.011 | 0.49 (0.08–3.10) | 0.446 |
| Skin and soft tissue | 37 (8.6) | 35 (9.2) | 2 (4.2) | 0.369 | ||
| Thoracic cavity | 41 (9.6) | 41(10.8) | 0 | 0.033 | 0 | 0.997 |
| Abdominal cavity | 49 (11.4) | 45 (11.8) | 4 (8.3) | 0.472 | ||
| BSI | 50 (11.7) | 37 (9.7) | 13 (27.1) | <0.001 | 0.35 (0.08–1.58) | 0.172 |
| Empirical antibiotic treatment | 398 (93.0) | 355 (93.4) | 43 (89.6) | 0.496 | ||
| β-lactam/β-lactamase inhibitor combinations | 68 (15.9) | 62 (16.3) | 6 (12.5) | 0.496 | ||
| Cephalosporins | 47 (11.0) | 44 (11.6) | 3 (6.3) | 0.266 | ||
| Carbapenems | 15 (3.5) | 13 (3.4) | 2 (4.2) | 1.000 | ||
| Fluoroquinolones | 31 (7.2) | 27 (7.1) | 4 (8.3) | 0.989 | ||
| Aminoglycosides | 9 (2.1) | 8 (2.1) | 1 (2.1) | 1.000 | ||
| Combination therapy | 224 (52.3) | 198 (52.1) | 26 (54.2) | 0.788 | ||
| Effective empirical antibiotic treatment | 239 (55.8) | 211 (55.5) | 28 (58.3) | 0.712 | ||
| Length of antibiotic treatment (days) | 9.0 (6.0–13.0) | 9.0 (6.0–14.0) | 6.0 (4.0–9.0) | 0.001 | ||
| < 9.0 | 207 (48.4) | 172 (45.3) | 35 (72.9) | <0.001 | 4.05 (1.41–11.61) | 0.009 |
| Length of hospitalization (days) | 21.0 (11.0–28.0) | 22.0 (13.0–29.0) | 12.0 (6.0–19.0) | <0.001 | ||
| < 21.0 | 210 (49.1) | 172 (45.3) | 38 (79.2) | <0.001 | 1.69 (0.53–5.41) | 0.378 |
| ICU admission | 73 (17.1) | 69 (18.2) | 4 (8.3) | 0.088 | 2.98 (0.36–25.02) | 0.315 |
| Mechanical ventilation | 44 (10.3) | 37 (9.7) | 7 (14.6) | 0.430 | ||
| Septic shock | 49 (11.4) | 34 (8.9) | 15 (31.3) | <0.001 | 5.08 (1.08–23.90) | 0.040 |
| Laboratory examination results | ||||||
| Haemoglobin (g/L; normal range 115–150) | 107.0 (96.0–120.0) | 108.0 (97.0–122.0) | 100.0 (85.0–106.0) | <0.001 | ||
| < 110 | 239 (55.8) | 202 (53.2) | 37 (77.1) | 0.002 | 1.63 (0.56–4.75) | 0.367 |
| Platelet count (×109/L; normal range 125–350) | 195.0 (129.3–275.8) | 203.0 (135.0–282.0) | 137.0 (101.0–224.0) | 0.026 | ||
| < 100.0 | 45 (10.5) | 35 (9.2) | 10 (20.8) | 0.013 | 0.77 (0.22–2.69) | 0.680 |
| White-cell count (×109/L; normal range 4.0–10.0) | 8.2 (5.5–11.1) | 8.1 (5.6–11.0) | 8.4 (5.1–12.3) | 0.646 | ||
| > 10.0 | 144 (33.6) | 124 (32.6) | 20 (41.7) | 0.212 | ||
| < 4.0 | 40 (9.3) | 33 (8.7) | 7 (14.6) | 0.289 | ||
| Neutrophils count (×109/L; normal range 1.8–6.3) | 6.6 (4.2–9.5) | 6.4 (4.2–9.2) | 6.9 (3.9–11.2) | 0.444 | ||
| Lymphocytes count (×109/L; normal range 1.1–3.2) | 0.9 (0.6–1.2) | 0.9 (0.6–1.3) | 0.7 (0.4–1.0) | 0.106 | ||
| < 1.0 | 249 (58.2) | 214 (56.3) | 35 (72.9) | 0.028 | 2.70 (0.90–8.13) | 0.077 |
| PCTa (ng/mL; normal range 0–0.5) | 0.5 (0.4–2.1) | 0.5 (0.3–1.9) | 0.5 (0.4–3.7) | 0.906 | ||
| ≥ 1.0 | 96 (33.4) | 84 (33.2) | 12 (35.3) | 0.808 | ||
| Albumin (g/L; normal range 40–55) | 32.0 (29.0–36.0) | 33.0 (29.0–36.0) | 30.0 (27.0–33.0) | 0.002 | ||
| < 30.0 | 145 (33.9) | 119 (31.3) | 26 (54.2) | 0.002 | 4.50 (1.58–12.83) | 0.005 |
Notes: aOnly 287 patients’ data are available. Bold values indicate statistical significance.
