Skip to main content

C-reactive protein predicts complications in community-associated S. aureus bacteraemia: a cohort study



Staphylococcus aureus (S. aureus) bacteraemia is increasingly acquired from community settings and is associated with a mortality rate of up to 40% following complications. Identifying risk factors for complicated S. aureus bacteraemia would aid clinicians in targeting patients that benefit from expedited investigations and escalated care.


In this prospective observational cohort study, we aimed to identify risk factors associated with a complicated infection in community-onset S. aureus bacteraemia. Potential risk factors were collected from electronic medical records and included: - patient demographics, symptomology, portal of entry, and laboratory results.


We identified several potential risk factors using univariate analysis. In a multiple logistic regression model, age, haemodialysis, and entry point from a diabetic foot ulcer were all significantly protective against complications. Conversely, an unknown entry point of infection, an entry point from an indwelling medical device, and a C-reactive protein concentration of over 161 mg/L on the day of admission were all significantly associated with complications.


We conclude that several factors are associated with complications including already conducted laboratory investigations and portal of entry of infection. These factors could aid the triage of at-risk patients for complications of S. aureus bacteraemia.

Peer Review reports


S. aureus is a Gram-positive bacterium that can cause many diseases including bacteraemia. Cases of S. aureus bacteraemia [SAB] are common and incidence rates remain stable [1, 2]. In Australia, SAB occurs at an annual rate of 10 per 100,000 people [3] and is mostly acquired from the community [4]. Risk of mortality remains high with international data suggesting rates of up to 40% [5] with Australian mortality currently reported at 18.3% [3]. Complications of SAB are common and include infective endocarditis, osteomyelitis, and severe sepsis. These complications are responsible for the need of greater interventions, longer admissions to hospital and increased risk of mortality.

Several studies have aimed to identify specific factors that may aid clinicians in identifying complications in SAB [6,7,8]. There is debate on gender, age and co-morbidity burden, with increased incidence of infection noted, but polarising results on outcome [9,10,11]. The antibiotic resistance profile of S. aureus [12], whether it persists with treatment [13], and where it is acquired are also possible risk factors for complications [11].

Predictive biomarkers have also been studied. C-reactive protein [CRP] is an acute phase reactant released from the liver and is raised in response to tissue injury and metabolic stressors. Several conditions that increase CRP have been observed including inflammatory processes, cancer, pregnancy and infection [14,15,16].

Although CRP has been rejected as a diagnostic marker for SAB due to the lack of specificity [17], it has also been investigated as a predictive factor of SAB complications. There are limitations to extrapolating these results to a clinical setting. Firstly, international studies on SAB often include healthcare-acquired infections and do not accurately reflect the increasingly common community-associated setting [18]. Other studies do not focus solely upon S. aureus, limiting the translation of the findings [19]. It is important to identify factors, including CRP, that are associated with an increased risk of complicated SAB acquired from the community. This is to better inform management guidelines and impact on mortality rates.

In this study, we aimed to identify potential predicting factors for complication of community-associated SAB infections from all adult cases of SAB that presented to a single institution in Australia. We ascertained several factors that could predict complications and suggest that CRP may be a very useful and readily available biomarker.


Sample selection

University Hospital Geelong [UHG] is a 500-bed tertiary referral hospital in the south-west of Victoria, Australia. It has a catchment of a population of 600,000 and has a specialised Infectious Diseases service. From June 2015 to September 2018, 236 adult cases of SAB were prospectively identified at UHG. All adult cases admitted to the UHG with an initial positive blood culture of S. aureus flagged within 48 h of admission to hospital were included into the study (Fig. 1). Paediatric cases, culture negative cases, or a blood culture positive after 48 h of admission to hospital [healthcare-acquired] were excluded. Cases not admitted to UHG or transferred to another hospital before a complication was diagnosed were also excluded.

Fig. 1



Cases included into the study were categorised on whether they were an uncomplicated or complicated. Categorisation were based upon definitions from the Infectious Disease Society of America Definition [20]. This defines uncomplicated SAB as a SAB which has no infective endocarditis or metastatic infection, no presence of an indwelling medical device within the patient, defervescence within 72 h of antibiotic commencement and no positive blood cultures after 72 h following commencement of antibiotics. Complicated SAB was classified as cases of SAB which did not match the uncomplicated definition.

Data collection

Cases were prospectively identified through the microbiology service (Australian Clinical Laboratories) and clinical data was collected from electronic medical records [EMR]. Data collected included patient demographics, symptomology and portal of entry, microbiological details, and biomarker and blood cell counts from the day of admission. These variables were selected as routinely collected data and their potential as predictive variables for SAB complications. These factors are further detailed below.


Demographics included age, gender and co-morbidities. Co-morbidity burden was determined through the Charlson age co-morbidity index [CACI] [21]. The CACI considers age, HIV status, renal and liver function, diabetic status, and peripheral vascular conditions as well as other conditions [21]. Other co-morbidities not used in the CACI, such as injecting drug use, immunosuppressive status, skin conditions and haemodialysis were also collected, as these co-morbidities are frequently associated with S. aureus infections [22,23,24,25,26].

