Novel Metabolites Are Associated with Coagulation Markers in Children with Congenital Heart Disease: A Metabolomic Study

Novel Metabolites Are Associated with Coagulation Markers in Children with Congenital Heart Disease: A Metabolomic 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 Novel Metabolites Are Associated with Coagulation Markers in Children with Congenital Heart Disease: A Metabolomic Study View ORCID Profile Shengxu Li , Dave Watson , Alissa Jorgenson , Zainab Adelekan , Kathleen Garland , Benjamin Deonovic , Leah Zupancich , Weihong Tang , David M. Overman , View ORCID Profile Marnie T. Huntley doi: https://doi.org/10.1101/2025.11.07.25339794 Shengxu Li 1 Children’s Minnesota Research Institute, Children’s Minnesota , Minneapolis, MN 55404 2 Cardiovascular and Critical Care Research Center, Children’s Minnesota , Minneapolis, MN 55404 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shengxu Li For correspondence: Shengxu.li{at}childrensmn.org mhuntley{at}chc-pa.org Dave Watson 1 Children’s Minnesota Research Institute, Children’s Minnesota , Minneapolis, MN 55404 3 Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic , Rochester, MN 55905 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alissa Jorgenson 4 Clinical and Translational Science Institute, University of Minnesota , Minneapolis, MN 55414 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zainab Adelekan 1 Children’s Minnesota Research Institute, Children’s Minnesota , Minneapolis, MN 55404 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathleen Garland 5 Hematology and Oncology, Children’s Minnesota , Minneapolis, MN 55404 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin Deonovic 1 Children’s Minnesota Research Institute, Children’s Minnesota , Minneapolis, MN 55404 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leah Zupancich 6 The Children’s Heart Clinic , Minneapolis, MN 55404 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Weihong Tang 7 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota , Minneapolis, MN 55454 Find this author on Google Scholar Find this author on PubMed Search for this author on this site David M. Overman 2 Cardiovascular and Critical Care Research Center, Children’s Minnesota , Minneapolis, MN 55404 6 The Children’s Heart Clinic , Minneapolis, MN 55404 8 Mayo Clinic-Children’s Minnesota Cardiovascular Collaborative Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marnie T. Huntley 6 The Children’s Heart Clinic , Minneapolis, MN 55404 8 Mayo Clinic-Children’s Minnesota Cardiovascular Collaborative Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marnie T. Huntley For correspondence: Shengxu.li{at}childrensmn.org mhuntley{at}chc-pa.org Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Children with congenital heart disease (CHD) have an increased risk of developing thrombosis. Coagulation markers are used to guide clinical anti-coagulation decisions. We aimed to identify circulating metabolites that are associated with coagulation markers in children with CHD. Methods Plasma samples were separated from whole blood under consistent conditions (consistent timing of blood draws and the same processing procedures). Untargeted metabolomic data were measured by Metabolon in plasma from young patients (age range: 0 days-24 years) with CHD before cardiac surgery. Coagulation markers included activated partial thromboplastin time (aPTT), prothrombin time (PT), activated clotting time (ACT), and international normalized ratio. Associations of individual metabolites with four coagulation markers were assessed with multivariable regression models, with false discovery rate (FDR) correction for multiple comparison. Results Out of 1,115 metabolites measured in samples from 203 patients, 776 met the quality control criteria. In total, 418 metabolites were associated with at least one marker, with one (valine) associated with ACT, three (gamma-CEHC, retinol, and behenoyl sphingomyelin) with PT, and 415 with aPTT (FDR q value < 0.05); among these, behenoyl sphingomyelin was associated with both PT and aPTT. Among the metabolites associated with aPTT, the top three were ornithine, X-21364 (identity unknown), and androstenediol disulfate. Metabolic pathway analysis based on the 415 metabolites significantly associated with aPTT suggested three pathways with an FDR q value<0.05: valine, leucine and isoleucine biosynthesis; histidine metabolism; and arginine and proline metabolism. Conclusion We have identified promising novel metabolites and metabolic pathways associated with coagulation markers in children with CHD. Future studies are warranted to confirm these findings and examine the implications of the links between these metabolites and coagulation markers. HIGHLIGHTS In this metabolomic study, we identified 418 metabolites associated with coagulation markers in young patients with CHD; These metabolites indicated three novel metabolic pathways in this patient population; Plasma metabolites clustered into