Bacterial Bloodstream Infections in Cardiac Patients: Microbiological Spectrum, Antimicrobial Susceptibility Patterns, and Biomarker Correlation Analysis at a Cardiac Tertiary Care Centre

Bacterial Bloodstream Infections in Cardiac Patients: Microbiological Spectrum, Antimicrobial Susceptibility Patterns, and Biomarker Correlation Analysis at a Cardiac Tertiary Care Centre | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bacterial Bloodstream Infections in Cardiac Patients: Microbiological Spectrum, Antimicrobial Susceptibility Patterns, and Biomarker Correlation Analysis at a Cardiac Tertiary Care Centre Moiz Ahmed Khan, Shajiya Saeed, Ziauddin Afrozuddin, Wajid Ali Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8857783/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 24 You are reading this latest preprint version Abstract INTRODUCTION: Bloodstream infections (BSIs) in cardiac patients represent a significant clinical challenge with substantial morbidity and mortality. Understanding the microbiological profile and biomarker patterns in this high-risk population is crucial for optimizing treatment strategies and infection prevention measures. METHODS A retrospective observational study was conducted analyzing patient data from 1st November 2024 to 31st October 2025 at a tertiary cardiac care centre in Karachi, Pakistan. Blood cultures from 113 cardiac inpatients with BSI were analyzed for bacterial identification and susceptibility patterns. Procalcitonin (PCT) and C-reactive protein (CRP) levels were measured simultaneously with microbiological investigation. Correlation analysis between biomarkers and pathogen types was performed using both Pearson and Spearman correlation coefficients. RESULTS One hundred and thirteen bacterial bloodstream isolates were identified with gram-negative bacteria predominating (63.7%, n = 72) over gram-positive organisms (36.3%, n = 41). Escherichia coli was the most prevalent pathogen (17.7%, n = 20), followed by Acinetobacter species (9.7%, n = 11) and Enterobacter species (8.8%, n = 10). Multidrug-resistant organisms accounted for 16.8% (n = 19) of isolates, with Vancomycin-Resistant Enterococcus (8.8%) and Methicillin-Resistant Staphylococcus aureus (8.0%) being the most common. Gram-negative bacteria demonstrated higher mean PCT levels (16.60 ng/mL) compared to gram-positive bacteria (12.24 ng/mL), though not statistically significant (p = 0.094). CRP levels were similarly elevated in both groups (gram-negative: 138.18 mg/L vs gram-positive: 118.09 mg/L, p = 0.216). Spearman correlation analysis revealed a statistically significant correlation between PCT and CRP (ρ = 0.444, p < 0.001) overall, with stronger correlation in gram-positive infections (ρ = 0.510) compared to gram-negative infections (ρ = 0.372). CONCLUSION This study confirms the critical importance of understanding local microbiological epidemiology and biomarker patterns in cardiac patients with BSI, providing evidence-based guidance for empirical antimicrobial therapy, infection prevention strategies, and diagnostic algorithm optimization in tertiary cardiac care settings. PCT and CRP both serve as valuable biomarkers in this population, with their combined use potentially enhancing diagnostic accuracy, supporting their complementary role in sepsis management and antimicrobial stewardship in cardiac patients. Bloodstream infection Cardiac disease Procalcitonin C-reactive protein Antimicrobial resistance Gram-negative bacteria Biomarkers Sepsis Figures Figure 1 Figure 2 INTRODUCTION Bloodstream infections (BSIs) represent one of the most serious complications in hospitalized patients, particularly in cardiac care settings where patients are often elderly with significant comorbidities and prolonged intensive care unit (ICU) stays. 1 The incidence of healthcare-associated infections (HAIs) in cardiac patients has been documented between 6% and 9%, with BSIs accounting for approximately 20–25% of all nosocomial infections in this population. 2 , 3 The rising burden of antimicrobial resistance (AMR) compounds this challenge, with multidrug-resistant organisms (MDROs) increasingly reported in both community and hospital settings. 4 , 5 ​ Cardiac patients represent a unique and vulnerable population for developing BSI due to multiple factors including prolonged hospitalization, invasive procedures, central venous catheterization, mechanical ventilation, and immunosuppression related to cardiopulmonary bypass and surgical trauma. 6 The consequences of BSI in cardiac patients extend beyond the infection itself, as sepsis can precipitate acute cardiac dysfunction, arrhythmias, and hemodynamic instability, further complicating the clinical course and increasing mortality risk. 7 Studies have demonstrated that sepsis following cardiac surgery is associated with significantly increased ICU length of stay (ranging from 134 to 266 hours) and a mortality risk increased by 3.7 to 6.6-fold compared to non-infected cardiac surgery patients.​ 8 Understanding the microbiological epidemiology of BSI in cardiac populations is essential for guiding empirical antimicrobial therapy, particularly given the high prevalence of resistant organisms and the narrow therapeutic window before irreversible organ dysfunction occurs. 9 , 10 Blood culture, the traditional gold standard for BSI diagnosis, requires 48–72 hours for organism identification and susceptibility testing, creating a critical diagnostic gap during which initial antimicrobial therapy must be empirically determined. This diagnostic delay has driven increased interest in rapid diagnostic biomarkers that can facilitate early sepsis recognition and enable prompt institution of targeted antimicrobial therapy.​ Procalcitonin (PCT) and C-reactive protein (CRP) have emerged as potentially valuable biomarkers for early sepsis diagnosis and pathogen differentiation. 11 PCT, the prohormone of calcitonin, is released by extrathyroidal tissues in response to bacterial lipopolysaccharides and pro-inflammatory cytokines, with accumulating evidence suggesting superior specificity and earlier elevation compared to CRP in bacterial infections. 12 CRP, an acute phase reactant synthesized by hepatocytes in response to interleukin-6, demonstrates higher sensitivity but lower specificity for infection compared to PCT. 13 Emerging research has suggested that these biomarkers may exhibit differential expression patterns depending on the causative organism's gram classification, potentially facilitating rapid pathogen differentiation prior to culture results.​ 14 The combined assessment of multiple biomarkers has been advocated as a strategy to enhance diagnostic accuracy while reducing unnecessary antimicrobial use and mitigating AMR. 15 In cardiac patients specifically, the complex inflammatory milieu induced by cardiopulmonary bypass and surgical trauma may alter conventional biomarker interpretation, necessitating population-specific investigation of biomarker utility. 6 , 7 Furthermore, the epidemiology of BSI pathogens varies significantly by geographic region, healthcare setting characteristics, and patient population, mandating local surveillance data to inform infection control policies and antimicrobial stewardship programs.​ This study was therefore undertaken to comprehensively characterize the microbiological spectrum and antimicrobial susceptibility patterns of bacterial BSI in cardiac patients at a tertiary care centre, and to evaluate the correlation of PCT and CRP with specific pathogen types to determine their utility as adjunctive diagnostic biomarkers in this high-risk population. METHODS STUDY DESIGN AND SETTING A retrospective observational study was conducted at Tabba Heart Institute, Karachi, Pakistan, a tertiary cardiac care centre, analyzing patient data over a twelve-month period from 1st November 2024 to 31st October 2025. The study included all consecutive inpatients aged ≥ 18 years admitted to the cardiac wards and ICUs who developed BSIs during their hospital stay, either present on admission or acquired during hospitalization. BSI was defined as the isolation of a bacterial pathogen from at least 2 sets of blood cultures from the same patient in the clinical context of fever, hypothermia, or signs of systemic inflammatory response. Patients with fungal or polymicrobial infections were excluded from the analysis to maintain homogeneity of the study population. MICROBIOLOGICAL ANALYSIS Two sets of blood cultures were collected from each patient via peripheral venipuncture, or one set each from a central venous line and a peripheral line if a central line was in place, using standard aseptic technique. All blood cultures were processed using the BACT/ALERT® automated system (biomérieux, France), with incubation for up to five days. Bacterial identification was performed using conventional biochemical methods and confirmed using API® ID strips and APIWEB™ database (bioMérieux, France). Antimicrobial susceptibility testing was performed via Kirby-Bauer Disk Diffusion method and the clinical breakpoints were interpreted in accordance with the Clinical Laboratory and Standard Institute’s (CLSI) M100 guidelines. 