Incidence and Risk Factors of Postoperative Delirium in Children with Congenital Heart Disease: A Retrospective Nationwide Inpatient Sample Database Study

Incidence and Risk Factors of Postoperative Delirium in Children with Congenital Heart Disease: A Retrospective Nationwide Inpatient Sample Database Study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Incidence and Risk Factors of Postoperative Delirium in Children with Congenital Heart Disease: A Retrospective Nationwide Inpatient Sample Database Study Ru-Gang An , Jin-Qing Tang , Jing Xu , Hao Xie , Bo-Fei Dong , Yan Wang , Zheng-Quan Tan , Ming-Qiang Zhou doi: https://doi.org/10.1101/2025.08.01.25332649 Ru-Gang An 1 Department of Anesthesiology, The First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University) , No.98, Fenghuang Road, Zunyi, Guizhou Province, 563000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jin-Qing Tang 1 Department of Anesthesiology, The First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University) , No.98, Fenghuang Road, Zunyi, Guizhou Province, 563000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jing Xu 2 Department of Neonatology, The First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University) , No.98, Fenghuang Road, Zunyi, Guizhou Province, 563000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hao Xie 3 Division of Orthopaedic Surgery, Department of Orthopaedics, Nanfang Hospital, Southern Medical University , Guangzhou, Guangdong, 510515, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bo-Fei Dong 4 The Second School of Clinical Medicine, Southern Medical University , Guangzhou, Guangdong, 510515, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yan Wang 5 Department of Rehabilitation Medicine, Guizhou Aerospace Hospital , Zunyi, China. No.615, Dalian Road, Zunyi, Guizhou Province, 563000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zheng-Quan Tan 1 Department of Anesthesiology, The First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University) , No.98, Fenghuang Road, Zunyi, Guizhou Province, 563000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ming-Qiang Zhou 1 Department of Anesthesiology, The First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University) , No.98, Fenghuang Road, Zunyi, Guizhou Province, 563000, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: zyyyzmq{at}163.com Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Postoperative delirium (POD) is an important neurocognitive complication following congenital heart disease (CHD) surgery in pediatric patients. While recognized for its association with adverse clinical outcomes, comprehensive population-based studies examining POD incidence and risk factors in this vulnerable population remain scarce. Methods Using the Nationwide Inpatient Sample (NIS) database (2010-2019), a retrospective study was performed of pediatric patients (<18 years) undergoing CHD-related surgeries. We evaluated demographics (race, sex, age), hospital characteristics (region of the hospital, type of admission, bed size of hospital, teaching status of hospital, type of insurance, location of hospital), mortality, length of stay (LOS), total charges, perioperative complications, and comorbidities. Results Among 37,535 CHD surgery patients identified, POD was diagnosed in 717 (1.9%). Children with POD exhibited higher comorbidity burdens, increased total medical costs, prolonged LOS and increased rates of in-hospital mortality (P < 0.0001). Independent predictors of POD included coagulopathy, drug abuse, fluid and electrolyte disorders, other neurological disorders, and paralysis. Additionally, POD was linked to in-hospital complications, including prolonged mechanical ventilation, acute renal failure, acute myocardial infarction, pneumonia, and respiratory failure. Conclusions Although the incidence of POD following CHD surgery in children is relatively low, investigating its predisposing factors is clinically valuable for optimizing patient management and improving outcomes. Introduction Congenital heart disease (CHD) represents the most prevalent birth defect globally, affecting approximately 1% of live births ( 1 , 2 ). Although advances in medical technology and surgical techniques have dramatically improved survival rates for children with CHD, making cardiac surgery a critical life-saving intervention, postoperative complications remain a significant concern( 3 , 4 ). Currently, approximately 40,000 pediatric cardiac surgeries are performed annually in the United States alone, with this number projected to increase due to enhanced diagnostic capabilities and improved healthcare accessibility ( 5 ). Postoperative delirium (POD) is one of the most common and serious complications following CHD surgery in children( 6 , 7 ). POD is an acute neuropsychiatric syndrome characterized by alterations in attention, awareness, and