Prevalence and Outcomes of Postoperative Infection Among Pediatric Congenital Heart Disease Patients in Intensive Care Units: A Single Center Retrospective Study

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This study aims to investigate the prevalence, clinical outcomes, and associated risk factors for postoperative infections in pediatric CHD patients. Methods This retrospective study included 1,131 pediatric CHD patients who underwent surgery at Xinhua Hospital Affiliated to Shanghai Jiao Tong University between October 2020 and July 2024. Patients were classified into infected (n = 131) and non-infected (n = 1000) groups. Clinical characteristics, infection status, and outcomes were analyzed. Statistical analyses were performed using Mann-Whitney U tests and logistic regression models to assess the impact of infections on hospitalization duration, ICU stay, and mechanical ventilation time. Results Infections occurred in 11.6% of patients and were independently associated with prolonged hospitalization (adjusted OR = 3.10, 95% CI: 1.84–5.20), ICU stay (aOR = 3.51, 95% CI: 2.01–6.12), and mechanical ventilation (aOR = 1.85, 95% CI: 1.09–3.11). Other independent predictors of prolonged stay included cyanotic lesions, pulmonary hypertension, and high Society of Thoracic Surgeons-European Association for Cardiothoracic Surgery (STAT) Mortality Category. Among infected patients, multidrug-resistant organism infection (+ 11.54 days), delayed pathogen sampling (≥ postoperative day 5; +14.92 days), prematurity (+ 29.00 days), and extracardiac malformations were significantly associated with extended hospitalization. Conclusions Postoperative infections are significantly associated with prolonged hospitalization and delayed recovery in pediatric CHD patients, particularly among those with high STAT category, cyanotic heart disease, and pulmonary hypertension. Early detection and targeted infection control measures may help improve outcomes in this high-risk population. Future research should focus on multi-center prospective studies and advanced diagnostic tools to enable timely and effective infection management. Pediatric congenital heart disease postoperative infection ICU stay mechanical ventilation hospital stay multidrug-resistant pathogens Figures Figure 1 Figure 2 1. Introduction Congenital heart disease (CHD) is one of the most common birth defects, affecting approximately 1% of live births worldwide[ 1 ]. Advances in surgical techniques and intensive care have significantly improved the survival rates of children with CHD, yet the postoperative period remains critical due to complications, particularly infections[ 2 ]. Postoperative infections in pediatric congenital heart disease patients are a major cause of morbidity and prolonged hospitalization, often leading to adverse outcomes such as increased intensive care unit (ICU) stay and mechanical ventilation time[ 3 ]. These infections can complicate the recovery process, further stressing already compromised cardiovascular and immune systems, thus making prevention and timely treatment essential. The prevalence of postoperative infections in pediatric CHD patients varies across different populations and hospital settings[ 4 ]. Factors such as the complexity of the congenital defect, the type of surgery performed[ 5 ], and the duration of cardiopulmonary bypass (CPB) time[ 6 ], mechanical ventilation[ 7 ] all influence infection rates[ 8 ]. Infections in this patient population often involve respiratory[ 9 ], urinary, bloodstream infections[ 10 ], or surgical site infection[ 11 ] with causative agents frequently being multidrug-resistant pathogens. This highlights the importance of comprehensive infection surveillance and management strategies in pediatric ICUs, particularly for children with CHD. Understanding the relationship between infections and clinical outcomes in pediatric CHD patients is crucial for improving healthcare protocols. Retrospective studies, such as those by the EPIC III investigators have shed light on the critical role of infections in ICU patients, demonstrating that infection is associated with higher in-hospital mortality and prolonged ICU stays[ 12 ]. However, there remains a need for more specific studies focusing on pediatric populations with congenital heart disease. This retrospective analysis aims to investigate the prevalence and clinical outcomes of postoperative infections in pediatric CHD patients in a single-center ICU, contributing valuable insights into infection control and patient management strategies. 2. Methods 2.1 Patients This retrospective study included pediatric patients diagnosed with CHD who underwent surgical intervention at Xinhua Hospital Affiliated to Shanghai Jiao Tong University from October 2020 to July 2024. A total of 1,131 patients were reviewed, with 131 developing postoperative infections, while 1,000 patients remained non-infected during their ICU stay as shows Fig. 1 . The inclusion criteria comprised patients aged 0–18 years who underwent corrective or palliative surgery for CHD. Patients with pre-existing infections or immune deficiencies, antibiotic use in the week prior to surgery, and unplanned secondary surgery during the same hospital stay were excluded from the analysis. According to previous studies[ 13 , 14 ], complex congenital heart disease (CCHD) is defined as: Univentricular Hearts, Truncus Arteriosus Communis, Interrupted or Hypoplastic Aortic Arch, Transposition of the Great Arterie, Atrioventricular Septal Defect, Totally Anomalous Pulmonary Venous Drainage, Pulmonary Atresia and Tetralogy of Fallot. Additionally, according to previous studies[ 15 , 16 ], the definition of cyanotic versus acyanotic heart defects, was also noted. 2.2 Ethical Statement The study was conducted following the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Xinhua Hospital Affiliated to Shanghai Jiao Tong University(Number: XHEC-WJW-2020-014). Written informed consent was waived by the IRB of Xinhua Hospital Affiliated to Shanghai Jiao Tong University due to the retrospective nature of the study, and all patient data were anonymized before analysis. 2.3 Surgical Procedure Surgical procedures were categorized based on the complexity of the cardiac anomaly and included both open-heart surgeries with CPB and less invasive surgeries without CPB. The majority of patients (84.1%) required CPB during surgery. Standard perioperative prophylactic antibiotics were administered to all patients, and adjustments were made based on infection risk or culture results as per the hospital’s infection control guidelines. In our center, the standard antibiotic prophylaxis protocol consists of a single dose of cefuroxime (50mg/kg) administered intravenously within 60 minutes prior to surgery and every 3 hours in operating room. The total duration of antibiotic therapy does not exceed 24 hours[17, 18].For patients requiring delayed sternal closure (DSC), prophylactic antibiotic coverage is extended to 48 hours following chest closure, consistent with published infection prevention bundles[19]. 2.4 Data Collection Data on patient demographics, clinical characteristics, perioperative variables, and postoperative outcomes were collected from electronic medical records. Variables of interest included age, weight, type of congenital heart defect, presence of cyanotic heart disease, preoperative pulmonary hypertension, STAT, length of ICU stay, hospital stay, and ventilation duration. The classification of infection categories in our study adheres to the definitions established by the International Sepsis Forum[20]. Patients were categorized into non-infected, possible, probable, and confirmed infection groups based on clinical and microbiological criteria[12, 20]. Confirmed infection was defined as the presence of clinical signs consistent with infection in combination with microbiological evidence.Probable infections were defined as cases with strong clinical evidence of infection (e.g., fever, elevated inflammatory markers, imaging abnormalities) along with incomplete microbiological confirmation, such as a positive culture below diagnostic thresholds or a positive Gram stain without definitive identification. Possible infections referred to cases with clinical signs suggestive of infection but lacking microbiological or radiological confirmation, often due to prior antibiotic exposure or limited sampling opportunities. Clinical indicators included new-onset fever (>38°C), elevated inflammatory markers such as C-reactive protein (CRP > 8 mg/L), white blood cell count (WBC > 15×10⁹/L), and procalcitonin (PCT > 0.5 ng/mL). 2.5 Follow-up Patients were followed from the date of admission to their discharge or death. Postoperative infections were defined as those occurring within 30 days post-surgery, and infection sites included bloodstream, respiratory, urinary, and surgical site infections. The follow-up also included monitoring for infection-related complications, specifically prolonged mechanical ventilation, extended ICU stays, or lengthier hospitalizations. Durations of hospitalization, ICU stay, and mechanical ventilation were classified as prolonged if they surpassed the 75th percentile for each metric. The 75th percentile values were derived from our own study population rather than from an international database or local standards. This approach was chosen to ensure that our analysis accurately reflects the characteristics of the specific group under investigation, as different populations may have distinct distributions of variables. 2.6 Statistical Analysis Data analysis was performed using R software (R Core Team, version 4.4.1, Vienna, Austria). Continuous variables were expressed as medians with interquartile ranges (IQR) and were compared using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages and compared using the chi-square test. To evaluate the impact of suspected or confirmed infections on hospitalization duration and ICU length of stay, univariable and multivariable logistic regression analyses were performed. Results of the regression analyses are reported as adjusted odds ratios (aOR) with 95% confidence intervals (CI). For patients with suspected or confirmed infections, multivariable linear regression was used to examine the association between the timing of pathogen detection and hospital stay duration, adjusting for relevant clinical factors. Sampling time was modeled as a continuous variable using restricted cubic splines to explore nonlinear relationships. Inflection points in the spline curves were used to determine thresholds indicating significant changes in hospitalization risk. In infections without pathogen confirmation, infection onset timing was defined as the first postoperative day with clinical signs prompting empirical antibiotic use. This proxy was applied as the pathogen detection time in the spline model. A two-tailed p -value of <0.05 was considered statistically significant. 