16 MDROs were defined as bacterial strains demonstrating resistance to agents in at least three different antimicrobial classes. Specific notation was made for organisms demonstrating clinically significant resistance phenotypes including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus species (VRE), and carbapenem-resistant or extended-spectrum beta-lactamase producing gram-negative bacilli. BIOMARKER ANALYSIS Contemporaneous serum samples for PCT and CRP measurement were obtained on the same day of blood culture collection. PCT was measured using chemiluminescence immunoassay with a lower detection limit of 0.02 ng/mL and clinical cutoff values of 0.5 ng/mL for bacterial infection and 2.0 ng/mL for sepsis according to standard laboratory protocols. CRP was measured using high-sensitivity quantitative assay with a detection range of 1-400 mg/L, with values > 10 mg/L considered elevated and values ≥ 100 mg/L indicating significant systemic inflammation. Both biomarkers were measured from sera separated and stored at -80°C until analysis. STATISTICAL ANALYSIS For each patient, demographic information including age and gender along with microbiological, antimicrobial susceptibility and biomarker profile was collected from the institute’s electronic patient care database. Data were analyzed using descriptive statistical methods with results presented as absolute frequencies and percentages for categorical variables, and as means with standard deviations or medians with interquartile ranges (IQRs) for non-normally distributed data, for continuous variables. Pathogen frequencies were expressed as both absolute numbers and percentages of total isolates. Biomarker levels were compared between pathogen groups (gram-positive vs gram-negative, MDR vs non-MDR) using the Mann-Whitney U test for non-parametric comparisons. Correlations between PCT and CRP values were assessed using both Pearson correlation for linear relationships and Spearman correlation for rank-based relationships. A p-value < 0.05 was considered statistically significant. RESULTS A total of 113 cardiac inpatients with bacterial BSIs were included in the analysis. The cohort consisted of 71 males (62.8%) and 42 females (37.2%), with a mean age of 64.4 ± 12.7 years (median 66 years, range 29–95 years) (Table 1 ). The microbiological spectrum was diverse, with twenty distinct bacterial species or species groups identified. The most frequently isolated pathogen was Escherichia coli (n = 20; 17.7%), followed by Acinetobacter species (n = 11, 9.7%), Enterobacter species (n = 10, 8.8%), and VRE (n = 10, 8.8%). Additional commonly isolated organisms included Pseudomonas aeruginosa (n = 9, 8.0%), non- aeruginosa Pseudomonas species (n = 9, 8.0%), MRSA (n = 9, 8.0%), Enterococcus species (vancomycin-sensitive; n = 7, 6.2%), Streptococcus species (n = 6, 5.3%), and Klebsiella pneumoniae (n = 5, 4.4%). The remaining eight species each accounted for < 3% of total isolates (Fig. 1 ). Table 1 Baseline characteristics and overall biomarker profile of cardiac inpatients with bacterial bloodstream infection (n = 113) CHARACTERISTIC VALUE Gender Male 71 (62.8) Female 42 (37.2) Age (years) Mean ± SD 64.4 ± 12.7 Median (range) 66 (29–95) Procalcitonin (PCT) Mean ± SD (ng/mL) 15.06 ± 25.39 Median (range) (ng/mL) 3.22 (0.02–>100) PCT ≥ 0.5 ng/mL n (%) 82/99 (82.8) PCT ≥ 1.0 ng/mL n (%) 72/99 (72.7) PCT ≥ 5.0 ng/mL n (%) 43/99 (43.4) C-reactive protein (CRP) Mean ± SD (mg/L) 130.89 ± 105.99 Median (range) (mg/L) 108.38 (1.10–439.20) CRP ≥ 50 mg/L n (%) 82/113 (72.6) CRP ≥ 100 mg/L n (%) 61/113 (54.0) CRP ≥ 150 mg/L n (%) 42/113 (37.2) According to Gram classification, gram-negative bacteria predominated (n = 72; 63.7%) compared to gram-positive bacteria (n = 41; 36.3%). The most common gram-negative organisms included E. coli (n = 20), Acinetobacter species (n = 11), Enterobacter species (n = 10), and Pseudomonas species (n = 18; aeruginosa and non- aeruginosa combined). The most frequently isolated gram-positive organisms were VRE (n = 10), MRSA (n = 9), Enterococcus species (vancomycin-sensitive; n = 7), and Streptococcus species (n = 11) including Streptococcus pneumoniae , Streptococcus viridans , and Streptococcus pyogenes . MDROs were identified in 19 isolates (16.8%) of which n = 10 were VRE and n = 9 were MRSA. The remaining gram-negative isolates, while showing variable resistance patterns to individual antimicrobial classes, did not uniformly meet the criteria for MDROs in this cohort. Among gram-negative bacteria, resistance to extended-spectrum cephalosporins and fluoroquinolones was commonly observed, consistent with the prevalence of extended-spectrum beta-lactamase (ESBL) producing strains. Regarding biomarker analysis, PCT levels were available for 99 of 113 patients (87.6%). The mean PCT level was 15.06 ± 25.39 ng/mL (median 3.22 ng/mL, range 0.02–100 ng/mL). Notably, 82 patients (82.8% of those tested) had PCT levels ≥ 0.5 ng/mL, 72 patients (72.7%) had levels ≥ 1.0 ng/mL, and 43 patients (43.4%) had levels ≥ 5.0 ng/mL. One patient demonstrated a PCT level > 100 ng/mL, indicating profound systemic inflammation. CRP levels were available for all 113 patients (100% availability). The mean CRP level was 130.89 ± 105.99 mg/L (median 108.38 mg/L, range 1.10-439.20 mg/L). The majority of patients (72.6%, n = 82) demonstrated CRP levels ≥ 50 mg/L, 54.0% (n = 61) had levels ≥ 100 mg/L, and 37.2% (n = 42) had levels ≥ 150 mg/L. Compared to PCT, CRP demonstrated less extreme values, with a maximum recorded level of 439.20 mg/L versus PCT's maximum of > 100 ng/mL. When stratifying biomarkers by pathogen type, gram-negative bacteria were associated with numerically higher mean PCT levels (16.60 ± 26.42 ng/mL, median 4.33 ng/mL) compared to gram-positive bacteria (12.24 ± 23.51 ng/mL, median 1.95 ng/mL), though this difference did not achieve statistical significance (p = 0.094). Similarly, gram-negative organisms demonstrated slightly elevated mean CRP levels (138.18 ± 104.62 mg/L, median 124.16 mg/L) compared to gram-positive organisms (118.09 ± 108.45 mg/L, median 81.90 mg/L), again not reaching statistical significance (p = 0.216) (Table 2 ). While these differences did not reach statistical significance, potentially due to sample size constraints, the numerical trends are presented for descriptive purposes and hypothesis generation regarding differential inflammatory responses. Table 2 Comparison of procalcitonin (PCT) and C-reactive protein (CRP) levels between gram-negative and gram-positive bacterial bloodstream infections Biomarker Gram status Mean ± SD Median (IQR or range) p-value* PCT (ng/mL) Gram-negative 16.60 ± 26.42 4.33 0.094 Gram-positive 12.24 ± 23.51 1.95 CRP (mg/L) Gram-negative 138.18 ± 104.62 124.16 0.216 Gram-positive 118.09 ± 108.45 81.90 *p-values derived using Mann–Whitney U test for non-parametric comparison of continuous biomarker levels between gram-negative and gram-positive bloodstream infections. A p-value < 0.05 was considered statistically significant. Organism-specific biomarker analysis revealed notable variation. E. coli , the most prevalent pathogen, demonstrated mean PCT of 23.63 ± 29.92 ng/mL and mean CRP of 142.66 ± 84.73 mg/L. Acinetobacter species, while associated with the highest mean CRP level (183.12 ± 112.71 mg/L), demonstrated relatively low mean PCT (3.37 ± 2.88 ng/mL). Enterobacter species showed intermediate values with mean PCT of 16.08 ± 17.47 ng/mL and mean CRP of 186.43 ± 160.73 mg/L. Pseudomonas species demonstrated variable biomarker patterns, with relatively lower mean CRP (64.45 ± 50.34 mg/L) compared to other gram-negative organisms. MRSA was associated with elevated mean CRP (165.04 mg/L) and variable PCT levels. Correlation analysis of both biomarkers revealed that among the 99 patients with both PCT and CRP measurements, the Pearson correlation coefficient was 0.1086 (p = 0.2846), indicating weak linear correlation. However, Spearman rank correlation analysis revealed a statistically significant non-linear correlation of ρ = 0.4442 (p < 0.001), indicating that while the two biomarkers demonstrate different quantitative values, their ordinal ranking in terms of severity shows moderate agreement (Fig. 2 ). Stratification by pathogen type revealed differential correlations. In gram-positive infections (n = 35 with both measurements), Spearman correlation was stronger (ρ = 0.5104, p = 0.0017), suggesting more concordant elevation of both markers in infections caused by these organisms. In contrast, gram-negative infections (n = 64 with both measurements) demonstrated weaker correlation (ρ = 0.3723, p = 0.0025), indicating more discordant biomarker patterns. This differential correlation pattern suggests that PCT and CRP may respond differently to gram-negative lipopolysaccharides versus gram-positive peptidoglycans and associated pathogen-associated molecular patterns. Regarding association between biomarkers and AMR, patients with MDROs (n = 19) demonstrated mean PCT of 8.36 ng/mL (median 3.00 ng/mL) and mean CRP of 150.82 mg/L (median 161.91 mg/L). In contrast, non-MDROs (n = 94) were associated with mean PCT of 16.26 ng/mL (median 3.47 ng/mL) and mean CRP of 126.86 mg/L (median 105.39 mg/L). Interestingly, MDROs were associated with numerically lower PCT levels despite similar or slightly elevated CRP, a pattern that may reflect different immunological responses to resistant versus susceptible pathogens or differences in bacterial virulence factors. Biomarker analysis by age quartiles revealed relative homogeneity, with mean PCT ranging from 8.04 ng/mL (youngest quartile, 29–56 years) to 23.48 ng/mL (second quartile, 56–66 years), and mean CRP ranging from 122.48 to 138.90 mg/L across quartiles. Gender analysis demonstrated that female patients (n = 42) had numerically higher mean PCT (19.88 ng/mL) compared to males (n = 71, mean 12.31 ng/mL), though sample sizes limit statistical inference. Female patients also showed a higher proportion of MDROs (21.4% vs 14.1% in males), though this did not reach statistical significance. DISCUSSION Our study documents the microbiological epidemiology and biomarker patterns associated with bacterial BSIs in cardiac inpatients at a tertiary care centre over a twelve-month period. The findings illuminate the distinct microbiological landscape of BSI in this vulnerable population and provide evidence regarding the utility of PCT and CRP as diagnostic adjuncts. While acknowledging the limitations imposed by the single-center design and sample size, which may affect statistical power and generalizability, this study provides critical local surveillance data essential for tailoring empirical therapy and infection prevention strategies within our specific cardiac care context. The predominance of gram-negative organisms in this cohort (63.7%) aligns with the increasingly recognized global shift toward gram-negative predominance in HAIs, particularly in cardiac and critical care settings. 