Sage Journals: Discover world-class research

Abstract

Objective

Staphylococcus aureus causes infections ranging from mild skin conditions to life-threatening diseases such as endocarditis. The incidence of S. aureus infections is increasing due to the rise in invasive procedures and immunosuppression. Inflammatory markers such as C-reactive protein level, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio are useful for monitoring disease severity. This study aimed to identify the clinical and laboratory predictors of mortality in patients with S. aureus infections.

Methods

This retrospective observational study included patients aged >18 years with S. aureus growth who were treated at a tertiary hospital between 1 November 2013 and 1 February 2022. Infections were classified as pneumonia or infections of the bloodstream, bone and joint, and skin and soft tissue.

Results

Univariate analysis revealed that age >73 years, emergency department admission, and methicillin resistance were significant mortality risk factors. Multivariate analysis showed that age >73 years, pneumonia, and C-reactive protein level >86 mg/L increased the mortality rate by 3.5-, 7.6-, and 4.4-fold, respectively.

Conclusion

C-reactive protein level, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio are accessible, cost-effective markers that aid in predicting mortality. Integrating these biomarkers with clinical parameters such as advanced age and pneumonia can improve early risk stratification in patients with S. aureus infections.

Keywords

C-reactive protein systemic inflammation index mortality neutrophil-to-lymphocyte ratio platelet-to-lymphocyte ratio

Objective

Staphylococcus aureus is an important pathogen that can cause various clinical manifestations, ranging from mild skin and soft tissue infections to serious life-threatening infections such as endocarditis. S. aureus, which can cause community- and hospital-acquired infections, can be found in the human body as both a colonizer and a pathogen. The S. aureus colonization rate—one of the risk factors for the development of S. aureus infections—has been continuously rising in the community, growing from 35% to 65.9% over the past 20 years.^1,2 The epidemiology of S. aureus infections is changing, and the methicillin resistance rate of S. aureus is increasing.³ Along with community-acquired skin and soft tissue infections caused by strains containing specific virulence factors and those resistant to β-lactam antibiotics, healthcare-associated infections, particularly infective endocarditis and prosthetic infections, are being reported more frequently.⁴ Owing to the increasing rates of invasive procedures, methicillin resistance, and the growing number of immunosuppressed patients, there has been a rise in the incidence of S. aureus infections and the frequency of related complications.⁵ Laboratory parameters play an important role in the development of complications. C-reactive protein (CRP) is an acute-phase reactant synthesized by the liver in response to tissue damage. Polymorphonuclear neutrophils are the most important cells of the innate immune system, playing a key role in cellular defense against bacterial infections.⁶ Conversely, lymphocytes are cells of the adaptive immune system, and the innate and adaptive immune system components function in coordination against bacterial infections. The physiological response of the immune system to various stressors is typically characterized by an increase in the neutrophil count and a decrease in the lymphocyte count. The neutrophil-to-lymphocyte ratio (NLR), calculated as a ratio of neutrophil count to lymphocyte count, is a marker of systemic inflammation and has been shown to predict disease severity and prognosis in various conditions, such as sepsis, malignancies, and cardiovascular diseases.^7
–9

Other complete blood count parameters that change dynamically in response to various pathological processes are the platelet-to-lymphocyte ratio (PLR) and systemic inflammation index (SII). An increased PLR in cases of severe systemic inflammation indicates an imbalance between proinflammatory and anti-inflammatory responses.¹⁰ In patients with peritonitis, elevated NLR and PLR have been identified as risk factors for treatment failure, while a low SII has been shown to reduce this risk.¹¹ In patients with S. aureus infections, NLR has also been shown to be elevated and associated with increased mortality.¹² The mortality rate associated with S. aureus infections ranges from 10% to 30%.^13,14 Owing to the more lethal course of S. aureus infections compared with that of many other infectious diseases that cause global mortality and morbidity, such as acquired immunodeficiency syndrome, tuberculosis, and viral hepatitis, it represents a significant clinical condition that warrants further investigation.¹⁴ In this study, the clinical and laboratory findings of infections caused by S. aureus strains isolated from various clinical samples as well as their methicillin resistance rates and disease outcomes were evaluated. This study aimed to identify clinical and laboratory markers that can predict mortality and guide the management of these infections.

Methods

This was a single-center, retrospective, observational study that enrolled patients aged >18 years with S. aureus growth in cultures at a tertiary education and research hospital. Using the hospital’s information system, we collected the data of patients in whom S. aureus was detected in clinical samples (blood, urine, wound, sputum, and catheter tips) between 1 November 2013 and 1 February 2022. Blood cultures were analyzed using an automated system (BACTEC^TM FX, Becton-Dickinson Diagnostic Systems), and bacteriological identification and antibiotic susceptibility testing were performed using the Phoenix™ 100 BD system (Becton-Dickinson Diagnostic Systems). Antibiotic susceptibility was evaluated according to the Clinical Laboratory Standards Institute criteria from 2013 to 2015 and the European Committee on Antimicrobial Susceptibility Testing criteria from 2016 onward. Methicillin resistance was determined using the cefoxitin disk diffusion method.

Bloodstream infections confirmed by laboratory tests without an identifiable focus are termed “primary bacteremia.”¹⁵ S. aureus infections were categorized as follows: intravascular catheter infection, endocarditis, and thrombophlebitis as bloodstream infections; arthritis, spondylodiscitis, prosthetic infection, and osteomyelitis as bone joint infections; cellulitis, diabetic foot infection, and abscess as skin and soft tissue infections; and infections without any identifiable focus as pneumonia. Clinical findings for catheter infections included fever, hyperemia, discharge, tenderness, and local heat; those for cellulitis included fever, hyperemia, edema, tenderness, and local heat; those for septic shock included tachycardia and hypotension; those for pneumonia included fever, increased secretion, cough, and radiographic evidence of pulmonary infiltration; and those for urinary system infections included fever, abdominal pain, pollakiuria, and dysuria. Laboratory parameters obtained at patients’ admission were evaluated. NLR, PLR, and SII were calculated using complete blood count data. Biochemical parameters, including CRP, aspartate transaminase (AST), alanine transaminase (ALT), total bilirubin, urea, and creatinine, were examined. Patient demographics, type of admission, comorbidities, infection diagnoses, clinical and laboratory findings, and disease outcomes were analyzed.