3. Results The baseline demographic and clinical characteristics of the study cohort, which comprised 1,131 pediatric CHD patients, are presented in Table 1 . The median age of the entire cohort was 541 days (IQR: 153–1813 days), with a statistically significant difference between infected and non-infected groups (248 vs. 577 days, p = 0.002). Male patients predominated (52.8%), and infection prevalence was higher in males (62.6% infected vs. 51.5% non-infected, p = 0.017). Infected patients had significantly lower median weight (7.30 kg [IQR: 4.67–14.00] vs. 10.50 kg [IQR: 6.50–18.00], p < 0.001). Additionally, cyanotic CHD(diagnosed at enrollment based on clinical evaluation and standardized criteria) was significantly more common in the infected group, with 28.2% of infected patients being cyanotic, compared to 11.8% in the non-infected group (p < 0.001). Preoperative pulmonary hypertension (PH) was also more prevalent in infected patients (32.1% vs. 23.2%, p = 0.026). Combined extracardiac malformation and CCHD were strongly associated with infection (Combined extracardiac malformation: 19.1% vs. 7.0%; CCHD: 59.5% vs. 37.0%; both p < 0.001). Although cardiopulmonary bypass (CPB) use and delayed sternal closure showed no intergroup differences, infected patients required longer mechanical ventilation (median 24.0 hours [IQR: 8.5–91.0] vs. 7.5 hours [IQR: 4.0–22.0], p < 0.001) and prolonged ICU stays (median 6.00 days [IQR: 3.00–10.00] vs. 3.00 days [IQR: 2.00–4.00], p < 0.001). To further investigate potential perioperative risk factors associated with infection, we performed a multivariable logistic regression analysis incorporating relevant clinical variables (Supplementary Table 1). The results demonstrated that the presence of combined extracardiac malformations was independently associated with an increased risk of infection (OR 2.388, 95% CI 1.363–4.184, p = 0.002). Additionally, higher STAT categories (STAT 3–5) were significantly predictive of infection, with STAT 4–5 showing a particularly strong association (OR 6.719, 95% CI 2.969–15.209, p < 0.001), highlighting the impact of surgical complexity on postoperative infectious outcomes. Table 1. Baseline Characteristics. Characteristic All (n=1131) Infection (n=131) Non infection (n=1000) P Gender, male, n(%) 597 (52.8) 82 ( 62.6) 515 ( 51.5) 0.017 Age(day),median[IQR] 541[153,1813] 248[106,1129] 577 [162, 1862] 0.002 Weight(kg),median[IQR] 10.00 [6.20, 17.50] 7.30 [4.67, 14.00] 10.50 [6.50, 18.00] <0.001 Cyanotic heart disease, n(%) 155 (13.7) 37 ( 28.2) 118 ( 11.8) <0.001 Preoperative PH, n(%) 274 (24.2) 42 ( 32.1) 232 ( 23.2) 0.026 Combined Extracardiac malformation, n(%) 95 ( 8.4) 25 ( 19.1) 70 ( 7.0) <0.001 CCHD, n(%) 448 (39.6) 78 ( 59.5) 370 ( 37.0) <0.001 Preterm, n(%) 14 ( 1.2) 3 ( 2.3) 11 ( 1.1) 0.215 CPB, n(%) 951 (84.1) 111 ( 84.7) 840 ( 84.0) 0.829 Second-stage surgery, n(%) 113 (10.0) 19 ( 14.5) 94 ( 9.4) 0.067 Delayed sternal closure, n(%) 15 ( 1.3) 4 ( 3.1) 11 (1.1) 0.085 STAT <0.001 STAT=1 192 16 176 STAT=2 491 37 454 STAT=3 295 30 265 STAT =4 and 5 153 48 105 Ventilator length (hours),median[IQR] 8.0 [4.0, 23.0] 24.0 [8.5, 91.0] 7.5 [4.0, 22.0] <0.001 ICU(days),median[IQR] 3.0 [2.0, 5.0] 6.0 [3.0, 10.0] 3.0 [2.0, 4.0] <0.001 Hospital stay (days),median[IQR] 12.0 [9.0, 17.0] 18.0 [13.0, 27.5] 12.0 [9.0, 16.0] <0.001 PCT , Mean± SD 0.33 ± 0.10 0.35± 0.15 0.32± 0.07 0.055 CRP(mg/L), Mean± SD 2.43 ± 1.04 2.54± 0.64 2.41± 1.16 0.209 WBC(10^9/L), Mean± SD 9.63± 4.91 9.60± 5.03 9.65± 4.86 0.912 GRAM(%), Mean± SD 35.23± 17.81 35.92± 17.79 34.75± 17.63 0.476 LYM(%), Mean± SD 54.11± 17.60 53.08± 18.16 54.71± 17.15 0.479 PLT(10^9/L), Mean± SD 317.59± 113.72 314.30± 126.18 319.81± 107.23 0.569 Abbreviations: PH, pulmonary hypertensive；CCHD, Complex congenital heart disease; CPB, cardiopulmonary bypass; ICU, intensive care unit; IQR, interquartile range; PCT, Procalcitonin; CRP, C reactive protein; WBC, white blood count; GRAM, neutrophile granulocyte ; LYM, Lymphocytes; PLT platelets,STAT, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Mortality Categories .Due to the small number of cases in STAT categories 4 and 5, the two categories were combined for statistical analysis Although baseline preoperative levels of PCT, CRP, and WBC counts showed no significant differences between groups, the median hospital stay for patients with infections was significantly longer, at 18.0 days (IQR: 13.0–27.5) compared to 12.0 days (IQR: 9.0–16.0) in non-infected patients (p < 0.001), further emphasizing the adverse impact of infections on recovery times. The impact of infection on postoperative outcomes was evaluated through multivariable logistic regression analyses, adjusting for confounding variables including gender, age, weight, cyanotic heart disease, pulmonary hypertension (PH), extracardiac malformations, CPB, delayed sternal closure, and surgical complexity (STAT category). Infection was identified as a significant independent risk factor for prolonged hospitalization (adjusted OR: 3.10, 95% CI: 1.84–5.20, p < 0.001), ICU stay (adjusted OR: 3.51, 95% CI: 2.01–6.12, p < 0.001), and mechanical ventilation duration (adjusted OR: 1.85, 95% CI: 1.09–3.11, p = 0.021) (see Supplementary Figure 1-3 ). These findings suggest that infection independently contributes to increased postoperative resource utilization and delayed recovery. Additionally, when stratifying patients by infection status (confirmed, probable, and possible) the impact of infection status on hospitalization duration, ICU stay, and mechanical ventilation time is summarized in Table 2 . Patients with confirmed infections experienced significantly longer hospital stays, ICU stays, and mechanical ventilation times compared to non-infected patients. The prevalence of prolonged hospitalization (defined as >17 days) was 76.1% in the confirmed infection group, compared to only 21.3% in non-infected patients (adjusted OR: 7.193, 95% CI: 3.024-17.762, p 5 days) were notably more common in the confirmed infection group, with 78.3% of these patients experiencing extended ICU stays compared to 21.4% in non-infected patients (adjusted OR: 6.499, 95% CI: 2.665-16.779, p 23 hours) was observed in 84.8% of patients with confirmed infections, compared to 21.6% in non-infected patients (adjusted OR: 6.557, 95% CI: 2.581-19.216, p < 0.001). Table 2. Impact of Infection Status on Hospitalization Duration, ICU Stay, and Mechanical Ventilation Time Hospital stay Crude analysis Adjusted analysis (P75= 17days) Total number Prolonged hospital stay, n (%) cOR 95% CI P value aOR 95% CI P value Non-infection 1000 213(21.30) ref ref ref ref Confirmed 46 35 (76.09) 14.981 7.463-30.075 <0.001 7.193 3.024-17.762 <0.001 Probable 40 25 (62.50) 7.847 4.054-15.192 <0.001 3.110 1.286-7.532 0.011 Possible 45 10 (22.22) 1.345 0.654-2.768 0.420 1.406 0.552-3.306 0.453 ICU stay Total number Prolonged ICU stay， n (%) Crude analysis Adjusted analysis (P 75= 5 days) cOR 95% CI P value aOR 95% CI P value Non-infection 1000 214 (21.40) ref ref ref ref Confirmed 46 36 (78.26) 20.400 9.912-41.986 <0.001 6.499 2.665-16.779 <0.001 Probable 40 23 (57.50) 7.667 4.001-14.692 <0.001 3.333 1.310-8.438 <0.010 Possible 45 10 (22.22) 1.619 0.785 -3.339 0.190 1.882 0.684- 4.780 0.199 MV time Total number Prolonged MV time, n (%) Crude analysis Adjusted analysis (P75= 23 hr) cOR 95% CI P value aOR 95% CI P value Non -infection 1000 216(21.60) ref ref ref ref Confirmed 46 39 (84.78) 20.709 9.133-46.959 <0.001 6.557 2.581-19.216 <0.001 Probable 40 20 (50.00) 3.717 1.964-7.036 0.001 3.625 1.254-13.558 0.031 Possible 45 8 (17.78) 0.804 0.369-1.752 0.582 0.613 0.212-1.594 0.337 Adjusting factors included: gender, age, weight, cyanotic, pulmonary hypertension (PH), Extracardiac malformation, CPB time, secondary surgery, Delayed sternal closure, and STAT. Abbreviations: cOR:crude odd ratio; aOR: adjusted odd ratio; STAT, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Mortality Categories . Postoperative infection characteristics are detailed in Figure 2 . Analysis of infection sites (Figure 2A) revealed respiratory infections as the predominant type (69.75%, 113/162), followed by bloodstream infections (16.05%, 26/162) and catheter-related infections (8.64%, 14/162). Pathogen resistance profiling (Figure 2B) demonstrated non-resistant organisms in 72.84% (118/162) of cases, with multidrug-resistant organisms (MDROs) comprising 27.16% (44/162). Comparative analysis of hospitalization duration (Figure 2C) showed significantly prolonged stays for MDRO-related infections versus non-MDRO cases, particularly in bloodstream and respiratory infections. A total of 162 pathogens were identified. Gram-negative bacteria were the most common (n=93), followed by Gram-positive bacteria (n=50), fungi (n=5), viruses (n=5), and other atypical organisms (n=9). The detailed pathogen distribution is provided in Supplementary Table 2, and their corresponding antibiotic susceptibility profiles, is provided in the Supplementary Table3. To compare the differences in hospital length of stay between infections caused by multidrug-resistant (MDR) and non-MDR strains, we conducted a multiple linear regression analysis on 131 infection-defined cases. The results identified multidrug-resistant strains, pathogen sampling time, high STAT, and prematurity as significant predictors of prolonged hospitalization. In our study, “pathogen sampling time” was defined as the duration from ICU admission to the time of microbiological sample collection for pathogen identification. To better capture the nonlinear relationship between sampling time and hospital stay, we performed cubic spline analysis, which revealed inflection points at approximately 2 and 4 days. Accordingly, we categorized pathogen sampling time into three clinically meaningful groups: early sampling (≤2 days postoperatively), intermediate sampling (2–4 days), and delayed sampling (≥4 days). This stratification was applied in regression analysis to improve interpretability. In contrast, gender, age, weight, cyanotic heart disease, preoperative pulmonary hypertension (PH), secondary surgery, and STAT category were not statistically significant. Patients with infections caused by multidrug-resistant strains had a notably longer hospital stay, with an increase of 11.54 days (95% CI: 2.57-20.52, p = 0.012). Additionally, the timing of pathogen sampling was significantly associated with extended hospital stays, showing an increase of 6.71 days (95% CI: 1.41-12.01, p = 0.013) per delayed sampling. Other factors, such as concomitant malformations and prematurity, also contributed to longer hospital stays. Specifically, patients with higher STAT had an increase of 4.05 days (95% CI: 1.64-6.48, p = 0.001), while premature infants experienced the most significant impact, with an increase of 29.00 days (95% CI: 7.90-50.10, p = 0.007). The detailed results are summarized in Table 3. Table 3. Factors Associated with the Length of Hospital Stay in Infected Children Variables B (Estimate） SE P-value 95% CI (Lower - Upper) Multidrug-resistant strains 10.11 5.07 0.048 0.067-20.15 Pathogen sampling time ≤ 2 days postoperatively reference - - - 2-4 days postoperatively 0.16 4.65 0.895 -8.58-9.83 ≥ 4 days postoperatively 14.92 6.10 0.016 2.84-27.01 STAT 4.05 1.22 0.001 1.64-6.48 Prematurity 29.00 10.66 0.007 7.90-50.10 R² = 0.37, Adjusted R² = 0.31 Abbreviations: STAT, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Mortality Categories . 4. Discussion This retrospective study underscores the significant clinical impact of postoperative infections on pediatric CHD patients in the ICU. Among the 1,131 patients reviewed, 131 (11.6%) developed infections after surgery. Compared with non-infected patients, those with infections experienced markedly prolonged ICU stays, extended durations of mechanical ventilation, and increased overall hospitalization time, highlighting the adverse influence of infection on postoperative recovery. The findings of our study are consistent with previous research that underscores the overall infections burden in pediatric CHD patients, which is a leading cause of morbidity and prolonged ICU stays[21, 22]. Similar to the results reported by Ding et al.