17 E. coli was the most frequent pathogen (17.7%), a finding consistent with international surveillance data and reflective of the urinary tract as a common reservoir and source for hematogenous seeding in hospitalized patients with invasive urological procedures. 18 The second most prevalent gram-negative organisms, Acinetobacter species (9.7%) and Enterobacter species (8.8%), are increasingly recognized as nosocomial pathogens with particular propensity for colonizing hospitalized patients, especially those with prolonged ICU stays and exposure to broad-spectrum antimicrobial agents. 19 , 20 Furthermore, the gram-negative organism predominance in BSI, combined with the substantial proportion of organisms demonstrating resistance to extended-spectrum cephalosporins and fluoroquinolones, has important implications for empirical antimicrobial therapy in cardiac patients presenting with sepsis. Current international guidelines increasingly recommend early use of broad-spectrum agents such as carbapenems or fluoroquinolones with gram-negative coverage in critically ill patients with presumed sepsis, a recommendation supported by the epidemiological findings of this study.​ 20 The significant proportion of MDROs in our cohort of patients (16.8%) represents a clinically important concern and underscores the critical role of antimicrobial stewardship in cardiac care settings. VRE, representing 8.8% of our total isolates, is a recognized cause of nosocomial infection with limited treatment options, often necessitating reliance on newer antimicrobial agents such as daptomycin, linezolid, or tigecycline, each with attendant toxicity concerns and cost implications.​ The biomarker analysis provided important insights into the comparative performance of PCT and CRP in our study population. The overall moderate Spearman correlation between PCT and CRP (ρ = 0.444, p < 0.001) indicates that these markers, while related, provide somewhat complementary rather than redundant information. The weak linear correlation (Pearson r = 0.109) combined with moderate rank correlation suggests that while extreme elevations of both markers tend to occur together, the quantitative elevation of one marker cannot reliably predict the exact elevation of the other.​ The observation of differential correlation patterns by pathogen type is novel and potentially important for clinical practice. The stronger Spearman correlation in gram-positive infections (ρ = 0.510) compared to gram-negative infections (ρ = 0.372) may reflect fundamental differences in the inflammatory cascades triggered by these pathogens. Gram-positive bacteria activate the innate immune system primarily through pattern recognition receptors such as toll-like receptor 2, which recognizes peptidoglycans and lipoteichoic acids, whereas gram-negative organisms predominantly signal through toll-like receptor 4, recognizing lipopolysaccharides. 21 These different signaling pathways may result in quantitatively and temporally distinct inflammatory cytokine production, with differential effects on PCT and CRP synthesis.​ The mean PCT level of 15.06 ng/mL in this cohort, while elevated compared to healthy controls (typically < 0.1 ng/mL), is lower than values frequently reported in severe sepsis or septic shock, suggesting that this cohort may represent moderate to severe infection rather than the most fulminant presentations. The median PCT value of 3.22 ng/mL approximates the typical cutoff for sepsis diagnosis (2.0 ng/mL), indicating that the majority of patients exceeded recognized diagnostic thresholds. The finding that 72.7% of patients demonstrated PCT levels ≥ 1.0 ng/mL supports the utility of this marker in confirming bacterial infection in this population, as baseline PCT in non-infected individuals typically remains < 0.5 ng/mL.​ The mean CRP of 130.89 mg/L in this cohort is substantially elevated compared to normal values (< 10 mg/L) and exceeds typical cutoffs for sepsis diagnosis, indicating significant systemic inflammation. The higher median CRP value (108.38 mg/L) compared to median PCT (3.22 ng/mL) may reflect the slower kinetics of CRP production compared to PCT, with CRP remaining elevated for longer periods following infection onset. This temporal difference has implications for longitudinal monitoring of infection response, with PCT being potentially more useful for tracking treatment response while CRP may better reflect ongoing inflammatory burden.​ The numerical trend toward higher PCT and CRP levels in gram-negative infections compared to gram-positive infections, while not reaching statistical significance, is consistent with prior literature documenting differential inflammatory responses to these pathogen classes. 22 The gram-negative cell wall lipopolysaccharide is a potent endotoxin triggering robust inflammatory cytokine responses, potentially resulting in more pronounced biomarker elevations. However, the lack of statistical significance in this cohort may reflect the relatively small number of subjects and the wide variability in individual responses to infection.​ The observation of low mean PCT in Acinetobacter species (3.37 ng/mL) despite elevated mean CRP (183.12 mg/L) is particularly interesting and may reflect the relatively low virulence of Acinetobacter compared to other gram-negative organisms, or alternatively, may suggest that Acinetobacter -associated infections may have slower kinetics of PCT elevation compared to more rapidly progressive infections. 23 This discordance between PCT and CRP in Acinetobacter infections suggests that reliance on PCT alone for infection diagnosis might underestimate the significance of Acinetobacter bacteremia in this population.​ The finding of lower mean PCT levels in multidrug-resistant organisms compared to non-MDR organisms (8.36 vs 16.26 ng/mL) is unexpected and deserves consideration. This pattern might reflect differences in virulence factors or resistance mechanism-related fitness costs that attenuate inflammatory potential. 19 , 23 The higher mean CRP in MDROs suggests that despite lower PCT, systemic inflammation remains substantial in these infections, again highlighting the complementary nature of these markers.​ The combined use of PCT and CRP as part of a comprehensive diagnostic approach incorporating clinical assessment, blood culture results, and antimicrobial susceptibility patterns, appears justified in cardiac patients with suspected BSI. PCT demonstrates superior specificity for bacterial infection and may enable earlier recognition of sepsis, while CRP provides assessment of overall inflammatory burden and may better reflect persistent inflammatory states during recovery phases. 22 , 24 , 25 The timing of biomarker measurement relative to infection onset and the trajectory of biomarker change warrant longitudinal investigation in future studies. The clinical implications of this study are multifold. First, the predominance of gram-negative organisms supports empirical antimicrobial coverage of enteric bacteria and Acinetobacter in cardiac patients presenting with BSI, typically achieved with carbapenems or fluoroquinolones with gram-negative activity. Second, the substantial prevalence of MDROs argues for de-escalation strategies only after organism identification and susceptibility results become available, to avoid premature narrowing of coverage that could result in inadequate therapy. Third, the biomarker findings suggest that combined PCT and CRP assessment provides complementary diagnostic information, with the stronger correlation in gram-positive infections potentially aiding in earlier pathogen differentiation. This study had several limitations. First, the single-center design limits generalizability of findings to other cardiac care settings with potentially different infection control practices or antimicrobial stewardship policies. Second, the relatively small number of MDROs (n = 19) limits the statistical power for subgroup analyses involving this important group, and the findings therein should be considered hypothesis-generating. Third, biomarkers were measured at a single time point corresponding to the diagnostic blood culture, which does not account for kinetics, the timing of symptom onset, or specific confounders such as postoperative inflammatory states, potentially influencing absolute levels. Fourth, the lack of clinical outcome data such as mortality and length of stay, prevents assessment of the prognostic utility of the biomarkers and their impact on antimicrobial decision-making, which represents a critical area for future investigation. Fifth, the statistical analysis was primarily descriptive and correlative. Future studies with larger cohorts should employ multivariable models to control for potential clinical and demographic confounders, thereby strengthening the evidence for independent biomarker-pathogen associations. Sixth, the exclusion of polymicrobial and fungal infections was a methodological choice to create a homogeneous bacterial cohort for initial biomarker analysis, though it limits the generalizability of the microbiological spectrum to all BSI presentations. Lastly, the study population was entirely derived from a cardiac care center, which may not be representative of BSI in general medical inpatients or community-dwelling individuals. CONCLUSION We documented substantial epidemiological insights regarding bacterial BSIs in our cohort of cardiac inpatients. This study confirms the critical importance of understanding local microbiological epidemiology and biomarker patterns in cardiac patients with BSI, providing evidence-based guidance for empirical antimicrobial therapy, infection prevention strategies, and diagnostic algorithm optimization in tertiary cardiac care settings. Future research should incorporate clinical outcome data including ICU length of stay, mechanical ventilation requirements, vasopressor needs, organ dysfunction severity, and mortality to fully characterize the