Ethical approval for this study was obtained from the Clinical Research Ethics Committee of Izmir Bozyaka Training and Research Hospital, University of Health Sciences (Date: 26.01.2022, Decision no: 2022/20). The study complies with the principles of the Declaration of Helsinki. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁶

Statistical methods

Data were analyzed using IBM SPSS version 26. The normality of continuous variables was assessed through graphical methods and normality tests, revealing that these variables did not follow a normal distribution. Comparisons between independent groups were performed using the nonparametric Mann–Whitney U test, with results reported as median values and interquartile ranges (IQR: 25th–75th percentiles). Receiver operating characteristic curve analysis was conducted to assess the predictive performance of the markers of mortality, and optimal cutoff values were determined using the Youden Index to generate new dichotomous variables.

Categorical independent variables were presented in cross tables as frequencies and percentages. Comparisons between groups were made using chi-square and Fisher’s Exact tests; in addition, univariate odds ratios (ORs) were calculated. Variables with significant or borderline significant univariate associations were included in the multivariate logistic regression analysis. In this analysis, “pneumonia findings” was considered an independent variable and “pneumonia as the primary focus of infection” was treated as the dependent variable. Multivariate ORs were calculated using the backward stepwise (Wald) method. The significance level for all statistical tests was set at α = 0.05, using a two-tailed test.

Results

The study included 254 patients. The mean age of the patients was 63.8 ± 15.8 years, and the median age was 66 (IQR: 55–76) years. In terms of hospital admissions, 72% were from the emergency department and 28% from outpatient clinics. Of all S. aureus infections, 27% were caused by methicillin-resistant S. aureus (MRSA). The overall mortality rate among patients was 19.2%. The median age of nonsurvivors was significantly higher than that of survivors (p < 0.001). The mortality rate was significantly higher among patients admitted through the emergency department (p = 0.011), those with MRSA infections (p = 0.022), those presenting with catheter-related infections (p = 0.023), and those with pneumonia as the primary focus of infection (p < 0.001). A comparative analysis of demographic data between survivors and nonsurvivors is shown in Table 1.

Table 1.

Baseline characteristics and clinical findings of patients according to mortality status.

		Total (n = 254)		Nonsurvivors (n = 49)		Survivors (n = 205)		p*
Age, median (IQR) (years)		66 (55–76)		76 (62–81)		65 (54–73)		<0.001
Sex, n (%)								0.900
Female		116	(46%)	22	(45%)	94	(46%)
Male		138	(54%)	27	(55%)	111	(54%)
Hospital admission type, n (%)								0.011
Emergency		183	(72%)	43	(88%)	140	(68%)
Outpatient clinic		71	(28%)	6	(12%)	65	(32%)
Methicillin resistance, n (%)								0.022
MRSA		68	(27%)	20	(41%)	48	(23%)
MSSA		186	(73%)	29	(59%)	157	(77%)
Comorbidity, n (%)								0.810
Yes		223	(88%)	44	(90%)	179	(87%)
No		31	(12%)	5	(10%)	26	(13%)
DM, n (%)	Yes	109	(43%)	21	(43%)	88	(43%)	0.990
DM, n (%)	No	145	(57%)	28	(57%)	117	(57%)	0.990
HT, n (%)	Yes	101	(40%)	15	(31%)	86	(42%)	0.190
HT, n (%)	No	153	(60%)	34	(69%)	119	(58%)	0.190
CRF, n (%)	Yes	98	(39%)	15	(31%)	83	(40%)	0.260
CRF, n (%)	No	156	(61%)	34	(69%)	122	(60%)	0.260
CVA, n (%)	Yes	26	(10%)	7	(14%)	19	(9%)	0.430
CVA, n (%)	No	228	(90%)	42	(86%)	186	(91%)	0.430
CAD, n (%)	Yes	21	(8%)	4	(8%)	17	(8%)	1.00
CAD, n (%)	No	233	(92%)	45	(92%)	188	(92%)	1.00
Malignancy, n (%)	Yes	17	(7%)	5	(10%)	12	(6%)	0.330
Malignancy, n (%)	No	237	(93%)	44	(90%)	193	(94%)	0.330
COPD, n (%)	Yes	13	(5%)	5	(10%)	8	(4%)	0.130
COPD, n (%)	No	241	(95%)	44	(90%)	197	(96%)	0.130
Clinical finding
Fever, n (%)	Present	161	(63%)	26	(53%)	135	(66%)	0.130
Fever, n (%)	Absent	93	(37%)	23	(47%)	70	(34%)	0.130
Catheter infection findingsn (%)	Present	40	(16%)	2	(4%)	38	(19%)	0.023
Catheter infection findingsn (%)	Absent	214	(84%)	47	(96%)	167	(81%)	0.023
Pneumonia findingsn (%)	Present	23	(9%)	15	(31%)	8	(4%)	<0.001
Pneumonia findingsn (%)	Absent	231	(91%)	34	(69%)	197	(96%)	<0.001
Cellulite findingsn (%)	Present	23	(9%)	4	(8%)	19	(9%)	1.00
Cellulite findingsn (%)	Absent	231	(91%)	45	(92%)	186	(91%)	1.00
Draining wound n (%)	Present	18	(7%)	1	(2%)	17	(8%)	0.210
Draining wound n (%)	Absent	236	(93%)	48	(98%)	188	(92%)	0.210
Back pain n (%)	Present	12	(5%)	1	(2%)	11	(5%)	0.470
Back pain n (%)	Absent	242	(95%)	48	(98%)	194	(95%)	0.470
Septic shock n (%)	Present	7	(3%)	1	(2%)	6	3%	1.00
Septic shock n (%)	Absent	247	(97%)	48	(98%)	199	97%	1.00
Arthritis n (%)	Present	9	(4%)	0	(0%)	9	(4%)	0.210
Arthritis n (%)	Absent	245	(96%)	49	(100%)	196	(96%)	0.210

CAD: coronary artery disease; CRF: chronic renal failure; COPD: chronic obstructive pulmonary disease; CVA: cerebrovascular accident; DM: diabetes mellitus; MSSA: methicillin-susceptible Staphylococcus aureus; MRSA: methicillin-resistant S. aureus; HT: hypertension.

