Abstract
This study aimed to describe the epidemiological and clinical features of patients admitted to non-intensive care hospital wards due to Staphylococcus aureus bacteremia (SAB) and to identify predictors of mortality to improve patient outcomes. This single-center retrospective study included hospitalized patients with SAB between 2016 and 2024. We retrieved clinical and microbiological data retrospectively from the electronic medical record system. The research comprised 356 patients with SAB. The 30-day and in-hospital mortality rates were 7.3% (n = 26) and 9.8% (n = 35), respectively. The multivariate analysis revealed neutrophil-to-lymphocyte ratio (NLR) (HR = 1.08; 95% CI = 1.02–1.13; p = 0.002), CRP (HR = 1.01; 95% CI = 1-1.02 ; p = 0.04), and albumin (HR = 0.83; 95% CI = 0.73-0.95; p = 0.008) as predictors for 30-day mortality. Pneumonia (HR = 15.03; 95% CI = 2.05–109.71; p = 0.008), leukemia (HR = 28.72; 95% CI = 1.56-525.92; p = 0.002), and sepsis (HR = 7.06; 95% CI = 1.02–48.53; p = 0.002) were identified as significant risk factors for mortality. Using the Cox regression analysis, age (HR: 1.05, CI:1.01–1.10, p = 0.01), leukemia (HR: 0.80, CI:0.71–0.90, p < 0.001), and low albumin level (HR: 11.76; CI:1.76–78.42, p = 0.01) were identified as independent risk factors affecting in-hospital mortality. We used the receiver operating characteristic (ROC) curve to predict the30-day mortality. The area under the ROC curve values were 0.619 (p = 0.044) for NLR, 0.692 (p = 0.001) for CRP, and 0.791 (p < 0.001) for albumin. The highest sensitivity and specificity at 30-day mortality were obtained from CRP and albumin, with a sensitivity of 65.4% and a specificity of 78.5% for albumin. Elevated NLR and CRP levels, along with decreased albumin levels, may predict poor clinical outcomes and could assist clinicians in optimizing the management of this bacterial infection. As a result, early diagnosis and appropriate antibiotic treatments are crucial in reducing mortality in SAB.
Keywords: Staphylococcus aureus, Bacteremia, Mortality, Methicillin-resistance, Charlson comorbidity index, Risk factors
Subject terms: Bacterial infection, Infectious-disease epidemiology, Microbiology, Medical research
Introduction
Staphylococcus aureus is a leading causative agent in respiratory tract infections, skin and soft tissue infections, surgical site infections, prosthetic device infections, cardiovascular infections, and bacteremia1. The local epidemiology, antibiotic resistance, and incidence of Staphylococcus aureus may vary between geographic regions and over time. The treatment remains challenging due to the emergence of multidrug- resistant strains such as MRSA. Notably, hospital-acquired SAB is more likely to be due to methicillin-resistant strains2,3.
Staphylococcus aureus is also an important cause of hospital-acquired (HA) and community-acquired (CA) bacteremia, and poses a significant threat due to its high morbidity and mortality rates4,5. The estimated incidence of Staphylococcus aureus bacteremia (SAB) is between 20 and 30 cases per 100,000 person-years, with 30-day mortality ranging from 15 to 40%, even with effective antibacterial therapies and source control strategies6,7. The guidelines define the standard management approach for SAB. These include diagnostic and therapeutic measures that should be implemented in all patients, such as bedside infectious disease consultation, follow-up blood cultures, echocardiography, source control, and adequate and timely antibiotic treatment8,9.
Several factors have been frequently associated with poor outcomes in patients with SAB, including advanced age, underlying comorbidities, immunosuppression, and the presence of sepsis or septic shock. Furthermore, specific infection foci—such as pneumonia, infective endocarditis, or bacteremia of unknown origin—have been identified as independent predictors of poor prognosis2,6,10. These variables emphasize the importance of early diagnosis, appropriate antimicrobial therapy, and targeted management strategies in improving clinical outcomes in SAB.
It is essential to understand which patients are at risk for SAB and its complications, how laboratory findings vary during the course of SAB, and which factors predict mortality. As such, healthcare professionals must stay updated about the manifestations and complications of SAB. This study aimed to describe the current epidemiology and clinical features of SAB and to identify mortality predictors in non-ICU patients. The study also aimed to identify important laboratory factors related to mortality, in order to support better risk evaluation and earlier treatment in this patient group.
Materials and methods
Study design
We conducted this retrospective cohort study between January 2016 and January 2024 at a 507-bed tertiary hospital in İstanbul, Turkey. The study comprised hospitalized patients with SAB. Eligible patients met the following criteria: (1) age ≥ 18 years, (2) hospitalized in non-intensive care unit wards, and 3) ≥ one blood culture positive for S. aureus accompanied by signs and symptoms of infection.
Patients were excluded if they had: (1) polymicrobial growth in blood cultures, (2) missing clinical data, or (3) been hospitalized in an intensive care unit. For patients with multiple episodes of SAB, only the first episode was included in the analysis. The patient’s demographic characteristics, laboratory parameters, and radiological imaging findings were retrieved from their electronic medical record system. Due to the retrospective nature of the study, (Clinical Research Ethics Committee of University of Health Sciences Turkey, Bakırköy Dr. Sadi Konuk Training and Research Hospital; approval number:2023-08-15, date:17.04.2023) waived the need of obtaining informed consent. This study complied with the Declaration of Helsinki.