Symptomology and portal of entry

Symptoms were described on admission and recorded in the EMR by the admitting medical officer. These included fever, rigors, nausea, lethargy, and pain. Pain included both local and generalised pain.

Portal of entry was defined as previously described from Smit et al. [27] with a few modifications. Briefly Smit et al. lists skin and soft tissue infection [SSTI], indwelling medical device associated [IMD], injecting drug use inoculation [IDU], other injections, other entry points, or unknown [27]. We did not include other injections and other entry points as we only had two other entry points observed and no other injections were observed. We instead included community-acquired pneumonia and separated diabetic foot ulcer from SSTI due to the potential increases risk of poor outcomes from these entry points [5, 28, 29]. SSTIs included pressure ulcers, non-diabetic foot ulcers, cellulitis, and other infections of the skin and soft tissue such as abscesses and boils. Pneumonia was defined as community-acquired pneumonia where there were no other entrance points of infection. IMD included catheters and recent implanted devices. IDU was defined as a SAB infection directly attributable to injecting drug use. An unknown entry point was defined as a case that didn’t have any defined entry point.

Laboratory test factors

Microbiological data included whether the bacteraemia was persistent, as defined as having three or more days of consecutive positive blood cultures. The antibiotic sensitivity of the organism was determined through the VITEK 2 system [Biomerieux] and cefoxitin discs and were classified via definitions used by the pathology services. Those pan-sensitive were designated as penicillin-sensitive [PSSA], isolates resistant to penicillin were classified as methicillin-sensitive [MSSA], those resistant toward cefoxitin but no other classes were classified as non-multi resistant [nm-MRSA] and those resistant to cefoxitin and other antibiotic classes were classified as methicillin-resistant [MRSA]. It was also noted if the SAB was part of a poly-microbial infection.

Biomarkers and blood tests were ordered at the discretion of the clinical team in charge of the patient on the day of admission. Results of available biomarkers and bloods were collected and included CRP concentration, platelet, leukocyte, neutrophil and lymphocyte cell counts. If a biomarker was collected more than once, the most divergent from the healthy range was chosen.

Statistical analysis

Data presentation

The outcome for the study was considered as a binary variable: Complicated infections (Yes/No).The explanatory/predictor variables of interest are both continuous and categorical variables. These include demographics, self-reported symptomology, portal of entry and microbiological and laboratory assessment variables. All categorical data are presented as % [n], and non-normal continuous data are presented as medians and interquartile ranges [IQR].

Univariate analysis- stage 1

Pearson’s Chi squared and Fisher’s Exact tests and univariable logistic regression were used to determine whether there were associations between the outcome: complicated infections (Yes/No) and potential categorical predictor/exploratory variables. Whereas univariable logistic regression was used to explore potential associations between complicated infections (Yes/No) and continuous exploratory variables. Only results from the univariable logistic regressions are presented.

Receiver operating characteristic (ROC) curve analysis- stage 2

Significant blood markers determined from univariate analysis were also assessed for a potential cut-off value. The main blood marker of interest was CRP concentration. To determine a potential optimal cut-off values, Receiver Operating Characteristic (ROC) curve analysis was used and a cut-off value was predicted using the Youden Index.

Multiple logistic regression- stage 3

Exploratory variables were included in the multiple logistic regression modelling, if preliminary tests had a Wald statistic p-value < 0.2. A stepwise forward and backward elimination approach was used to model certain potential predictors while variables deemed as potential confounders by the literature were included to adjust other associations the multiple logistic regression model. Post estimation analysis such as Hosmer-Lemeshow goodness of fit test, percentage or correctly classified and area under the curve were used to assess the appropriateness of the model. Only patients with complete data were included in the logistic regression modelling. For this exploratory analysis, a level of significance (α) of 5 and 95% confidence intervals were considered appropriate and reported. Although in the discussion of the final adjusted logistic regression model, a more conservative alpha level of 0.01 has been considered. All data were analysed using both Stata SE version 15.1 [30] and SPSS version 24.0 [31].


Description of the cohort

From 2015 until 2018, 236 cases of SAB presented to UHG. After applying exclusion criteria, 158 cases of SAB were included into the study (Fig. 1). Complicated SAB represented 67.6% (n = 107) cases of the study and 32.4% (n = 51) were categorised as uncomplicated.


The median age was 68.5 years old (IQR: 29), CACI scores were a median of 5 (IQR: 6) and females represented 39.2% of the cohort. There were no significant associations of CACI score or gender with presentation. Intravenous drug use was low (9.5%, n = 15) in the cohort. As summarised in Table 1, age was associated with complications (OR: 1.03, 95% CI: [1.01, 1.05], p = 0.003) and haemodialysis was inversely associated with complications (OR: 0.07, 95% CI: [0.01, 0.61], p = 0.016).

Table 1 Demographic comparison between uncomplicated and complicated SAB

Self-reported symptomology

Self-reported symptomology on admission is summarised in Table 2. Pain was the most common reported symptom (65.8%, n = 104), followed by fever (57.6%, n = 91). Lethargy was the least common reported symptom (19.6% n = 31). Reporting rigors was inversely associated with complications (OR: 0.49, 95% CI: [0.24, 0.99], p = 0.046).