cognition, typically emerging within hours to days after surgery ( 8 ). The reported incidence varies substantially, ranging from 10% to 80%, depending on diagnostic criteria and patient populations ( 9 , 10 ). Evidence demonstrates that pediatric CHD patients developing POD experience significantly worse clinical outcomes, including longer periods of mechanical ventilation, extended durations of hospital and ICU admissions, and heightened healthcare expenses, and long-term cognitive impairment( 11 – 13 ).Beyond financial burdens, POD has been shown to adversely affect patients, families, and healthcare systems, leading to higher mortality rates, progressive functional impairment, and reduced quality of life( 14 , 15 ). The etiology of POD in pediatric CHD patients is multifactorial. Numerous risk factors have been recognized, such as the patient’s age, the severity of the CHD, and any cognitive impairments present before surgery( 16 – 18 ). Additionally, factors such as cyanotic heart disease, developmental delays, and postoperative electrolyte disturbances have been linked to an increased risk of delirium( 10 , 19 ). Although research on POD in pediatric patients undergoing CHD surgery is increasing, many current studies are limited by small sample sizes, single-center designs, or inconsistent diagnostic criteria. To address this gap, we leveraged the National Inpatient Sample (NIS) database to analyze over 30,000 pediatric CHD surgery cases surgeries. This study aims to elucidate the epidemiology of POD, identify key risk factors, and inform targeted prevention strategies to improve clinical outcomes in this vulnerable population. Materials and Methods Data source This study employed data from the NIS, a principal database within the Healthcare Cost and Utilization Project (HCUP) supported by the Agency for Healthcare Research and Quality (AHRQ). As the largest all-payer database of hospital admissions in the United States, the NIS provides a comprehensive and stratified sample of over 1,000 hospitals, representing approximately 20% of annual hospitalizations nationwide. This database includes detailed information on patient demographics, hospital characteristics, length of stay (LOS), total hospitalization charges, payer types, in-hospital mortality, and diagnostic and procedural codes based on the International Classification of Diseases, Ninth and tenth revisions, Clinical Modification (IICD-9-CM and ICD-10-CM). For this retrospective analysis, de-identified data were extracted, ensuring compliance with ethical standards for the use of publicly available datasets. The NIS database’s robust sampling methodology and extensive coverage make it a reliable source for studying the incidence and risk factors of POD in children with CHD. Data access and ethics statement This cross-sectional nationwide study analyzed fully de-identified patient data from the 2010-2019 US National Inpatient Sample (NIS) database (HCUP). Research data access occurred between 01/09/2024 and 15/12/2024 with no accessible protected health information at any phase. All NIS records were pre-anonymized prior to release with no identifiable personal information retained. The Institutional Review Board of The First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University) granted exemption from full ethical review and waived informed consent requirements, as researchers accessed only de-identified data in compliance with HCUP Data Use Agreement protocols. Data collection The study analyzed 2010-2019 NIS data, and the ICD-9-CM and ICD-10-CM was utilized to identify patients who had surgery for CHD and to assess the incidence of POD. Study inclusion criteria encompassed patients under 18 years of age who underwent surgical intervention for CHD. POD was diagnosed using particular ICD-9-CM and ICD-10-CM diagnostic codes (Supplemental Table S1). Initially, 38,398 cases were identified. After excluding patients with missing data for key variables, such as gender, total hospital charges, length of stay, expected payment method, elective admission status, hospital bed size and death, the final analysis comprised 37,535 cases. ( Fig 1 ) Download figure Open in new tab Fig 1. Exclusion process for pediatric patients undergoing congenital heart disease with postoperative delirium. Patient demographics (including race, sex, and age), hospital characteristics (region of the hospital, type of admission, bed size of hospital, teaching status of hospital, location of hospital, type of insurance), and outcome measures such as length of stay (LOS), and total hospital charges (TOTCHG) and in-hospital mortality were extracted from the database. Additionally, information on preoperative comorbidities and perioperative complications was collected using ICD-9-CM and ICD-10-CM diagnostic codes (Table1). Perioperative complications included: pneumonia, blood transfusion, continuous