Definitions
S. aureus bacteremia was defined as at least one positive blood culture for S. aureus from a peripheral venous blood-culture sample taken from a patient with clinical signs of infection. Bacteremia was considered community-acquired if the positive blood culture was obtained at or within 48 h of admission, or when there was evidence of S.aureus infection at another body site on admission5. Bacteremia was considered hospital-acquired (HA) if the positive blood culture was taken more than 48 h after admission, and there was no evidence of S.aureus infection on admission. Persistent SAB was defined as the continued growth of S. aureus in blood cultures at or beyond 72 h after the initiation of appropriate antimicrobial therapy8.
We defined the source of SAB according to CDC criteria11. The infective focus was classified as 1) infective endocarditis, 2) osteoarticular (including all bone and joint infections with or without prosthetic devices) infections, 3) pneumonia, 4) skin and soft tissue infections, (5) central venous catheter infections, (6) Primary bacteremia. Primary bacteremia was considered when the source of infection was unclear. Abdominal infection, surgical site infection, septic arthritis, and intracardiac device (ICD) infections were the other foci of infection causing Staphylococcus aureus bacteremia.We defined infective endocarditis using the modified Duke Criteria12. We used the Charlson comorbidity index (CCI) score to assess the severity of comorbidities and their impact on mortality prediction. Comorbidities were classified into three groups: mild (CCI score 1–2), moderate (CCI score 3–4), and severe (CCI score ≥ 5)13. We considered antimicrobial therapy appropriate if the antimicrobial agent had in vitro activity against the isolated S. aureus strain. Since all patients were managed in non-ICUwards, the quick Sequential Organ Failure Assessment (qSOFA) score was used as a screening tool for sepsis14. Coronavirus disease 2019 (COVID-19) co-infection was defined as co-existence with laboratory-confirmed COVID-19 and SAB. We also examined the previous history of COVID-19 within the last three months. The clinical spectrum of SARS-CoV-2 Infection was performed according to the “COVID-19 Treatment Guidelines Panel”15.
Microbiological data
All S. aureus isolates were identified using VITEK 2 Compact (bioMérieux, Marcy l’Etoile, France) automated system. Antimicrobial susceptibilities of the isolates, including oxacillin and vancomycin, were determined by using the disk diffusion test or automated systems according to the criteria of the Clinical and Laboratory Standards Institute16. The primary outcomes of the study were to determine 30-day and in-hospital mortality rates and to identify the factors associated with mortality. The association of methicillin resistance and hospital-acquired SAB with mortality are captured as secondary outcomes.
Statistical analyses
Statistical analyses were performed by using the Statistical Package for Social Sciences version 25.0 for Windows (SPSS Inc., Chicago, IL, USA). Descriptive data were presented as mean (standard deviation), frequency, median (interquartile range), and percentage values. Categorical variables were compared using the Chi-Square test and Fisher’s Exact test. The normality of continuous variables was tested with the Kolmogorov-Smirnov test. Student’s t-test was used for comparing the normally distributed continuous variables, and the Mann-Whitney U test was used for comparing the continuous variables which were not normally distributed. For the analysis of mortality, a multivariate model was constructed to identify independent predictors. In addition to significant variables (p < 0.05) in the univariate analysis, the established predictor variables for mortality were included in the multivariate model. Cox-regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). The “p” values less than or equal to 0.05 (p ≤ 0.05) were considered statistically significant. Receiver operating characteristic (ROC) curve analysis was applied to determine the accuracies of laboratory parameters that were significant in the multivariate analysis.
Results
A total of 356 patients with SAB were identified during the study period. The study patients were predominantly male (58%) with a mean age of (± SD) 56.9 ± 17.6 years. Of all SAB cases, 59 (16.5%) were community-acquired, and 297 (83.5%) were hospital-acquired. A total of 91.3% patients (n = 325) had at least one documented comorbidity. A median CCI score of 3 (IQR:2–6) further highlights the complexity and severity of the comorbidities.
The 30-day hospital mortality rate was determined to be 7.3%. When comparing the patients according to 30-day mortality, patients who died within 30 days had higher rates of several comorbidities, including coronary artery disease (CAD) (69.2%), congestive heart failure (CHF) (30.8%), solid tumors (11.5%), leukemia (11.5%), and lymphoma (7.7%). Additionally, sepsis at presentation was significantly higher in non-survivors than in survivors (30.8% vs. 8.8%, p = 0.003). Pneumonia was more common among the non-survivors than the survivors. (n = 11/26, 42.3% vs. n = 62/330, 18.8%, p = 0.004). Similarly, there was a higher incidence of leukemia in non-survivors compared to the survivors (n = 3/26, 11.5% vs. n = 8/330, 2.4%,p = 0.01). Also, non-survivors had a higher median CCI score compared to survivors (n = 6, IQR: 4-7.25 vs. n = 3, IQR: 2–6, p < 0.001). However, the rate of empirically appropriate antibiotic use was similar between the two groups (n = 22/26, 84.6% vs. n = 274/330, 83%, p > 0.05).
