Preoperative, Operative, and Immediate Postoperative Predictors of Onset of Low Cardiac Output Syndrome in Congenital Heart Disease

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Allan, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8080625/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Low cardiac output syndrome (LCOS) is a leading cause of morbidity and mortality after congenital heart disease (CHD) surgery. Early, transparent risk estimation at pediatric cardiac intensive care unit admission could guide monitoring and resource allocation. Objective To develop and evaluate a multivariable model that estimates the risk of LCOS using routinely available preoperative, intraoperative, and immediate postoperative variables. Methods In this single-center retrospective observational cohort, children ≤ 18 years undergoing CHD surgery between February 2023 and November 2024 were included. LCOS was defined using prespecified criteria from the Pediatric Cardiac Critical Care Consortium (PC4). Candidate predictors were screened in univariate analyses; independent associations were estimated with multivariable logistic regression and backward elimination. Results Among 191 patients, 46 (24%) developed LCOS. Independent predictors were higher surgical complexity (Risk Adjustment for Congenital Heart Surgery [RACHS-1] ≥ 4; adjusted odds ratio [AOR] 3.69, 95% CI 1.38–9.81), preoperative inotrope use (AOR 2.75, 1.02–7.43), longer cardiopulmonary bypass duration (AOR 1.01 per minute, 1.00–1.02), and a greater number of prior cardiac operations (AOR 2.00 per operation, 1.34–2.97); higher operative weight was protective (AOR 0.92 per kilogram, 0.87–0.97). Model performance metrics were area under the receiver-operating characteristic curve (AUROC) 0.879 and area under the precision–recall curve (AUPRC) 0.706; at a prespecified decision threshold, accuracy 0.817, positive predictive value (PPV) 0.657, sensitivity (recall) 0.50, F1 score 0.568, and negative predictive value (NPV) 0.853. Conclusions A parsimonious, interpretable model derived from routinely collected data identifies children at increased risk of LCOS at ICU arrival and can inform early intervention and staffing. Prospective multicenter validation and dynamic updating with continuous postoperative physiology are warranted. Congenital heart disease Low cardiac output syndrome Pediatric cardiac surgery Risk prediction Logistic regression Intensive care Propensity score Figures Figure 1 Introduction Low-cardiac-output syndrome (LCOS) arises in the postoperative period when there is a clinically significant imbalance between systemic oxygen delivery and tissue oxygen consumption [ 1 ]. Among infants and children undergoing repair or palliation of congenital heart disease (CHD), LCOS has been documented in 24–71% of procedures, with the incidence varying according to the diagnostic criteria applied [ 2 – 8 ]. Up to one half of early postoperative deaths in CHD are attributed to LCOS [ 9 ], yet an universally accepted definition is still lacking: a recent review identified 262 distinct sets of diagnostic criteria published before 2020 [ 10 ]. In routine practice, clinicians monitor several indicators that consistently feature in most definitions of LCOS, including: a reduced invasively measured cardiac index; elevated serum lactate; widened arterio-venous oxygen difference; a greater core-to-peripheral temperature gradient; oliguria/acute kidney injury; and increasing requirements for vasoactive or inotropic medications as reflected by the vasoactive-inotropic score (VIS); each of these variables is tracked throughout the first 72 h after surgery [ 1 – 3 , 6 , 8 , 10 ]. To optimize postoperative management, monitoring, and staffing, it is critical to identify patients at elevated risk of LCOS and to obtain this estimate immediately after surgery. While the literature consistently links LCOS with patient-level and operative factors—such as younger age, lower preoperative oxygen saturation, bidirectional shunting, longer cardiopulmonary bypass, and residual postoperative shunt—large-scale assessments of preoperative scoring systems are sparse, and most studies do not provide a risk estimate