eight modules that were also associated with coagulation markers. INTRODUCTION Thrombosis and bleeding are common after heart surgery in children with congenital heart disease (CHD) 1 , 2 . Both thrombosis and bleeding increase risk of death and prolong the length of hospital stay in these children. Coagulation markers are important clinical indicators that guide clinical anti-coagulation regimens. However, the hemostatic system is still under development in young children, and coagulation markers are known to be less correlated with hemostasis in children than in adults 3 , 4 , which exacerbate the challenges of designing effective anti-coagulation strategies in children with CHD scheduled to have corrective heart surgery. Furthermore, evidence-based clinical guidelines for preventing and treating thrombosis and bleeding in children with CHD are lacking 1 , 5 . Despite these challenges, coagulation markers in children with CHD are still widely used for anti-coagulation decision-making. To overcome the challenges in anticoagulation practice in children with CHD, it is necessary to have a better understanding of common coagulation markers in children. However, factors that may influence the measurements of these individual markers in children are not well understood. In recent years, metabolomic technology, which simultaneously measures up to tens of thousands of metabolites in biofluids, has made it possible to detect metabolic alterations and to elucidate new metabolic mechanisms on a hypothesis-free basis 6 . The non-targeted metabolomic approach—mass spectrometry coupled with liquid chromatography or gas chromatography—has been increasingly used to study complex pathophysiological processes 6 . In our previous report, we used non-targeted metabolomic technology and identified novel metabolites and metabolic pathways for thrombosis after heart surgery in children with CHD 7 . In the current study, we aimed to identify novel metabolites and metabolic pathways that are associated with common coagulation markers in the same cohort of patients with CHD scheduled to undergo cardiac surgery. METHODS The data that support the findings of this study are available from the corresponding authors upon reasonable request. The current study was approved by the Institutional Review Board of Children’s Minnesota. Study sample Young patients (aged 0-24 years) with CHD who planned to have corrective cardiac surgery at the Cardiovascular Care Center at Children’s Minnesota were eligible for the study. Patients with the following conditions were excluded: 1) body weight < 1800 grams at the time of surgery; 2) receiving extracorporeal membrane oxygenation at the time of surgery; 3) known clotting or bleeding disorder (e.g., Von Willebrand disease, Factor V Leiden, Factor VIII or Factor IX deficiency, methylenetetrahydrofolate reductase gene mutation); 4) isolated patent ductus arteriosus ligation; 5) parent or legal guardian unable to provide informed consent due to diminished capacity; and 6) having a second surgery during the same hospitalization. Data and blood sample collection began in December 2019 and ended in August 2022. Clinical data collection Clinical data, including demographics, diagnoses, pre-operation treatments, medical history (i.e., thrombosis or bleeding events), coagulation markers, and other relevant laboratory tests, were retrieved from the electronic medical record by A.J and Z.A. The coagulation markers in the current study included activated partial thromboplastin time (aPTT), prothrombin time (PT), activated clotting time (ACT), and international normalized ratio (INR). These markers were measured or calculated as part of routine clinical care. Blood sample collection and processing Blood samples were collected right before incision across the study period. Immediately after the induction of anesthesia and before incision, 0.5-1.0 ml of whole blood was drawn through a central line into an EDTA tube, which was then inverted 8-10 times to assure anticoagulation. The blood sample was immediately put into an ice bag and transferred to the lab at Children’s Minnesota for immediate separation of plasma. A laboratory technician further inverted the tube manually a minimum of 20 times prior to centrifuging the sample for 10-15 minutes at a minimum of 2000 g. Separated plasma was aliquoted into a pre-chilled, barcoded cryovial (0.7 mL) provided by Metabolon (Morrisville, NC, a service provider for the study). Plasma samples were flash-frozen at −80°C before being shipped to Metabolon for metabolomic quantification. Metabolomic quantification Metabolon quantified all plasma samples, which were shipped in two separate batches. Metabolon used the untargeted, ultrahigh performance liquid chromatography-tandem mass spectroscopy-based metabolomic quantification protocol and followed rigorous protocols for sample handling, processing, and metabolites profiling, including the use of internal standards, blank samples, and technical replicates. Metabolon