Table 2 Symptomology comparison between uncomplicated and complicated SAB

Portal of entry

An unknown entry point of infection was most common, representing 36.1% (n = 57) of the cohort (Table 3) and was strongly associated with complications (OR: 4.54, 95% CI: [1.95, 10.57], p < 0.001). Conversely, entry points from SSTIs (OR: 0.35, 95% CI: [0.17, 0.71], p = 0.003) or DFUs (OR: 0.16, 95% CI: [0.05, 0.54], p = 0.003) were inversely associated with complications.

Table 3 Portal of entry compared between uncomplicated and complicated SAB

Microbiological and laboratory assessment

As summarised in Table 4, most S. aureus isolates were methicillin sensitive (74.1%, n = 117). The prevalence of antibiotic resistant S. aureus was low within the study (2.5% MRSA (n = 4), 5.1% nm-MRSA (n = 8)) and was not associated with complicated SAB (p = 0.754 for MRSA and p = 0.746 for nm-MRSA). While poly-microbial S. aureus bacteraemia was inversely associated with complications, it was not statistically significant (OR: 0.38, 95% CI: [0.14, 1.04], p = 0.061).

Table 4 Laboratory results comparisons between uncomplicated and complicated SAB

Data on blood biomarkers and from full blood examinations collected on the day of admission were analysed and compared (Table 4). Univariable logistic regressions suggested significant associations between complications and the continuous variable of CRP, white blood cell count, and neutrophil cell count. Potentially important cut-off values were determined for each of these variables using Youden’s Index and used in further analyses.

As shown in Table 4, it was observed that the presence of a CRP concentration over 161 mg/L was associated with complications (OR: 3.29, 95% CI: [1.50, 7.24], p = 0.003). In addition, a white cell count over 14.85 × 109 cells/L (OR: 3.09, 95% CI:[1.31, 7.29], p = 0.01) or a neutrophil cell count over 9.25 × 109 cells/L (OR: 2.06, 95% CI:[1.02, 4.15], p = 0.043) were also associated with complications.

Multiple logistic regression analysis

The goodness of fit test suggested that the final multiple logistic regression model shown in Table 5 was a reasonably fitting model (p = 0.744). When all other independent variables were held constant in the model, CRP concentration over 161 mg/L on the day of admission was significantly associated with complications (adjusted odds ratio (ORadj): 3.56; 95% CI: [1.22, 10.40], p = 0.020). Age (ORadj: 1.03, 95% CI: [1.01, 1.06], p = 0.014), an unknown entry point (ORadj: 3.94, 95% CI: [1.33, 11.62], p = 0.013) and a entry point from an IMD (ORadj:4.58, 95% CI: [1.14, 18.35], p = 0.032) were also significantly associated with complications. Conversely, haemodialysis (ORadj: 0.09, 95% CI: [0.01, 0.71], p = 0.023) and an entry point from a DFU (ORadj: 0.12, 95% CI: [0.02, 0.77], p = 0.025) were inversely associated with complicated disease.

Table 5 Multiple logistic regression: potential risk factors for complications of S. aureus bacteraemia


Current reported global mortality rates of SAB range between 10 and 35% [3, 5, 32, 33] and are related to complications of the infection. Identifying complications earlier may improve implementation of appropriate therapy, identify the need for additional interventions and reduce this mortality rate [34]. To that end, several attempts to identify risk factors for complications have been attempted [35,36,37,38]. These studies are varied in definitions, methodology and populations. They also often focus upon one specific complication. Predictive models have also been attempted [39,40,41,42], but focus upon predicting mortality, a specific complication, or are hospital-acquired infections and thus may not be generalizable to the increasingly prevalent community-acquired setting of infection.

To our knowledge, no study has focussed specifically upon community-associated SAB; the predominant cause in Australia. In this study, we aimed to identify risk factors for complications from demographics, self-reported symptoms, portal of entry, and microbiological profile of the bacteria and from routine biomarkers in SAB.

Complicated SAB represented 67.6% of the cohort, with the median age of the cohort 68.5 years (IQR:29.0). Age was significantly associated with complications and this is also reflected in other studies [5, 32, 36, 43].

Haemodialysis (ORadj: 0.09, 95% CI: [0.01, 0.71], p = 0.023) was significantly inversely associated with complications in SAB. This may be due to the increased medical attention received by those who are on haemodialysis. It may be also be due to the portal of entry from an infected line, with early removal associated with improved outcomes [44]. However, it must be noted that patients with haemodialysis have been previously associated with increased risk for infective endocarditis [8] in all-cause bacteraemia infections.

Significantly, portals of entry were strongly associated with complications in SAB. In this study, an unknown portal of entry was the most common entry point of infection. The higher prevalence of unknown entry point in this study when compared to others, may be due to our definition of portal of entry rather than the infective focus that other studies have used [3, 45].

An unknown portal of entry was significantly associated with complications (ORadj: 3.94, 95% CI: [1.33, 11.62], p = 0.013), as seen previously [46]. This may be due the increased risk of persistence due to the inability to identify the portal of entry and remove the infective focus, increasing the likelihood of S. aureus creating metastatic foci.