Pearson chi-square test, Fisher’s Exact test.

An evaluation of the focus of the infection showed that 37% of the patients had bloodstream infections, 20% had skin and soft tissue infections, and 19% had pneumonia. The most frequently obtained cultures were blood (71.7%), abscess (10.2%), and wound (9.8%). When the distribution of patients with positive blood cultures was analyzed according to the infection focus, bloodstream infections (including catheter-related infections, endocarditis, and thrombophlebitis) and pneumonia emerged as the most common foci (Table 2).

Table 2.

Distribution of patients with blood culture growth according to infection focus.

Infection focus	n (%)
Bloodstream infection	93 (50.9)
Pneumonia	33 (18.0)
Primary bacteremia	16 (8.7)
Bone joint infection	16 (8.7)
Skin and soft tissue infection	14 (7.7)
Urinary tract infection	11 (6.0)

An evaluation of the effect of laboratory parameters on mortality showed that deceased patients had significantly lower platelet counts (p = 0.005) and higher NLR (p = 0.023). Biochemical parameters showed higher CRP, urea, AST, and total bilirubin levels in deceased patients (p values were 0.009, 0.002, <0.001, and <0.001, respectively) (Table 3).

Table 3.

Effect of laboratory parameters on mortality.

	Total (n = 254)	Nonsurvivors (n = 49)	Survivors (n = 205)	p*
	Median (IQR)	Median (IQR)	Median (IQR)	p*
WBC	12,700 (9040–16,620)	13,660 (9820–17,100)	12,510 (9040–16,440)	0.420
Neutrophil count	10,400 (6480–14,280)	11,900 (8800–15,320)	10,190 (6030–14,100)	0.110
Lymphocyte count	985 (560–1520)	780 (450–1240)	1020 (580–1600)	0.055
Platelet count	203,000 (139,000–283,000)	172,000 (120,000–239,000)	212,000 (145,000–303,000)	0.005
CRP	156 (74–252)	189 (121–326)	153 (65–243)	0.009
Urea	69 (36–117)	88 (59–139)	66 (34–114)	0.002
Creatinine	1.58 (0.99–4.33)	1.4 (0.96–3.9)	1.7 (0.99–4.4)	0.420
AST	21 (15–35)	29 (18–50)	19 (14–32)	<0.001
ALT	18 (11–28)	18 (13–35)	17 (11–27)	0.290
Total bilirubin	0.7 (0.45–1.02)	0.9 (0.7–1.57)	0.63 (0.43–0.97)	<0.001
NLR	10.8 (5.4–20.2)	14.4 (8.9–20.9)	10.1 (5.3–19.6)	0.023
PLR	229 (137–345)	231 (125–327)	228 (140–350)	0.500
SII	2210 (1091–3941)	2249 (1268–4071)	2207 (1077–3906)	0.880

ALT: alanine aminotransferase; AST: aspartate aminotransferase; CRP: C-reactive protein; NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; WBC: white blood cell count

Mann–Whitney U test. IQR: Interquartile range (25th–75th percentiles).

Parameters such as advanced age, low lymphocyte count, low platelet count, elevated urea level, elevated AST level, and elevated total bilirubin level were significantly associated with mortality. The ideal cutoff values for predicting mortality were as follows: age >73 years (sensitivity: 57%, specificity: 76%), lymphocyte count ≤1340/μL (sensitivity: 86%, specificity: 37%), platelet count ≤191,000/µL (sensitivity: 61%, specificity: 58%), and urea level >41 mg/dL (sensitivity: 90%, specificity: 35%) (Table 4).

Table 4.

ROC analysis of laboratory parameters affecting mortality.

Parameter	AUC (95% CI)	p (area = 0.5)	Cutoff	Sensitivity (%)	Specificity (%)
Age (years)	0.663 (0.601–0.721)	<0.001	>73	57	76
WBC (×10³/µL)	0.537 (0.474–0.600)	0.43	>13,650	51	60
Neutrophil count (/µL)	0.572 (0.509–0.634)	0.11	>8740	76	41
Lymphocyte count (/µL)	0.588 (0.525–0.649)	0.043	≤1340	86	37
Platelet count (/µL)	0.628 (0.566–0.688)	0.004	≤191,000	61	58
CRP (mg/L)	0.619 (0.557–0.679)	0.006	>86	90	34
Urea (mg/dL)	0.639 (0.577–0.698)	0.001	>41	90	35
Creatinine (mg/dL)	0.537 (0.474–0.599)	0.41	≤1.69	61	50
AST (IU/L)	0.657 (0.595–0.715)	<0.001	>23	67	63
ALT (IU/L)	0.549 (0.485–0.611)	0.30	>73	12	98
Total bilirubin (mg/dL)	0.674 (0.612–0.731)	<0.001	>0.7	73	60
NLR	0.605 (0.541–0.665)	0.012	>7.5	82	42
PLR	0.531 (0.467–0.593)	0.51	≤342	84	28
SII	0.507 (0.443–0.570)	0.88	>1856	53	40

p (area = 0.5): p-value for the null hypothesis that the AUC is equal to 0.5.