applied several procedures to the raw metabolite data: first, the data were batch-adjusted by scaling raw metabolite levels to have a within-batch median of 1, then missing data were imputed to the median, and finally a log transformation was applied. In addition to chemical names of quantified metabolites, Metabolon-specific chemical ID, along with the Human Metabolome Database (HMDB) ( https://www.hmdb.ca/ ) ID, was used to facilitate data analyses. Some metabolites did not have an HMDB ID. We performed additional quality assurance via 5 within-batch and 10 between-batch duplicate samples, which were blinded to Metabolon. Spearman correlations were calculated between all duplicate groups for each metabolite. Only metabolites with call rates (i.e., non-missing data rates) ≥ 75% and with all Pearson correlation coefficients ≥ 0.5 were included for analysis. Among duplicate pairs, one sample was randomly selected for analysis. Statistical analysis Individual metabolite analysis Multiple linear regression models were used to examine the associations of individual metabolites with each coagulation marker, while adjusting for age, race, sex, personal history of thrombosis, personal history of postoperative bleeding, oxygen saturation, hemoglobin, anticoagulation medication, and STAT (Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery) score, which ranks the complexity of the planned surgery. For coagulation markers, PT, aPTT, ACT, and INR were natural log-transformed to increase normality. We normalized the batch-adjusted, median-imputed, and natural log-transformed concentration of individual metabolites to z-scores before further analysis, so regression effect sizes are interpretable as the predicted change per one standard deviation increase on the log scale of concentration. Robust standard errors were used to account for non-normality of residuals errors. To control for multiple comparisons, the positive false discovery rate (FDR) was controlled at the 5% level, specifically using a 0.05 significance threshold for the q-value—the FDR analogue of the p-value 8 , 9 . Controlling the FDR means that among the metabolites identified as significant, we expect at most 5% to be false positives. Regression models and Q-values were computed using SAS Enterprise Guide software (version 7.12, Cary, NC, USA), specifically the “GENMOD” procedure with the “REPEATED” option and the “MULTTEST” procedure with the “PFDR” option, respectively. Pathway analysis We used MetaboAnalyst 6.0 ( https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml ), an online bioinformatics tool developed by Xia J., et al 10 , 11 , to analyze metabolomics pathways (a.k.a., MetPA) 12 . The HMDB IDs for the metabolites that showed a significant association with aPTT (i.e., q-value < 0.05) were input into the MetPA platform. In the current study, pathway enrichment was determined by over-representation analysis, and the pathway impact was determined by the sum of the importance measures of the matched metabolites normalized by the sum of the importance measures of all metabolites in each pathway. Weighted Gene Co-expression Network Analysis (WGCNA) We performed WGCNA to identify modules (or clusters) of metabolites that were correlated together 13 . Metabolomic data are well suited for such analysis because metabolites in the body are inherently and closely correlated across interconnected metabolic pathways. WGCNA was used to detect and define modules of metabolites based on pair-wise correlations. Each module was summarized at a patient level using “eigenmetabolites”, the first principal component of the metabolites within a given module, and these eigenmetabolites were then examined for correlation with coagulation markers. This analysis was performed via the “WGCNA” library in R (version 4.3.0) 13 – 15 . To further explore the metabolic implications of the identified modules, we also performed pathway analysis for individual modules where possible. RESULTS From December 2019 to July 2021, 203 patients aged 0-24 years were enrolled with unique surgical encounters. Characteristics and coagulation markers of the study sample are shown in Table 1 . A detailed quality control procedure for metabolomic data has been described previously 7 . Our final dataset included 776 metabolites that met our filtering criteria. View this table: View inline View popup Download powerpoint Table 1. Characteristics of the study sample In individual metabolite analyses, valine was associated with ACT; gamma-CEHC, retinol, and behenoyl sphingomyelin (d18:1/22:0) were associated with PT; and none were associated with INR. In comparison with the few metabolites associated with ACT, PT, or INR, 415 metabolites were associated with aPTT (see Table 2 for the top 10 metabolites and Table S1 for the full list). Behenoyl sphingomyelin was the only metabolite associated with both PT and aPTT. Pathway analysis by MetaboAnalyst 6.0 for the 415 metabolites significantly associated with aPTT suggests three