A portal of entry from an indwelling medical device (ORadj: 4.58, 95% CI: [1.14, 18.35], p = 0.025) was also significantly associated with complications. This may be due to the definition of complication which automatically categorises those with a prosthesis or cardiac device as a complicated case, regardless of whether the medical device is implicated in the infection [20]. Conversely, a portal of entry from a diabetic foot ulcer (ORadj: 0.12, 95% CI: [0.02, 0.77], p = 0.012) was inversely associated with complications. This may be due to increased management of diabetic foot ulcers but could also be due to the suppressed immune effects and reduced blood supply to the ulcer [47].

Investigation into predictive factors from routinely performed blood tests in a clinical setting showed three possible markers associated with complications in univariable analysis. Cut-off values showed high confidence intervals in white blood cell count and neutrophil cell count. This was due to the difference in numbers between the complicated and uncomplicated groups.

It was identified that a CRP concentration of over 161 mg/L on the day of admission was significantly associated with complications (ORadj: 3.56; 95% CI: [1.22, 10.40], p = 0.020). CRP is an acute phase protein of the pentraxin family. It is raised following an inflammatory injury and peaks the following day [48]. It aids in bacterial clearance by activating the classical pathway of the complement system through binding to Cq1 [49]. Interestingly, an inverse relationship has been observed in sepsis cases where a decreased expression of the HLA-DR marker on monocytes was associated with persistently increased CRP concentration over time [19]. CRP has been investigated as a predictive risk factor in adult SAB, with differing outcomes and study populations reflecting all different results. As seen in Table 6, prior studies are varied in terms of setting, population, outcome assessed, and potential cut-off value identified [3.0 mg/L to 850 mg/L]. Our study in comparison, is mid-range in terms of study number, has the benefit of being a prospective collection, and attempts to include all complications. Conversely, our study is set in a community-onset SAB population, limiting the generalisability of the findings to all SAB cases.

Table 6 Summary of C-reactive protein association studies with bacteraemia

CRP and full blood examinations are already routine blood immunoassays utilised in the diagnosis of infection and therefore have known parameters and remains stable when affected by delays into processing [60, 61]. Therefore, these assays could quickly and efficiently be implemented into a management guideline. CRP could be an additional tool for resource poor healthcare settings to manage the condition by prioritising those with high CRP to have further diagnostic tests to assess for complications. Furthermore, given the increasing positive data on CRP as a predictive marker, it should be highly considered as an accessory marker for triaging patients. Future studies can help confirm the most appropriate cut-off value.

It also highlights the potential for other biomarkers to be predictive. Other studies have found several including, procalcitonin, IL-6, IL-10, IL-1β, IL-17, TNFa and IFNy [62,63,64,65,66,67]. However, these markers are not routinely used in a clinical setting and to implement them into clinical practice would require significant additional resources.

There are several limitations to this study. Firstly, this study is from a single centre and the sample of patients is relatively modest. This limits the generalisability of the findings in other settings and potentially a loss of power to detect weaker associations that may exist. However, Pasco and colleagues demonstrated that the Geelong region population closely reflects the Australian population, through comparing the Barwon Statistical Division that represents Geelong and the national average, and showed the most divergent factor [Country of birth] differed by 9.5% [67]. Nonetheless, results of the study are promising, and we plan to extend this work to a larger study with multiple sites to further validate our model.


In this study, we aimed to identify potential predictive factors of complications in adult community-associated SAB. This included a quantified cut-off predictive marker of any potential blood markers already used in the clinical setting. It was identified that a history of haemodialysis was protective against complications in this cohort. The importance of the portal of entry in the outcome of SAB was observed. An unknown entry point and an entry point originating from an IMD were both significantly associated with complications, whereas a portal of entry from a DFU was protective against complications. Most importantly, we identified that a CRP over 161 mg/L on the day of admission was significantly associated with complicated disease and might offer a useful predictive tool to aid clinicians in the management of adult cases of community-associated SAB.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


S. aureus :

Staphylococcus aureus


Staphylococcus aureus bacteraemia


C-reactive protein


University Hospital Geelong


Electronic medical records


Charlson age comorbidity index


Human immunodeficiency virus


Skin and soft tissue infection


Indwelling medical device


Injecting drug use


Penicillin-sensitive Staphylococcus aureus


Methicillin-sensitive Staphylococcus aureus


Non-multiple methicillin-resistant Staphylococcus aureus


Methicillin-resistant Staphylococcus aureus


Interquartile range


Receiving operating curve


Confidence interval


Odds ratio

ORadj :

Adjusted odds ratio


Area under the curve




  1. 1.

    Rhee Y, Aroutcheva A, Hota B, Weinstein RA, Popovich KJ. Evolving epidemiology of Staphylococcus aureus bacteremia. Infect Control Hosp Epidemiol. 2015;36(12):1417–22.

    Article  PubMed  Google Scholar 

  2. 2.

    Petersen ALA. Staphylococcus aureus bacteraemia cases in Denmark 2017. Denmark: Statens Serum Institut; 2018.

    Google Scholar 

  3. 3.