When evaluating risk factors for mortality, the following variables were found to be statistically significant in the univariate analysis: age >73 years (p < 0.001), presentation to the emergency department (p = 0.009), presence of methicillin resistance (p = 0.015), clinical signs of catheter infection (p = 0.024), clinical signs of pneumonia (p < 0.001), neutrophil count >8740/μL (p = 0.035), lymphocyte count ≤1340/μL (p = 0.004), platelet count ≤191,000/μL (p = 0.019), CRP level >86 mg/L (p = 0.002), urea level >41 mg/dL (p = 0.002), AST level >23 U/L (p < 0.001), ALT level >73 U/L (p = 0.006), total bilirubin level >0.7 mg/dL (p < 0.001), and NLR >7.5 (p = 0.003) (Table 5). Variables with statistically or borderline significant ORs were included as independent variables in the multivariate logistic regression analysis. A statistically significant model was obtained using the backward stepwise elimination method based on the Wald statistic. Admission type, methicillin resistance, urea, ALT level, and AST level were among the variables excluded from the final model. Age >73 years, pneumonia as the primary focus of infection, and CRP levels >86 mg/L increased the risk of mortality by 3.5-fold (p = 0.001; OR = 3.5; 95% confidence interval (CI): 1.64–7.68), 7.6-fold (p < 0.001; OR = 7.65; 95% CI: 3.31–17.68), and 4.4-fold (OR = 4.40; 95% CI: 1.48–13.12), respectively. Additionally, total bilirubin levels >0.7 mg/dL, NLR >7.5, and PLR ≥342 increased the risk of mortality by 2.8-fold (OR = 2.80; 95% CI: 1.26–6.23), 3.16-fold (OR = 3.21; 95% CI: 1.24–8.08), and 3.4-fold (OR = 3.48; 95% CI 1.28–9.48), respectively (Table 6).

Table 5.

Mortality analysis according to ROC-based cutoff values.

Parameter	Total (n = 254)	Nonsurvivors (n = 49)	Survivors (n = 205)	p-value
Age >73 years, n (%)	77 (30%)	28 (57%)	49 (24%)	<0.001
WBC >13,650/µL, n (%)	107 (42%)	25 (51%)	82 (40%)	0.210
Neutrophil count >8740/µL, n (%)	158 (62%)	37 (76%)	121 (59%)	0.048
Lymphocyte count ≤1340/µL, n (%)	171 (67%)	42 (86%)	129 (63%)	0.004
Platelet ≤ 191,000/µL, n (%)	117 (46%)	30 (61%)	87 (42%)	0.027
CRP >86 mg/L, n (%)	180 (71%)	44 (90%)	136 (66%)	0.002
Urea >41 mg/dL, n (%)	178 (70%)	44 (90%)	134 (65%)	0.001
Creatinine ≤1.69 mg/dL, n (%)	132 (52%)	30 (61%)	102 (50%)	0.190
AST >23 IU/L, n (%)	108 (43%)	33 (67%)	75 (37%)	<0.001
ALT >73 IU/L, n (%)	11 (4%)	6 (12%)	5 (2%)	0.008
Total bilirubin >0.7 mg/dL, n (%)	120 (47%)	36 (73%)	84 (41%)	<0.001
NLR >7.5, n (%)	159 (63%)	40 (82%)	119 (58%)	0.004
PLR ≤342, n (%)	189 (74%)	41 (84%)	148 (72%)	0.140
SII >1856, n (%)	150 (59%)	26 (53%)	124 (60%)	0.430

Table 6.

Assessment of risk factors influencing mortality using logistic regression analysis.

Variable	Univariate OR (95% CI)	p*	Multivariate OR (95% CI)	p**
Age (>73 or ≤73 years)	4.24 (2.21–8.14)	<0.001	3.55 (1.64–7.68)	0.001
Sex (Female/Male)	0.96 (0.51–1.8)	0.900	NI	–
Type of admission (Emergency/OPD)	3.33 (1.35–8.21)	0.009	–	NS
Comorbidity (Yes/No)	1.28 (0.46–3.52)	0.630	NI	–
Methicillin resistance (MRSA/MSSA)	2.26 (1.17–4.34)	0.015	–	NS
Infection focus (pneumonia/other)	7.60 (3.74–15.43)	<0.001	7.65 (3.31–17.68)	<0.001
WBC count (>13,650 or ≤13,650)	1.56 (0.84–2.92)	0.160	NI	–
Neutrophil count (>8740 or ≤8740)	2.14 (1.05–4.35)	0.035	NI	–
Lymphocyte count (≤1340 or >1340)	3.53 (1.51–8.26)	0.004	NI	–
Platelet count (≤191,000 or >191,000)	2.14 (1.13–4.05)	0.019	NI	–
CRP (>86 or ≤86 mg/L)	4.46 (1.69–11.77)	0.002	4.40 (1.48–13.12)	0.008
Urea (>41 or ≤41 mg/dL)	4.66 (1.77–12.28)	0.002	–	NS
Creatinine (≤1.69 or >1.69 mg/dL)	1.59 (0.84–3.01)	0.150	NI	–
AST (>23 or ≤23 IU/L)	3.58 (1.85–6.93)	<0.001	–	NS
ALT (>73 or ≤73 IU/L)	5.58 (1.63–19.13)	0.006	–	NS
Total bilirubin (>0.7 or ≤0.7 mg/dL)	3.99 (2.00–7.97)	<0.001	2.80 (1.26–6.23)	0.011
NLR (>7.5 or ≤7.5)	3.21 (1.48–6.97)	0.003	3.16 (1.24–8.08)	0.016
PLR (≤342 or >342)	1.97 (0.87–4.47)	0.100	3.48 (1.28–9.48)	0.015
SII (>1856 or ≤1856)	0.74 (0.39–1.38)	0.340	NI	–

ALT: alanine aminotransferase; AST: aspartate aminotransferase; WBC: white blood cell; CRP: C-reactive protein; MRSA: methicillin-resistant Staphylococcus aureus; MSSA: methicillin-sensitive S. aureus; NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SII: systemic immune-inflammation index; NI: not included in multivariable analysis; NS: not significant; OR: odds ratio.