[23], we observed that the duration of mechanical ventilation and ICU stays were significantly longer in patients with confirmed infections, further emphasizing the burden of infections on recovery outcomes. However, the incidence of postoperative infections in children with CHD varies substantially across institutions. In high-income countries, reported rates range from 1.2% to 48%, depending on the case mix and monitoring methods[2]. Data from the National Center for Cardiovascular Diseases in China indicate that the postoperative ICU infection rate after CHD surgery ranges between 1.3% and 15%[4]. Our center has established a comprehensive infection prevention and control (IPC) program for many years, incorporating standard bundle measures such as hand hygiene, chlorhexidine oral care, early extubation, head-of-bed elevation, glycemic control, gastric decompression, and venous thromboembolism prophylaxis. Despite these efforts, our study still observed an overall infection rate of 11.6%, which may be related to the underlying disease complexity and patient characteristics. In particular, the elevated infection rates observed in patients with high STAT categories, cyanotic congenital heart disease, and preoperative PH underscore the presence of high-risk features. These findings suggest that such vulnerable subgroups may require more tailored and intensified infection control strategies to mitigate postoperative infectious complications. Among infected patients, we further identified several key factors associated with prolonged hospital stays, including MDROs, delayed pathogen identification (≥ postoperative day 4), prematurity, and higher STAT categories. Patients who underwent delayed microbiological sampling had significantly longer hospitalizations compared with those sampled within the first two postoperative days. This delay may limit the timely initiation of targeted antimicrobial therapy, thereby prolonging empirical broad-spectrum antibiotic use and increasing selective pressure for MDRO development. Our results align with those of Wang et al[3]., who reported that MDRO infections were strongly associated with longer ICU and hospital stays in pediatric cardiac surgical patients. These findings reinforce the negative impact of antimicrobial resistance on recovery and support the importance of early and accurate pathogen detection. Compared with the global ICU data from the EPIC III study[12], our study focused exclusively on pediatric populations, demonstrating that infection rates and outcomes may differ significantly between adults and children, particularly in those undergoing high-risk cardiac surgeries. The higher incidence of multidrug-resistant pathogens in our study also suggests an evolving challenge in infection control that has been similarly noted in other reports[24],necessitating targeted antimicrobial strategies in pediatric ICUs[25, 26]. A notable strength of strengths of this study is the comprehensive approach used in classifying postoperative infections, distinguishing between possible, probable, and confirmed cases based on both clinical and microbiological criteria[20]. This classification system, allowed us to evaluate the differential impacts of infection severity on outcomes such as ICU stay, mechanical ventilation time, and overall hospitalization[25]. This methodology aligns with prior studies, including the EPIC III investigation, which utilized detailed infection definitions and multivariate analyses to assess clinical outcomes [12]. Notably, our inclusion of probable and possible infections reflects real-world pediatric ICU practice, where empirical treatment is often initiated before pathogen confirmation due to the time-sensitive nature of critical care. This comprehensive approach enables a more accurate assessment of the infection burden across different levels of diagnostic certainty and better mirrors clinical decision-making processes. Moreover, our study uniquely focuses on pediatric CHD patients, allowing for a more targeted understanding of infection risks and outcomes in this vulnerable population. Stratification by infection status, along with adjustments for variables such as pulmonary hypertension and secondary surgeries, yielded more precise insights into the elevated risks associated with confirmed infections—an aspect that is often generalized in broader ICU studies [27]. These findings underscore the need for early recognition and timely intervention in high-risk pediatric patients to prevent severe postoperative complications. This study has several limitations inherent to its retrospective, single-center design, which may introduce selection bias and restrict the generalizability of findings to populations with differing surgical practices or socioeconomic contexts. Although we adjusted for key variables such as age and disease complexity, residual confounding from unmeasured factors—including preoperative nutritional status, socioeconomic disparities, and single ventricular physiological —could influence outcomes[28]. Furthermore, the absence of advanced diagnostic tools like Presepsin may limit precision in differentiating sepsis from non-infectious inflammation. Future research should incorporate novel diagnostic biomarkers such as Presepsin to improve early infection identification and diagnostic accuracy in critically ill pediatric patients. Additionally, due to the retrospective nature of this study, we were unable to systematically capture post-discharge follow-up data, including readmissions or late infectious complications in patients discharged with suspected but unconfirmed infections. To address this limitation, a prospective follow-up initiative is planned to systematically collect standardized data on post-discharge outcomes, including recurrent infections, rehospitalizations, and long-term sequelae. Such a program will be instrumental in capturing late complications and refining our understanding of the long-term impact of postoperative infections in this population. Due to sample size limitations, we were unable to match a sufficient number of cases to perform a robust propensity score matching analysis, thereby precluding the ability to strengthen causal inference. Future multicenter prospective studies integrating standardized perioperative protocols, novel biomarkers and personalized risk stratification models (e.g., genetic/immunological profiling) are needed to validate these findings and optimize infection prevention strategies in high-risk pediatric CHD cohorts[12]. Longitudinal studies examining the long-term outcomes of postoperative infections and their potential effects on cardiac function and overall quality of life in CHD patients would also be valuable. By addressing these areas, future research can help improve the quality of care and outcomes for pediatric congenital heart disease patients. Conclusion This study highlights the clinical relevance of postoperative infections in pediatric patients with congenital heart disease, showing that such infections are significantly associated with prolonged ICU stays, increased mechanical ventilation duration, and extended hospitalization. These findings underscore the importance of strengthening infection prevention protocols and optimizing postoperative care strategies in pediatric intensive care settings. A more comprehensive approach to early infection detection and targeted management may help reduce the burden of complications associated with postoperative infections. Abbreviations CHD – Congenital Heart Disease；ICU – Intensive Care Unit；CPB – Cardiopulmonary Bypass；CCHD – Complex Congenital Heart Disease；PH – Pulmonary Hypertension；OR – Odds Ratio；CI – Confidence Interval；IQR – Interquartile Range；EPIC – Extended Prevalence of Infection in Intensive Care Declarations Clinical trial number Not applicable. Ethical Approval The study was conducted following the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Xinhua Hospital Affiliated to Shanghai Jiao Tong University（Number: XHEC-WJW-2020-014）. Written informed consent was waived by the IRB of Xinhua Hospital Affiliated to Shanghai Jiao Tong University due to the retrospective nature of the study, and all patient data were anonymized before analysis. Patient consent for publication Consent obtained from parent(s)/guardian(s). Data Availability Statement The datasets generated and analyzed during the current study are not publicly available due to patient privacy and institutional data-sharing policies but are available from the corresponding author upon reasonable request and with permission from Xinhua Hospital Affiliated to Shanghai Jiao Tong University. This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University. Due to the retrospective nature of the study, the requirement for written informed consent was waived. All patient data were anonymized prior to analysis to ensure privacy and confidentiality. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by grants from the Natural Science Foundation of China (82273647), Medical Engineering Cross Fund of Shanghai Jiao Tong University (YG2025ZD24) Contributions XJ and JG contributed equally to the conception and design of the study. WS was responsible for data collection and management. TZ and SC performed the data analysis and interpretation. JH and HW drafted the manuscript, while HW and JH critically revised it for important intellectual content. All authors reviewed and approved the final version of the manuscript. JH and HW were the corresponding authors and supervised the overall conduct of the study. Acknowledgements Not applicable. References Hoffman, J.I. and S. Kaplan, The incidence of congenital heart disease. J Am Coll Cardiol, 2002. 39 (12): p. 1890-900. Murni, I.K., et al., Perioperative infections in congenital heart disease. Cardiol Young, 2017. 27 (S6): p. S14-S21. Wang, X., et al., Nosocomial Infections After Pediatric Congenital Heart Disease Surgery: Data from National Center for Cardiovascular Diseases in China. Infect Drug Resist, 2024. 17 : p. 1615-1623. Wang, X., et al., Healthcare-associated infection management in 62 ICUs for patients with congenital heart disease in China: a survey study. Int J Surg, 2024. 110 (4): p. 2025-2033. de Araujo Motta, F., et al., Risk Adjustment for Congenital Heart Surgery Score as a Risk Factor for Candidemia in Children Undergoing Congenital Heart Defect Surgery. Pediatr Infect Dis J, 2016. 35 (11): p. 1194-1198. Costello, J.M., et al., Risk factors for surgical site infection after cardiac surgery in children. Ann Thorac Surg, 2010. 89 (6): p. 1833-41; discussion 1841-2. Mahle, W.T., et al., Early Extubation After Repair of Tetralogy of Fallot and the Fontan Procedure: An Analysis of The Society of Thoracic Surgeons Congenital Heart Surgery Database. Ann Thorac Surg, 2016. 102 (3): p. 850-858. Kansy, A., et al., Major infection after pediatric cardiac surgery: external validation of risk estimation model. Ann Thorac Surg, 2012. 94 (6): p. 2091-5. Ren, C., et al., Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China. J Cardiothorac Surg, 2021. 16 (1): p. 71. Haughey, B.S., S.C. White, and M.D. Seckeler, Catheter-associated bloodstream infection incidence and outcomes in congenital cardiac surgery. Congenit Heart Dis, 2019. 14 (5): p. 811-813. Ribeiro, A.C.L., et al., Risk Factors for Surgical Site Infection in Patients Undergoing Pediatric Cardiac Surgery. Arq Bras Cardiol, 2023. 120 (12): p. e20220592. Vincent, J.L., et al., Prevalence and Outcomes of Infection Among Patients in Intensive Care Units in 2017. JAMA, 2020. 323 (15): p. 1478-1487. Erikssen, G., et al., Achievements in congenital heart defect surgery: a prospective, 40-year study of 7038 patients. Circulation, 2015. 131 (4): p. 337-46; discussion 346. Xiang, L., et al., Effect of family socioeconomic status on the prognosis of complex congenital heart disease in children: an observational cohort study from China. Lancet Child Adolesc Health, 2018. 