    Coombs G, Daley D, Gottlieb T, Turnidge JD, Bell JM. Australian group on antimicrobial resistance: Sepsis outcome programs 2015 report. Sydney: Australian Commission on Safety and Quality in Health Care; 2017.

    Google Scholar 

  4. 4.

    Bai AD, Showler A, Burry L, Steinberg M, Ricciuto DR, Fernandes T, Chiu A, Raybardhan S, Science M, Fernando E, Tomlinson G, Bell CM, Morris AM. Impact of infectious disease consultation on quality of care, mortality, and length of stay in Staphylococcus aureus bacteremia: results from a large multicenter cohort study. Clin Infect Dis. 2015;60(10):1451–61.

    Article  PubMed  Google Scholar 

  5. 5.

    Kaasch AJ, Barlow G, Edgeworth JD, Fowler VG, Hellmich M, Hopkins S, et al. Staphylococcus aureus bloodstream infection: a pooled analysis of five prospective, observational studies. J Inf Secur. 2014;68(3):242–51.

    Google Scholar 

  6. 6.

    Bayer AS, Ward JI, Ginzton LE, Shapiro SM. Evaluation of new clinical criteria for the diagnosis of infective endocarditis. Am J Med. 1994;96(3):211–9.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Fowler VG Jr, Justice A, Moore C, Benjamin DK Jr, Woods CW, Campbell S, et al. Risk factors for hematogenous complications of intravascular catheter-associated Staphylococcus aureus bacteremia. Clin Infect Dis. 2005;40(5):695–703.

    Article  PubMed  Google Scholar 

  8. 8.

    Mostaghim AS, Lo HYA, Khardori N. A retrospective epidemiologic study to define risk factors, microbiology, and clinical outcomes of infective endocarditis in a large tertiary-care teaching hospital. SAGE Open Med. 2017;5:2050312117741772.

    Article  Google Scholar 

  9. 9.

    Smit J, López-Cortés LE, Kaasch AJ, Søgaard M, Thomsen RW, Schønheyder HC, Rodríguez-Baño J, Nielsen H. Gender differences in the outcome of community-acquired Staphylococcus aureus bacteraemia: a historical population-based cohort study. Clin Microbiol and Infect. 2017;23(1):27–32.

    CAS  Article  Google Scholar 

  10. 10.

    Anderson DJ, Moehring RW, Sloane R, Schmader KE, Weber DJ, Fowler VG Jr, et al. Bloodstream infections in community hospitals in the 21st century: a multicenter cohort study. PLOS ONE. 2014;9(3):e91713.

    Article  Google Scholar 

  11. 11.

    Melzer M, Welch C. Thirty-day mortality in UK patients with community-onset and hospital-acquired meticillin-susceptible Staphylococcus aureus bacteraemia. J Hosp Infect. 2013;84(2):143–50.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Wehrhahn MC, Robinson JO, Pascoe EM, Coombs GW, Pearson JC, O'Brien FG, Tan HL, New D, Salvaris P, Salvaris R, Murray RJ. Illness severity in community-onset invasive Staphylococcus aureus infection and the presence of virulence genes. J Infect Dis. 2012;205(12):1840–8.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Khatib R, Johnson LB, Fakih MG, Riederer K, Khosrovaneh A, Shamse Tabriz M, Sharma M, Saeed S. Persistence in Staphylococcus aureus bacteremia: incidence, characteristics of patients and outcome. Scand J Infect Dis. 2006;38(1):7–14.

    Article  PubMed  Google Scholar 

  14. 14.

    Li Z, Feng J, Sun X. Is C-reactive protein a specific marker in melanoma? J Clin Oncol. 2015;33(31):3673–4.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Mei Z, Li H, Serdula MK, Flores-Ayala RC, Wang L, Liu J-M, et al. C-reactive protein increases with gestational age during pregnancy among Chinese women. Am J Hum Biol. 2016;28(4):574–9.

    Article  PubMed  Google Scholar 

  16. 16.

    Sankaran G, Zacharia B, Roy A, Purayil SP. Current clinical and bacteriological profile of septic arthritis in young infants: a prospective study from a tertiary referral Centre. Eur J Orthop Surg Traumatol. 2018;28(4):573–8.

    Article  PubMed  Google Scholar 

  17. 17.

    Povoa P, Almeida E, Moreira P, Fernandes A, Mealha R, Aragao A, et al. C-reactive protein as an indicator of sepsis. Intensive Care Med. 1998;24.

  18. 18.

    Benoit JB, Frank DN, Bessesen MT. Genomic evolution of Staphylococcus aureus isolates colonizing the nares and progressing to bacteremia. PLOS ONE. 2018;13(5):e0195860.

    Article  Google Scholar 

  19. 19.

    Cajander S, Rasmussen G, Tina E, Magnuson A, Söderquist B, Källman J, et al. Dynamics of monocytic HLA-DR expression differs between bacterial etiologies during the course of bloodstream infection. PLOS ONE. 2018;13(2):e0192883.

    Article  Google Scholar 

  20. 20.