Evaluation of laboratory parameters revealed that lymphocyte count, platelet count, and albumin levels were significantly lower in non-survivors, whereas CRP and procalcitonin levels were significantly higher. (Table 1). For determining the independent predictors of mortality, a multivariate model was constructed. In addition to variables that were significant in the univariate analysis, the established predictor variables for mortality, such as age, sepsis at presentation, and methicillin resistance were included in the multivariate model. The multivariate analysis revealed neutrophil-to-lymphocyte ratio (NLR) (HR = 1.08; 95% CI = 1.02–1.13; p = 0.002), CRP (HR = 1.01; 95% CI = 1-1.02 ; p = 0.04) and albumin (HR = 0.83; 95% CI = 0.73–0.95; p = 0.008) as predictors for 30-day mortality. Depending on the results of the multivariate analysis of risk factors for 30-day mortality in patients with SAB, as shown in Table 1, pneumonia was identified as a significant risk factor for mortality (HR = 15.03; 95% CI = 2.05–109.71; p = 0.008). Pneumonia significantly increased the risk of 30-day mortality, with a hazard ratio of approximately 15. Also leukemia (HR = 28.72; 95% CI = 1.56-525.92; p = 0.002), and presence of sepsis (HR = 7.06; 95% CI = 1.02–48.53; p = 0.002) were significant risk factors for death.
Table 1.
Univariate and multivariate analysis of the factors associated with 30-day mortality in patients with Staphylococcus aureus bacteremia.
| Total n = 356 |
Univariate analysis | Multivariate analysis | |||||
|---|---|---|---|---|---|---|---|
| Non-survivors n = 26 |
Survivors n = 330 |
P(1) value |
OR (95%CI) |
P(1) value |
HR (95%CI) |
||
|
Male gender (n, %) |
208 (58.4) |
16 (61.5) |
192 (58.2) |
0.73 |
0.87 (0.38–1.97) |
||
|
Age (mean ± SD) |
56.9 ± 17.6 | 62.38 ± 15.229 | 56.50 ± 17.784 | 0.11 | 0.36 |
0.97 (0.92–1.03) |
|
|
Hospital-acquired SAB |
297 (83,4) |
22 (84,6) |
275 (83,3) |
0.86 |
1.1 (0.36–3.31) |
||
|
Length of stay (day) (median, IQR) |
15 (8–27) |
14.5 (9–25.5) |
15 (8–27) |
0.66 | |||
| Sepsis (n, %) |
37 (10.4) |
8 (30.8) |
29 (8.8) |
0.003 |
4.61 (1.84–11.52) |
0.04 |
7.06 (1.02–48.53) |
|
Persistent bacteremia (n,%) |
28 (7.9) |
2 (7.7) |
26 (7.9) |
1 |
0.97 (0.21–4.35) |
||
|
Comorbidity (n, %) |
325 (91.3) |
26 (100) |
299 (90.6) |
0.10 |
1.08 (1.05–1.12) |
||
|
Hypertension (n, %) |
221 (62.1) |
19 (73.1) |
202 (61.2) |
0.23 |
1.72 (0.70–4.20) |
||
|
Coronary artery disease (n, %) |
156 (43.8) |
18 (69.2) |
138 (41.8) |
0.007 |
3.13 (1.32–7.40) |
0.84 |
1.23 (0.15–10.05) |
|
Congestive heart failure (n, %) |
49 (13.8) |
8 (30.8) |
41 (12.4) |
0.009 |
3.13 (1.28–7.66) |
0.59 |
1.70 (0.23–12.09) |
|
Peripheral vascular disease (n, %) |
22 (6.2) |
1 (3.8) |
21 (6.4) |
0.60 |
0.58 (0.08–4.55) |
||
|
Cerebrovascular disease (n, %) |
72 (20.2) |
6 (23.1) |
66 (20) |
0.70 |
1.20 (0.46–3.10) |
||
| Dementia (n, %) |
31 (8.7) |
2 (7.7) |
29 (8.8) |
0.84 |
0.86 (0.19–3.84) |
||
|
COPD (n, %) |
29 (8.1) |
4 (15.4) |
25 (7.6) |
0.16 |
2.21 (0.70–6.94) |
||
|
Connective tissue disease (n, %) |
10 (2.8) |
1 (3.8) |
9 (2.7) |
0.74 |
1.42 (0.17–11.71) |
||
|
Peptic ulcer disease (n, %) |
2 (0.6) |
0 |
2 (0.6) |
0.69 |
0.92 (0.90–0.95) |
||
|
Mild liver disease (n, %) |
2 (0.6) |
0 |
2 (0.6) |
0.69 |
0.92 (0.90–0.95) |
||
|
Severe liver disease (n, %) |
22 (6.2) |
2 (7.7) |
20 (6.1) |
0.73 |
1.29 (0.28–5.85) |
||
|
Diabetes mellitus (n, %) |
152 (42.7) |
12 (46.2) |
140 (42.4) |
0.71 |
1.16 (0.52–2.59) |
||
| Hemiplegia (n, %) |
31 (8.7) |
2 (7.7) |
29 (8.8) |
0. 84 |
0.86 (0.19–3.84) |
||
|
Moderate to severe CKD (n, %) |
170 (47.8) |
10 (38.5) |
160 (48.5) |
0.32 |
0.66 (0.29–1.50) |
||
|
Diabetes with complications (n, %) |
86 (24.2) |