2 (6): p. 430-439. Rohit, M. and S. Shrivastava, Acyanotic and Cyanotic Congenital Heart Diseases. Indian J Pediatr, 2018. 85 (6): p. 454-460. Ellis, C.L., J.C. Rutledge, and M.A. Underwood, Intestinal microbiota and blue baby syndrome: probiotic therapy for term neonates with cyanotic congenital heart disease. Gut Microbes, 2010. 1 (6): p. 359-66. Bratzler, D.W., et al., Clinical practice guidelines for antimicrobial prophylaxis in surgery. Am J Health Syst Pharm, 2013. 70 (3): p. 195-283. Paioni, P., et al., Swiss recommendations on perioperative antimicrobial prophylaxis in children. Swiss Med Wkly, 2022. 152 : p. w30230. Delgado-Corcoran, C., et al., Reducing Pediatric Sternal Wound Infections: A Quality Improvement Project. Pediatr Crit Care Med, 2017. 18 (5): p. 461-468. Calandra, T., J. Cohen, and I.C.U.C.C. International Sepsis Forum Definition of Infection in the, The international sepsis forum consensus conference on definitions of infection in the intensive care unit. Crit Care Med, 2005. 33 (7): p. 1538-48. Yu, X., et al., Risk factors of nosocomial infection after cardiac surgery in children with congenital heart disease. BMC Infect Dis, 2020. 20 (1): p. 64. Sen, A.C., et al., Postoperative Infection in Developing World Congenital Heart Surgery Programs: Data From the International Quality Improvement Collaborative. Circ Cardiovasc Qual Outcomes, 2017. 10 (4). Ding, N., et al., Analysis of risk factors for prolonged stay in the intensive care unit after cardiac surgery in children with pneumonia. Cardiol Young, 2024: p. 1-7. Bateman, R.M., et al., 36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016. Crit Care, 2016. 20 (Suppl 2): p. 94. Wirz, Y., et al., Effect of procalcitonin-guided antibiotic treatment on clinical outcomes in intensive care unit patients with infection and sepsis patients: a patient-level meta-analysis of randomized trials. Crit Care, 2018. 22 (1): p. 191. Rostad, C.A., et al., Bacterial infections after pediatric heart transplantation: Epidemiology, risk factors and outcomes. J Heart Lung Transplant, 2017. 36 (9): p. 996-1003. Zeng, M., et al., Guidelines for the diagnosis, treatment, prevention and control of infections caused by carbapenem-resistant gram-negative bacilli. J Microbiol Immunol Infect, 2023. 56 (4): p. 653-671. Wittenberg, R.E., et al., Preoperative Malnutrition Increases Risk of In-Hospital Mortality, Major Infection, and Longer Intensive Care Unit Stay After Ventricular Septal Defect Closure. J Am Heart Assoc, 2024. 13 (13): p. e032662. Additional Declarations No competing interests reported. Supplementary Files supplementary.doc Graphicalabstract.jpg Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in BMC Infectious Diseases → Version 1 posted Editor assigned by journal 06 May, 2025 Editorial decision: Revision requested 22 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers invited by journal 07 Apr, 2025 Submission checks completed at journal 06 Apr, 2025 First submitted to journal 31 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5647863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439585293,"identity":"73e31054-1f94-44b8-9f62-138c995dbcfd","order_by":0,"name":"Xianting Jiao","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianting","middleName":"","lastName":"Jiao","suffix":""},{"id":439585294,"identity":"a1fc5d45-0731-4cb2-bf50-493b240ee850","order_by":1,"name":"Jiawei Gao","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Gao","suffix":""},{"id":439585295,"identity":"a2b02ba7-0be2-4c85-a739-5097331100c9","order_by":2,"name":"Wenyuan Shang","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenyuan","middleName":"","lastName":"Shang","suffix":""},{"id":439585296,"identity":"178a723e-ad84-40e3-9fe6-cd2ef4b8aabb","order_by":3,"name":"Tingting Zhang","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Zhang","suffix":""},{"id":439585297,"identity":"fae18266-9ad4-45bd-87ca-663f535f79b7","order_by":4,"name":"Sun Chen","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Chen","suffix":""},{"id":439585298,"identity":"2bf46f9a-65a0-4dbe-a65d-a530300423f2","order_by":5,"name":"Huiying Wang","email":"","orcid":"","institution":"XinHua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huiying","middleName":"","lastName":"Wang","suffix":""},{"id":439585299,"identity":"3bcf2e8f-7096-4e6f-a33e-933282c34c22","order_by":6,"name":"Jihong Huang","email":"data:image/png;base64,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","orcid":"","institution":"XinHua Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jihong","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-12-15 14:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5647863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5647863/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-12398-w","type":"published","date":"2026-01-05T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80280436,"identity":"ac244693-3f72-4db5-b39f-ac33cd959027","added_by":"auto","created_at":"2025-04-10 05:41:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37877,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart and the number of patients with infection\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5647863/v1/4fbe4964a9847df6acc23234.jpg"},{"id":80279195,"identity":"b51e8408-3841-477a-8434-d4dbd5833200","added_by":"auto","created_at":"2025-04-10 05:33:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39612,"visible":true,"origin":"","legend":"\u003cp\u003ePathogen Strain Distribution, Infection Sites, and Their Impact on Hospitalization Duration. (A) Pie chart showing the distribution of infection sites among patients. (B) Pie chart depicting the distribution of pathogens based on resistance profile. (C) Bar graph comparing hospital stay duration among culture-negative patients, those infected with non-MDROs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BSI, Bloodstream Infections; SSI, Surgical Site Infection; CR, Carbapenem-Resistant; VRE, Vancomycin-Resistant Enterococci; ESBL, Extended-Spectrum Beta-Lactamase-producing bacteria; MRSA, Methicillin-Resistant Staphylococcus aureus; MDRAB, Multidrug-Resistant Acinetobacter baumannii; MDROs, Multidrug-Resistant Organisms* p\u0026lt;0.05, *** p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5647863/v1/105a9fb9d29464d87d8d2069.jpg"},{"id":100069082,"identity":"600d10af-ac09-4af5-8785-461a785c98e1","added_by":"auto","created_at":"2026-01-12 16:08:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":867082,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5647863/v1/b27e51df-4a62-413b-8121-30346cf69bbe.pdf"},{"id":80279206,"identity":"76a9137b-5fed-49a3-92ce-15c5c18428f3","added_by":"auto","created_at":"2025-04-10 05:33:11","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2624892,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.doc","url":"https://assets-eu.researchsquare.com/files/rs-5647863/v1/4450de20b313252cc08c1853.doc"},{"id":80280437,"identity":"7ebb3b13-db8f-4159-87ce-d90b6d0f1000","added_by":"auto","created_at":"2025-04-10 05:41:11","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":66204,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5647863/v1/a131bd474ab6fe2d77109f27.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Outcomes of Postoperative Infection Among Pediatric Congenital Heart Disease Patients in Intensive Care Units: A Single Center Retrospective Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCongenital heart disease (CHD) is one of the most common birth defects, affecting approximately 1% of live births worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Advances in surgical techniques and intensive care have significantly improved the survival rates of children with CHD, yet the postoperative period remains critical due to complications, particularly infections[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Postoperative infections in pediatric congenital heart disease patients are a major cause of morbidity and prolonged hospitalization, often leading to adverse outcomes such as increased intensive care unit (ICU) stay and mechanical ventilation time[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These infections can complicate the recovery process, further stressing already compromised cardiovascular and immune systems, thus making prevention and timely treatment essential.\u003c/p\u003e \u003cp\u003eThe prevalence of postoperative infections in pediatric CHD patients varies across different populations and hospital settings[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Factors such as the complexity of the congenital defect, the type of surgery performed[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and the duration of cardiopulmonary bypass (CPB) time[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], mechanical ventilation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] all influence infection rates[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Infections in this patient population often involve respiratory[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], urinary, bloodstream infections[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], or surgical site infection[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] with causative agents frequently being multidrug-resistant pathogens. This highlights the importance of comprehensive infection surveillance and management strategies in pediatric ICUs, particularly for children with CHD.\u003c/p\u003e \u003cp\u003eUnderstanding the relationship between infections and clinical outcomes in pediatric CHD patients is crucial for improving healthcare protocols. Retrospective studies, such as those by the EPIC III investigators have shed light on the critical role of infections in ICU patients, demonstrating that infection is associated with higher in-hospital mortality and prolonged ICU stays[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, there remains a need for more specific studies focusing on pediatric populations with congenital heart disease. This retrospective analysis aims to investigate the prevalence and clinical outcomes of postoperative infections in pediatric CHD patients in a single-center ICU, contributing valuable insights into infection control and patient management strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eThis retrospective study included pediatric patients diagnosed with CHD who underwent surgical intervention at Xinhua Hospital Affiliated to Shanghai Jiao Tong University from October 2020 to July 2024. A total of 1,131 patients were reviewed, with 131 developing postoperative infections, while 1,000 patients remained non-infected during their ICU stay as shows Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe inclusion criteria comprised patients aged 0\u0026ndash;18 years who underwent corrective or palliative surgery for CHD. Patients with pre-existing infections or immune deficiencies, antibiotic use in the week prior to surgery, and unplanned secondary surgery during the same hospital stay were excluded from the analysis. According to previous studies[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], complex congenital heart disease (CCHD) is defined as: Univentricular Hearts, Truncus Arteriosus Communis, Interrupted or Hypoplastic Aortic Arch, Transposition of the Great Arterie, Atrioventricular Septal Defect, Totally Anomalous Pulmonary Venous Drainage, Pulmonary Atresia and Tetralogy of Fallot. Additionally, according to previous studies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the definition of cyanotic versus acyanotic heart defects, was also noted.