invasive mechanical ventilation, urinary tract infection, acute renal failure, wound dehiscence/non-healing, acute myocardial infarction, arrhythmia, postoperative shock, and respiratory failure. View this table: View inline View popup Table 1. Variables used in binary logistic regression analysis Data analysis Statistical analysis was performed using IBM SPSS Statistics 29.0. Descriptive statistics were used to summarize patient demographics, hospital characteristics, and clinical outcomes. Continuous variables, such as age and length of stay (LOS), were analyzed using independent t-tests, while categorical variables, such as gender, race, and insurance type, were compared using chi-square tests. Univariate analyses evaluated potential associations between postoperative delirium POD and demographic/clinical variables. Independent predictors were discerned by means of stepwise binary logistic regression, incorporating patient demographics, hospital characteristics, and perioperative complications. Results are presented as adjusted odds ratios (OR) with corresponding 95% confidence intervals (CI). Given the substantial cohort size, a conservative significance threshold (α = 0.001) was adopted to minimize Type I error risk. All tests were two-tailed, and missing data were addressed through complete case analysis to maintain methodological rigor. Results The incidence of postoperative delirium in patients with CHD surgery From 2010 to 2019, a total of 37,535 children undergoing congenital heart disease surgery were identified in the NIS database. Among these, 717 cases (1.9%) were diagnosed with postoperative delirium during hospitalization. The annual incidence of postoperative delirium exhibited slight fluctuations over the study period, ranging from 1.4% in 2010 to 2.6% in 2014, with an overall stable trend ( Table 2 ). A detailed year-by-year analysis revealed that the highest incidence occurred in 2014 (2.6%, n=98), followed by 2015 (2.4%, n=93) and 2013 (2.1%, n=77). In contrast, the lowest rates were observed in 2010 (1.4%, n=50) and 2016 (1.5%, n=62). Notably, no consistent upward or downward trajectory was observed across the decade, suggesting that postoperative delirium incidence remained relatively stable in this population ( Fig 2 ). Download figure Open in new tab Fig 2. Annual Incidence of Postoperative Delirium in pediatric Patients Undergoing congenital heart disease View this table: View inline View popup Table 2. Patient characteristics and outcomes after congenital heart disease surgery (2010–2019) Hospital characteristics and patient demographics in two surgical groups Significant differences were observed in age-related characteristics between POD and without POD in children undergoing congenital heart surgery. Patients with POD were significantly older (median age: 0.0 [0.0-6.0] years vs 0.0 [0.0-3.0] years, P<0.001) ( Table 2 ). Age distribution analysis revealed markedly different patterns, with the POD group having lower proportions of infants (0-2 years: 64.7% vs 71.1%) but higher proportions of older children (6-12 years: 16.5% vs 11.9%; 13-17 years: 10.2% vs 7.4%, P<0.001) ( Table 2 and Fig.3A & B).The elective admission rate was significantly lower in the POD group compared to non-POD patients (48.1% vs 61.6%, P<0.001) ( Table 2 ). Regional variations were also significant, with higher POD proportions in the Midwest/North Central (24.7% vs 20.5%) and West (25.5% vs 22.4%) regions (P<0.001) ( Table 2 and Fig.3C & D). However, there were no statistical differences in variables such as race, gender, insurance type, hospital type, region of hospital, bed size of hospital, etc. ( Table 2 and Fig.4 A & B & C and D). Download figure Open in new tab Fig 3. Patient demographics between the two surgical groups. A: Age distribution analysis of postoperative delirium patients. B: Analysis of age distribution of patients without postoperative delirium. C: Hospital Region of patients with postoperative delirium. D: Hospital Region of patients without postoperative delirium. Download figure Open in new tab Fig 4. Racial distribution and incidence of Postoperative Complications Related to Postoperative Delirium. A Racial distribution analysis of delirium patients. B Racial distribution analysis of non-delirium patients. C Analysis of Insurance Types for Patients with Delirium. D Analysis of Insurance Types for Non-delirium Patients. E Analysis of the number of hospital beds for patients with delirium. F Analysis of the number of hospital beds for non-delirium patients. Adverse outcomes of postoperative delirium after CHD in Children CHD children with POD demonstrated a significantly greater comorbidity burden, with 48.40% presenting ≥3 coexisting conditions compared to 11.80% in non-POD cases (P < 0.001) ( Table 2 ). The mortality rate tripled in the delirium group (11.4% vs. 3.1%; P < 0.001) ( Table 2 ). Hospitalization