Abbreviations: OR, odds ratio; CI, confidence interval; BSI, bloodstream infection; ICU, intensive care unit; PCT, procalcitonin.
Microbiology Characteristics
A total of 428 strains of causative pathogens were isolated from different clinical samples during the study period. The most common samples were sputum (40.9%), followed by blood (14.5%), drainage fluid (13.1%), and urine (8.4%). Gram-negative bacteria (GNB) were the leading causative pathogens of nosocomial infections in GI cancer patients, accounting for 36.7%, followed by MDRGNB (27.6%), polymicrobial (13.6%), Gram-positive bacteria (9.6%), and fungi (9.6%). Of the isolated GNB, the most frequently isolated pathogens were E. coli (11.9%), Klebsiella pneumoniae (10.0%), and Pseudomonas aeruginosa (9.1%). Among the MDRGNB group, ESBL-producing E. coli (13.6%), Acinetobacter baumannii (4.7%), and ESBL-producing Klebsiella pneumoniae (3.7%) were the most frequently isolated pathogens. The isolated Gram-positive bacteria mainly included Staphylococcus aureus (3.7%), Streptococcus pneumoniae (2.8%), and Streptococcus anginosus (1.6%). The detailed microbiological characteristics were presented in Table 3.
Table 3.
Causative Pathogens of All Nosocomial Infection Episodes in Gastrointestinal Cancer Patients
| Causative Organisms | All N =428 | Univariate Analysis | Multivariate Analysis | |||
|---|---|---|---|---|---|---|
| Survivor N=380, n (%) | Non-Survivor N = 48, n (%) | P-value | OR (95% CI) | P-value | ||
| Gram-negative bacteria | 157 (36.7) | 146 (38.4) | 11 (22.9) | 0.036 | ||
| Escherichia coli | 51 (11.9) | 50 (13.2) | 1 (2.1) | 0.026 | 0.06 (0.00–1.34) | 0.075 |
| Klebsiella pneumoniae | 43 (10.0) | 42 (11.1) | 1 (2.1) | 0.090 | 0.23 (0.02–2.20) | 0.202 |
| Pseudomonas aeruginosa | 39 (9.1) | 36 (9.5) | 3 (6.3) | 0.642 | ||
| Enterobacter spp. | 24 (5.6) | 18 (4.7) | 6 (12.5) | 0.061 | 3.29 (0.76–14.23) | 0.112 |
| Gram-positive bacteria | 41(9.6) | 38 (10.0) | 3 (6.3) | 0.568 | ||
| Staphylococcus aureus | 16 (3.7) | 13 (3.4) | 3 (6.3) | 0.569 | ||
| Streptococcus pneumoniae | 12 (2.8) | 12 (3.2) | 0 | 0.433 | ||
| Coagulase-negative Staphylococci | 6 (1.4) | 6 (1.6) | 0 | 1.000 | ||
| Streptococcus anginosus | 7 (1.6) | 7 (1.8) | 0 | 1.000 | ||
| Fungi | 41 (9.6) | 31 (8.2) | 10 (20.8) | 0.011 | ||
| Candida albicans | 31 (7.2) | 24 (6.3) | 7 (14.6) | 0.074 | 3.96 (0.93–16.91) | 0.063 |
| Candida spp. | 5 (1.2) | 3 (0.8) | 2 (4.2) | 0.099 | 6.18 (0.21–185.43) | 0.294 |
| Eurotium | 5 (1.2) | 4 (1.1) | 1 (2.1) | 0.450 | ||
| MDRGNB | 118 (27.6) | 103 (27.1) | 15 (31.3) | 0.545 | ||
| ESBL-producing Escherichia coli | 58 (13.6) | 47 (12.4) | 11 (22.9) | 0.124 | ||
| ESBL-producing Klebsiella pneumoniae | 16 (3.7) | 16 (4.2) | 0 | 0.274 | ||
| MDR Pseudomonas aeruginosa | 7 (1.6) | 7 (1.8) | 0 | 1.000 | ||