7 (26.9) |
79 (23.9) |
0.73 |
1.17 (0.47–2.88) |
||
|
Solid tumor (n, %) |
13 (3.7) |
3 (11.5) |
10 (3) |
0.03 |
4.17 (1.07–16.22) |
0.69 |
1.58 (0.15–16.10) |
| Leukemia (n, %) |
11 (3.1) |
3 (11.5) |
8 (2.4) |
0.01 |
5.25 (1.30–21.13) |
0.02 |
28.72 (1.56–525.92) |
|
Lymphoma (n, %) |
5 (1.4) |
2 (7.7) |
3 (0.9) |
0.005 |
9.08 (1.44–56.99) |
0.86 |
1.20 (0.13–10.87) |
|
IVDU (n, %) |
10 (2.8) |
1 (3.8) |
9 (2.7) |
0.74 |
1.42 (0.17–11.71) |
||
|
Charlson Comorbidity Index (median, IQR) |
3 (2–6) |
6 (4–7.25) |
3 (2–6) |
< 0.001 | 0.24 |
1.15 (0.90–1.47) |
|
|
Previous history of COVID- 19 within the last 3 months (n, %) |
38 (10.7) |
2 (7.7) |
36 (10.9) |
0.60 |
0.68 (0.15–3) |
||
|
COVID-19 co-infection (n, %) |
25 (7) |
2 (7.7) |
23 (7) |
0.89 |
1.11 (0.24–5) |
||
|
Appropriate empirical treatment (n, %) |
296 (83.1) |
22 (84.6) |
274 (83) |
1 |
1.12 (0.37–3.38) |
||
|
Methicillin resistance (n, %) |
105 (29.5) |
7 (6.7) |
98 (29,7) |
0.76 |
0.87 (0.35–2.14) |
0.54 |
1.53 (0.38–6.11) |
|
Central venous catheter (n, %) |
155 (43.5) |
10 (38.5) |
145 (43.9) |
0.58 |
0.79 (0.35–1.81) |
||
|
Hemodialysis (n, %) |
123 (34.6) |
5 (19.2) |
118 (35.8) |
0.08 |
0.42 (0.15–1.16) |
||
|
Central venous catheter infection (n, %) |
122 (34.3) |
7 (26.9) |
115 (34.8) |
0.41 |
0.68 (0.28–1.68) |
||
|
Endocarditis (n, %) |
17 (4.7) |
0 |
17 (5.2) |
0.62 |
0.92 (0.89–0.95) |
||
|
Pneumonia (n, %) |
73 (20.5) |
11 (42.3) |
62 (18.8) |
0.004 |
3.17 (1.38–7.23) |
0.008 |
15.03 (2.05–109.71) |
|
Soft tissue infection (n, %) |
41 (11.5) |
1 (3.8) |
40 (12.1) |
0.20 |
0.29 (0.03–2.19) |
||
|
Primary bacteremia (n, %) |
43 (12.1) |
4 (15.4) |
39 (11.8) |
0.59 |
1.35 (0.44–4.14) |
||
|
Abdominal infection (n, %) |
18 (5.1) |
2 (7.7) |
16 (4.8) |
0.52 |
1.63 (0.35–7.53) |
||
|
Surgical site infection (n, %) |
6 (1.7) |
1 (3.8) |
5 (1.5) |
0.37 |
2.60 (0.29–23.12) |
||
|
İnternal medicine services (n, %) |
71 (19.9) |
1 (3.8) |
70 (21.2) |
0.03 |
0.14 (0.02–1.11) |
0.90 |
1.17 (0.09–14.54) |
|
WBC (median, IQR) |
10,385 (6932- 14,897) |
9520 (5390- 15,335) |
10,490 (7097- 14,892) |
0.52 | |||
|
Neutrophil (median, IQR) |
8300 (5035- 12,165) |
7905 (4110- 13,625) |
8300 (5287- 12,155) |
0.77 | |||
|
Lymphocyte (median, IQR) |
880 (572- 1340) |
590 (375–1062) |
885 (600–1340) |
0.02 | 0.06 |
1 (1–1.002) |
|
|
NLR (median, IQR) |
9.05 (4.80- 16.9) |
15.6 (4.5–25.6) |
8.7 (4.8–16.1) |
0.04 | 0.002 |
1.08 (1.02–1.13) |
|
|
PLT (median, IQR) |
198,200 (135,000-271,750) |
136,500 (58,000-222,250) |
201,000 (142,000-272,300) |
0.005 | 0.85 |
1 (1–1) |
|
|
PLR (median, IQR) |
225.1 (128.3-357.1) |
290.6 (109.2-475.3) |
220.4 (129-347.8) |
0.41 | |||
|
CRP (median, IQR) |
138 (73–224) |
233 (161–325) |
136 (72–218) |
0.001 | 0.04 |
1.01 (1–1.02) |
|
|
Procalcitonin (median, IQR) |
1.8 (0.3–8.4) |
4.4 (2.9–8.9) |
1.4 (0.3–7) |
0.02 | 0.26 |
0.97 (0.93–1.02) |
|
|
Creatinine (median, IQR) |
1.4 (0.7–4.5) |
1.2 (0.6–3.4) |
1.4 (0.7–4.7) |
0.31 | |||
|
ALT (median, IQR) |
18 (11–30) |
15 (11.2–23.7) |
19 (11–30) |
0.19 | |||
|
AST (median, IQR) |
23 (14–42) |
22 (12.7–51) |
23 (14–42) |
0.94 | |||
|
Albumin (median, IQR) |
30 (26–33) |
23 (19.7–27) |
30 (26–34) |
< 0.001 | 0.008 | 0.83 (0.73–0.95) | |
Significant values are in [bold].
COPD, Chronic Obstructive Pulmonary Disease; CKD, Chronic Kidney Disease; ICU, intensive care unit; IVDU, Iv drug use; MRSA, Methicillin-resistant Staphylococcus aureus; MSSA, Methicillin-susceptible Staphylococcus aureus; GCS, Glasgow Coma Scale; AST, Aspartate amintransferase; ALT, Alanine amino transferase ; NLR, Neutrophil/ Lymphocyte ratio. OR, Odds ratio; HR, Hazard ratio; CI, Confidence interval. (1): P-values < 0.05 were considered statistically significant.