duration showed marked prolongation, with median length of stay (LOS) extending fourfold in POD patients (32 days vs. 8 days; P < 0.001) (Table2). This clinical deterioration translated into substantial economic consequences, as the total charges of hospitalization increased by $451,634.5 ($639,345 vs. $187,710.5, P < 0.001) ( Table 2 ) Risk Factors of POD Following Surgery for Congenital Heart Disease in Children Logistic regression analysis identified several significant predictors of POD in pediatric CHD surgical patients, revealing the following indicators: older age groups (6-12 years, OR=1.613; CI=1.300–2.001; P<0.001), increased comorbidity burden (1 comorbidity: OR=5.129; CI=3.529–7.456; P<0.001; 2 comorbidities: OR=12.997; CI=9.004–18.759; P<0.001;3 comorbidities: OR=31.436; CI=22.022–44.873; P<0.001) ( Table 3 ). comorbidities including, coagulopathy (OR=2.147; CI=1.724–2.675; P<0.001), drug abuse (OR=50.18; CI=39.558–63.665; P<0.001), fluid and electrolyte disorders (OR=1.595; CI=1.334–1.908; P<0.001), other neurological disorders (OR=35.21; CI=29.115–42.598; P<0.001) and Paralysis (OR=2.182; CI=1.418-3.358; P<0.001) ( Table 4 ). View this table: View inline View popup Table 3. Risk factors associated with postoperative delirium following Surgery for Congenital Heart Disease in Children View this table: View inline View popup Table 4. Relationship between postoperative Delirium and preoperative comorbidities Risk factors associated with postoperative delirium after CHD surgery POD occurred more frequently in pediatric CHD patients with acute myocardial infarction, acute renal failure, pneumonia, urinary tract infection, continuous trauma ventilation, arrhythmia, wound dehiscence/non-healing, postoperative shock and respiratory failure ( P < 0.001) ( Table 5 ). Multivariable regression analysis revealed a significant association between POD and acute renal failure (OR = 3.314; 95% CI = 2.768–3.969; P < 0.001), acute myocardial infarction (OR = 1.754; CI = 1.344–2.288; P < 0.001), pneumonia (OR = 3.431; CI = 2.763–4.262; P < 0.001), continuous trauma ventilation (OR = 1.905; CI = 1.614–2.248; P < 0.001), and respiratory failure (OR = 2.114; CI = 1.788–2.500; P < 0.001) ( Table 5 ). View this table: View inline View popup Download powerpoint Table 5. Relationship between postoperative delirium and postoperative complications Discussion POD represents a significant complication in pediatric CHD surgery that substantially impacts recovery outcomes. This large-scale health economic analysis characterizes POD epidemiology across pediatric CHD surgical populations. Our study identified an overall POD incidence of 1.9% between 2010-2019, demonstrating temporal stability. our findings contrast markedly with Mao et al.’s single-center study reporting 25.4% POD incidence following pediatric CHD surgery( 16 ). This disparity likely reflects fundamental methodological differences between multicenter administrative data analysis and prospective clinical assessments. The ICD diagnostic criteria demonstrate more stringent parameters relative to DSM psychiatric classifications, leading to lower case identification rates( 20 , 21 ). Several inherent limitations of the NIS database contribute to potential underestimation of delirium incidence. The database exclusively captures in-hospital delirium cases and demonstrates high specificity with correspondingly low sensitivity, creating systematic underdetection( 20 – 22 ). Furthermore, ICD-9-CM diagnostic codes cannot adequately document critical delirium manifestations including disorganized thinking, altered consciousness, cognitive deficits, and perceptual disturbances( 23 ). These methodological constraints collectively suggest that our reported incidence rates represent conservative estimates of true POD prevalence in this vulnerable population. Regarding patient demographics, our analysis identified older age as a significant risk factor for POD following CHD surgery in the pediatric population. Children aged 6 years and above constituted a substantially higher proportion of the delirium cohort compared to the non-delirium group, emerging as an independent predictor of POD development. Ours finding contradicts previous reports demonstrating that younger age was associated with higher likelihood of delirium development( 7 , 16 , 24 ). We attribute this discrepancy to fundamental methodological differences: earlier single-center studies conducted predominantly at tertiary referral institutions enrolled populations skewed toward neonates with critical CHD requiring emergent intervention. In contrast, the nationwide NIS database captures a broader spectrum of school-aged pediatric patients undergoing non-emergent elective procedures across community hospitals and academic medical centers. This population-representative sampling includes substantial proportions of school-aged children undergoing elective