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e2.2 Ethical Statement\u003c/p\u003e\n\u003cp\u003eThe study was conducted following the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Xinhua Hospital Affiliated to Shanghai Jiao Tong University(Number: XHEC-WJW-2020-014). Written informed consent was waived by the IRB of Xinhua Hospital Affiliated to Shanghai Jiao Tong University\u0026nbsp;due to the retrospective nature of the study, and all patient data were anonymized before analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3 Surgical Procedure\u003c/p\u003e\n\u003cp\u003eSurgical procedures were categorized based on the complexity of the cardiac anomaly and included both open-heart surgeries with CPB and less invasive surgeries without CPB. The majority of patients (84.1%) required CPB during surgery. Standard perioperative prophylactic antibiotics were administered to all patients, and adjustments were made based on infection risk or culture results as per the hospital\u0026rsquo;s infection control guidelines. In our center, the standard antibiotic prophylaxis protocol consists of a single dose of cefuroxime (50mg/kg) administered intravenously within 60 minutes prior to surgery and every 3 hours in operating room. The total duration of antibiotic therapy does not exceed 24 hours[17, 18].For patients requiring delayed sternal closure (DSC), prophylactic antibiotic coverage is extended to 48 hours following chest closure, consistent with published infection prevention bundles[19].\u003c/p\u003e\n\u003cp\u003e2.4 Data Collection\u003c/p\u003e\n\u003cp\u003eData on patient demographics, clinical characteristics, perioperative variables, and postoperative outcomes were collected from electronic medical records. Variables of interest included age, weight, type of congenital heart defect, presence of cyanotic heart disease, preoperative pulmonary hypertension, STAT, length of ICU stay, hospital stay, and ventilation duration. The classification of infection categories in our study adheres to the definitions established by the International Sepsis Forum[20]. Patients were categorized into non-infected, possible, probable, and confirmed infection groups based on clinical and microbiological criteria[12, 20]. Confirmed infection was defined as the presence of clinical signs consistent with infection in combination with microbiological evidence.Probable infections were defined as cases with strong clinical evidence of infection (e.g., fever, elevated inflammatory markers, imaging abnormalities) along with incomplete microbiological confirmation, such as a positive culture below diagnostic thresholds or a positive Gram stain without definitive identification. Possible infections referred to cases with clinical signs suggestive of infection but lacking microbiological or radiological confirmation, often due to prior antibiotic exposure or limited sampling opportunities. Clinical indicators included new-onset fever (\u0026gt;38\u0026deg;C), elevated inflammatory markers such as C-reactive protein (CRP \u0026gt; 8 mg/L), white blood cell count (WBC \u0026gt; 15\u0026times;10⁹/L), and procalcitonin (PCT \u0026gt; 0.5 ng/mL).\u003c/p\u003e\n\u003cp\u003e2.5 Follow-up\u003c/p\u003e\n\u003cp\u003ePatients were followed from the date of admission to their discharge or death. Postoperative infections were defined as those occurring within 30 days post-surgery, and infection sites included bloodstream, respiratory, urinary, and surgical site infections. The follow-up also included monitoring for infection-related complications, specifically prolonged mechanical ventilation, extended ICU stays, or lengthier hospitalizations. Durations of hospitalization, ICU stay, and mechanical ventilation were classified as prolonged if they surpassed the 75th percentile for each metric. The 75th percentile values were derived from our own study population rather than from an international database or local standards. This approach was chosen to ensure that our analysis accurately reflects the characteristics of the specific group under investigation, as different populations may have distinct distributions of variables.\u003c/p\u003e\n\u003cp\u003e2.6 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eData analysis was performed using R software (R Core Team, version 4.4.1, Vienna, Austria). Continuous variables were expressed as medians with interquartile ranges (IQR) and were compared using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages and compared using the chi-square test. To evaluate the impact of suspected or confirmed infections on hospitalization duration and ICU length of stay, univariable and multivariable logistic regression analyses were performed. Results of the regression analyses are reported as adjusted odds ratios (aOR) with 95% confidence intervals (CI). For patients with suspected or confirmed infections, multivariable linear regression was used to examine the association between the timing of pathogen detection and hospital stay duration, adjusting for relevant clinical factors. Sampling time was modeled as a continuous variable using restricted cubic splines to explore nonlinear relationships. Inflection points in the spline curves were used to determine thresholds indicating significant changes in hospitalization risk. In infections without pathogen confirmation, infection onset timing was defined as the first postoperative day with clinical signs prompting empirical antibiotic use. This proxy was applied as the pathogen detection time in the spline model. A two-tailed\u0026nbsp;\u003cstrong\u003ep\u003c/strong\u003e-value of \u0026lt;0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe baseline demographic and clinical characteristics of the study cohort, which comprised 1,131 pediatric CHD patients, are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e. The median age of the entire cohort was 541 days (IQR: 153\u0026ndash;1813 days), with a statistically significant difference between infected and non-infected groups (248 vs. 577 days, p = 0.002). Male patients predominated (52.8%), and infection prevalence was higher in males (62.6% infected vs. 51.5% non-infected,\u0026nbsp;p\u0026nbsp;= 0.017). Infected patients had significantly lower median weight (7.30 kg [IQR: 4.67\u0026ndash;14.00] vs. 10.50 kg [IQR: 6.50\u0026ndash;18.00],\u0026nbsp;p\u0026nbsp;\u0026lt; 0.001). Additionally, cyanotic CHD(diagnosed at enrollment based on clinical evaluation and standardized criteria) was significantly more common in the infected group, with 28.2% of infected patients being cyanotic, compared to 11.8% in the non-infected group (p \u0026lt; 0.001). Preoperative pulmonary hypertension (PH) was also more prevalent in infected patients (32.1% vs. 23.2%,\u0026nbsp;p\u0026nbsp;= 0.026). Combined extracardiac malformation\u0026nbsp;and CCHD\u0026nbsp;were strongly associated with infection (Combined extracardiac malformation: 19.1% vs. 7.0%; CCHD: 59.5% vs. 37.0%; both\u0026nbsp;p\u0026nbsp;\u0026lt; 0.001). Although cardiopulmonary bypass (CPB) use and delayed sternal closure showed no intergroup differences, infected patients required longer mechanical ventilation (median 24.0 hours [IQR: 8.5\u0026ndash;91.0] vs. 7.5 hours [IQR: 4.0\u0026ndash;22.0], p \u0026lt; 0.001) and\u0026nbsp;prolonged ICU stays (median 6.00 days [IQR: 3.00\u0026ndash;10.00] vs. 3.00 days [IQR: 2.00\u0026ndash;4.00],\u0026nbsp;p\u0026nbsp;\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTo further investigate potential perioperative risk factors associated with infection, we performed a multivariable logistic regression analysis incorporating relevant clinical variables (Supplementary Table 1). The results demonstrated that the presence of combined extracardiac malformations was independently associated with an increased risk of infection (OR 2.388, 95% CI 1.363\u0026ndash;4.184, p = 0.002). Additionally, higher STAT categories (STAT 3\u0026ndash;5) were significantly predictive of infection, with STAT 4\u0026ndash;5 showing a particularly strong association (OR 6.719, 95% CI 2.969\u0026ndash;15.209, p \u0026lt; 0.001), highlighting the impact of surgical complexity on postoperative infectious outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline Characteristics. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9059%;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003cp\u003e(n=1131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9954%;\"\u003e\n \u003cp\u003eInfection\u003c/p\u003e\n \u003cp\u003e(n=131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6646%;\"\u003e\n \u003cp\u003eNon infection (n=1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9879%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eGender, male, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e597 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e82 ( 62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e515 ( 51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eAge(day),median[IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e541[153,1813]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e248[106,1129]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e577 [162, 1862]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eWeight(kg),median[IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e10.00 [6.20, 17.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e7.30 [4.67, 14.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e10.50 [6.50, 18.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eCyanotic heart disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e155 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e37 ( 28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e118 ( 11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003ePreoperative PH, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e274 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e42 ( 32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e232 ( 23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eCombined Extracardiac malformation, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e95 ( 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e25 ( 19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e70 ( 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eCCHD, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e448 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e78 ( 59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e370 ( 37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003ePreterm, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e14 ( 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e3 ( 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e11 ( 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eCPB, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e951 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e111 ( 84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e840 ( 84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eSecond-stage surgery, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e113 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e19 ( 14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e94 ( 9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eDelayed sternal closure, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e15 ( 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e4 ( 3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e11 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eSTAT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eSTAT=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eSTAT=2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eSTAT=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eSTAT =4 and \u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eVentilator length (hours),median[IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e8.0 [4.0, 23.