The rate of in-hospital mortality was determined to be 9.8% and risk factors associated with in-hospital mortality were also evaluated. In univariate analysis the following variables were found to be significantly associated with in-hospital mortality: age (p = 0.007), sepsis at presentation (p = 0.001), CAD (p = 0.006), CHF (p = 0.007), chronic obstructive pulmonary disease (COPD; p = 0.04), leukemia (p = 0.02), lymphoma (p = 0.02), higher CCI (p = 0.001), pneumonia (p = 0.01), hospitalization in internal medicine wards (p = 0.03), lymphopenia (p = 0.04), thrombocytopenia (p = 0.005), elevated C-reactive protein (CRP; p = 0.002), elevated procalcitonin (p = 0.009), and hypoalbuminemia (p < 0.001). A multivariate analysis model was created by adding “methicillin resistance” and “HA bacteremia” to the variables that were significant in the univariate analysis. Using the Cox regression analysis, age (HR: 1.05, CI:1.01–1.10, p = 0.01), leukemia (HR: 0.80, CI:0.71–0.90, p < 0.001), and low albumin level (HR: 11.76, CI:1.76–78.42, p = 0.01) were identified as independent risk factors affecting in-hospital mortality.
Among the 356 patients included in our study, 251 (70.5%) had methicillin-susceptible Staphylococcus aureus (MSSA) bacteremia, while 105 (29.5%) had methicillin-resistant Staphylococcus aureus (MRSA) bacteremia. Although the median CCI score was higher in the MRSA group compared to the MSSA group (n = 4, IQR:2–6 vs. n = 3, IQR:2–6), this difference was not statistically significant (p > 0.05). The majority of patients in both MSSA and MRSA groups were male (n = 144/251, 57.4% vs. n = 64/105, 61%), with no significant difference between them (p > 0.05). However, what is particularly concerning is that a significant portion of infections in both groups were hospital-acquired, accounting for 84.9% (n = 213/251) of MSSA and 84.0% (n = 80/105) of MRSA infections.
In this study, patients with MRSA bacteremia had a significantly higher prevalence of peripheral vascular disease than those with MSSA bacteremia (n = 10, 9.5% vs. n = 12, 4.8% p = 0.009). In contrast, dementia was more frequently observed among patients with MSSA bacteremia (10.8%) than in those with MRSA bacteremia (n = 4, 3.8% vs. n = 27, 10.8% p = 0.034 ). The 30-day mortality was similar in the patients with MSSA and the MRSA bacteremia (n = 19/251, 7.6% vs. n = 7/105, 6.7% p > 0.05). A comparative overview of patients with MSSA bacteremia and MRSA bacteremia is presented in Table 2.
Table 2.
Comparison of community-acquired or hospital-acquired Staphylococcus aureus bacteremia, and MSSA or MRSA bacteremia.
| CA-SAB n = 59 |
HA-SAB n = 297 |
P(1) value |
MSSA n = 251 |
MRSA n = 105 |
P(1) Value |
|
|---|---|---|---|---|---|---|
|
Male gender (n, %) |
45 (76.3) |
163 (54.9) |
0.002 |
144 (57.4) |
64 (61) |
0.532 |
|
Age (mean ± SD) |
51.07 ± 15.9 56 | 58.10 ± 17.772 | 0.003 | 57.94 ± 17.619 | 54,53 ± 17.602 | 0.097 |
|
Sepsis (n, %) |
6 (10.2) |
31 (10.4) |
0.95 |
28 (11.2) |
9 (8.6) |
0.46 |
|
Persistent bacteremia (n, %) |
2 (3.4) |
26 (8.8) |
0.19 |
19 (7.6) |
9 (8.6) |
0.74 |
|
Comorbidity (n, %) |
45 (76.3) |
280 (94.3) |
< 0.001 |
231 (92) |
94 (89.5) |
0.444 |
|
Hypertension (n, %) |
23 (39) |
198 (66.7) |
< 0.001 |
161 (64.1) |
60 (57.1) |
0. 214 |
|
Coronary artery disease (n, %) |
20 (33.9) |
136 (45.8) |
0.093 |
112 (44.6) |
44 (41.9) |
0. 638 |
|
Congestive heartfailure (n, %) |
4 (6.8) |
45 (15.2) |
0.08 |
39 (15.5) |
10 (9.5) |
0. 133 |
|
Peripheral vascular disease(n, %) |
6 (10.2) |
16 (5.4) |
0.16 |
12 (4.8) |
10 (9.5) |
0.009 |
|
Cerebrovascular disease(n, %) |
6 (10.2) |
66 (22.2) |
0.035 |
56 (22.3) |
16 (15.2) |
0.13 |
|
Dementia (n, %) |
3 (5.1) |
28 (9.4) |
0.028 |
27 (10.8) |
4 (3.8) |
0.034 |
|
COPD (n, %) |
3 (5.1) |
26 (8.8) |
0.34 |
23 (9.2) |
6 (5.7) |
0.278 |
|
Connective tissue disease(n, %) |
3 (5.1) |
7 (2.4) |
0.247 |
7 (2.8) |
3 (2.9) |
0.972 |
|
Peptic ulcer disease(n, %) |
0 |
2 (0.7) |
0.527 |
1 (0.4) |
1 (1) |
0. 524 |
|
Mild liver disease(n, %) |
0 |
2 (0.7) |
0.527 |
1 (0.4) |
1 (1) |
0. 524 |
|