| Acinetobacter baumannii | 20 (4.7) | 18 (4.7) | 2 (4.2) | 1.000 | ||
| Stenotrophomonas maltophilia | 14 (3.3) | 12 (3.2) | 2 (4.2) | 1.000 | ||
| CRE | 3 (0.7) | 3 (0.8) | 0 | 1.000 | ||
| Enterococcus | 10 (2.3) | 6 (1.6) | 4 (8.3) | 0.016 | 3.47 (0.42–28.94) | 0.250 |
| Anaerobes | 3 (0.7) | 3 (0.8) | 0 | 1.000 | ||
| Polymicrobial | 58 (13.6) | 53 (13.9) | 5 (10.4) | 0.501 | ||
Note: Bold values indicate statistical significance.
Abbreviations: MDRGNB, multidrug-resistant gram-negative bacteria; ESBL, extended-spectrum β-lactamase; MDR, multidrug-resistant; CRE, carbapenem-resistant Enterobacteriaceae.
Antimicrobial Susceptibility Analysis
We then performed antimicrobial susceptibility analysis of the isolated causative pathogens. It showed that the isolated GNB exhibited high sensitivity to amikacin (94.5%), meropenem (92.4%), imipenem (91.2%), piperacillin/tazobactam (83.4%), and cefepime (78.0%), while they were mainly resistant to ceftriaxone (48.7%), sulfamethoxazole-trimethoprim (45.4%), aztreonam (39.1%), and ciprofloxacin (29.2%) (Figure 1A). Compared with the isolated GNB, the isolated Gram-positive bacteria exhibited high sensitivity to tigecycline (100.0%), linezolid (100.0%), minocycline (100.0%), and vancomycin (98.0%), while they were mainly resistant to clindamycin (75.0%), penicillin (51.1%), tetracycline (45.2%), and sulfamethoxazole-trimethoprim (44.4%) (Figure 1B).
Figure 1.
Antimicrobial susceptibility comparison among gastrointestinal cancer patients with nosocomial infections. (A) The isolated Gram-negative bacteria. (B) The isolated Gram-positive bacteria.
Abbreviations: TGC, tigecycline; LVX, levofloxacin; CIP, ciprofloxacin; CRO, ceftriaxone; CAZ, ceftazidime; FEP, cefepime; MEM, meropenem; IMP, imipenem; ATM, aztreonam; TZP, piperacillin/tazobactam; GEN, gentamicin; SXT, sulfamethoxazole-trimethoprim; AMK, amikacin; PEN, penicillin; CLI, clindamycin; MFX, moxifloxacin; TEC, teicoplanin; VAN, vancomycin; LNZ, linezolid; MNO, minocycline; TCY, tetracycline.
Prognostic Factors for In-Hospital Mortality of Gastrointestinal Cancer Patients with Nosocomial Infections
A total of 48 (11.2%) patients died of nosocomial infections during hospitalization. The results of the univariate analysis identified several variables were statistically significant for in-hospital mortality (Tables 1–3). The results of the multivariate analysis revealed that ECOG performance status ≥two scores (OR 9.99; 95% CI 3.57–27.92), length of antibiotic treatment <9.0 days (OR 4.05; 95% CI 1.41–11.61), existence of septic shock (OR 5.08; 95% CI 1.08–23.90), and hypoproteinemia (OR 4.50; 95% CI 1.58–12.83) were independent risk factors for in-hospital mortality of hospitalized GI cancer patients with nosocomial infections.