    Liu C, Bayer A, Cosgrove SE, Daum RS, Fridkin SK, Gorwitz RJ, Kaplan SL, Karchmer AW, Levine DP, Murray BE, J Rybak M, Talan DA, Chambers HF, Infectious Diseases Society of America. Clinical practice guidelines by the Infectious Diseases Society of America for the treatment of methicillin-resistant Staphylococcus aureus infections in adults and children. Clin Infect Dis. 2011;52(3):e18–55.

    Article  PubMed  Google Scholar 

  21. 21.

    Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–51.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Tong SYC, Davis JS, Eichenberger E, Holland TL, Fowler VG Jr. Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev. 2015;28(3):603–61.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Le Moing V, Alla F, Doco-Lecompte T, Delahaye F, Piroth L, Chirouze C, et al. Staphylococcus aureus Bloodstream Infection and Endocarditis--A Prospective Cohort Study. PLOS ONE. 2015;10(5):e0127385.

  24. 24.

    Joost I, Kaasch A, Pausch C, Peyerl-Hoffmann G, Schneider C, Voll RE, et al. Staphylococcus aureus bacteremia in patients with rheumatoid arthritis – data from the prospective INSTINCT cohort. J Inf Secur. 2017;74(6):575–84.

    Google Scholar 

  25. 25.

    Hon KL, Tsang KYC, Kung JSC, Leung TF, Lam CWK, Wong CK. Clinical Signs, Staphylococcus and Atopic Eczema-Related Seromarkers. Molecules. 2017;22:2.

    Article  Google Scholar 

  26. 26.

    McNicholas S, Fe Talento A, O'Gorman J, Hannan MM, Lynch M, Greene CM, et al. Reduced pro-inflammatory responses to Staphylococcus aureus bloodstream infection and low prevalence of enterotoxin genes in isolates from patients on haemodialysis. Eur J Clin Microbiol Infect Dis. 2017;36(1):33–42.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Smit J, Rieg SR, Wendel AF, Kern WV, Seifert H, Schønheyder HC, Kaasch AJ. Onset of symptoms, diagnostic confirmation, and occurrence of multiple infective foci in patients with Staphylococcus aureus bloodstream infection: a look into the order of events and potential clinical implications. Infection. 2018;46(5):651–8.

    Article  PubMed  Google Scholar 

  28. 28.

    Wehrhahn MC, Robinson JO, Pearson JC, O'Brien FG, Tan HL, Coombs GW, et al. Clinical and laboratory features of invasive community-onset methicillin-resistant Staphylococcus aureus infection: a prospective case–control study. Eur J Clin Microbiol Infect Dis. 2010;29(8):1025–33.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Silva V, Almeida F, Carvalho JA, Castro AP, Ferreira E, Manageiro V, Tejedor-Junco MT, Caniça M, Igrejas G, Poeta P. Emergence of community-acquired methicillin-resistant Staphylococcus aureus EMRSA-15 clone as the predominant cause of diabetic foot ulcer infections in Portugal. Eur J Clin Microbiol Infect Dis. 2020;39(1):179–86.

    Article  PubMed  Google Scholar 

  30. 30.

    StataCorp. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.; 2017.

  31. 31.

    Corp. I. IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.: IBM Corp.; 2016.

  32. 32.

    Holmes NE, Robinson JO, van Hal SJ, Munckhof WJ, Athan E, Korman TM, et al. Morbidity from in-hospital complications is greater than treatment failure in patients with Staphylococcus aureus bacteraemia. BMC Infectious Diseases. 2018;18(1):107-.

  33. 33.

    Papakonstantinou PE, Samonis G, Andrianaki AM, Christofaki M, Dimopoulou D, Papadakis J, Gikas A, Kofteridis DP. Epidemiology, microbiological and clinical features, treatment, and outcomes of infective endocarditis in Crete, Greece. Infect Chemother. 2018;50(1):21–8.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    van Hal SJ, Jensen SO, Vaska VL, Espedido BA, Paterson DL, Gosbell IB. Predictors of mortality in Staphylococcus aureus bacteremia. Clin Microbiol Rev. 2012;25(2):362–86.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Melzer M, Welch C. Does the presence of a urinary catheter predict severe sepsis in a bacteraemic cohort? J Hosp Infect. 2017;95(4):376–82.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Chaudry MS, Gislason GH, Kamper A-L, Rix M, Dahl A, Østergaard L, et al. The impact of hemodialysis on mortality risk and cause of death in Staphylococcus aureus endocarditis. BMC Nephrol. 2018;19(1):216.

    Article  Google Scholar 

  37. 37.

    San-Juan R, Pérez-Montarelo D, Viedma E, Lalueza A, Fortún J, Loza E, Pujol M, Ardanuy C, Morales I, de Cueto M, Resino-Foz E, Morales-Cartagena MA, Fernández-Ruiz M, Rico A, Romero MP, Fernández de Mera M, López-Medrano F, Orellana MÁ, Aguado JM, Chaves F. Pathogen-related factors affecting outcome of catheter-related bacteremia due to methicillin-susceptible Staphylococcus aureus in a Spanish multicenter study. Eur J Clin Microbiol Infect Dis. 2017;36(10):1757–65.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Subbaraju P, Rai S, Morakhia J, Midha G, Kamath A, Saravu K. Clinical - microbiological characterization and risk factors of mortality in infective endocarditis from a tertiary care academic hospital in southern India. Indian Heart J. 2018;70(2):259–65.