Severe liverdisease (n, %) |
6 (10.2) |
16 (5.4) |
0.163 |
14 (5.6) |
8 (7.6) |
0.466 |
|
Diabetes mellitus (n, %) |
20 (33.9) |
132 (44.9) |
0.135 |
110 (43.8) |
42 (40) |
0.506 |
|
Hemiplegia (n, %) |
0 |
31 (10.4) |
0.009 |
23 (9.2) |
8 (7.6) |
0.637 |
|
Moderate to severe CKD (n, %) |
15 (25.4) |
155 (52.2) |
< 0.001 |
123 (49) |
47 (44.8) |
0.465 |
|
Diabetes with complications (n, %) |
7 (11.9) |
79 (26.6) |
0.016 |
57 (22.7) |
29 (27.6) |
0.324 |
|
Solid tumor(n, %) |
3 (5.1) |
29 (9.8) |
0.251 |
10 (4) |
3 (2.9) |
0.605 |
|
Leukemia (n, %) |
1 (1.7) |
10 (3.4) |
0.498 |
4 (1.6) |
7 (6.7) |
0.012 |
|
Lymphoma (n, %) |
0 |
5 (1.7) |
0. 316 |
2 (0.8) |
3 (2.9) |
0.132 |
|
IVDU (n, %) |
8 (13.6) |
2 (0.7) |
< 0.001 | 9(3.6) | 1(1) | 0.170 |
|
Charlson Comorbidity Index (median, IQR) |
2 (0–4) |
4 (2–6) |
< 0.001 | 3 (2–6) | 4 (2–6) | 0.867 |
|
Previoushistory of COVID-19 withinthelast 3 months (n, %) |
4 (6.8) |
34 (11.4) |
0.289 |
29 (11.6) |
9 (8.6) |
0.406 |
|
COVID-19 co-infection (n, %) |
4 (6.8) |
21 (7.1) |
0.8 |
21 (8.4) |
4 (3.8) |
0.125 |
|
Appropriate empirical treatment (n, %) |
51 (86.4) |
245 (82.5) |
0.459 |
204 (81.3) |
92 (87.6) |
0.145 |
|
Urinary catheterization (n, %) |
23 (39) |
176 (59.3) |
0.004 |
147 (58.6) |
52 (49.5) |
0.117 |
|
Central venous catheter (n, %) |
10 (16.9) |
145 (48.8) |
< 0.001 |
114 (45.4) |
41 (39) |
0.269 |
|
Hemodialysis (n, %) |
9 (15.3) |
48 (16.2) |
0.862 |
87 (34.7) |
36 (34.3) |
0.946 |
|
GCS < 15 (n, %) |
6 (10.2) |
117 (39.4) |
0.000 |
77 (30.7) |
25 (23.8) |
0.191 |
|
30 days mortality (n, %) |
18 (30.5) |
84 (28.3) |
0.73 |
19 (7.6) |
7 (6.7) |
0.765 |
|
In hospital mortality (n, %) |
4 (6.8) |
22 (7.4) |
0.866 |
26 (10.4) |
9 (8.6) |
0.606 |
|
Infectiveendocarditis (n, %) |
5 (8.5) |
12 (4) |
0.145 |
10 (4) |
7 (6.7) |
0. 279 |
|
Central Venous catheter infection (n, %) |
5(8.5) | 117(39.4) | < 0.001 |
88 (35.1) |
34 (32.4) |
0.627 |
|
Pneumonia (n, %) |
10 (16.9) |
63 (21.2) |
0.459 |
56 (22.3) |
17 (16.2) |
0.192 |
|
Soft tissue infection(n, %) |
18 (30.5) |
23 (7.7) |
< 0.001 |
25 (10) |
16 (15.2) |
0.155 |
|
Osteomyelitis (n, %) |
2 (3.4) |
5 (1.7) |
0.389 |
5 (2) |
2 (1.9) |
0.957 |
|
Septic arthritis (n, %) |
4 (6.8) |
12 (4) |
0. 316 |
9 (3.6) |
7 (6.7) |
0.201 |
|
Primary bacteremia (n, %) |
5 (8.5) |
38 (12.8) |
0.352 |
33 (13.1) |
10 (9.5) |
0.339 |
|
Abdominal infection (n, %) |
2 (3.4) |
16 (5.4) |
0.522 |
12 (4.8) |
6 (5.7) |
0.714 |
|
Surgical site infection(n, %) |
0 |
6 (2) |
0.595 |
4 (1.6) |
2 (1.9) |
0.835 |
|
ICD infection (n, %) |
1 (1.7) |
1 (0.3) |
0.304 |
1 (0.4) |
1 (1) |
0.524 |
|
İnternalmedical services (n, %) |
42 (14.7) |
17 (23.9) |
0.062 |
202 (80.5) |
83 (79) |
0.758 |
|
WBC (median, IQR) |
10,300 (6810- 15,350) |
10,500 (7095- 14,495) |
0. 923 |
10,800 (7620- 15,260) |
9480 (5800- 13,750) |
0.005 |
|
Neutrophil (median, IQR) |
8480 (4820- 13,400) |
8260 (5230- 12,025) |
0. 971 |
8460 (5750- 13,300) |
7320 (4110- 11,545) |
0.006 |
|
Lymphocyte (median, IQR) |
1040 (620- 1680) |
850 (550–1265) |
0. 089 |
870 (580–1300) |
960 (545- 1450) |
0.678 |
|
NLR (median, IQR) |
7.3 (4.3- 16.6) |
9.4 (4.9–17.1) |
0. 244 |
9.7 (5.3–18.3) |
6.8 (4.05- 13.3) |
0.002 |
|
PLT (median, IQR) |
204,000 (135,000- 272,000) |
196,000 (13,490- 271,500) |
0. 731 |
201,000 (144,000- 273,200) |
188,000 (108,500- 251,500) |
0.112 |
|
PLR (median, IQR) |
198.8 (129.5- 334.3) |
228 (126.3- 359.4) |
0. 497 |
231 (129.8- 359.5) |
211.2 (113.7 –326.7) |
0.225 |
|
CRP (median, IQR) |
182 (88- 244) |
136 (71–219) |
0. 020 |
139 (73–223) |
137 (73–244) |
0.877 |
|
Procalcitonin (median, IQR) |
2.7 (0.6- 14.5) |
1.7 (0.3–6.5) |
0.215 |
1.9 (0.3–9) |
1.1 (0.4–5.1) |
0.413 |
|
Creatinine (median, IQR) |
0.9 (0.6- 2.1) |
1.5 (0.7–4.9) |
0. 015 |
1.4 (0.7–4.7) |
1.2 (0.6–4.2) |
0.288 |
|
ALT (median, IQR) |
23 (15–35) |
17 (10–29) |
0. 023 |
17 (11–30) |
22 (10–30) | 0.911 |
|
AST (median, IQR) |
27 (15–51) |
22 (14–38) |
0. 044 |
23 (14–39) |
22 (14–46) |
0.920 |
|
Albumin (median, IQR) |
29 (24–35) |
30 (26–33) |
0. 381 |
30 (26–34) |
30 (25–33) |