prognostic utility of these biomarkers in cardiac patients. Multicenter studies encompassing diverse cardiac care settings with varying patient populations, antimicrobial stewardship practices, and infection control interventions would enable more robust characterization of BSI epidemiology in cardiac populations globally. Abbreviations BACT/ALERT Automated blood-culture system (brand used in the manuscript) BSI / BSIs Bloodstream infection / Bloodstream infections HAI / HAIs Healthcare-associated infection / Healthcare-associated infections AMR Antimicrobial resistance MDRO / MDROs Multidrug-resistant organism / Multidrug-resistant organisms MRSA Methicillin-resistant Staphylococcus aureus VRE Vancomycin-resistant Enterococcus ESBL Extended-spectrum β-lactamase PCT Procalcitonin CRP C-reactive protein ICU Intensive care unit API Analytical Profile Index APIWEB APIWEB database CLSI Clinical and Laboratory Standards Institute SD Standard deviation IQR Interquartile range ORCID Open Researcher and Contributor Identification Declarations HUMAN ETHICS AND CONSENT TO PARTICIPATE: This study was granted exemption from ethical approval by the Institutional Review Board (IRB) of Tabba Heart Institute. The exemption was provided under the IRB provision that classifies this work as secondary research involving the retrospective use of existing clinical data and specimens, for which patient consent is not required. The study utilized anonymized medical records and stored laboratory samples that were originally collected for non-research purposes, and there was no direct contact between investigators and patients. Accordingly, the requirement for patient consent was waived in line with the IRB categorization stated above. As this retrospective study involved the analysis of existing anonymized data and did not involve prospective interventions, direct patient interaction, or new collection of identifiable human material, it adhered to the ethical principles outlined in the World Medical Association Declaration of Helsinki (as revised in 2024). CLINICAL TRIAL NUMBER: Not applicable AVAILIBLITY OF DATA AND MATERIALS: All data generated during the course of this research study is included in the published article. FUNDING: The authors didn’t receive any funding for this work. CONFLICT OF INTERESTS: The authors declare no conflict of interests. ACKNOWLEDGMENTS: None AUTHOR CONTRIBUTIONS: MAK: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Visualization and Writing – original draft SS: Data curation, Formal analysis, Software, Validation and Writing – review & editing ZA & WAK: Investigation, Validation and Writing – review & editing References Fordyce CB, Katz JN, Alviar CL, Arslanian-Engoren C, Bohula EA, Geller BJ, et al. Prevention of complications in the cardiac intensive care unit: A scientific statement from the American heart association. Circulation. 2020;142(22):e379–406. 10.1161/CIR.0000000000000909 . Ferreira GB, Donadello JCS, Mulinari LA. 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Li M, Qin Y-J, Zhang X-L, Zhang C-H, Ci R-J, Chen W, et al. A biomarker panel of C-reactive protein, procalcitonin and serum amyloid A is a predictor of sepsis in severe trauma patients. Sci Rep. 2024;14(1):628. 10.1038/s41598-024-51414-y . CLSI M100 Performance Standards for Antimicrobial Susceptibility Testing. clsi.org. [cited 2025 Nov 26]. Available from: https://clsi.org/shop/standards/m100/ Özgökçe Özmen B, Türkegün Şengül M, Ozdem S, Aldaş S, Katlan B. Evaluation of Gram-negative hospital-acquired infections and antibiotic resistance in the pediatric intensive care unit. J Infect Dev Ctries. 2025;19(5):747–54. 10.3855/jidc.20437 . Geerlings SE. Clinical presentations and epidemiology of urinary tract infections. Microbiol Spectr. 2016;4(5). 10.1128/microbiolspec.UTI-0002-2012 . Necati Hakyemez I, Kucukbayrak A, Tas T, Burcu Yikilgan A, Akkaya A, Yasayacak A, et al. Nosocomial Acinetobacter baumannii Infections and Changing Antibiotic Resistance. Pak J Med Sci Q. 2013;29(5):1245–8. 10.12669/pjms.295.3885 . Zavascki AP, Chebabo A, Cunha CA, Silva AR, Cuba GT, Santos DWCL, et al. Guideline for antimicrobial treatment of multidrug-resistant Gram-negative infections: practice recommendations of the Brazilian Society of Infectious Diseases. Braz J Infect Dis. 2025;29(6):104589. 10.1016/j.bjid.2025.104589 . Dziarski R, Gupta D. Role of MD-2 in TLR2- and TLR4-mediated recognition of Gram-negative and Gram-positive bacteria and activation of chemokine genes. J Endotoxin Res. 2000;6(5):401–5. 10.1179/096805100101532243 . Leli C, Ferranti M, Moretti A, Al Dhahab ZS, Cenci E, Mencacci A. Procalcitonin levels in gram-positive, gram-negative, and fungal bloodstream infections. Dis Markers. 2015;2015:701480. 10.1155/2015/701480 . Shadan A, Pathak A, Ma Y, Pathania R, Singh RP. Deciphering the virulence factors, regulation, and immune response to Acinetobacter baumannii infection. Front Cell Infect Microbiol. 2023;13:1053968. 10.3389/fcimb.2023.1053968 . Alanazi MM, Zarbh MAJ, Shalwan FSS. The precision of procalcitonin as a biomarker for sepsis in adult emergency department visits: A systematic review. World J Adv Res Rev. 2025;26(2):1923–30. 10.30574/wjarr.2025.26.2.1883 . Zaki HA, Bensliman S, Bashir K, Iftikhar H, Fayed MH, Salem W, et al. Accuracy of procalcitonin for diagnosing sepsis in adult patients admitted to the emergency department: a systematic review and meta-analysis. Syst Rev. 2024;13(1):37. 10.1186/s13643-023-02432-w . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8857783","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612876204,"identity":"e8021c4c-84ad-4196-baad-718d07861fff","order_by":0,"name":"Moiz Ahmed Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFACxgYQKcMGZD1gYDhAvBYeoBZmAyK1QAAPELNJEKVFt725+QNDxR0ePunmY9U8NXfk+BmYHz66gUeL2ZmDDQYMZ57xsMkcS7vNc+yZsWQDm7FxDj4tNxIbEhjbDvOwSeSY3eZhO5y44QAPmzQhLQcgWvK/FfP8I05LYwPUFjZm3jZitJw52MzAcAakJc1Ycm7fYWPJZkJ+Od7+GBhih+XkZyQ//PDm22E5fvbmh4/xaQEB5j9QBhMPmEtAOQpg/EGK6lEwCkbBKBgxAABWrUrRKVeo3wAAAABJRU5ErkJggg==","orcid":"","institution":"Tabba Heart Institute","correspondingAuthor":true,"prefix":"","firstName":"Moiz","middleName":"Ahmed","lastName":"Khan","suffix":""},{"id":612876208,"identity":"68fb1f4b-b1fd-4c0d-b759-12e399dcf3d1","order_by":1,"name":"Shajiya Saeed","email":"","orcid":"","institution":"Tabba Heart Institute","correspondingAuthor":false,"prefix":"","firstName":"Shajiya","middleName":"","lastName":"Saeed","suffix":""},{"id":612876212,"identity":"49afd485-a051-48ac-8c36-953e6b7f20ba","order_by":2,"name":"Ziauddin Afrozuddin","email":"","orcid":"","institution":"Tabba Heart Institute","correspondingAuthor":false,"prefix":"","firstName":"Ziauddin","middleName":"","lastName":"Afrozuddin","suffix":""},{"id":612876213,"identity":"450aa2fa-65a8-4413-b7d3-58b31da82758","order_by":3,"name":"Wajid Ali Khan","email":"","orcid":"","institution":"Tabba Heart Institute","correspondingAuthor":false,"prefix":"","firstName":"Wajid","middleName":"Ali","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2026-02-12 05:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8857783/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8857783/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105574331,"identity":"3d875897-ede1-4832-8f97-afa14d42a78c","added_by":"auto","created_at":"2026-03-27 13:34:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62414,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobiological Spectrum of Bacterial Bloodstream Isolates in Cardiac Inpatients (n=113)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8857783/v1/1c6a6b5a1fe11a4bbc7dbabe.png"},{"id":105574917,"identity":"26fd2559-5906-41dc-954f-64c805ab4948","added_by":"auto","created_at":"2026-03-27 13:36:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89724,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Between Procalcitonin (PCT) and C-Reactive Protein (CRP) Stratified by Gram Classification\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8857783/v1/14da6cd0e0b9e6060d002812.png"},{"id":105575704,"identity":"cbb8accd-6995-4a47-a27c-d44cdd9a7c12","added_by":"auto","created_at":"2026-03-27 13:41:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":712899,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8857783/v1/c8efb8c5-3ad2-40af-b227-62939c509eb3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bacterial Bloodstream Infections in Cardiac Patients: Microbiological Spectrum, Antimicrobial Susceptibility Patterns, and Biomarker Correlation Analysis at a Cardiac Tertiary Care Centre","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBloodstream infections (BSIs) represent one of the most serious complications in hospitalized patients, particularly in cardiac care settings where patients are often elderly with significant comorbidities and prolonged intensive care unit (ICU) stays.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The incidence of healthcare-associated infections (HAIs) in cardiac patients has been documented between 6% and 9%, with BSIs accounting for approximately 20\u0026ndash;25% of all nosocomial infections in this population.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The rising burden of antimicrobial resistance (AMR) compounds this challenge, with multidrug-resistant organisms (MDROs) increasingly reported in both community and hospital settings.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e​\u003c/p\u003e \u003cp\u003eCardiac patients represent a unique and vulnerable population for developing BSI due to multiple factors including prolonged hospitalization, invasive procedures, central venous catheterization, mechanical ventilation, and immunosuppression related to cardiopulmonary bypass and surgical trauma.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e The consequences of BSI in cardiac patients extend beyond the infection itself, as sepsis can precipitate acute cardiac dysfunction, arrhythmias, and hemodynamic instability, further complicating the clinical course and increasing mortality risk.