Discussion
GI cancer patients represent a particular group of people since these patients are predisposed to developing immunosuppression due to a variety of different mechanisms.15,16 As a result, infections remain one of the most common complications for these patients during hospitalization.17 Besides, nosocomial infections in cancer patients are associated with prolonged hospitalization, delayed initiation of chemotherapy, increased medical costs, and substantial morbidity and mortality. Therefore, we performed this study to describe the bacteria distribution, antibiotic resistance pattern, and clinical outcomes of nosocomial infections in hospitalized GI cancer patients.
The global epidemiology of nosocomial infections in cancer patients has changed over time.2 Throughout the 1960s and 1970s, GNB were the most common causative pathogens in cancer patients. During the next 20 years, the causative pathogens shifted from GNB to Gram-positive bacteria due to chemotherapy-induced severe oral mucositis, the widespread use of central venous catheters, and antibacterial prophylaxis administration.18 However, in recent years, a new re-emergence of GNB as leading causative pathogens of infections were reported worldwide.19–21 In consistent, our results showed that 36.7% of cases were caused by GNB, and 9.6% of cases were caused by Gram-positive bacteria. Similar to previously published studies,6,15,22,23 we found that E. coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa were the most frequently isolated causative pathogens of nosocomial infections caused by GNB. Among the isolated Gram-positive bacteria, Staphylococcus aureus was the most commonly isolated causative pathogen, followed by Streptococcus pneumoniae. Similarly, Antonio et al reported that Staphylococcus aureus and Streptococcus pneumoniae were the most frequently isolated causative pathogens of nosocomial infections caused by Gram-positive bacteria among elderly cancer patients in Spain.22 Nosocomial infections due to fungi were observed in 9.6% of cases. Among these, Candida albicans was the most commonly isolated causative pathogen. This is consistent with the previous study conducted by Calik Basaran et al in a tertiary care academic center in Turkey.24 In the present study, it is noteworthy that MDR strains were detected in 27.6% of infection episodes. ESBL-producing E. coli and Acinetobacter baumannii were the most frequently isolated causative pathogens of nosocomial infections caused by MDRGNB. Besides, there were no MDR Gram-positive bacteria detected in our study. Previous studies reported that ESBL-producing Enterobacteriaceae was the leading causative pathogen for nosocomial infections in cancer patients caused by MDR strains.13,15,25
Immunosuppressed cancer patients are prone to developing septic shock due to severe infections during hospitalization. Therefore, appropriate and adequate empirical antibiotic treatment plays an important role in reducing infection-related mortality.2 However, previously published guidelines only addressed the antimicrobial therapy of nosocomial infections in cancer patients with febrile neutropenia (FN).26–28 Gudiol et al reported that β-lactam + β-lactamase inhibitor combination was the most frequent empirical antimicrobial regimen in dealing with infections among cancer patients, followed by oxymino-β-lactams.2 In our study, the results of antimicrobial susceptibility analysis demonstrated that the isolated GNB exhibited high sensitivity to amikacin, meropenem, imipenem, and piperacillin/tazobactam. However, the isolated GNB exhibited high resistance to ceftriaxone, sulfamethoxazole-trimethoprim, and aztreonam. Ashour et al also reported a comparable observation among hospitalized cancer patients in Egypt.6 It is reported that the colonization of ESBL-producers has been increased dramatically in recent years.2 In the present study, ESBL-producing Enterobacteriaceae was the most commonly isolated MDR strains. Ceftriaxone and aztreonam resistance are potential markers for the presence of ESBL. The isolated Gram-positive bacteria exhibited high sensitivity to tigecycline, linezolid, and vancomycin since there were no MDR strains detected in this group. A previous study reported a meager resistance rate to these antibiotics in non-neutropenic cancer patients in Taiwan, China.29