0.984 |
Significant values are in [bold].
(1): P-values < 0.05 were considered statistically significant. CA, Community-acquired; COPD, Chronic Obstructive Pulmonary Disease; CKD, Chronic Kidney Disease; GCS, Glasgow Coma Scale; HA, Hospital-acquired; ICD, Implantable cardioverter defibrillators; ICU, intensive care unit; IVDU, IV drug use; MRSA, Methicillin-resistant Staphylococcus aureus; MSSA, Methicillin-susceptible Staphylococcus aureus; SAB, S. aureus bacteremia.
The highest sensitivity and specificity at 30-day mortality were obtained from CRP and albumin, with a sensitivity of 65.4%, and albumin, with a specificity of 78.5% ( Fig. 1; Table 3). In the ROC curve analysis for predicting 30-day mortality, the area under the curve (AUC) was 0.619 for NLR (p = 0.044), 0.692 for CRP ( p = 0.001), and 0.638 for albumin (p = 0.020).
Fig. 1.
Receiver operating characteristic curves of laboratory parameters for 30-day mortality.
Table 3.
Diagnostic performance of laboratory parameters in predicting 30-day mortality.
| Parameters | p | AUC | 95% CI | cut off | sensitivity (%) | specifity (%) |
|---|---|---|---|---|---|---|
| CRP | 0.001 | 0.692 | 0.582–0.801 | 183 | 65.4 | 65.2 |
| NLR | 0.044 | 0.619 | 0.491–0.747 | 12.05 | 61.5 | 62.4 |
| albumin | 0.000 | 0.791 | 0.703–0.879 | 2.5 | 65.4 | 78.5 |
Discussion
In this study, we evaluated the demographic characteristics, laboratory parameters, clinical outcomes, and mortality-related factors of 356 patients with SAB over eight years. The frequency of hospital-acquired SAB was 83.5% indicated an important clinical concern for nosocomial infections. This percentage is significantly higher than previously reported in large multicenter studies. For instance, Austin et al. reported a hospital-acquired SAB rate of 30% among 2,139 patients, while Le Moing et al. found a rate of 54% among 2,091 patients17,18. The higher rate of hospital-acquired SAB in our study may be related to the differences in patient characteristics, the frequent use of invasive procedures, antimicrobial stewardship policies, or high clinical workload among healthcare staff. The mortality rates were similar between community-acquired and hospital-acquired SAB in our study, consistent with previous studies19.
Our study’s methicillin resistance rate was 29.5% which aligns closely with the World Health Organization’s 2021 surveillance data for Turkey, reporting a rate of 31%20. The studies of Yılmaz et al. and Basetti et al. revealed that the rates of MRSA bacteremia were 39.2% and 54%, respectively21,22. These variations could be attributed to differences in healthcare settings, infection control measures, and patient populations.
Our findings showed that severe comorbid conditions, particularly hematologic malignancies such as leukemia, may significantly contribute to poor outcomes, even outside the ICU setting. Pneumonia as a primary focus of infection was also strongly associated with increased mortality. This may be related to delayed diagnosis, respiratory complications, and suboptimal treatment response. These findings highlight the importance of early identification of high-risk patients and prompt, targeted management, even outside the ICU setting. Most studies on SAB have been conducted in patients hospitalised in the intensive care unit (ICU) and non-ICU hospital wards3,21. However, we intentionally excluded patients in intensive care units from our analysis to focus on the other risk factors, thereby providing a comprehensive understanding of SAB.
According to some studies, males are more likely to develop SAB than females, but females may have a higher mortality rate6,21,22. However, in a large study by Kang et al. involving 1,974 patients, SAB-related mortality was similar between males (21.2%) and females (21.9%), with no statistically significant difference (p = 0.786)23. In our cohort, 58% of the patients were male, and the 30-day mortality rates were 7.69% in men and 6.75% in women. Consistent with previous findings, we observed no significant difference in outcomes based on sex.