    Article  PubMed  Google Scholar 

  39. 39.

    Kobayashi D, Yokota K, Takahashi O, Arioka H, Fukui T. A predictive rule for mortality of inpatients with Staphylococcus aureus bacteraemia: a classification and regression tree analysis. Eur J Intern Med. 2014;25(10):914–8.

    Article  PubMed  Google Scholar 

  40. 40.

    Fowler VG Jr, Olsen MK, Corey G, et al. Clinical identifiers of complicated staphylococcus aureus bacteremia. Arch Intern Med. 2003;163(17):2066–72.

    Article  PubMed  Google Scholar 

  41. 41.

    Tubiana S, Duval X, Alla F, Selton-Suty C, Tattevin P, Delahaye F, et al. The VIRSTA score, a prediction score to estimate risk of infective endocarditis and determine priority for echocardiography in patients with Staphylococcus aureus bacteremia. J Inf Secur. 2016;72(5):544–53.

    Google Scholar 

  42. 42.

    Jorgensen SCJ, Lagnf AM, Bhatia S, Rybak MJ. A new simplified predictive model for mortality in methicillin-resistant Staphylococcus aureus bacteremia. Eur J Clin Microbiol Infect Dis. 2019;38(5):843–50.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Aguilar-Company J, Pigrau C, Fernández-Hidalgo N, Rodríguez-Pardo D, Falcó V, Lung M, Pellisé F, Almirante B. Native vertebral osteomyelitis in aged patients: distinctive features. An observational cohort study. Infection. 2018;46(5):679–86.

    Article  PubMed  Google Scholar 

  44. 44.

    El Zakhem A, Chaftari A-M, Bahu R, El Helou G, Shelburne S, Jiang Y, et al. Central line-associated bloodstream infections caused by Staphylococcus aureus in cancer patients: clinical outcome and management. Ann Med. 2014;46(3):163–8.

    Article  PubMed  Google Scholar 

  45. 45.

    Qi R, Joo H-S, Sharma-Kuinkel B, Berlon NR, Park L, Fu C-L, et al. Increased in vitro phenol-soluble modulin production is associated with soft tissue infection source in clinical isolates of methicillin-susceptible Staphylococcus aureus. J Inf Secur. 2016;72(3):302–8.

    Google Scholar 

  46. 46.

    Courjon J, Demonchy E, Degand N, Risso K, Ruimy R, Roger P-M. Patients with community-acquired bacteremia of unknown origin: clinical characteristics and usefulness of microbiological results for therapeutic issues: a single-center cohort study. Ann Clin Microbiol Antimicrob. 2017;16(1):40.

    Article  Google Scholar 

  47. 47.

    Catrina S-B, Zheng X. Disturbed hypoxic responses as a pathogenic mechanism of diabetic foot ulcers. Diabetes Metab Res Rev. 2016;32(Suppl 1):179–85.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Peri G, Introna M, Corradi D, Iacuitti G, Signorini S, Avanzini F, Pizzetti F, Maggioni AP, Moccetti T, Metra M, Cas LD, Ghezzi P, Sipe JD, Re G, Olivetti G, Mantovani A, Latini R. PTX3, a prototypical long pentraxin, is an early indicator of acute myocardial infarction in humans. Circulation. 2000;102(6):636–41.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    McGrath FDG, Brouwer MC, Arlaud GJ, Daha MR, Hack CE, Roos A. Evidence that complement protein C1q interacts with C-reactive protein through its globular head region. J Immunol. 2006;176(5):2950–7.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Horino T, Sato F, Hosaka Y, Hoshina T, Tamura K, Nakaharai K, Kato T, Nakazawa Y, Yoshida M, Hori S. Predictive factors for metastatic infection in patients with bacteremia caused by methicillin-sensitive Staphylococcus aureus. Am J Med Sci. 2015;349(1):24–8.

    Article  PubMed  Google Scholar 

  51. 51.

    Pravin Charles MV, Kalaivani R, Venkatesh S, Kali A, Seetha KS. Evaluation of procalcitonin as a diagnostic marker in neonatal sepsis. Indian J Pathol Microbiol. 2018;61(1):81–4.

    Article  PubMed  Google Scholar 

  52. 52.

    Bernstein DT, Haruno LS, Daram S, Dawson JR, Zhang W, Rosenfeld SB. Patient factors associated with methicillin-resistant Staphylococcus aureus septic arthritis in children. Orthopedics. 2018;41(2):e277–e82.

    Article  PubMed  Google Scholar 

  53. 53.

    Bouchard M, Shefelbine L, Bompadre V. C-Reactive Protein Level at Time of Discharge Is Not Predictive of Risk of Reoperation or Readmission in Children With Septic Arthritis. Front Surg. 2019;6(68).

  54. 54.