The overall in-hospital mortality of all patients was 11.2% (48/428) in the study. This finding was lower as compared with the previous retrospective studies performed in other centers (17.0–32.0%).15,22–24 It may be attributed to the fact that only a small number of patients developed BSIs during hospitalization in our study. Besides, there were few patients who developed neutropenia due to the appropriate administration of G-CSF. The results of multivariate analysis revealed that ECOG performance status, length of antibiotic treatment, septic shock, and serum albumin level were associated with the in-hospital mortality of GI cancer patients with nosocomial infections. Cancer patients with higher ECOG performance status were associated with worse prognoses. We found that ECOG performance status ≥two scores was significantly correlated with increased in-hospital mortality rate in our study. It suggests that we should focus more on these patients once they were confirmed as infection episodes during hospitalization. Length of antibiotic treatment <9.0 days and septic shock were also identified as risk factors associated with unfavorable clinical outcomes, which is aligned with the previous studies conducted in Spain22,30 and Taiwan, China.31 In addition, we also found that hypoproteinemia was an independent risk factor of in-hospital mortality. Similarly observation was made in a retrospective study conducted in China.32 Previous studies revealed that low serum albumin levels were associated with poor nutrition status and weight loss in cancer patients, and hypoproteinemia was positively associated with short - and long-term mortality in all hospitalized patients.32,33
Our study also has several limitations. First, this is a retrospective study. Although partial infection episodes were determined under the clinical manifestations of patients by qualified physicians, there might be potential bias in the study. Furthermore, the chemotherapy regimens, radiotherapy dosage, prior infection history, and prior antibiotic treatment were not collected. Moreover, considering this retrospective study was carried out in one hospital, further prospective multicenter studies are warranted to verify our results.
Conclusions
The burden of nosocomial infections in GI cancer patients is considerably high, with GNB being predominantly isolated causative pathogens. MDR strains were detected in 27.6% of isolates. The isolated GNB exhibited high sensitivity to amikacin, meropenem, imipenem, and piperacillin/tazobactam, while the isolated Gram-positive bacteria exhibited high sensitivity to tigecycline, linezolid, and vancomycin. Our findings also suggest that ECOG performance status ≥two scores, inadequate antibiotic treatment, septic shock, and hypoproteinemia were associated with increased in-hospital mortality. Surveillance on serum albumin level, adequate antibiotic treatment, early identification and prompt treatment of septic shock could advance the prognosis of nosocomial infections in GI cancer patients.
Funding Statement
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Ethics Approval and Consent to Participate
This study was approved by the ethics committee of the First Affiliated Hospital of Xi’an Jiaotong University (No: XJTU1AF2020LSK-049). Waiving of informed consent was obtained due to the retrospective noninterventional study design. Furthermore, we confirm that the patient data accessed complied with data protection privacy laws.
Author Contributions
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.
Disclosure
No potential conflict of interest was reported by the authors.
References