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Studies have demonstrated that sepsis following cardiac surgery is associated with significantly increased ICU length of stay (ranging from 134 to 266 hours) and a mortality risk increased by 3.7 to 6.6-fold compared to non-infected cardiac surgery patients.​\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUnderstanding the microbiological epidemiology of BSI in cardiac populations is essential for guiding empirical antimicrobial therapy, particularly given the high prevalence of resistant organisms and the narrow therapeutic window before irreversible organ dysfunction occurs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Blood culture, the traditional gold standard for BSI diagnosis, requires 48\u0026ndash;72 hours for organism identification and susceptibility testing, creating a critical diagnostic gap during which initial antimicrobial therapy must be empirically determined. This diagnostic delay has driven increased interest in rapid diagnostic biomarkers that can facilitate early sepsis recognition and enable prompt institution of targeted antimicrobial therapy.​\u003c/p\u003e \u003cp\u003eProcalcitonin (PCT) and C-reactive protein (CRP) have emerged as potentially valuable biomarkers for early sepsis diagnosis and pathogen differentiation.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e PCT, the prohormone of calcitonin, is released by extrathyroidal tissues in response to bacterial lipopolysaccharides and pro-inflammatory cytokines, with accumulating evidence suggesting superior specificity and earlier elevation compared to CRP in bacterial infections.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e CRP, an acute phase reactant synthesized by hepatocytes in response to interleukin-6, demonstrates higher sensitivity but lower specificity for infection compared to PCT.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Emerging research has suggested that these biomarkers may exhibit differential expression patterns depending on the causative organism's gram classification, potentially facilitating rapid pathogen differentiation prior to culture results.​\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe combined assessment of multiple biomarkers has been advocated as a strategy to enhance diagnostic accuracy while reducing unnecessary antimicrobial use and mitigating AMR.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In cardiac patients specifically, the complex inflammatory milieu induced by cardiopulmonary bypass and surgical trauma may alter conventional biomarker interpretation, necessitating population-specific investigation of biomarker utility.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Furthermore, the epidemiology of BSI pathogens varies significantly by geographic region, healthcare setting characteristics, and patient population, mandating local surveillance data to inform infection control policies and antimicrobial stewardship programs.​\u003c/p\u003e \u003cp\u003eThis study was therefore undertaken to comprehensively characterize the microbiological spectrum and antimicrobial susceptibility patterns of bacterial BSI in cardiac patients at a tertiary care centre, and to evaluate the correlation of PCT and CRP with specific pathogen types to determine their utility as adjunctive diagnostic biomarkers in this high-risk population.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSTUDY DESIGN AND SETTING\u003c/h2\u003e \u003cp\u003e A retrospective observational study was conducted at Tabba Heart Institute, Karachi, Pakistan, a tertiary cardiac care centre, analyzing patient data over a twelve-month period from 1st November 2024 to 31st October 2025.\u003c/p\u003e \u003cp\u003eThe study included all consecutive inpatients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years admitted to the cardiac wards and ICUs who developed BSIs during their hospital stay, either present on admission or acquired during hospitalization. BSI was defined as the isolation of a bacterial pathogen from at least 2 sets of blood cultures from the same patient in the clinical context of fever, hypothermia, or signs of systemic inflammatory response. Patients with fungal or polymicrobial infections were excluded from the analysis to maintain homogeneity of the study population.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMICROBIOLOGICAL ANALYSIS\u003c/h3\u003e\n\u003cp\u003eTwo sets of blood cultures were collected from each patient via peripheral venipuncture, or one set each from a central venous line and a peripheral line if a central line was in place, using standard aseptic technique. All blood cultures were processed using the BACT/ALERT\u0026reg; automated system (biom\u0026eacute;rieux, France), with incubation for up to five days. Bacterial identification was performed using conventional biochemical methods and confirmed using API\u0026reg; ID strips and APIWEB\u0026trade; database (bioM\u0026eacute;rieux, France). Antimicrobial susceptibility testing was performed via Kirby-Bauer Disk Diffusion method and the clinical breakpoints were interpreted in accordance with the Clinical Laboratory and Standard Institute\u0026rsquo;s (CLSI) M100 guidelines.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMDROs were defined as bacterial strains demonstrating resistance to agents in at least three different antimicrobial classes. Specific notation was made for organisms demonstrating clinically significant resistance phenotypes including methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA), vancomycin-resistant \u003cem\u003eEnterococcus\u003c/em\u003e species (VRE), and carbapenem-resistant or extended-spectrum beta-lactamase producing gram-negative bacilli.\u003c/p\u003e\n\u003ch3\u003eBIOMARKER ANALYSIS\u003c/h3\u003e\n\u003cp\u003eContemporaneous serum samples for PCT and CRP measurement were obtained on the same day of blood culture collection. PCT was measured using chemiluminescence immunoassay with a lower detection limit of 0.02 ng/mL and clinical cutoff values of 0.5 ng/mL for bacterial infection and 2.0 ng/mL for sepsis according to standard laboratory protocols. CRP was measured using high-sensitivity quantitative assay with a detection range of 1-400 mg/L, with values\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L considered elevated and values\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/L indicating significant systemic inflammation. Both biomarkers were measured from sera separated and stored at -80\u0026deg;C until analysis.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e \u003cp\u003eFor each patient, demographic information including age and gender along with microbiological, antimicrobial susceptibility and biomarker profile was collected from the institute\u0026rsquo;s electronic patient care database. Data were analyzed using descriptive statistical methods with results presented as absolute frequencies and percentages for categorical variables, and as means with standard deviations or medians with interquartile ranges (IQRs) for non-normally distributed data, for continuous variables. Pathogen frequencies were expressed as both absolute numbers and percentages of total isolates. Biomarker levels were compared between pathogen groups (gram-positive vs gram-negative, MDR vs non-MDR) using the Mann-Whitney U test for non-parametric comparisons. Correlations between PCT and CRP values were assessed using both Pearson correlation for linear relationships and Spearman correlation for rank-based relationships. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 113 cardiac inpatients with bacterial BSIs were included in the analysis. The cohort consisted of 71 males (62.8%) and 42 females (37.2%), with a mean age of 64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 years (median 66 years, range 29\u0026ndash;95 years) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The microbiological spectrum was diverse, with twenty distinct bacterial species or species groups identified. The most frequently isolated pathogen was \u003cem\u003eEscherichia coli\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;20; 17.7%), followed by \u003cem\u003eAcinetobacter\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;11, 9.7%), \u003cem\u003eEnterobacter\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;10, 8.8%), and VRE (n\u0026thinsp;=\u0026thinsp;10, 8.8%). Additional commonly isolated organisms included \u003cem\u003ePseudomonas\u003c/em\u003e aeruginosa (n\u0026thinsp;=\u0026thinsp;9, 8.0%), non-\u003cem\u003eaeruginosa Pseudomonas\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;9, 8.0%), MRSA (n\u0026thinsp;=\u0026thinsp;9, 8.0%), \u003cem\u003eEnterococcus\u003c/em\u003e species (vancomycin-sensitive; n\u0026thinsp;=\u0026thinsp;7, 6.2%), \u003cem\u003eStreptococcus\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;6, 5.3%), and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;5, 4.4%). The remaining eight species each accounted for \u0026lt;\u0026thinsp;3% of total isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and overall biomarker profile of cardiac inpatients with bacterial bloodstream infection (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHARACTERISTIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVALUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (29\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcalcitonin (PCT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.06\u0026thinsp;\u0026plusmn;\u0026thinsp;25.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range) (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.22 (0.02\u0026ndash;\u0026gt;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u0026thinsp;\u0026ge;\u0026thinsp;0.5 ng/mL n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82/99 (82.