The 30-day mortality rates have been reported to range from 15.3 to 21.5% in different series19,21,22,24. In our study, the 30-day all-cause mortality was 7.3%, suggesting a possible decline in SAB-related mortality. Some of these differences may be explained by variations in study design and patient characteristics, including differences in standards of care, underlying comorbidities, and advanced age.
We found the 30-day and in-hospital mortality rates similar in the MSSA and the MRSA bacteremia, consistent with some other studies19,25. Cosgrove et al. and Blot et al. showed that mortality rates were significantly higher for the MRSA group than the MSSA group (OR,1.93; 95% CI,1.54–2.42; p < 0.001), [OR,1.93; 95% CI,1.18 to 3.18; p = 0.009]7,26. A possible explanation for the similar mortality rates is the growing awareness of MRSA infection management through education of healthcare workers and continuous surveillance of SAB for effective treatment, and measuring effectiveness in our hospital. We cannot rule out that in some patients, the final cause of death was not an uncontrolled infection but the deterioration of the patient’s underlying comorbidities as a result of the infection.
Previous research has shown that the mortality rates in SAB rise with increased age, comorbidity load, and comorbid disorders22,27,28. In our study, the median CCI score was significantly higher in non-survivors compared to survivors (p < 0.001). Similarly, studies by Sharma et al. and la Vega et al. have found no association between comorbidity assessed by the CCI (p = 0.157, p = 0.144 )29,30. In contrast, Lesens et al. and Kim et al. reported a significant link between higher CCI scores and increased 30-day mortality in patients with SAB3,25.
Improving SAB-related outcomes requires a better understanding of risk factors and optimization of timely diagnosis and appropriate treatment strategies31. Differences in comorbidities profiles may partly explain the differences in reported mortality rates. SAB-related mortality differs according to the primary focus of infection, with the highest rates observed in patients with bacteraemic pulmonary infections19,32. In our study, patients with pneumonia demonstrated a mortality rate of 42.3%. Pneumonia significantly increased the risk of 30-day mortality, with a hazard ratio of approximately 15. (Table 1). These findings are consistent with some other studies showing high staphylococcal pneumonia mortality rates33–36. The proportion of patients presenting with sepsis was significantly higher among non-survivors compared to survivors (30.8% vs. 8.8%, p = 0.003) in our study like in previous studies10,31. Our findings emphasise the importance of detecting and managing deep foci of infection as soon as SAB is identified.
Furthermore, the site of SAB and source control are crucial for clinical outcomes.
Our findings, particularly identifying NLR, CRP, and albumin as independent predictors of 30-day mortality, have practical implications for patient care. These findings suggest that CRP and albumin may serve as useful biomarkers in predicting short-term mortality in patients with SAB. This is consistent with previous literature reporting the prognostic value of inflammatory markers in bloodstream infections37.
In addition to the infection focus, inflammatory biomarkers may help identify patients at increased risk of mortality. High NLR values are associated with a worsening prognosis regarding morbidity or mortality38. In the study of Greenberg et al., the NLR was associated with increased mortality in SAB (OR, 1.93; 95% CI 1.17–3.17, p = 0.01)39. In our study, NLR, CRP, and albumin were all found to be independent predictors of 30-day mortality. Similarly, Jacobsson et al. found CRP and albumin predictors of SAB mortality (p = 0.015, p = 0.023)36. These findings underscore the importance of identifying patients with SAB who are at high risk for mortality to provide timely and effective therapies.
This study had several limitations. First, because it was designed retrospectively, it was not possible to avoid selection and evaluation biases. Second, the data were collected from a single center, which may reduce the generalizability of the results. Furthermore, due to the retrospective design and the limited availability of detailed medical records, we were unable to obtain vital signs or other clinical information needed to assess the severity of sepsis. Despite these limitations, the study had several strengths. It focuses on patients with SAB who were treated in non-ICU wards, a category that is not frequently investigated. Furthermore, additional laboratory parameters were included in the multivariate analysis, which strengthens the findings. While this study offers valuable insights into the clinical course and outcomes of patients with SAB, the findings should be interpreted with caution and validated by future prospective, multicenter studies.
Conclusions
Finally, determining mortality directly attributable to infection remains challenging. Our findings underscore the critical role of continuous surveillance in the management and treatment of SAB. The findings further emphasize the importance of the primary focus of infection on clinical outcomes. Future prospective, multicenter studies are needed to confirm these findings and to identify modifiable risk factors that may improve outcomes in patients with SAB.
Author contributions
DB, YEÖ and ZY conceptualized and designed the study. SA, EE acquired the data. DB, AIS, ECÜ, FBE analyzed and interpreted the data. SŞ, ZÇ, HKK and KKY drafted and critically revised the manuscript. All authors approved the final version of the manuscript.
Funding
This research received no external funding.
Data availability
Data is provided within the manuscript.
Competing interests
The authors declare no competing interests.
Human ethics and consent to participate
not applicable. ‘Due to the retrospective nature of the study, waived the need of obtaining informed consent.
Ethics approval
This study was approved by the University of Health Sciences, Turkey, Bakırköy Dr. Sadi Konuk Training and Research Hospital Clinical Research Ethics Committee (approval number: 2023-08-15, date:17.04.2023). This study complied with the Declaration of Helsinki.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
Data is provided within the manuscript.