usable at ICU arrival [ 11 – 17 ]. In this study, we present the findings of a statistical analysis that integrates preoperative, intraoperative, and immediate postoperative variables to estimate the risk of developing LCOS during the early postoperative intensive care period. By providing transparent risk estimates several hours before overt hemodynamic deterioration, the model is intended to support timely clinical intervention, improve patient outcomes, and facilitate more efficient allocation of intensive-care resources. Method Design setting This retrospective observational study received approval from the Cleveland Clinic Institutional Review Board (IRB #20–390). All procedures were conducted in accordance with the ethical standards of the responsible institutional and/or regional review committees and with the principles of the 1975 Declaration of Helsinki. Participants Children ≤ 18 years of age who were admitted to the Pediatric Cardiac Intensive Care Unit (PCICU) at Cleveland Clinic Children’s after cardiothoracic surgery for CHD between February 2023 and November 2024 were enrolled in this study. Patients older than 18 years were excluded from study. Reporting followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline (Supplementary Table-1) [ 18 ]. Data source and measurements Clinical and perioperative data were obtained retrospectively from the institutional electronic medical record (EPIC Systems, Verona, WI) and the Pediatric Cardiac Critical Care Consortium (PC4) registry database. Laboratory values, hemodynamic parameters, and echocardiographic findings were extracted from routinely recorded clinical documentation. Data accuracy was verified by two independent investigators through cross-checking random samples of entries against the source charts. Variables Patient characteristics included chronological age, operative weight, prematurity, documented genetic anomalies, single-ventricle physiology, the presence of a bidirectional shunt on echocardiography, preoperative endotracheal intubation, and preoperative vasoactive/inotropic support; together, these variables reflected preoperative physiological status. In the operative and postoperative periods, procedural complexity was classified as high risk for the Risk Adjustment for Congenital Heart Surgery (RACHS-1) scores > 3 and Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) categories > 2. Procedure-specific metrics comprised total cardiopulmonary bypass duration, aortic cross-clamp time, and circulatory-arrest time, as well as the number of prior cardiac operations and whether a heart transplant was performed. Intraoperative transesophageal or epicardial echocardiography findings of residual shunt or lesion and the decision to leave the sternum open postoperatively (delayed sternal closure) were also recorded. Variables for the multivariable analysis were selected based on the univariate results. In this study, LCOS was defined according to the PC4 registry criteria [ 1 , 5 ]: (1) a vasoactive–inotropic score (VIS) > 15 at any time; (2) a threefold increase in VIS within any 48-hour interval, including postoperative escalation of support, with the resultant VIS ≥ 10; (3) an arteriovenous oxygen (AVO₂) difference > 40% measured invasively or by near-infrared spectroscopy (NIRS); or (4) documentation of LCOS in a physician note. PC4 prospectively records the earliest qualifying time point for each case. Statistical Analysis All eligible patients meeting inclusion criteria during the study period were included using a consecutive sampling approach to minimize selection bias. Continuous variables are summarized as mean ± SD or median (IQR) according to distribution, and categorical variables as counts. Normality was assessed using the Shapiro–Wilk test (α = 0.05); the null hypothesis of normality was retained when p > 0.05. Group comparisons were performed with the χ² or Fisher exact test for categorical data, the Student t -test for normally distributed continuous data, and the Wilcoxon rank-sum test for non-normal data. Propensity-score distributions between LCOS and non-LCOS were compared using a shift plot and the Mann–Whitney U test. The shift plot computes a shift