Mantel–Haenszel common odds ratio estimate, ** Logistic regression; backward stepwise (Wald).

OR (constant = −5.914).

Model performance metrics for the final step of the multivariate logistic regression analysis were as follows: Nagelkerke R² = 0.414, Hosmer–Lemeshow goodness-of-fit test p = 0.449, area under the ROC curve (AUC) = 0.845, sensitivity = 0.449, specificity = 0.956, Brier score = 0.103, likelihood ratio test χ² = 76.108 with p < 0.001, variance inflation factor (VIF) <1.101, and tolerance values >0.9.

Discussion

S. aureus is the most commonly identified pathogen in infective endocarditis, osteomyelitis, septic arthritis, and prosthetic joint infections.^4,17 Benfield et al.⁵ reported that community-acquired S. aureus infections predominantly present as skin and soft tissue infections and pneumonia, whereas hospital-acquired infections are more commonly associated with surgical site and vascular device infections. Following colorectal surgery, one of the most frequent postoperative complications is surgical site infections (SSIs), which significantly affect patient morbidity and healthcare burden.¹⁸ The high bacterial load in the gastrointestinal tract increases the risk of SSIs during surgical procedures. Among causative agents, S. aureus remains a leading pathogen due to its ability to colonize skin and surgical wounds.¹⁹ In a study involving 1440 cases of S. aureus bacteremia, 41% were classified as primary bacteremia, while secondary sources included bone and joint (16%), skin and soft tissue (16%), respiratory tract (15%), endovascular (6%), intra-abdominal (5%), and central nervous system (1%) infections.²⁰ Botheras et al.²¹ identified primary bacteremia (36.1%) as the most common focus, followed by skin and soft tissue infections (32.9%) and invasive device-related infections (15.8%). A surveillance study conducted in western Sweden between 2003 and 2005 reported that 83% of invasive S. aureus infections were bacteremic.²² In our study, the primary foci were bloodstream infections, skin and soft tissue infections, and pneumonia, with 71.7% of cases being bacteremic and the primary bacteremia rate being 6%.

Significant changes have been reported over the years in the predisposing conditions for S. aureus infections, with a rising incidence among older patients.⁵ Age is a strong determinant of the incidence and mortality of Staphylococcus aureus bacteremia (SAB), with the incidence increasing progressively after the age of 60 years. The highest incidence is observed during the first year of life and in individuals aged >90 years.^23,24 The incidence of SAB exceeds 100 per 100,000 person-years in those aged >70 years,²⁰ compared with 4.7 per 100,000 person-years in young, healthy individuals.²⁵ A Finnish study (1995–2001) indicated an overall increase in the SAB incidence, particularly among individuals aged >74 years.²⁶

Three studies^20,27,28 have reported median ages of 60 (IQR: 47–70), 62, and 62.8 (IQR: 46.3–76.2) years among patients with S. aureus infections, respectively. In our study, the mean patient age was 63.8 ± 15.8 years, with a median age of 66 (IQR: 55–76) years, which is slightly higher than those reported in previous studies. Recent literature has indicated median ages of 68 (IQR: 57–77) and 68.5 (IQR: 29; lower and upper quartiles not specified in the original source) years.^21,29

Studies have indicated that the age at which S. aureus infections occur is gradually increasing over the years; our results are in agreement with this trend. Physiological changes associated with aging and decreased bone marrow reserves diminish the immune response to infections,³⁰ while age-related comorbidities contribute to increased mortality.³¹ A study identified age ≥65 years as an independent risk factor for mortality in patients with SAB, revealing that mortality was 2.19 times higher (OR: 2.19, 95% CI: 1.45–3.32, p ≤ 0.001) and 3.58 times higher (OR: 3.58, 95% CI: 2.39–5.37; p ≤ 0.001) in patients aged ≥75 years.³² In another study, each 10-year increase in age was associated with a 1.38-fold increase in mortality for hospital-acquired cases and a 1.55-fold increase for community-acquired cases.⁵ A Finnish study reported a 28-day mortality rate of 17% across all age groups, with rates of 1% for individuals aged 1–14 years and 33% for those aged >74 years.²⁶ In our study, age >73 years increased the mortality rate by 4.24-fold.

Infections caused by resistant bacteria result in prolonged hospital stays, higher healthcare costs, and increased mortality.³³ MRSA was first identified in 1961³⁴ and has since become an increasingly significant global concern, particularly in studies conducted after 2000. A multicenter international cohort study evaluating over 83 million patients with SAB between 2000 and 2008 reported an MRSA rate of 4.4%.²³ In the United Kingdom, the incidence of MRSA isolates in bacteremic patients increased from 2% in 1990 to 40% in the early 2000s.³⁵ Another study reported that although the incidence of methicillin-sensitive S. aureus (MSSA) infections remained stable between 1997 and 2004, the incidence of MRSA infections rose from 0 to 7.4 per 100,000 population.³⁴ An 8-year study conducted between 1991 and 1998 reported an MRSA rate of 24.8% among 908 SAB episodes.²⁸ Clinical guidelines emphasize that the rates of methicillin resistance in both community and healthcare settings should be considered when planning empirical antibiotic therapy.³⁶ In our study, during the period from 2013 to 2022, the MRSA rate among S. aureus infections was found to be 27%.