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e24.0 [8.5, 91.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e7.5 [4.0, 22.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eICU(days),median[IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e3.0 [2.0, 5.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e6.0 [3.0, 10.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e3.0 [2.0, 4.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eHospital stay (days),median[IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e12.0 [9.0, 17.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e18.0 [13.0, 27.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e12.0 [9.0, 16.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003ePCT , Mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e0.33 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e0.35\u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e0.32\u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eCRP(mg/L), Mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e2.43 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e2.54\u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e2.41\u0026plusmn; 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eWBC(10^9/L), Mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e9.63\u0026plusmn; 4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e9.60\u0026plusmn; 5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e9.65\u0026plusmn; 4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eGRAM(%), Mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e35.23\u0026plusmn; 17.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e35.92\u0026plusmn; 17.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e34.75\u0026plusmn; 17.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003eLYM(%), Mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e54.11\u0026plusmn; 17.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e53.08\u0026plusmn; 18.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e54.71\u0026plusmn; 17.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4461%;\"\u003e\n \u003cp\u003ePLT(10^9/L), Mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.9059%;\"\u003e\n \u003cp\u003e317.59\u0026plusmn; 113.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9954%;\"\u003e\n \u003cp\u003e314.30\u0026plusmn; 126.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.6646%;\"\u003e\n \u003cp\u003e319.81\u0026plusmn; 107.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9879%;\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: PH, pulmonary hypertensive；CCHD, Complex congenital heart disease; CPB, cardiopulmonary bypass; ICU, intensive care unit; IQR, interquartile range; PCT, Procalcitonin; CRP, C reactive protein; WBC, white blood count; GRAM, neutrophile granulocyte ; LYM, Lymphocytes; PLT platelets,STAT, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Mortality Categories .Due to the small number of cases in STAT categories 4 and 5, the two categories were combined for statistical analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough baseline preoperative levels of PCT, CRP, and WBC counts showed no significant differences between groups, the median hospital stay for patients with infections was significantly longer, at 18.0 days (IQR: 13.0\u0026ndash;27.5) compared to 12.0 days (IQR: 9.0\u0026ndash;16.0) in non-infected patients (p \u0026lt; 0.001), further emphasizing the adverse impact of infections on recovery times.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impact of infection on postoperative outcomes was evaluated through multivariable logistic regression analyses, adjusting for confounding variables including gender, age, weight, cyanotic heart disease, pulmonary hypertension (PH), extracardiac malformations, CPB, delayed sternal closure, and surgical complexity (STAT category).\u003c/p\u003e\n\u003cp\u003eInfection was identified as a significant independent risk factor for prolonged hospitalization (adjusted OR: 3.10, 95% CI: 1.84\u0026ndash;5.20, p \u0026lt; 0.001), ICU stay (adjusted OR: 3.51, 95% CI: 2.01\u0026ndash;6.12, p \u0026lt; 0.001), and mechanical ventilation duration (adjusted OR: 1.85, 95% CI: 1.09\u0026ndash;3.11, p = 0.021) (see Supplementary Figure 1-3 ). These findings suggest that infection independently contributes to increased postoperative resource utilization and delayed recovery.\u003c/p\u003e\n\u003cp\u003eAdditionally, when stratifying patients by infection status (confirmed, probable, and possible) the impact of infection status on hospitalization duration, ICU stay, and mechanical ventilation time is summarized in\u003cstrong\u003e\u0026nbsp;Table 2\u003c/strong\u003e. Patients with confirmed infections experienced significantly longer hospital stays, ICU stays, and mechanical ventilation times compared to non-infected patients. The prevalence of prolonged hospitalization (defined as \u0026gt;17 days) was 76.1% in the confirmed infection group, compared to only 21.3% in non-infected patients (adjusted OR: 7.193, 95% CI: 3.024-17.762, p \u0026lt; 0.001). Similarly, prolonged ICU stays (defined as \u0026gt;5 days) were notably more common in the confirmed infection group, with 78.3% of these patients experiencing extended ICU stays compared to 21.4% in non-infected patients (adjusted OR: 6.499, 95% CI: 2.665-16.779, p \u0026lt; 0.001). Furthermore, prolonged mechanical ventilation (defined as \u0026gt;23 hours) was observed in 84.8% of patients with confirmed infections, compared to 21.6% in non-infected patients (adjusted OR: 6.557, 95% CI: 2.581-19.216, p \u0026lt; 0.001). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eImpact of Infection Status on Hospitalization Duration, ICU Stay, and Mechanical Ventilation Time\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"700\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHospital stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 237px;\"\u003e\n \u003cp\u003eCrude analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 197px;\"\u003e\n \u003cp\u003eAdjusted analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e(P75= 17days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eTotal number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eProlonged \u0026nbsp;hospital stay, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ecOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eNon-infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e213(21.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eConfirmed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e35 (76.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e14.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.463-30.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e7.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.024-17.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eProbable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e25 (62.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.054-15.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.286-7.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003ePossible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e10 (22.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.654-2.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.552-3.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eICU stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eTotal number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eProlonged \u0026nbsp;ICU stay，\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 237px;\"\u003e\n \u003cp\u003eCrude analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 197px;\"\u003e\n \u003cp\u003eAdjusted analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e(P 75= 5 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ecOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eNon-infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e214 (21.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eConfirmed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e36 (78.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e20.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e9.912-41.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.665-16.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eProbable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e23 (57.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.001-14.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.310-8.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026lt;0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003ePossible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e10 (22.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.785\u0026nbsp; \u0026nbsp;\u0026nbsp;-3.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.684- 4.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMV time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003enumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eProlonged MV time, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 237px;\"\u003e\n \u003cp\u003eCrude analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 197px;\"\u003e\n \u003cp\u003eAdjusted analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e(P75= 23 hr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ecOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eNon -infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e216(21.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eConfirmed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e39 (84.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e20.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e9.133-46.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.581-19.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eProbable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e20 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.964-7.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.254-13.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003ePossible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e8 (17.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.369-1.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.212-1.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdjusting factors included: gender, age, weight, cyanotic, pulmonary hypertension (PH), Extracardiac malformation, CPB time, secondary surgery, Delayed sternal closure, and STAT.\u003c/p\u003e\n\u003cp\u003eAbbreviations: cOR:crude odd ratio; aOR: adjusted odd ratio; STAT, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Mortality Categories .