procedures, thereby more accurately reflecting real-world epidemiological distributions. Our analysis further revealed that preexisting comorbidities substantially increased the risk of postoperative delirium in this population. Comorbid conditions may predispose pediatric patients to heightened physiological stress and chronic inflammation, which when combined with the surgical stress-induced inflammatory response, can potentially trigger neuroinflammation and precipitate postoperative delirium( 25 – 27 ). POD has been demonstrated to significantly extend length of hospital stay, escalate healthcare expenditures, and adversely impact patient survival outcomes ( 9 , 25 , 28 , 29 ). Our investigation yielded consistent results with these previous observations. Studies have demonstrated that POD is significantly associated with prolonged hospitalization, likely attributable to the extended monitoring and specialized care required for delirious patients( 9 , 30 ). Moreover, POD increases healthcare costs through multiple mechanisms, including heightened consumption of medical resources (e.g., frequent monitoring, pharmacological therapies, and nursing interventions) and the necessity of additional treatments for delirium-associated complications such as infections or respiratory failure( 28 , 31 ).Most concerningly, POD was correlated with elevated postoperative mortality, potentially mediated by delirium-induced life-threatening complications (e.g., infections, organ dysfunction) or heightened exposure to iatrogenic risks during the prolonged hospitalization( 6 , 32 ). Interestingly, Our regression analysis identified elective admission as a significant protective factor against POD, corroborating existing literature that demonstrates reduced delirium risk in electively admitted patients ( 33 , 34 ). This protective effect may be attributed to patients’ more stable baseline health status and the opportunity for comprehensive preoperative optimization in elective cases. The likelihood of POD was lower in the Northeast and South regions but higher in the Midwest/North Central and West regions. The exact etiology remains uncertain but is probably multifactorial in nature( 4 ). Logistic regression analysis revealed a statistically significant increase in POD rates among medium-scale facilities versus small-scale institutions. Medium hospitals may demonstrate enhanced capability for early identification of high-risk pediatric patients and implementation of preventive measures( 35 ). Existing evidence from pediatric cardiac surgery research indicates that implementing systematic preoperative screening, establishing validated risk stratification protocols, and applying standardized management approaches are essential components for optimizing postoperative outcomes in children with congenital heart disease( 4 , 9 ). Consequently, comprehensive evaluation of identifiable preoperative risk factors represents a fundamental prerequisite for effective prevention of postoperative delirium in this vulnerable patient population. Our logistic regression analysis produced findings that align with existing evidence from previous studies( 36 – 40 ). Consistent with our hypothesis, pre-existing neurological disorders demonstrated the strongest association with POD (OR = 35.21), underscoring the vulnerability of children with preexisting neurological conditions to postoperative neuroinflammation and delirium. Similarly, drug abuse (OR = 50.18) and coagulopathy (OR = 2.147) emerged as strong independent risk factors, likely due to their systemic impact on cerebral perfusion and cerebral neuronal injury( 41 , 42 ). In addition, This study has confirmed that fluid and electrolyte disturbances (OR = 1.595) are also related to the occurrence of POD ( 14 ). Furthermore, to the authors’ knowledge, paralysis (OR = 2.182) was identified for the first time as an independent risk factor for POD. These findings align with prior adult studies linking neurological and metabolic derangements to POD, though our study highlights their amplified effects in pediatric populations. Our retrospective analysis demonstrates significant associations between POD and specific perioperative complications following CHD surgery, including acute renal failure(OR = 3.314), acute myocardial infarction(OR =1.754), pneumonia(OR = 3.431), continuous trauma ventilation(OR = 1.905), respiratory failure(OR = 2.114). While our study identified statistically significant correlations between POD and perioperative complications in congenital heart surgery patients, the observational nature of this retrospective analysis precludes definitive causal attribution. Inherent limitations in database studies, including potential confounding variables and temporal ambiguity, prevent causal inference. These findings necessitate prospective validation through controlled longitudinal studies incorporating