Studies have reported conflicting results regarding mortality in MRSA versus MSSA bacteremia.^32,37 While some studies indicate higher mortality associated with MRSA bacteremia, others suggest the opposite. Romero et al.³⁷ revealed that the overall all-cause mortality was significantly higher in patients with MRSA bacteremia based on univariate analysis. In multivariate analysis, meningitis, inadequate source control, and MRSA bacteremia were identified as independent risk factors for increased mortality. Conversely, Harbarth et al.³⁸ reported that methicillin resistance did not significantly increase mortality in 184 patients with SAB (hazard ratio (HR): 1.1; 95% CI: 0.5–2.1; p = 0.30). In the study by Mylotte et al.,³⁹ factors associated with mortality in patients with SAB included end-stage renal failure, an APACHE III score >60, and a lifestyle score above 1, while MRSA was not found to increase the mortality risk. Various studies have indicated that methicillin resistance does not significantly affect outcomes when confounding factors such as age, length of hospital stay, comorbidity, disease severity, and appropriateness of antibiotic treatment are considered.^40,41 In our study, methicillin resistance was associated with mortality in univariate analysis; however, this was not identified as an independent risk factor in the multivariate analysis. This suggests that mortality risk is more closely related to factors such as patient age, infection focus, and disease severity, rather than methicillin resistance alone.

CRP levels in patients with complicated SAB are significantly elevated, with levels ≥161 mg/L increasing the risk of complications by 3.29 times (OR: 3.29, 95% CI: 1.50–7.24, p = 0.003).²¹ In a study involving 222 SAB patients, the median CRP level was 170 mg/L, and a CRP level ≥250 mg/L was identified as an independent risk factor for treatment failure.⁴² In our study, CRP levels were significantly higher among nonsurvivors, with a CRP level ≥86 mg/dL associated with a 4.46 fold increase in the mortality risk. Numerous studies have explored the impact of initial NLR values and their increase during follow-up on mortality. An NLR cutoff of ≥3 is associated with a 2.98-fold increase in the risk of bacterial sepsis (OR: 2.58; 95% CI: 1.67–3.97).⁴³ In bacteremic patients, CRP levels and NLR were significantly elevated, while lymphocyte counts were lower. An NLR of >10 and a lymphocyte count <1.0 × 10⁹/L are more reliable predictors of bacteremia than a CRP level ≥50 mg/L.⁷ A high initial NLR at ICU admission was significantly associated with increased mortality.⁴⁴ In patients with pulmonary source septic shock, an NLR ≥15 on day 3 was associated with a 6.96-fold increase in mortality (95% CI: 1.4–34.1, p < 0.017).⁴⁵ A meta-analysis involving 10,685 patients confirmed that an elevated NLR was correlated with a poor prognosis (HR = 1.75, 95% CI: 1.56–1.97).⁴⁶

Another hematological marker associated with mortality is PLR. A meta-analysis of 2403 septic patients revealed that PLR was significantly higher in those who died, suggesting its potential as a predictor of mortality in sepsis patients.¹⁰ Shen et al.⁴⁷ reported that a PLR >200 significantly increased the risk of mortality in patients with sepsis. In our study, an NLR ≥7.5 increased the risk of mortality by 3.21 times, while a PLR ≥342 raised it by 1.97 times. The mortality rate of SAB ranges from 10% to 30%.^13,14 A global analysis across 135 countries identified S. aureus as the leading cause of bacterial-related deaths, accounting for 1,105,000 deaths.⁴⁸ In 47 of the 53 countries evaluated, MRSA ranked second in mortality after Escherichia coli.

Studies have reported SAB mortality rates of 25%,⁴⁹ 28%,⁵⁰ and 34%.⁵¹ Stratified analysis based on the infection focus revealed mortality rates of approximately 5%, 13%, and up to 30% for low-risk (e.g. intravenous catheters), medium-risk (e.g. bone and joint), and high-risk (e.g. endovascular) groups, respectively.²⁸ Pneumonia and sepsis cases demonstrated a more fatal disease course compared with cases of soft tissue infections.⁵² In patients aged <21 years, pulmonary infection and endocarditis were identified as independent risk factors for mortality. Other factors associated with mortality included advanced age, methicillin resistance, hospital-acquired infections, primary bacteremia, endocarditis, and pneumonia.²⁸ Jensen et al.⁵¹ reported that failure to eliminate the infection focus (OR: 6.7), presence of septic shock (OR: 3.7), administration of low-dose penicillin (OR: 3.7), and age >60 years (OR: 2.4) were associated with an increased mortality risk. Allard et al.³² identified several independent risk factors, including age ≥65 years, female sex, comorbidities, inappropriate antibiotic therapy, primary bacteremia, endocarditis, and pneumonia, as the primary focus of infection. Conditions with the highest mortality rates were pneumonia (OR: 3.52) and endocarditis (OR: 2.75), and these were identified as the primary focus of infection. In our study, independent risk factors for mortality included age ≥73 years, pneumonia as the primary focus of infection, CRP level >86 mg/L, total bilirubin level >0.7 mg/dL, NLR >7.5, and PLR ≥342.

Our study has certain limitations due to its retrospective nature, including the inability to differentiate between community-acquired and hospital-acquired infections, lack of assessment of MRSA risk factors, and the failure to calculate a bacteremia score for disease progression. Additionally, important clinical parameters that may influence outcomes, such as the Sequential Organ Failure Assessment score and time interval between diagnosis and initiation of treatment, were not available in our dataset. These uncontrolled variables may have affected our results and limited the generalizability of our findings. To overcome the inherent limitations of single-center and retrospective analyses and to validate our findings, further multicenter studies with more comprehensive data collection are warranted.

Conclusion

Accessible and inexpensive markers such as NLR, PLR, and CRP can effectively predict mortality in patients with S. aureus infections. Evaluating the effects of advanced age and pneumonia as the primary focus of infection on mortality may provide more accurate prognostic insights, especially when interpreted in conjunction with CRP, NLR, and PLR values.

Footnotes

Acknowledgments

None.

Author contributions

Y.Ö: Conceptualization, methodology, software, data curation, writing–original draft preparation, visualization, investigation, and writing–reviewing and editing; NF.K.: conceptualization, data curation, visualization, and investigation; Ş.Ç.: visualization, supervision, software, and validation; A.A.: software, data curation, and validation; S.T.: supervision and software; ED.B.: software and validation. All authors have read and approved the final manuscript.