- 1.Global Burden of Disease Cancer C, Fitzmaurice C, Allen C, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA Oncol. 2017;3(4):524–548. doi: 10.1001/jamaoncol.2016.5688 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Gudiol C, Aguado JM, Carratala J. Bloodstream infections in patients with solid tumors. Virulence. 2016;7(3):298–308. doi: 10.1080/21505594.2016.1141161 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Gajdacs M, Urban E. Epidemiological trends and resistance associated with stenotrophomonas maltophilia bacteremia: a 10-year retrospective cohort study in a tertiary-care hospital in Hungary. Diseases. 2019;7:2. doi: 10.3390/diseases7020041 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Brand JS, Colzani E, Johansson ALV, et al. Infection-related hospitalizations in breast cancer patients: risk and impact on prognosis. J Infect. 2016;72(6):650–658. doi: 10.1016/j.jinf.2016.04.003 [DOI] [PubMed] [Google Scholar]
- 5.Kamboj M, Sepkowitz KA. Nosocomial infections in patients with cancer. Lancet Oncol. 2009;10(6):589–597. doi: 10.1016/S1470-2045(09)70069-5 [DOI] [PubMed] [Google Scholar]
- 6.Ashour HM, El-Sharif A. Species distribution and antimicrobial susceptibility of gram-negative aerobic bacteria in hospitalized cancer patients. J Transl Med. 2009;7:14. doi: 10.1186/1479-5876-7-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. doi: 10.1016/0021-9681(87)90171-8 [DOI] [PubMed] [Google Scholar]
- 8.Crowe MJ, Cooke EM. Review of case definitions for nosocomial infection — towards a consensus. J Hosp Infect. 1998;39(1):3–11. doi: 10.1016/S0195-6701(98)90237-7 [DOI] [PubMed] [Google Scholar]
- 9.Perdikouri EIA, Arvaniti K, Lathyris D, et al. Infections due to multidrug-resistant bacteria in oncological patients: insights from a five-year epidemiological and clinical analysis. Microorganisms. 2019;7:9. doi: 10.3390/microorganisms7090277 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Vincenzi B, Fioroni I, Pantano F, et al. Procalcitonin as diagnostic marker of infection in solid tumors patients with fever. Sci Rep. 2016;6:28090. doi: 10.1038/srep28090 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Shankar-Hari M, Phillips GS, Levy ML, et al. Developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315(8):775–787. doi: 10.1001/jama.2016.0289 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Gajdacs M, Batori Z, Abrok M, Lazar A, Burian K. Characterization of resistance in gram-negative urinary isolates using existing and novel indicators of clinical relevance: a 10-year data analysis. Life (Basel). 2020;10(2):16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.CLSI. Performance Standards for Antimicrobial Susceptibility Testing, Supplement M100S. 26th ed. Wayne, PA, USA: Clinical and Laboratory Standards Institute; 2016. [Google Scholar]
- 14.Ben-David D, Kordevani R, Keller N, et al. Outcome of carbapenem resistant klebsiella pneumoniae bloodstream infections. Clin Microbiol Infect. 2012;18(1):54–60. doi: 10.1111/j.1469-0691.2011.03478.x [DOI] [PubMed] [Google Scholar]
- 15.Marin M, Gudiol C, Garcia-Vidal C, Ardanuy C, Carratala J. Bloodstream infections in patients with solid tumors: epidemiology, antibiotic therapy, and outcomes in 528 episodes in a single cancer center. Medicine (Baltimore). 2014;93(3):143–149. doi: 10.1097/MD.0000000000000026 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Safdar A, Armstrong D. Infectious morbidity in critically ill patients with cancer. Crit Care Clin. 2001;17(3):531–570. doi: 10.1016/S0749-0704(05)70198-6 [DOI] [PubMed] [Google Scholar]
- 17.Fentie A, Wondimeneh Y, Balcha A, Amsalu A, Adankie BT. Bacterial profile, antibiotic resistance pattern and associated factors among cancer patients at University of Gondar Hospital, Northwest Ethiopia. Infect Drug Resist. 2018;11:2169–2178. doi: 10.2147/IDR.S183283 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Cordonnier C, Buzyn A, Leverger G, et al. Epidemiology and risk factors for gram-positive coccal infections in neutropenia: toward a more targeted antibiotic strategy. Clin Infect Dis. 2003;36(2):149–158. doi: 10.1086/345435 [DOI] [PubMed] [Google Scholar]
- 19.Gajdacs M. The concept of an ideal antibiotic: implications for drug design. Molecules. 2019;24(5):892. doi: 10.3390/molecules24050892 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wisplinghoff H, Seifert H, Wenzel RP, Edmond MB. Current trends in the epidemiology of nosocomial bloodstream infections in patients with hematological malignancies and solid neoplasms in hospitals in the United States. Clin Infect Dis. 2003;36(9):1103–1110. doi: 10.1086/374339 [DOI] [PubMed] [Google Scholar]