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePostoperative infection characteristics are detailed in \u003cstrong\u003eFigure 2\u003c/strong\u003e. Analysis of infection sites (Figure 2A) revealed respiratory infections as the predominant type (69.75%, 113/162), followed by bloodstream infections (16.05%, 26/162) and catheter-related infections (8.64%, 14/162). Pathogen resistance profiling (Figure 2B) demonstrated non-resistant organisms in 72.84% (118/162) of cases, with multidrug-resistant organisms (MDROs) comprising 27.16% (44/162). Comparative analysis of hospitalization duration (Figure 2C) showed significantly prolonged stays for MDRO-related infections versus non-MDRO cases, particularly in bloodstream and respiratory infections. A total of 162 pathogens were identified. Gram-negative bacteria were the most common (n=93), followed by Gram-positive bacteria (n=50), fungi (n=5), viruses (n=5), and other atypical organisms (n=9). The detailed pathogen distribution is provided in Supplementary Table 2, and their corresponding antibiotic susceptibility profiles, is provided in the Supplementary Table3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo compare the differences in hospital length of stay between infections caused by multidrug-resistant (MDR) and non-MDR strains, we conducted a multiple linear regression analysis on 131 infection-defined cases. The results identified multidrug-resistant strains, pathogen sampling time, high STAT, and prematurity as significant predictors of prolonged hospitalization. In our study, \u0026ldquo;pathogen sampling time\u0026rdquo; was defined as the duration from ICU admission to the time of microbiological sample collection for pathogen identification. To better capture the nonlinear relationship between sampling time and hospital stay, we performed cubic spline analysis, which revealed inflection points at approximately 2 and 4 days. Accordingly, we categorized pathogen sampling time into three clinically meaningful groups: early sampling (\u0026le;2 days postoperatively), intermediate sampling (2\u0026ndash;4 days), and delayed sampling (\u0026ge;4 days). This stratification was applied in regression analysis to improve interpretability. In contrast, gender, age, weight, cyanotic heart disease, preoperative \u0026nbsp;pulmonary hypertension (PH), secondary surgery, and STAT category were not statistically significant. Patients with infections caused by multidrug-resistant strains had a notably longer hospital stay, with an increase of 11.54 days (95% CI: 2.57-20.52, p = 0.012). Additionally, the timing of pathogen sampling was significantly associated with extended hospital stays, showing an increase of 6.71 days (95% CI: 1.41-12.01, p = 0.013) per delayed sampling. Other factors, such as concomitant malformations and prematurity, also contributed to longer hospital stays. Specifically, patients with higher STAT had an increase of 4.05 \u0026nbsp;days (95% CI: 1.64-6.48, p = 0.001), while premature infants experienced the most significant impact, with an increase of 29.00 days (95% CI: 7.90-50.10, p = 0.007). The detailed results are summarized in Table 3. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFactors Associated with the Length of Hospital Stay in Infected Children\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eB (Estimate）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e95% CI (Lower - Upper)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eMultidrug-resistant strains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.067-20.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003ePathogen sampling time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026le; 2 days \u0026nbsp;postoperatively\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e2-4 days postoperatively\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e-8.58-9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026ge; 4 days postoperatively\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e14.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2.84-27.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eSTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.64-6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003ePrematurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e29.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e7.90-50.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eR\u0026sup2; = 0.37, Adjusted R\u0026sup2; = 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: STAT, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Mortality Categories .\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis retrospective study underscores the significant clinical impact of postoperative infections on pediatric CHD patients in the ICU. Among the 1,131 patients reviewed, 131 (11.6%) developed infections after surgery. Compared with non-infected patients, those with infections experienced markedly prolonged ICU stays, extended durations of mechanical ventilation, and increased overall hospitalization time, highlighting the adverse influence of infection on postoperative recovery.\u003c/p\u003e\n\u003cp\u003eThe findings of our study are consistent with previous research that underscores the overall infections burden in pediatric CHD patients, which is a leading cause of morbidity and prolonged ICU stays[21, 22]. Similar to the results reported by Ding et al.[23], we observed that the duration of mechanical ventilation and ICU stays were significantly longer in patients with confirmed infections, further emphasizing the burden of infections on recovery outcomes. However, the incidence of postoperative infections in children with CHD varies substantially across institutions. In high-income countries, reported rates range from 1.2% to 48%, depending on the case mix and monitoring methods[2]. Data from the National Center for Cardiovascular Diseases in China indicate that the postoperative ICU infection rate after CHD surgery ranges between 1.3% and 15%[4].\u003c/p\u003e\n\u003cp\u003eOur center has established a comprehensive infection prevention and control (IPC) program for many years, incorporating standard bundle measures such as hand hygiene, chlorhexidine oral care, early extubation, head-of-bed elevation, glycemic control, gastric decompression, and venous thromboembolism prophylaxis. Despite these efforts, our study still observed an overall infection rate of 11.6%, which may be related to the underlying disease complexity and patient characteristics.\u003c/p\u003e\n\u003cp\u003eIn particular, the elevated infection rates observed in patients with high STAT categories, cyanotic congenital heart disease, and preoperative PH underscore the presence of high-risk features. These findings suggest that such vulnerable subgroups may require more tailored and intensified infection control strategies to mitigate postoperative infectious complications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong infected patients, we further identified several key factors associated with prolonged hospital stays, including MDROs, delayed pathogen identification (\u0026ge;\u0026nbsp;postoperative day 4), prematurity, and higher STAT categories. Patients who underwent delayed microbiological sampling had significantly longer hospitalizations compared with those sampled within the first two postoperative days. This delay may limit the timely initiation of targeted antimicrobial therapy, thereby prolonging empirical broad-spectrum antibiotic use and increasing selective pressure for MDRO development. \u0026nbsp;Our results align with those of Wang et al[3]., who reported that MDRO infections were strongly associated with longer ICU and hospital stays in pediatric cardiac surgical patients. These findings reinforce the negative impact of antimicrobial resistance on recovery and support the importance of early and accurate pathogen detection.\u003c/p\u003e\n\u003cp\u003eCompared with the global ICU data from the EPIC III study[12], our study focused exclusively on pediatric populations, demonstrating that infection rates and outcomes may differ significantly between adults and children, particularly in those undergoing high-risk cardiac surgeries. The higher incidence of multidrug-resistant pathogens in our study also suggests an evolving challenge in infection control that has been similarly noted in other reports[24],necessitating targeted antimicrobial strategies in pediatric ICUs[25, 26].\u003c/p\u003e\n\u003cp\u003eA notable strength of strengths of this study is the comprehensive approach used in classifying postoperative infections, distinguishing between possible, probable, and confirmed cases based on both clinical and microbiological criteria[20]. This classification system, allowed us to evaluate the differential impacts of infection severity on outcomes such as ICU stay, mechanical ventilation time, and overall hospitalization[25]. This methodology aligns with prior studies, including the EPIC III investigation, which utilized detailed infection definitions and multivariate analyses to assess clinical outcomes [12]. Notably, our inclusion of probable and possible infections reflects real-world pediatric ICU practice, where empirical treatment is often initiated before pathogen confirmation due to the time-sensitive nature of critical care. This comprehensive approach enables a more accurate assessment of the infection burden across different levels of diagnostic certainty and better mirrors clinical decision-making processes. Moreover, our study uniquely focuses on pediatric CHD patients, allowing for a more targeted understanding of infection risks and outcomes in this vulnerable population. Stratification by infection status, along with adjustments for variables such as pulmonary hypertension and secondary surgeries, yielded more precise insights into the elevated risks associated with confirmed infections\u0026mdash;an aspect that is often generalized in broader ICU studies [27]. These findings underscore the need for early recognition and timely intervention in high-risk pediatric patients to prevent severe postoperative complications.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations inherent to its retrospective, single-center design, which may introduce selection bias and restrict the generalizability of findings to populations with differing surgical practices or socioeconomic contexts. Although we adjusted for key variables such as age and disease complexity, residual confounding from unmeasured factors\u0026mdash;including preoperative nutritional status, socioeconomic disparities, and single ventricular physiological \u0026mdash;could influence outcomes[28]. Furthermore, the absence of advanced diagnostic tools like Presepsin may limit precision in differentiating sepsis from non-infectious inflammation. Future research should incorporate novel diagnostic biomarkers such as Presepsin to improve early infection identification and diagnostic accuracy in critically ill pediatric patients. Additionally, due to the retrospective nature of this study, we were unable to systematically capture post-discharge follow-up data, including readmissions or late infectious complications in patients discharged with suspected but unconfirmed infections. To address this limitation, a prospective follow-up initiative is planned to systematically collect standardized data on post-discharge outcomes, including recurrent infections, rehospitalizations, and long-term sequelae. Such a program will be instrumental in capturing late complications and refining our understanding of the long-term impact of postoperative infections in this population. Due to sample size limitations, we were unable to match a sufficient number of cases to perform a robust propensity score matching analysis, thereby precluding the ability to strengthen causal inference. Future multicenter prospective studies integrating standardized perioperative protocols, novel biomarkers and personalized risk stratification models (e.g., genetic/immunological profiling) are needed to validate these findings and optimize infection prevention strategies in high-risk pediatric CHD cohorts[12]. Longitudinal studies examining the long-term outcomes of postoperative infections and their potential effects on cardiac function and overall quality of life in CHD patients would also be valuable. By addressing these areas, future research can help improve the quality of care and outcomes for pediatric congenital heart disease patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the clinical relevance of postoperative infections in pediatric patients with congenital heart disease, showing that such infections are significantly associated with prolonged ICU stays, increased mechanical ventilation duration, and extended hospitalization. These findings underscore the importance of strengthening infection prevention protocols and optimizing postoperative care strategies in pediatric intensive care settings. A more comprehensive approach to early infection detection and targeted management may help reduce the burden of complications associated with postoperative infections.