Data availability statement

The study data are available from the corresponding author on reasonable request.

Declaration of conflicting interests

The authors have no conflicts of interest to declare.

Funding

No funding was received for this study.

ORCID iDs

Yeliz Özdemir

Şebnem Çalık

References

1.
Kuehnert MJ Kruszon-Moran D Hill HA , et al. Prevalence of Staphylococcus aureus nasal colonization in the United States, 2001–2002. J Infect Dis 2006; 193: 172–179.

2.
Russakoff B Wood C Lininger MR , et al. A quantitative assessment of Staphylococcus aureus community carriage in Yuma, Arizona. J Infect Dis 2023; 227: 1031–1041.

3.
Ippolito G Leone S Lauria FN , et al. Methicillin-resistant Staphylococcus aureus: the superbug. Int J Infect Dis 2010; 14: S7–11.

4.
Tong SY Davis JS Eichenberger E , et al. Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev 2015; 28: 603–661.

5.
Benfield T Espersen F Frimodt-Møller N , et al. Increasing incidence but decreasing in-hospital mortality of adult Staphylococcus aureus bacteraemia between 1981 and 2000. Clin Microbiol Infect 2007; 13: 257–263.

6.
Kolaczkowska E Kubes P. Neutrophil recruitment and function in health and inflammation. Nat Rev Immunol 2013; 13: 159–175.

7.
De Jager CP Van Wijk PT Mathoera RB , et al. Lymphocytopenia and neutrophil–lymphocyte count ratio predict bacteremia better than conventional infection markers in an emergency care unit. Crit Care 2010; 14: R192.

8.
Gibson PH Croal BL Cuthbertson BH , et al. Preoperative neutrophil–lymphocyte ratio and outcome from coronary artery bypass grafting. Am Heart J 2007; 154: 995–1002.

9.
Halazun KJ Aldoori A Malik HZ , et al. Elevated preoperative neutrophil to lymphocyte ratio predicts survival following hepatic resection for colorectal liver metastases. Eur J Surg Oncol 2008; 34: 55–60.

10.
Wang G Mivefroshan A Yaghoobpoor S , et al. Prognostic value of platelet to lymphocyte ratio in sepsis: a systematic review and meta-analysis. Biomed Res Int 2022; 2022: 9056363.

11.
Zhou D Yang H Zeng L , et al. Calculated inflammatory markers derived from complete blood count results, along with routine laboratory and clinical data, predict treatment failure of acute peritonitis in chronic peritoneal dialysis patients. Ren Fail 2023; 45: 2179856.

12.
Chen CY Hsu SC Hsieh HL , et al. Microbial etiology of pneumonia in patients with decreased renal function. PLoS One 2019; 14: e0216367.

13.
Kern WV. Management of Staphylococcus aureus bacteremia and endocarditis: progresses and challenges. Curr Opin Infect Dis 2010; 23: 346–358.

14.
Van Hal SJ Jensen SO Vaska VL , et al. Predictors of mortality in Staphylococcus aureus bacteremia. Clin Microbiol Rev 2012; 25: 362–386.

15.
Centers for Disease Control and Prevention. Patient safety component manual, https://www.cdc.gov/nhsn/PDFs/pscManual/4PSC_CLABScurrent.pdf (2024, accessed 9 June 2025).

16.
von Elm E Altman DG Egger M ; STROBE Initiativeet al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 2008; 61: 344–349.

17.
Vogkou CT Vlachogiannis NI Palaiodimos L , et al. The causative agents in infective endocarditis: a systematic review comprising 33,214 cases. Eur J Clin Microbiol Infect Dis 2016; 35: 1227–1245.

18.
Mulita F Liolis E Akinosoglou K , et al. Postoperative sepsis after colorectal surgery: a prospective single-center observational study and review of the literature. Prz Gastroenterol 2022; 17: 47–51.

19.
Panos G Mulita F Akinosoglou K , et al. Risk of surgical site infections after colorectal surgery and the most frequent pathogens isolated: a prospective single-centre observational study. Med Glas (Zenica) 2021; 18: 438–443.

20.
Laupland KB Ross T Gregson DB. Staphylococcus aureus bloodstream infections: risk factors, outcomes, and the influence of methicillin resistance in Calgary, Canada, 2000–2006. J Infect Dis 2008; 198: 336–343.

21.
Botheras CL Bowe SJ Cowan R , et al. C-reactive protein predicts complications in community-associated S. aureus bacteraemia: a cohort study. BMC Infect Dis 2021; 21: 312.

22.
Jacobsson G Dashti S Wahlberg T , et al. The epidemiology of and risk factors for invasive Staphylococcus aureus infections in western Sweden. Scand J Infect Dis 2007; 39: 6–13.

23.
Laupland KB Lyytikäinen O Søgaard M ; International Bacteremia Surveillance Collaborativeet al. The changing epidemiology of Staphylococcus aureus bloodstream infection: a multinational population-based surveillance study. Clin Microbiol Infect 2013; 19: 465–471.

24.
Frederiksen MS Espersen F Frimodt-Møller N , et al. Changing epidemiology of pediatric Staphylococcus aureus bacteremia in Denmark from 1971 through 2000. Pediatr Infect Dis J 2007; 26: 398–405.

25.
Landrum ML Neumann C Cook C , et al. Epidemiology of Staphylococcus aureus blood and skin and soft tissue infections in the US military health system, 2005–2010. JAMA 2012; 308: 50–59.

26.
Lyytikäinen O Ruotsalainen E Järvinen A , et al. Trends and outcome of nosocomial and community-acquired bloodstream infections due to Staphylococcus aureus in Finland, 1995–2001. Eur J Clin Microbiol Infect Dis 2005; 24: 399–404.