    Tascini C, Aimo A, Arzilli C, Sbrana F, Ripoli A, Ghiadoni L, Bertone C, Passino C, Attanasio V, Sozio E, Taddei E, Murri R, Fantoni M, Paciosi F, Francisci D, Pasticci MB, Pallotto C, di Caprio G, Carozza A, Maffei S, Emdin M. Procalcitonin, white blood cell count and C-reactive protein as predictors of S. aureus infection and mortality in infective endocarditis. Int J Cardiol. 2020;301:190–4.

    Article  PubMed  Google Scholar 

  55. 55.

    Mölkänen T, Ruotsalainen E, Rintala EM, Järvinen A. Predictive value of C-reactive protein (CRP) in identifying fatal outcome and deep infections in Staphylococcus aureus bacteremia. PLoS One. 2016;11(5):e0155644.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Lin Z, Vasudevan A, Tambyah PA. Use of erythrocyte sedimentation rate and C-reactive protein to predict osteomyelitis recurrence. J Orthop Surg (Hong Kong). 2016;24(1):77–83.

    CAS  Article  Google Scholar 

  57. 57.

    Chiappini E, Camposampiero C, Lazzeri S, Indolfi G, De Martino M, Galli L. Epidemiology and Management of Acute Haematogenous Osteomyelitis in a Tertiary Paediatric Center. Int J Environ Res Public Health. 2017;14(5).

  58. 58.

    Tang A, Caballero AR, Bierdeman MA, Marquart ME, Foster TJ, Monk IR, et al. Staphylococcus aureus Superantigen-Like Protein SSL1: A Toxic Protease. Pathogens. 2019;8(1).

  59. 59.

    Garcia Del Pozo E, Collazos J, Carton JA, Camporro D, Asensi V. Factors predictive of relapse in adult bacterial osteomyelitis of long bones. BMC Infect Dis. 2018;18(1):635-.

  60. 60.

    Aziz N, Fahey JL, Detels R, Butch AW. Analytical performance of a highly sensitive C-reactive protein-based immunoassay and the effects of laboratory variables on levels of protein in blood. Clin Diagn Lab Immunol. 2003;10(4):652–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Ledue TB, Rifai N. Preanalytic and analytic sources of variations in C-reactive protein measurement: implications for cardiovascular disease risk assessment. Clin Chem. 2003;49(8):1258–71.

    Article  PubMed  Google Scholar 

  62. 62.

    Yan ST, Sun LC, Jia HB, Gao W, Yang JP, Zhang GQ. Procalcitonin levels in bloodstream infections caused by different sources and species of bacteria. Am J Emerg Med. 2017;35(4):579–83.

    Article  PubMed  Google Scholar 

  63. 63.

    Dey I, Bishayi B. Role of Th17 and Treg cells in septic arthritis and the impact of the Th17/Treg -derived cytokines in the pathogenesis of S. aureus induced septic arthritis in mice. Microb Pathog. 2017;113(Supplement C):248–64.

    CAS  Article  Google Scholar 

  64. 64.

    Leech JM, Lacey KA, Mulcahy ME, Medina E, McLoughlin RM. IL-10 plays opposing roles during Staphylococcus aureus systemic and localized infections. J Immunol. 2017;198(6):2352–65.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Alves De Souza C, Queiroz Alves De Souza A, Queiroz Alves De Souza MDS, Dias Leite JA, Silva De Morais M, Barem Rabenhorst SH. A link between osteomyelitis and IL1RN and IL1B polymorphisms-a study in patients from Northeast Brazil. Acta Orthop. 2017;88(5):556–61.

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Friebe A, Volk HD. Stability of tumor necrosis factor alpha, interleukin 6, and interleukin 8 in blood samples of patients with systemic immune activation. Arch Pathol Lab Med. 2008;132(11):1802–6.

    Article  PubMed  Google Scholar 

  67. 67.

    Parker D, Planet PJ, Soong G, Narechania A, Prince A. Induction of type I interferon signaling determines the relative pathogenicity of Staphylococcus aureus strains. PLOS Pathog. 2014;10(2):e1003951.

    Article  Google Scholar 

  68. 68.

    Pasco JA, Nicholson GC, Kotowicz MA. Cohort profile: Geelong osteoporosis study. Int J Epidemiol. 2012;41(6):1565–75.

    Article  PubMed  Google Scholar 

Download references


We acknowledge Australian Clinical Laboratory Services [Formerly St. John of God Pathology Services] for their aid in identifying clinical cases of SAB.


The authors declare no funding was involved in the production of this research.

Author information




CB collected, analysed, and wrote the manuscript. SB analysed the data, was a major contributor in writing and editing the manuscript. RC interpreted patient data regarding portal of entry of infection and complication status as well as editing of the manuscript. EA conceptualised the idea of the manuscript, interpreted patient data regarding portal of entry of infection and complication status as well as editing of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Carly L. Botheras.

Ethics declarations

Ethics approval and consent to participate

Exemption of ethical approval was given from the Barwon Health Research, Ethics, Governance, and Integrity unit (ratified by the Barwon Health HREC, Reference number: 16.33) and from Deakin University HREC (Reference number: 2017–217). Barwon Health Research, Ethics, Governance Integrity unit provided permission to access the data following ethical approval. Data was anonymised before use.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


  • S. aureus bacteraemia
  • Complications
  • Epidemiology
  • C-reactive protein