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHD \u0026ndash; Congenital Heart Disease；ICU \u0026ndash; Intensive Care Unit；CPB \u0026ndash; Cardiopulmonary Bypass；CCHD \u0026ndash; Complex Congenital Heart Disease；PH \u0026ndash; Pulmonary Hypertension；OR \u0026ndash; Odds Ratio；CI \u0026ndash; Confidence Interval；IQR \u0026ndash; Interquartile Range；EPIC \u0026ndash; Extended Prevalence of Infection in Intensive Care\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted following the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Xinhua Hospital Affiliated to Shanghai Jiao Tong University（Number: XHEC-WJW-2020-014）. Written informed consent was waived by the IRB of\u0026nbsp;Xinhua Hospital Affiliated to Shanghai Jiao Tong University due to the retrospective nature of the study, and all patient data were anonymized before analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent obtained from parent(s)/guardian(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to patient privacy and institutional data-sharing policies but are available from the corresponding author upon reasonable request and with permission from Xinhua Hospital Affiliated to Shanghai Jiao Tong University.\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University. Due to the retrospective nature of the study, the requirement for written informed consent was waived. All patient data were anonymized prior to analysis to ensure privacy and confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Natural Science Foundation of China (82273647), Medical Engineering Cross Fund of Shanghai Jiao Tong University (YG2025ZD24)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXJ and JG contributed equally to the conception and design of the study. WS was responsible for data collection and management. TZ and SC performed the data analysis and interpretation. JH and HW drafted the manuscript, while HW and JH critically revised it for important intellectual content. All authors reviewed and approved the final version of the manuscript. JH and HW were the corresponding authors and supervised the overall conduct of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoffman, J.I. and S. Kaplan, \u003cem\u003eThe incidence of congenital heart disease.\u003c/em\u003e J Am Coll Cardiol, 2002. \u003cstrong\u003e39\u003c/strong\u003e(12): p. 1890-900.\u003c/li\u003e\n\u003cli\u003eMurni, I.K., et al., \u003cem\u003ePerioperative infections in congenital heart disease.\u003c/em\u003e Cardiol Young, 2017. \u003cstrong\u003e27\u003c/strong\u003e(S6): p. 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Rutledge, and M.A. Underwood, \u003cem\u003eIntestinal microbiota and blue baby syndrome: probiotic therapy for term neonates with cyanotic congenital heart disease.\u003c/em\u003e Gut Microbes, 2010. \u003cstrong\u003e1\u003c/strong\u003e(6): p. 359-66.\u003c/li\u003e\n\u003cli\u003eBratzler, D.W., et al., \u003cem\u003eClinical practice guidelines for antimicrobial prophylaxis in surgery.\u003c/em\u003e Am J Health Syst Pharm, 2013. \u003cstrong\u003e70\u003c/strong\u003e(3): p. 195-283.\u003c/li\u003e\n\u003cli\u003ePaioni, P., et al., \u003cem\u003eSwiss recommendations on perioperative antimicrobial prophylaxis in children.\u003c/em\u003e Swiss Med Wkly, 2022. \u003cstrong\u003e152\u003c/strong\u003e: p. w30230.\u003c/li\u003e\n\u003cli\u003eDelgado-Corcoran, C., et al., \u003cem\u003eReducing Pediatric Sternal Wound Infections: A Quality Improvement Project.\u003c/em\u003e Pediatr Crit Care Med, 2017. \u003cstrong\u003e18\u003c/strong\u003e(5): p. 461-468.\u003c/li\u003e\n\u003cli\u003eCalandra, T., J. Cohen, and I.C.U.C.C. International Sepsis Forum Definition of Infection in the, \u003cem\u003eThe international sepsis forum consensus conference on definitions of infection in the intensive care unit.\u003c/em\u003e Crit Care Med, 2005. \u003cstrong\u003e33\u003c/strong\u003e(7): p. 1538-48.\u003c/li\u003e\n\u003cli\u003eYu, X., et al., \u003cem\u003eRisk factors of nosocomial infection after cardiac surgery in children with congenital heart disease.\u003c/em\u003e BMC Infect Dis, 2020. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 64.\u003c/li\u003e\n\u003cli\u003eSen, A.C., et al., \u003cem\u003ePostoperative Infection in Developing World Congenital Heart Surgery Programs: Data From the International Quality Improvement Collaborative.\u003c/em\u003e Circ Cardiovasc Qual Outcomes, 2017. \u003cstrong\u003e10\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eDing, N., et al., \u003cem\u003eAnalysis of risk factors for prolonged stay in the intensive care unit after cardiac surgery in children with pneumonia.\u003c/em\u003e Cardiol Young, 2024: p. 1-7.\u003c/li\u003e\n\u003cli\u003eBateman, R.M., et al., \u003cem\u003e36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016.\u003c/em\u003e Crit Care, 2016. \u003cstrong\u003e20\u003c/strong\u003e(Suppl 2): p. 94.\u003c/li\u003e\n\u003cli\u003eWirz, Y., et al., \u003cem\u003eEffect of procalcitonin-guided antibiotic treatment on clinical outcomes in intensive care unit patients with infection and sepsis patients: a patient-level meta-analysis of randomized trials.\u003c/em\u003e Crit Care, 2018. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 191.\u003c/li\u003e\n\u003cli\u003eRostad, C.A., et al., \u003cem\u003eBacterial infections after pediatric heart transplantation: Epidemiology, risk factors and outcomes.\u003c/em\u003e J Heart Lung Transplant, 2017. \u003cstrong\u003e36\u003c/strong\u003e(9): p. 996-1003.\u003c/li\u003e\n\u003cli\u003eZeng, M., et al., \u003cem\u003eGuidelines for the diagnosis, treatment, prevention and control of infections caused by carbapenem-resistant gram-negative bacilli.\u003c/em\u003e J Microbiol Immunol Infect, 2023. \u003cstrong\u003e56\u003c/strong\u003e(4): p. 653-671.\u003c/li\u003e\n\u003cli\u003eWittenberg, R.E., et al., \u003cem\u003ePreoperative Malnutrition Increases Risk of In-Hospital Mortality, Major Infection, and Longer Intensive Care Unit Stay After Ventricular Septal Defect Closure.\u003c/em\u003e J Am Heart Assoc, 2024. \u003cstrong\u003e13\u003c/strong\u003e(13): p. e032662.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Pediatric congenital heart disease, postoperative infection, ICU stay, mechanical ventilation, hospital stay, multidrug-resistant pathogens","lastPublishedDoi":"10.21203/rs.3.rs-5647863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5647863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackgroud\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePostoperative infections are a major complication in pediatric congenital heart disease (CHD) patients, leading to prolonged ICU stays, increased mechanical ventilation time, and extended hospitalization. This study aims to investigate the prevalence, clinical outcomes, and associated risk factors for postoperative infections in pediatric CHD patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis retrospective study included 1,131 pediatric CHD patients who underwent surgery at Xinhua Hospital Affiliated to Shanghai Jiao Tong University between October 2020 and July 2024. Patients were classified into infected (n\u0026thinsp;=\u0026thinsp;131) and non-infected (n\u0026thinsp;=\u0026thinsp;1000) groups. Clinical characteristics, infection status, and outcomes were analyzed. Statistical analyses were performed using Mann-Whitney U tests and logistic regression models to assess the impact of infections on hospitalization duration, ICU stay, and mechanical ventilation time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInfections occurred in 11.6% of patients and were independently associated with prolonged hospitalization (adjusted OR\u0026thinsp;=\u0026thinsp;3.10, 95% CI: 1.84\u0026ndash;5.20), ICU stay (aOR\u0026thinsp;=\u0026thinsp;3.51, 95% CI: 2.01\u0026ndash;6.12), and mechanical ventilation (aOR\u0026thinsp;=\u0026thinsp;1.85, 95% CI: 1.09\u0026ndash;3.11). Other independent predictors of prolonged stay included cyanotic lesions, pulmonary hypertension, and high Society of Thoracic Surgeons-European Association for Cardiothoracic Surgery (STAT) Mortality Category. Among infected patients, multidrug-resistant organism infection (+\u0026thinsp;11.54 days), delayed pathogen sampling (\u0026ge;\u0026thinsp;postoperative day 5; +14.92 days), prematurity (+\u0026thinsp;29.00 days), and extracardiac malformations were significantly associated with extended hospitalization.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePostoperative infections are significantly associated with prolonged hospitalization and delayed recovery in pediatric CHD patients, particularly among those with high STAT category, cyanotic heart disease, and pulmonary hypertension. Early detection and targeted infection control measures may help improve outcomes in this high-risk population. Future research should focus on multi-center prospective studies and advanced diagnostic tools to enable timely and effective infection management.\u003c/p\u003e","manuscriptTitle":"Prevalence and Outcomes of Postoperative Infection Among Pediatric Congenital Heart Disease Patients in Intensive Care Units: A Single Center Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 05:33:06","doi":"10.21203/rs.3.rs-5647863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2025-05-06T15:08:13+00:00","index":"","fulltext":""},{"type":"decision","content":"Revision requested","date":"2025-04-22T12:19:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-19T19:26:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T08:52:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121535958711244118461732683366846052938","date":"2025-04-09T19:03:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T10:39:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236512411163485019668628144729778853694","date":"2025-04-09T10:31:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335972030709468906508536853600939202193","date":"2025-04-07T14:07:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-07T09:18:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-07T01:35:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-03-31T16:19:44+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"0cfdbf10-ac4c-4136-8fb0-9a8fa6ac00f0","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:00:38+00:00","versionOfRecord":{"articleIdentity":"rs-5647863","link":"https://doi.org/10.1186/s12879-025-12398-w","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2026-01-05 15:57:16","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-04-10 05:33:06","video":"","vorDoi":"10.1186/s12879-025-12398-w","vorDoiUrl":"https://doi.org/10.1186/s12879-025-12398-w","workflowStages":[]},"version":"v1","identity":"rs-5647863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5647863","identity":"rs-5647863","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}