function for two (in)dependent groups using the robust Harrell–Davis quantile estimator with bias-corrected bootstrap confidence intervals [ 19 ]. Univariate screening for candidate predictors of LCOS employed point-biserial, tetrachoric, polychoric or Spearman correlations, with variables retained for multivariable modelling when p 0.05) were sequentially removed by backward elimination. Model discrimination is reported with adjusted pseudo-R² (AOR) and odds ratios with 5 % confidence intervals (CI). All tests were two-sided and a p -value < 0.05 denoted statistical significance. Analyses were performed in Stata 17 (StataCorp, College Station, TX and SAS 9.3©). Table 1 Demographic and preoperative characteristics by LCOS status LCOS No Yes P Value Correlation a Male (%) 81 (55.8) 30 (65.2) 0.34 Patient age expressed in days, median (IQR) 1162 (148–4172) 123 (8-497) < 0.001 -0.36 b Patient’s weight at the time of surgery (kg), median (IQR) 13.4 (5.8–39.1) 4.9 (3.1–8.9) < 0.001 -0.36 b Prematurity, n (%) 15 (10.3) 8 (17.4) 0.088 0.19 Genetic Anomaly, n (%) 38 (26.2) 14 (30.4) 0.126 0.07 Single Ventricle, n (%) 12 (8.3) 17 (37) < 0.001 0.58 Diagnostic Group, n (%) c NNA AS 10 (6.9) 0 (0) ASD 12 (8.3) 0 (0) AVC 9 (6.2) 2 (4.4) CHD 4 (2.8) 2 (4.4) CM 4 (2.8) 2 (4.4) COA 9 (6.2) 2 (4.4) D-TGA 4 (2.8) 2 (4.4) DORV 1 (0.7) 3 (6.5) HLHS 5 (3.5) 12 (26.1) PA 3 (2.1) 4 (8.7) TA 1 (0.7) 1 (2.2) TOF 13 (9) 2 (4.4) VSD 50 (34.5) 12 (26.1) Other 20 (13.8) 2 (4.4) Preop Intubation n (%) 6 (4.1) 17 (37) < 0.001 0.72 Preop Inotrope, n (%) 14 (9.7) 15 (32.6) < 0.001 0.48 Prior Cardiac Surgeries, n (%) 44 (30.3) 19 (41.3) 0.23 Preop Bidirectional, n (%) 24 (16.6) 12 (26.1) 0.06 0.19 Table 1 . a Tetrachoric Correlations unless otherwise specified, b Point-Biseral Correlation, c no comparison statistics were run. Abbreviations: Aortic-valve stenosis (AS), Atrial septal defect (ASD), Coarctation of the aorta (CoA), Dextro-transposition of the great arteries (D-TGA), Double-outlet right ventricle (DORV), Hypoplastic left heart syndrome (HLHS), Interrupted aortic arch (IAA), Pulmonary atresia (PA), Tetralogy of Fallot (TOF), Tricuspid-valve atresia (TA), Ventricular septal defect (VSD), Interquartile range (IQR). Results Between February 2023 and November 2024, 191 patients (80 female) admitted to the pediatric cardiac intensive care unit (PCICU) who met the study criteria were included; their demographic and baseline clinical characteristics are shown in Table 1 . According to the PC4 registry, 46 of these 191 patients (24%) developed LCOS. Children who developed LCOS were younger at surgery (123 days [8–497] vs 1162 [148–4172], p < 0.001) and had lower weight at time of cardiac surgery (4.9 kg [3.1–8.9] vs 13.4 kg [5.8–39.1], p < 0.001). Single-ventricle physiology was more common (37% vs 8.3%, p < 0.001), as were preoperative intubation (37% vs 4.1%, p < 0.001) and preoperative inotrope use (32.6% vs 9.7%, p < 0.001). Prematurity (17.4% vs 10.3%), genetic anomalies (30.4% vs 26.2%), prior cardiac surgery (41.3% vs 30.3%), and male sex (65.2% vs 55.8%) did not distinguish the groups at a statistically significant level (all p > 0.05). Diagnostic patterns suggested enrichment of Hypoplastic left heart syndrome (HLHS) (26.1% vs 3.5%) and Double-outlet right ventricle (DORV) (6.5% vs 0.7%) in LCOS, with no LCOS cases among Aortic-valve stenosis (AS) or Atrial septal defect (ASD); Ventricular septal defect (VSD) remained common in both groups (26.1% vs 34.5%). Preoperative bidirectional shunt was somewhat more frequent in LCOS (26.1% vs 16.6%). Operative and intraoperative features also diverged between groups. Heart transplantation was more frequent in LCOS (8.7% vs 0.7%). High-risk procedure categories were overrepresented (RACHS-1 > 4: 52.2% vs 10.3%; STAT > 3: 65.2% vs 29.7%). Open sternum postoperatively was notably more common (37% vs 1.4%), while residual shunt rates were similar (17.4% vs 15.2%). Procedural durations are reported as mean minutes ± SD and were longer in LCOS: cardiopulmonary bypass 122.2 ± 51.0 vs 82.5 ± 41.9, aortic cross-clamp 67.7 ± 43.2 vs 56.2 ± 36.6, and circulatory arrest 4.6 ± 10.7 vs 0.1 ± 1.25. See Table 2 . Table 2 Operative and Immediate