- 21.Gudiol C, Bodro M, Simonetti A, et al. Changing aetiology, clinical features, antimicrobial resistance, and outcomes of bloodstream infection in neutropenic cancer patients. Clin Microbiol Infect. 2013;19(5):474–479. doi: 10.1111/j.1469-0691.2012.03879.x [DOI] [PubMed] [Google Scholar]
- 22.Antonio M, Gudiol C, Royo-Cebrecos C, Grillo S, Ardanuy C, Carratala J. Current etiology, clinical features and outcomes of bacteremia in older patients with solid tumors. J Geriatr Oncol. 2019;10(2):246–251. doi: 10.1016/j.jgo.2018.06.011 [DOI] [PubMed] [Google Scholar]
- 23.Aydemir H, Piskin N, Kokturk F, Gokmen A, Akduman D. Health-care associated bacteremia in geriatric cancer patients with febrile neutropenia. J Geriatr Oncol. 2013;4(2):190–195. doi: 10.1016/j.jgo.2012.10.174 [DOI] [PubMed] [Google Scholar]
- 24.Calik Basaran N, Karaagaoglu E, Hascelik G, Durusu Tanriover M, Akova M. Prospective evaluation of infection episodes in cancer patients in a tertiary care academic center: microbiological features and risk factors for mortality. Turk J Haematol. 2016;33(4):311–319. doi: 10.4274/tjh.2015.0216 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ariza-Heredia EJ, Chemaly RF. Update on infection control practices in cancer hospitals. CA Cancer J Clin. 2018;68(5):340–355. doi: 10.3322/caac.21462 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Freifeld AG, Bow EJ, Sepkowitz KA, et al. Clinical practice guideline for the use of antimicrobial agents in neutropenic patients with cancer: 2010 update by the infectious diseases society of America. Clin Infect Dis. 2011;52(4):427–431. doi: 10.1093/cid/ciq147 [DOI] [PubMed] [Google Scholar]
- 27.Kochanek M, Schalk E, von Bergwelt-baildon M, et al. Management of sepsis in neutropenic cancer patients: 2018 guidelines from the Infectious Diseases Working Party (AGIHO) and Intensive Care Working Party (iCHOP) of the German Society of Hematology and Medical Oncology (DGHO). Ann Hematol. 2019;98(5):1051–1069. doi: 10.1007/s00277-019-03622-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Heinz WJ, Buchheidt D, Christopeit M, et al. Diagnosis and empirical treatment of fever of unknown origin (FUO) in adult neutropenic patients: guidelines of the Infectious Diseases Working Party (AGIHO) of the German Society of Hematology and Medical Oncology (DGHO). Ann Hematol. 2017;96(11):1775–1792. doi: 10.1007/s00277-017-3098-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Huang CC, Wu CJ, Wang LR, et al. Antimicrobial susceptibility of bacteremic isolates from cancer patients with or without neutropenia at a medical center in southern Taiwan. J Microbiol Immunol Infect. 2011;44(5):376–381. doi: 10.1016/j.jmii.2011.01.035 [DOI] [PubMed] [Google Scholar]
- 30.Palacios-Baena ZR, Gutierrez-Gutierrez B, De Cueto M, et al. Development and validation of the INCREMENT-ESBL predictive score for mortality in patients with bloodstream infections due to extended-spectrum-beta-lactamase-producing enterobacteriaceae. J Antimicrob Chemother. 2017;72(3):906–913. doi: 10.1093/jac/dkw513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Chen CY, Tien FM, Sheng WH, et al. Clinical and microbiological characteristics of bloodstream infections among patients with haematological malignancies with and without neutropenia at a medical centre in northern Taiwan, 2008–2013. Int J Antimicrob Agents. 2017;49(3):272–281. doi: 10.1016/j.ijantimicag.2016.11.009 [DOI] [PubMed] [Google Scholar]
- 32.Xiao T, Yang K, Zhou Y, et al. Risk factors and outcomes in non-transplant patients with extended-spectrum beta-lactamase-producing Escherichia coli bacteremia: a retrospective study from 2013 to 2016. Antimicrob Resist Infect Control. 2019;8:144. doi: 10.1186/s13756-019-0599-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Paccagnella A, Morassutti I, Rosti G. Nutritional intervention for improving treatment tolerance in cancer patients. Curr Opin Oncol. 2011;23(4):322–330. doi: 10.1097/CCO.0b013e3283479c66 [DOI] [PubMed] [Google Scholar]