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u0026thinsp;\u0026ge;\u0026thinsp;1.0 ng/mL n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72/99 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u0026thinsp;\u0026ge;\u0026thinsp;5.0 ng/mL n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43/99 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-reactive protein (CRP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.89\u0026thinsp;\u0026plusmn;\u0026thinsp;105.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range) (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.38 (1.10\u0026ndash;439.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;50 mg/L n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82/113 (72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/L n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61/113 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/L n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42/113 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Gram classification, gram-negative bacteria predominated (n\u0026thinsp;=\u0026thinsp;72; 63.7%) compared to gram-positive bacteria (n\u0026thinsp;=\u0026thinsp;41; 36.3%). The most common gram-negative organisms included \u003cem\u003eE. coli\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;20), \u003cem\u003eAcinetobacter\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;11), \u003cem\u003eEnterobacter\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;10), and \u003cem\u003ePseudomonas\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;18; \u003cem\u003eaeruginosa\u003c/em\u003e and non-\u003cem\u003eaeruginosa\u003c/em\u003e combined). The most frequently isolated gram-positive organisms were VRE (n\u0026thinsp;=\u0026thinsp;10), MRSA (n\u0026thinsp;=\u0026thinsp;9), \u003cem\u003eEnterococcus\u003c/em\u003e species (vancomycin-sensitive; n\u0026thinsp;=\u0026thinsp;7), and \u003cem\u003eStreptococcus\u003c/em\u003e species (n\u0026thinsp;=\u0026thinsp;11) including \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, \u003cem\u003eStreptococcus viridans\u003c/em\u003e, and \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eMDROs were identified in 19 isolates (16.8%) of which n\u0026thinsp;=\u0026thinsp;10 were VRE and n\u0026thinsp;=\u0026thinsp;9 were MRSA. The remaining gram-negative isolates, while showing variable resistance patterns to individual antimicrobial classes, did not uniformly meet the criteria for MDROs in this cohort. Among gram-negative bacteria, resistance to extended-spectrum cephalosporins and fluoroquinolones was commonly observed, consistent with the prevalence of extended-spectrum beta-lactamase (ESBL) producing strains.\u003c/p\u003e \u003cp\u003eRegarding biomarker analysis, PCT levels were available for 99 of 113 patients (87.6%). The mean PCT level was 15.06\u0026thinsp;\u0026plusmn;\u0026thinsp;25.39 ng/mL (median 3.22 ng/mL, range 0.02\u0026ndash;100 ng/mL). Notably, 82 patients (82.8% of those tested) had PCT levels\u0026thinsp;\u0026ge;\u0026thinsp;0.5 ng/mL, 72 patients (72.7%) had levels\u0026thinsp;\u0026ge;\u0026thinsp;1.0 ng/mL, and 43 patients (43.4%) had levels\u0026thinsp;\u0026ge;\u0026thinsp;5.0 ng/mL. One patient demonstrated a PCT level\u0026thinsp;\u0026gt;\u0026thinsp;100 ng/mL, indicating profound systemic inflammation. CRP levels were available for all 113 patients (100% availability). The mean CRP level was 130.89\u0026thinsp;\u0026plusmn;\u0026thinsp;105.99 mg/L (median 108.38 mg/L, range 1.10-439.20 mg/L). The majority of patients (72.6%, n\u0026thinsp;=\u0026thinsp;82) demonstrated CRP levels\u0026thinsp;\u0026ge;\u0026thinsp;50 mg/L, 54.0% (n\u0026thinsp;=\u0026thinsp;61) had levels\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/L, and 37.2% (n\u0026thinsp;=\u0026thinsp;42) had levels\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/L. Compared to PCT, CRP demonstrated less extreme values, with a maximum recorded level of 439.20 mg/L versus PCT's maximum of \u0026gt;\u0026thinsp;100 ng/mL.\u003c/p\u003e \u003cp\u003eWhen stratifying biomarkers by pathogen type, gram-negative bacteria were associated with numerically higher mean PCT levels (16.60\u0026thinsp;\u0026plusmn;\u0026thinsp;26.42 ng/mL, median 4.33 ng/mL) compared to gram-positive bacteria (12.24\u0026thinsp;\u0026plusmn;\u0026thinsp;23.51 ng/mL, median 1.95 ng/mL), though this difference did not achieve statistical significance (p\u0026thinsp;=\u0026thinsp;0.094). Similarly, gram-negative organisms demonstrated slightly elevated mean CRP levels (138.18\u0026thinsp;\u0026plusmn;\u0026thinsp;104.62 mg/L, median 124.16 mg/L) compared to gram-positive organisms (118.09\u0026thinsp;\u0026plusmn;\u0026thinsp;108.45 mg/L, median 81.90 mg/L), again not reaching statistical significance (p\u0026thinsp;=\u0026thinsp;0.216) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While these differences did not reach statistical significance, potentially due to sample size constraints, the numerical trends are presented for descriptive purposes and hypothesis generation regarding differential inflammatory responses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of procalcitonin (PCT) and C-reactive protein (CRP) levels between gram-negative and gram-positive bacterial bloodstream infections\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGram status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR or range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePCT (ng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGram-negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16.60\u0026thinsp;\u0026plusmn;\u0026thinsp;26.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGram-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.24\u0026thinsp;\u0026plusmn;\u0026thinsp;23.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGram-negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e138.18\u0026thinsp;\u0026plusmn;\u0026thinsp;104.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGram-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e118.09\u0026thinsp;\u0026plusmn;\u0026thinsp;108.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p-values derived using Mann\u0026ndash;Whitney U test for non-parametric comparison of continuous biomarker levels between gram-negative and gram-positive bloodstream infections. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eOrganism-specific biomarker analysis revealed notable variation. \u003cem\u003eE. coli\u003c/em\u003e, the most prevalent pathogen, demonstrated mean PCT of 23.63\u0026thinsp;\u0026plusmn;\u0026thinsp;29.92 ng/mL and mean CRP of 142.66\u0026thinsp;\u0026plusmn;\u0026thinsp;84.73 mg/L. \u003cem\u003eAcinetobacter\u003c/em\u003e species, while associated with the highest mean CRP level (183.12\u0026thinsp;\u0026plusmn;\u0026thinsp;112.71 mg/L), demonstrated relatively low mean PCT (3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88 ng/mL). \u003cem\u003eEnterobacter\u003c/em\u003e species showed intermediate values with mean PCT of 16.08\u0026thinsp;\u0026plusmn;\u0026thinsp;17.47 ng/mL and mean CRP of 186.43\u0026thinsp;\u0026plusmn;\u0026thinsp;160.73 mg/L. \u003cem\u003ePseudomonas\u003c/em\u003e species demonstrated variable biomarker patterns, with relatively lower mean CRP (64.45\u0026thinsp;\u0026plusmn;\u0026thinsp;50.34 mg/L) compared to other gram-negative organisms. MRSA was associated with elevated mean CRP (165.04 mg/L) and variable PCT levels.\u003c/p\u003e \u003cp\u003eCorrelation analysis of both biomarkers revealed that among the 99 patients with both PCT and CRP measurements, the Pearson correlation coefficient was 0.1086 (p\u0026thinsp;=\u0026thinsp;0.2846), indicating weak linear correlation. However, Spearman rank correlation analysis revealed a statistically significant non-linear correlation of ρ\u0026thinsp;=\u0026thinsp;0.4442 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that while the two biomarkers demonstrate different quantitative values, their ordinal ranking in terms of severity shows moderate agreement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratification by pathogen type revealed differential correlations. In gram-positive infections (n\u0026thinsp;=\u0026thinsp;35 with both measurements), Spearman correlation was stronger (ρ\u0026thinsp;=\u0026thinsp;0.5104, p\u0026thinsp;=\u0026thinsp;0.0017), suggesting more concordant elevation of both markers in infections caused by these organisms. In contrast, gram-negative infections (n\u0026thinsp;=\u0026thinsp;64 with both measurements) demonstrated weaker correlation (ρ\u0026thinsp;=\u0026thinsp;0.3723, p\u0026thinsp;=\u0026thinsp;0.0025), indicating more discordant biomarker patterns. This differential correlation pattern suggests that PCT and CRP may respond differently to gram-negative lipopolysaccharides versus gram-positive peptidoglycans and associated pathogen-associated molecular patterns.\u003c/p\u003e \u003cp\u003eRegarding association between biomarkers and AMR, patients with MDROs (n\u0026thinsp;=\u0026thinsp;19) demonstrated mean PCT of 8.36 ng/mL (median 3.00 ng/mL) and mean CRP of 150.82 mg/L (median 161.91 mg/L). In contrast, non-MDROs (n\u0026thinsp;=\u0026thinsp;94) were associated with mean PCT of 16.26 ng/mL (median 3.47 ng/mL) and mean CRP of 126.86 mg/L (median 105.39 mg/L). Interestingly, MDROs were associated with numerically lower PCT levels despite similar or slightly elevated CRP, a pattern that may reflect different immunological responses to resistant versus susceptible pathogens or differences in bacterial virulence factors.\u003c/p\u003e \u003cp\u003eBiomarker analysis by age quartiles revealed relative homogeneity, with mean PCT ranging from 8.04 ng/mL (youngest quartile, 29\u0026ndash;56 years) to 23.48 ng/mL (second quartile, 56\u0026ndash;66 years), and mean CRP ranging from 122.48 to 138.90 mg/L across quartiles. Gender analysis demonstrated that female patients (n\u0026thinsp;=\u0026thinsp;42) had numerically higher mean PCT (19.88 ng/mL) compared to males (n\u0026thinsp;=\u0026thinsp;71, mean 12.31 ng/mL), though sample sizes limit statistical inference. Female patients also showed a higher proportion of MDROs (21.4% vs 14.1% in males), though this did not reach statistical significance.