serial neurological assessments and stan. While our study benefits from a large, nationally representative sample and multivariate adjustment for confounders, several limitations inherent to the NIS database must be acknowledged. First, the NIS only captures in-hospital data, excluding post-discharge complications and long-term outcomes. This may lead to underestimation of POD incidence, as delayed-onset cases are unrecorded. Second, as an administrative database, the NIS lacks granular clinical details such as surgical duration, anesthesia type (e.g., volatile vs. intravenous agents), and specific perioperative medications (e.g., opioid dosing, benzodiazepine use) — all established POD risk factors. Additionally, delirium assessment methods varied across hospitals, relying solely on ICD codes rather than standardized diagnostic tools, potentially affecting detection sensitivity. Furthermore, coding discrepancies and documentation errors, common in large databases, may introduce misclassification bias. While the NIS provides high specificity for recorded complications, its low sensitivity could result in underreporting of true POD cases. Despite these constraints, the NIS remains valuable for analyzing rare outcomes in pediatric cardiac surgery due to its extensive sample size and generalizability. Future prospective studies incorporating detailed perioperative variables and standardized delirium assessments are needed to validate these findings. Conclusions POD represents a significant complication in pediatric CHD surgery, with an overall incidence of 1.9% (2010–2019). Key risk factors included older age, preoperative neurological disorders, drug abuse, and coagulopathy, while elective admission emerged as a protective factor, reflecting optimized preoperative management. Hospital characteristics revealed geographic disparities (higher Midwest/West incidence) and elevated rates at large institutions, suggesting variations in detection practices or resource allocation. POD was associated with prolonged hospitalization, increased costs, and 10-fold higher mortality, driven by complications such as acute renal failure and pneumonia. Paralysis was newly identified as a pediatric-specific risk factor, extending adult neuroinflammatory models to children. Data Availability The data used in this study were obtained from the Nationwide Inpatient Sample (NIS) database, part of the Healthcare Cost and Utilization Project (HCUP) sponsored by the Agency for Healthcare Research and Quality. The NIS is a large, publicly available all-payer inpatient care database in the United States, accessible through the HCUP website at https://hcup-us.ahrq.gov/nisoverview.jsp Supporting information Supplemental Table S1. POD was diagnosed using particular ICD-9-CM and ICD-10-CM diagnostic codes. Acknowledgments We would like to thank the research participants for their unreserved help and all those who supported us, whether directly or indirectly. Footnotes ↵ # These authors are correspondence authors References 1. ↵ Global, regional, and national burden of congenital heart disease , 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017 . Lancet Child Adolesc Health . 2020 ; 4 ( 3 ): 185 – 200 . 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Alcohol consumption may be associated with postoperative delirium in the elderly: the PNDABLE study . BMC Anesthesiol . 2023 ; 23 ( 1 ): 222 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted August 03, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Incidence and Risk Factors of Postoperative Delirium in Children with Congenital Heart Disease: A Retrospective Nationwide Inpatient Sample Database Study Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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Share Incidence and Risk Factors of Postoperative Delirium in Children with Congenital Heart Disease: A Retrospective Nationwide Inpatient Sample Database Study Ru-Gang An , Jin-Qing Tang , Jing Xu , Hao Xie , Bo-Fei Dong , Yan Wang , Zheng-Quan Tan , Ming-Qiang Zhou medRxiv 2025.08.01.25332649; doi: https://doi.org/10.1101/2025.08.01.25332649 Share This Article: Copy Citation Tools Incidence and Risk Factors of Postoperative Delirium in Children with Congenital Heart Disease: A Retrospective Nationwide Inpatient Sample Database Study Ru-Gang An , Jin-Qing Tang , Jing Xu , Hao Xie , Bo-Fei Dong , Yan Wang , Zheng-Quan Tan , Ming-Qiang Zhou medRxiv 2025.08.01.25332649; doi: https://doi.org/10.1101/2025.08.01.25332649 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Pediatrics Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15227) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6597) Geriatric Medicine (668) Health Economics (997) Health Informatics (4534) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00348045c1a06f3',t:'MTc3OTUzMTEzNg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();