27.
Souli M Ruffin F Choi SH , et al. Changing characteristics of Staphylococcus aureus bacteremia: results from a 21-year, prospective, longitudinal study. Clin Infect Dis 2019; 69: 1868–1877.

28.
Soriano A Martínez JA Mensa J , et al. Pathogenic significance of methicillin resistance for patients with Staphylococcus aureus bacteremia. Clin Infect Dis 2000; 30: 368–373.

29.
Van der Vaart TW Prins JM Goorhuis A , et al. The utility of risk factors to define complicated Staphylococcus aureus bacteremia in a setting with low methicillin-resistant S. aureus prevalence. Clin Infect Dis 2024; 78: 846–854.

30.
Prabhakar M Ershler WB Longo DL. Bone marrow, thymus and blood: changes across the lifespan. Aging Health 2009; 5: 385–393.

31.
Fuchs L Chronaki CE Park S , et al. ICU admission characteristics and mortality rates among elderly and very elderly patients. Intensive Care Med 2012; 38: 1654–1661.

32.
Allard C Carignan A Bergevin M , et al. Secular changes in incidence and mortality associated with Staphylococcus aureus bacteraemia in Quebec, Canada, 1991–2005. Clin Microbiol Infect 2008; 14: 421–428.

33.
Sancak B. [Staphylococcus aureus and antibiotic resistance]. Mikrobiyol Bul 2011; 45: 565–576.

34.
Jevons MP. “Celbenin” – resistant Staphylococci. BMJ 1961; 1: 124–125.

35.
Johnson AP Pearson A Duckworth G. Surveillance and epidemiology of MRSA bacteraemia in the UK. J Antimicrob Chemother 2005; 56: 455–462.

36.
Kalil AC Metersky ML Klompas M , et al. Management of adults with hospital-acquired and ventilator-associated pneumonia: 2016 clinical practice guidelines by the Infectious Diseases Society of America and the American Thoracic Society. Clin Infect Dis 2016; 63: e61–e111.

37.
Romero-Vivas J Rubio M Fernández C , et al. Mortality associated with nosocomial bacteremia due to methicillin-resistant Staphylococcus aureus. Clin Infect Dis 1995; 21: 1417–1423.

38.
Harbarth S Rutschmann O Sudre P , et al. Impact of methicillin resistance on the outcome of patients with bacteremia caused by Staphylococcus aureus. Arch Intern Med 1998; 158: 182–189.

39.
Mylotte JM Aeschlimann JR Rotella DL. Staphylococcus aureus bacteremia: factors predicting hospital mortality. Infect Control Hosp Epidemiol 1996; 17: 165–168.

40.
French GL Cheng AF Ling JM , et al. Hong Kong strains of methicillin-resistant and methicillin-sensitive Staphylococcus aureus have similar virulence. J Hosp Infect 1990; 15: 117–125.

41.
Hershow RC Khayr WF Smith NL. A comparison of clinical virulence of nosocomially acquired methicillin-resistant and methicillin-sensitive Staphylococcus aureus infections in a university hospital. Infect Control Hosp Epidemiol 1992; 13: 587–593.

42.
Holmes NE Robinson JO Van Hal SJ ; VANESSA study group, on behalf of the Australasian Society for Infectious Diseases (ASID) Clinical Research Network (CRN)et al. Morbidity from in-hospital complications is greater than treatment failure in patients with Staphylococcus aureus bacteraemia. BMC Infect Dis 2018; 18: 107.

43.
Ljungström L Pernestig AK Jacobsson G , et al. Diagnostic accuracy of procalcitonin, neutrophil–lymphocyte count ratio, C-reactive protein, and lactate in patients with suspected bacterial sepsis. PLoS One 2017; 12: e0181704.

44.
Salciccioli JD Marshall DC Pimentel MA , et al. The association between the neutrophil-to-lymphocyte ratio and mortality in critical illness: an observational cohort study. Crit Care 2015; 19: 13.

45.
Sarı R Karakurt Z Ay M , et al. Neutrophil to lymphocyte ratio as a predictor of treatment response and mortality in septic shock patients in the intensive care unit. Turk J Med Sci 2019; 49: 1336–1349.

46.
Huang Z Fu Z Huang W , et al. Prognostic value of neutrophil-to-lymphocyte ratio in sepsis: a meta-analysis. Am J Emerg Med 2020; 38: 641–647.

47.
Shen Y Huang X Zhang W. Platelet-to-lymphocyte ratio as a prognostic predictor of mortality for sepsis: interaction effect with disease severity-a retrospective study. BMJ Open 2019; 9: e022896.

48.
GBD 2019 Antimicrobial Resistance Collaborators. Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2022; 400: 2221–2248.

49.
European Antimicrobial Resistance Collaborators. The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis. Lancet Public Health 2022; 7: e897–e913.

50.
Mylotte JM Tayara A. Staphylococcus aureus bacteremia: predictors of 30-day mortality in a large cohort. Clin Infect Dis 2000; 31: 1170–1174.

51.
Jensen AG Wachmann CH Espersen F , et al. Treatment and outcome of Staphylococcus aureus bacteremia: a prospective study of 278 cases. Arch Intern Med 2002; 162: 25–32.

52.
Klevens RM Morrison MA Nadle J ; Active Bacterial Core surveillance (ABCs) MRSA Investigatorset al. Invasive methicillin-resistant Staphylococcus aureus infections in the United States. JAMA 2007; 298: 1763–1771.

Predictors of mortality in Staphylococcus aureus infections: The role of C-reactive protein,neutrophil-to-lymphocyte ratio,and platelet-to-lymphocyte ratio

Abstract

Objective

Methods

Results

Conclusion

Keywords

Objective

Methods

Statistical methods

Results

Discussion

Conclusion

Footnotes

Acknowledgments

Author contributions

Data availability statement

Declaration of conflicting interests

Funding

ORCID iDs

References