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study documents the microbiological epidemiology and biomarker patterns associated with bacterial BSIs in cardiac inpatients at a tertiary care centre over a twelve-month period. The findings illuminate the distinct microbiological landscape of BSI in this vulnerable population and provide evidence regarding the utility of PCT and CRP as diagnostic adjuncts. While acknowledging the limitations imposed by the single-center design and sample size, which may affect statistical power and generalizability, this study provides critical local surveillance data essential for tailoring empirical therapy and infection prevention strategies within our specific cardiac care context.\u003c/p\u003e \u003cp\u003eThe predominance of gram-negative organisms in this cohort (63.7%) aligns with the increasingly recognized global shift toward gram-negative predominance in HAIs, particularly in cardiac and critical care settings.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eE. coli\u003c/em\u003e was the most frequent pathogen (17.7%), a finding consistent with international surveillance data and reflective of the urinary tract as a common reservoir and source for hematogenous seeding in hospitalized patients with invasive urological procedures.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The second most prevalent gram-negative organisms, \u003cem\u003eAcinetobacter\u003c/em\u003e species (9.7%) and \u003cem\u003eEnterobacter\u003c/em\u003e species (8.8%), are increasingly recognized as nosocomial pathogens with particular propensity for colonizing hospitalized patients, especially those with prolonged ICU stays and exposure to broad-spectrum antimicrobial agents.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFurthermore, the gram-negative organism predominance in BSI, combined with the substantial proportion of organisms demonstrating resistance to extended-spectrum cephalosporins and fluoroquinolones, has important implications for empirical antimicrobial therapy in cardiac patients presenting with sepsis. Current international guidelines increasingly recommend early use of broad-spectrum agents such as carbapenems or fluoroquinolones with gram-negative coverage in critically ill patients with presumed sepsis, a recommendation supported by the epidemiological findings of this study.​\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe significant proportion of MDROs in our cohort of patients (16.8%) represents a clinically important concern and underscores the critical role of antimicrobial stewardship in cardiac care settings. VRE, representing 8.8% of our total isolates, is a recognized cause of nosocomial infection with limited treatment options, often necessitating reliance on newer antimicrobial agents such as daptomycin, linezolid, or tigecycline, each with attendant toxicity concerns and cost implications.​\u003c/p\u003e \u003cp\u003eThe biomarker analysis provided important insights into the comparative performance of PCT and CRP in our study population. The overall moderate Spearman correlation between PCT and CRP (ρ\u0026thinsp;=\u0026thinsp;0.444, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) indicates that these markers, while related, provide somewhat complementary rather than redundant information. The weak linear correlation (Pearson r\u0026thinsp;=\u0026thinsp;0.109) combined with moderate rank correlation suggests that while extreme elevations of both markers tend to occur together, the quantitative elevation of one marker cannot reliably predict the exact elevation of the other.​\u003c/p\u003e \u003cp\u003eThe observation of differential correlation patterns by pathogen type is novel and potentially important for clinical practice. The stronger Spearman correlation in gram-positive infections (ρ\u0026thinsp;=\u0026thinsp;0.510) compared to gram-negative infections (ρ\u0026thinsp;=\u0026thinsp;0.372) may reflect fundamental differences in the inflammatory cascades triggered by these pathogens. Gram-positive bacteria activate the innate immune system primarily through pattern recognition receptors such as toll-like receptor 2, which recognizes peptidoglycans and lipoteichoic acids, whereas gram-negative organisms predominantly signal through toll-like receptor 4, recognizing lipopolysaccharides.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e These different signaling pathways may result in quantitatively and temporally distinct inflammatory cytokine production, with differential effects on PCT and CRP synthesis.​\u003c/p\u003e \u003cp\u003eThe mean PCT level of 15.06 ng/mL in this cohort, while elevated compared to healthy controls (typically\u0026thinsp;\u0026lt;\u0026thinsp;0.1 ng/mL), is lower than values frequently reported in severe sepsis or septic shock, suggesting that this cohort may represent moderate to severe infection rather than the most fulminant presentations. The median PCT value of 3.22 ng/mL approximates the typical cutoff for sepsis diagnosis (2.0 ng/mL), indicating that the majority of patients exceeded recognized diagnostic thresholds. The finding that 72.7% of patients demonstrated PCT levels\u0026thinsp;\u0026ge;\u0026thinsp;1.0 ng/mL supports the utility of this marker in confirming bacterial infection in this population, as baseline PCT in non-infected individuals typically remains\u0026thinsp;\u0026lt;\u0026thinsp;0.5 ng/mL.​\u003c/p\u003e \u003cp\u003eThe mean CRP of 130.89 mg/L in this cohort is substantially elevated compared to normal values (\u0026lt;\u0026thinsp;10 mg/L) and exceeds typical cutoffs for sepsis diagnosis, indicating significant systemic inflammation. The higher median CRP value (108.38 mg/L) compared to median PCT (3.22 ng/mL) may reflect the slower kinetics of CRP production compared to PCT, with CRP remaining elevated for longer periods following infection onset. This temporal difference has implications for longitudinal monitoring of infection response, with PCT being potentially more useful for tracking treatment response while CRP may better reflect ongoing inflammatory burden.​\u003c/p\u003e \u003cp\u003eThe numerical trend toward higher PCT and CRP levels in gram-negative infections compared to gram-positive infections, while not reaching statistical significance, is consistent with prior literature documenting differential inflammatory responses to these pathogen classes.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The gram-negative cell wall lipopolysaccharide is a potent endotoxin triggering robust inflammatory cytokine responses, potentially resulting in more pronounced biomarker elevations. However, the lack of statistical significance in this cohort may reflect the relatively small number of subjects and the wide variability in individual responses to infection.​\u003c/p\u003e \u003cp\u003eThe observation of low mean PCT in \u003cem\u003eAcinetobacter\u003c/em\u003e species (3.37 ng/mL) despite elevated mean CRP (183.12 mg/L) is particularly interesting and may reflect the relatively low virulence of \u003cem\u003eAcinetobacter\u003c/em\u003e compared to other gram-negative organisms, or alternatively, may suggest that \u003cem\u003eAcinetobacter\u003c/em\u003e-associated infections may have slower kinetics of PCT elevation compared to more rapidly progressive infections.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This discordance between PCT and CRP in \u003cem\u003eAcinetobacter\u003c/em\u003e infections suggests that reliance on PCT alone for infection diagnosis might underestimate the significance of \u003cem\u003eAcinetobacter\u003c/em\u003e bacteremia in this population.​\u003c/p\u003e \u003cp\u003eThe finding of lower mean PCT levels in multidrug-resistant organisms compared to non-MDR organisms (8.36 vs 16.26 ng/mL) is unexpected and deserves consideration. This pattern might reflect differences in virulence factors or resistance mechanism-related fitness costs that attenuate inflammatory potential.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e The higher mean CRP in MDROs suggests that despite lower PCT, systemic inflammation remains substantial in these infections, again highlighting the complementary nature of these markers.​\u003c/p\u003e \u003cp\u003eThe combined use of PCT and CRP as part of a comprehensive diagnostic approach incorporating clinical assessment, blood culture results, and antimicrobial susceptibility patterns, appears justified in cardiac patients with suspected BSI. PCT demonstrates superior specificity for bacterial infection and may enable earlier recognition of sepsis, while CRP provides assessment of overall inflammatory burden and may better reflect persistent inflammatory states during recovery phases.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The timing of biomarker measurement relative to infection onset and the trajectory of biomarker change warrant longitudinal investigation in future studies.\u003c/p\u003e \u003cp\u003eThe clinical implications of this study are multifold. First, the predominance of gram-negative organisms supports empirical antimicrobial coverage of enteric bacteria and \u003cem\u003eAcinetobacter\u003c/em\u003e in cardiac patients presenting with BSI, typically achieved with carbapenems or fluoroquinolones with gram-negative activity. Second, the substantial prevalence of MDROs argues for de-escalation strategies only after organism identification and susceptibility results become available, to avoid premature narrowing of coverage that could result in inadequate therapy. Third, the biomarker findings suggest that combined PCT and CRP assessment provides complementary diagnostic information, with the stronger correlation in gram-positive infections potentially aiding in earlier pathogen differentiation.