pathways ( Figure 1 ): valine, leucine and isoleucine biosynthesis; histidine metabolism; and arginine and proline metabolism. Download figure Open in new tab Figure 1. Pathway analysis results from MetaboAnalyst 6.0 based on 415 significant metabolites associated with aPTT. The three named pathways were statistically significant (FDR q value < 0.05 for all). View this table: View inline View popup Download powerpoint Table 2. Metabolites showing the most significant associations with coagulation markers According to WGCNA, there were eight modules (clusters) among the 776 metabolites, which we named (by color) Turquoise, Pink, Red, Yellow, Brown, Blue, Black, and Green ( Figure 2 ). The Grey module included all metabolites that did not belong to any of those eight modules. Compositions of the metabolite classifications (for example, lipids or amino acids) for these modules can be found in Figure S1 ; membership details for each module are listed in Table S2 . Pathway analysis by MetaboAnalyst 6.0 disclosed a few suggestive metabolic pathways with an FDR q value less than 0.05 ( Table S3 ). Correlations between coagulation markers and eigenmetabolites are shown in Figure 3 ; as an example, PT was significantly and positively associated with the eigenmetabolite Turquoise, according to which pathway analysis suggested a few metabolic pathways including glycerophospholipid metabolism, arginine biosynthesis, nicotinate and nicotinamide metabolism, and histidine metabolism ( Table S3 ). Download figure Open in new tab Figure 2. Metabolomic modules according to WGCNA results. The y-axis represents relative hierarchical distance across submodules. WGCNA: weighted gene co-expression network analysis Download figure Open in new tab Figure 3. Associations of metabolite modules with coagulation markers. ACT: activated clotting time; aPTT: activated partial thromboplastin time; INR: international normalized ratio; PT: prothrombin time DISCUSSION In the current study, we identified one metabolite associated with ACT, two metabolites associated with PT, and a large number of metabolites (415) associated with aPTT. The metabolites associated with aPTT also indicated three metabolic pathways. We also demonstrated that metabolites in the blood tended to cluster into groups within which member metabolites were closely correlated. To our knowledge, the current study represents the first attempt to link metabolomic data to coagulation markers in young patients with CHD. Clustering of metabolites from the WGCNA analysis indicates, as expected, that metabolites in the body are closely correlated. Further, these modules (clusters) showed correlations with coagulation markers. The pathway analysis for these modules suggested a number of metabolic pathways, a finding that has implications for future research directions. The number of metabolomic studies of coagulating markers in the literature is limited. Valine was the only metabolite associated with ACT. Valine was a branched chain amino acid, the metabolism of which has been shown to promote thrombosis risk by enhancing tropomodulin-3 propionylation in platelets 16 . The inverse association of valine with ACT was consistent with such a connection between branched chain amino acid metabolism and thrombosis. Gamma-CEHC was among the three metabolites associated with PT and is a metabolite of vitamin E, which has both anti-coagulant 17 and antiplatelet properties 18 . Vitamin E is also shown to inhibit human platelets aggregation by a protein kinase C-dependent mechanism 18 . Interestingly, the serum level of vitamin E was associated with bleeding risk in patients receiving oral anti-coagulant therapy in an observational study 19 . The inverse association of gamma-CEHC with PT in the current study may suggest that rapid metabolism or depletion of vitamin E increases risk of thrombosis. Retinol, or vitamin A, has been shown to induce platelet aggregation via activation of phospholipase A2 20 , which is consistent with the inverse association of retinol with PT found in the current study. The fact that there was little overlap among metabolites associated with different coagulation markers suggests that these coagulation markers measure distinct aspects of the coagulation cascades. Behenoyl sphingomyelin, associated with both PT and aPTT in the current study, is synthesized from ceramide and is abundant in plasma membranes. Sphingomyelin has been suggested to be associated with atherosclerosis, lipoprotein metabolism, and diabetes 21 . Further, a recent study demonstrated that alterations to sphingomyelin metabolism affect hemostasis and thrombosis 22 . The relevance of the above metabolites to hemostasis and thrombosis offers validation for the metabolomic approach to studying coagulation markers. It was an unexpected finding that the majority of analyzed metabolites (415 in total) were associated with aPTT. It is not clear to us what causes underline the large number of associations. According to WGCNA analysis, however, one