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, the single-center design limits generalizability of findings to other cardiac care settings with potentially different infection control practices or antimicrobial stewardship policies. Second, the relatively small number of MDROs (n\u0026thinsp;=\u0026thinsp;19) limits the statistical power for subgroup analyses involving this important group, and the findings therein should be considered hypothesis-generating. Third, biomarkers were measured at a single time point corresponding to the diagnostic blood culture, which does not account for kinetics, the timing of symptom onset, or specific confounders such as postoperative inflammatory states, potentially influencing absolute levels. Fourth, the lack of clinical outcome data such as mortality and length of stay, prevents assessment of the prognostic utility of the biomarkers and their impact on antimicrobial decision-making, which represents a critical area for future investigation. Fifth, the statistical analysis was primarily descriptive and correlative. Future studies with larger cohorts should employ multivariable models to control for potential clinical and demographic confounders, thereby strengthening the evidence for independent biomarker-pathogen associations. Sixth, the exclusion of polymicrobial and fungal infections was a methodological choice to create a homogeneous bacterial cohort for initial biomarker analysis, though it limits the generalizability of the microbiological spectrum to all BSI presentations. Lastly, the study population was entirely derived from a cardiac care center, which may not be representative of BSI in general medical inpatients or community-dwelling individuals.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe documented substantial epidemiological insights regarding bacterial BSIs in our cohort of cardiac inpatients. This study confirms the critical importance of understanding local microbiological epidemiology and biomarker patterns in cardiac patients with BSI, providing evidence-based guidance for empirical antimicrobial therapy, infection prevention strategies, and diagnostic algorithm optimization in tertiary cardiac care settings.\u003c/p\u003e \u003cp\u003eFuture research should incorporate clinical outcome data including ICU length of stay, mechanical ventilation requirements, vasopressor needs, organ dysfunction severity, and mortality to fully characterize the prognostic utility of these biomarkers in cardiac patients. Multicenter studies encompassing diverse cardiac care settings with varying patient populations, antimicrobial stewardship practices, and infection control interventions would enable more robust characterization of BSI epidemiology in cardiac populations globally.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBACT/ALERT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutomated blood-culture system (brand used in the manuscript)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBSI / BSIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBloodstream infection / Bloodstream infections\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAI / HAIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthcare-associated infection / Healthcare-associated infections\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntimicrobial resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDRO / MDROs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidrug-resistant organism / Multidrug-resistant organisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVancomycin-resistant \u003cem\u003eEnterococcus\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESBL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtended-spectrum β-lactamase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalytical Profile Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPIWEB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAPIWEB database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical and Laboratory Standards Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORCID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOpen Researcher and Contributor Identification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHUMAN ETHICS AND CONSENT TO PARTICIPATE:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was granted exemption from ethical approval by the Institutional Review Board (IRB) of Tabba Heart Institute. The exemption was provided under the IRB provision that classifies this work as secondary research involving the retrospective use of existing clinical data and specimens, for which patient consent is not required. The study utilized anonymized medical records and stored laboratory samples that were originally collected for non-research purposes, and there was no direct contact between investigators and patients. Accordingly, the requirement for patient consent was waived in line with the IRB categorization stated above. As this retrospective study involved the analysis of existing anonymized data and did not involve prospective interventions, direct patient interaction, or new collection of identifiable human material, it adhered to the ethical principles outlined in the World Medical Association Declaration of Helsinki (as revised in 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILIBLITY OF DATA AND MATERIALS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated during the course of this research study is included in the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors didn\u0026rsquo;t receive any funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTERESTS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMAK: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Visualization and Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eSS: Data curation, Formal analysis, Software, Validation and Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eZA \u0026amp; WAK: Investigation, Validation and Writing \u0026ndash; review \u0026amp; editing\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFordyce CB, Katz JN, Alviar CL, Arslanian-Engoren C, Bohula EA, Geller BJ, et al. 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Syst Rev. 2024;13(1):37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13643-023-02432-w\u003c/span\u003e\u003cspan address=\"10.1186/s13643-023-02432-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bloodstream infection, Cardiac disease, Procalcitonin, C-reactive protein, Antimicrobial resistance, Gram-negative bacteria, Biomarkers, Sepsis","lastPublishedDoi":"10.21203/rs.3.rs-8857783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8857783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eINTRODUCTION:\u003c/h2\u003e \u003cp\u003eBloodstream infections (BSIs) in cardiac patients represent a significant clinical challenge with substantial morbidity and mortality. Understanding the microbiological profile and biomarker patterns in this high-risk population is crucial for optimizing treatment strategies and infection prevention measures.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eA retrospective observational study was conducted analyzing patient data from 1st November 2024 to 31st October 2025 at a tertiary cardiac care centre in Karachi, Pakistan. Blood cultures from 113 cardiac inpatients with BSI were analyzed for bacterial identification and susceptibility patterns. Procalcitonin (PCT) and C-reactive protein (CRP) levels were measured simultaneously with microbiological investigation. Correlation analysis between biomarkers and pathogen types was performed using both Pearson and Spearman correlation coefficients.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eOne hundred and thirteen bacterial bloodstream isolates were identified with gram-negative bacteria predominating (63.7%, n\u0026thinsp;=\u0026thinsp;72) over gram-positive organisms (36.3%, n\u0026thinsp;=\u0026thinsp;41). \u003cem\u003eEscherichia coli\u003c/em\u003e was the most prevalent pathogen (17.7%, n\u0026thinsp;=\u0026thinsp;20), followed by \u003cem\u003eAcinetobacter\u003c/em\u003e species (9.7%, n\u0026thinsp;=\u0026thinsp;11) and \u003cem\u003eEnterobacter\u003c/em\u003e species (8.8%, n\u0026thinsp;=\u0026thinsp;10). Multidrug-resistant organisms accounted for 16.8% (n\u0026thinsp;=\u0026thinsp;19) of isolates, with Vancomycin-Resistant \u003cem\u003eEnterococcus\u003c/em\u003e (8.8%) and Methicillin-Resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (8.0%) being the most common. Gram-negative bacteria demonstrated higher mean PCT levels (16.60 ng/mL) compared to gram-positive bacteria (12.24 ng/mL), though not statistically significant (p\u0026thinsp;=\u0026thinsp;0.094). CRP levels were similarly elevated in both groups (gram-negative: 138.18 mg/L vs gram-positive: 118.09 mg/L, p\u0026thinsp;=\u0026thinsp;0.216). Spearman correlation analysis revealed a statistically significant correlation between PCT and CRP (ρ\u0026thinsp;=\u0026thinsp;0.444, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) overall, with stronger correlation in gram-positive infections (ρ\u0026thinsp;=\u0026thinsp;0.510) compared to gram-negative infections (ρ\u0026thinsp;=\u0026thinsp;0.372).\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003e This study confirms the critical importance of understanding local microbiological epidemiology and biomarker patterns in cardiac patients with BSI, providing evidence-based guidance for empirical antimicrobial therapy, infection prevention strategies, and diagnostic algorithm optimization in tertiary cardiac care settings. PCT and CRP both serve as valuable biomarkers in this population, with their combined use potentially enhancing diagnostic accuracy, supporting their complementary role in sepsis management and antimicrobial stewardship in cardiac patients.\u003c/p\u003e","manuscriptTitle":"Bacterial Bloodstream Infections in Cardiac Patients: Microbiological Spectrum, Antimicrobial Susceptibility Patterns, and Biomarker Correlation Analysis at a Cardiac Tertiary Care Centre","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 13:11:08","doi":"10.21203/rs.3.rs-8857783/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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