cause is that metabolites tend to cluster ( Figure 1 ). Given the large number of associated metabolites, it is not feasible to discuss the links of all these metabolites individually to coagulation processes. The large number of associated metabolites with aPTT may indicate that aPTT is broadly associated with overall metabolic status in children with CHD and that extensive metabolic factors are involved in the coagulation process that the test measures. Ornithine, the metabolite with the lowest p value, belongs to the ornithine cycle and has been shown to be associated with coagulation markers in patients with COVID-19 infection 23 . It is worth noting that among the 10 metabolites showing the lowest p values, androstenediol (3beta,17beta) disulfate, X-21364 (identity unknown), X-24544 (identity unknown), 3-amino-2-piperidone, 21-hydroxypregnenolone disulfate, and 1-palmitoleoyl-GPC (16:1) were also associated with thrombosis after heart surgery in the same study sample 7 . The three metabolic pathways indicated by the 415 metabolites provide novel insight into the metabolic relevance of the pathways to the hemostatic status measured by aPTT. The branched chain amino acid biosynthesis pathway is indicated for both aPTT in the current study and thrombosis in our previous report 7 , suggesting that the pathway and its associated metabolites are important to the hemostatic balance. Despite the relevance of some of the identified metabolites to hemostasis and thrombosis, how exactly the identified metabolites affect coagulation markers, which involve numerous proteins and contributing factors, remains to be fully examined. Nevertheless, the identification of the metabolites indicates novel pathways for the hemostatic system, which suggests future research directions. The current study has strengths and limitations. We adopted rigorous quality assurance and control protocols, including consistency in the timing of blood draws and processing of blood samples and the use of blind duplicate blood samples. We used the “GENMOD” procedure with “REPEATED” option to account for potential deviation from normal distribution for the coagulation markers. As for limitations, the analyses were cross-sectional in nature, and the associations observed in the study might be subject to potential bias. The coagulation markers were retrieved from the electronic medical record and were not measured for research purposes. Further, children with CHD in the study had complex medical conditions, which could not be fully accounted for in statistical analyses. In pathway analysis, some metabolites do not have an identifier in the HMDB, which might have biased against or for certain metabolic pathways. Finally, our study sample was from young patients with CHD who needed corrective surgery, which limits the generalizability of our findings. In conclusion, we have identified novel metabolites associated with coagulation markers that are routinely measured in clinical practice. While few or no metabolites were associated with PT, ACT, and INR, the large number of associated metabolites associated with aPTT suggests that factors linked to aPTT are diverse and that aPTT is broadly influenced by metabolic status. The metabolic pathways indicated by the identified metabolites may help direct future research on how to assess hemostatic processes, particularly in children. Given that the current study was based on data from young patients with CHD, future studies are warranted to examine the metabolic signatures associated with coagulation status in the general pediatric population. Data Availability All data produced in the present study are available upon reasonable request to the authors. SOURCES OF FUNDING The current study was supported by a grant from the Research Committee of Children’s Minnesota. The funding agency did not have any role in conducting the study or interpreting the final results. DISCLOSURES None. SUPPLEMENTAL MATERIAL Table S1. Figure S1. Table S2. Table S3. ACKNOWLEDGEMENTS The current study involved many colleagues at Children’s Minnesota, including colleagues at the Minneapolis Central Lab, the Cardiovascular Care Center, the Children’s Heart Clinic, and anesthesiologists, whose contributions are greatly appreciated. The authors of the study also wish to thank the children who participated in the study and their families. The authors thank Martin Cozza for his help with editing the manuscript. Non-standard Abbreviations and Acronyms ACT activated clotting time CHD congenital heart disease CI confidence interval FDR false discovery rate INR international normalized ratio PT prothrombin time aPTT activated partial thromboplastin time STAT Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery WGCNA weighted gene co-expression network analysis References 1. ↵ McCrindle BW , Li JS , Manlhiot C , Tweddell JS , Giglia TM , Massicotte MP , Monagle P , Krishnamurthy R , Mahaffey KW , Michelson AD , et al. 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