postoperative characteristics by LCOS status LCOS No Yes P Value Correlation a Heart Transplant, n (%) 1 (0.7) 4 (8.7) 0.012 0.6 RACHS1 > 4 (high), n (%) 15 (10.3) 24 (52.2) 3 (high), n (%) 43 (29.7) 30 (65.2) < 0.001 0.51 Open Sternum, n (%) 2 (1.4) 17 (37) < 0.001 0.84 Residual Shunt, n (%) 22 (15.2) 8 (17.4) 0.168 0.05 Total cardiopulmonary bypass duration, in minutes, median (IQR) 76 (56–108) 117 (88–153) < 0.001 0.35 b Aortic cross-clamp time, in minutes, median (IQR) 48 (32–82) 72 (47–89) 0.052 0.14 b Duration of circulatory arrest during bypass, in minutes, median (IQR) 0 (0–0) 0 (0–0) N/A c N/A Table 2 . a Tetrachoric Correlations unless otherwise specified; b Point-Biseral Correlation; c 90% of both groups had zero duration There were 6 deaths (3.1%) in our cohort. 33 patients (17.2%) experienced at least one major complication. Seven patients (3.6%) sustained a cardiopulmonary arrest; 2 (1.0%) underwent extracorporeal cardiopulmonary resuscitation (E-CPR), and 2 (1.0%) progressed to brain death. 7 patients (3.6%) required extracorporeal life support (ECLS). Twenty-two patients (11.5%) developed a clinically significant arrhythmia necessitating treatment. Three patients (1.6%) developed renal dysfunction requiring continuous renal replacement treatment (CRRT). Ten patients (5.2%) developed sepsis. Twenty-six patients (13.6%) required a prolonged duration of mechanical ventilation; the overall MV duration was a median of 63 hours [IQR 13–171]. The median PCICU length of stay (LOS) was 2 days [IQR 1–9] and the median hospital LOS was 6 days [IQR 3.0–20.5]. All covariates associated with LCOS at p < 0.10 in univariate analyses and present in ≥ 5% of patients were entered into a logistic regression; the full model (pseudo-R²=0.33) and the backward-selected trimmed model are summarized in Table 3 . In the trimmed model, markers of operative complexity and preoperative instability independently increased LCOS risk, whereas higher operative weight was protective. Each additional prior cardiac surgery doubled the odds of LCOS (AOR 2.00, 95% CI 1.34–2.97); procedures with RACHS-1 ≥ 4 carried ~ 3.7-fold higher odds (AOR 3.69, 1.38–9.81); preoperative inotrope use was associated with higher odds (AOR 2.75, 1.02–7.43); and longer CPB duration conferred incremental risk (AOR 1.01 per minute, 1.00–1.02). Each additional kilogram at surgery was associated with lower odds (AOR 0.92, 0.87–0.97). Model performance was good (AUROC 0.879; PRAUC 0.706); accuracy 0.817, positive predictive value (PPV) 0.657, recall 0.50, F1 0.568, and negative predictive value (NPV) 0.853, indicating strong discrimination with moderate sensitivity. Table 3 Multivariable model predicting Low Cardiac Output Syndrome. AOR (CL) P Value Full Model Natural-logarithm transform of patient age in days 0.79 (0.56–1.12) 0.181 Prematurity (Ref = No) 1.44 (0.4–5.19) 0.576 Single Ventricle (Ref = No) 1.64 (0.43–6.24) 0.47 Number of Prior Cardiac Surgeries 2 (1.15–3.47) 0.014 Patient’s weight at the time of surgery (kg) 0.96 (0.9–1.01) 0.124 Preop Inotrope, n (%) 2.7 (0.92–7.98) 0.071 Having a RACHS-1 score of 4 or above (Ref = No) 3.61 (1.09–11.9) 0.035 STAT score of 3 or above 0.53 (0.16–1.79) 0.305 Preop Bidirectional (Ref = No) 1.33 (0.46–3.85) 0.596 Total cardiopulmonary bypass duration, in minutes 1.02 (1-1.04) 0.039 Aortic cross-clamp time, in minutes 0.99 (0.97–1.01) 0.422 Pseudo R2 = 0.33 Trimmed Model Number of Prior Cardiac Surgeries 2.00 (1.34–2.97) < 0.001 Patient’s weight at the time of surgery (kg) 0.92 (0.87–0.97) 0.004 On a Preoperative Inotrope (Ref = No) 2.75 (1.02–7.43) 0.046 Having a RACHS-1 score of 4 or above (Ref = No) 3.69 (1.38–9.81) 0.009 Total cardiopulmonary bypass duration 1.01 (1.00-1.02) 0.014 Table 3 . Multivariable logistic regression models predicting low cardiac output syndrome (LCOS). The Full model includes all candidate predictors meeting inclusion criteria, while the Trimmed model presents the most parsimonious set of independent predictors. Significant variables in the final model included number of prior cardiac surgeries, lower body weight, preoperative inotrope use, higher RACHS-1 category, and longer cardiopulmonary bypass duration. The final regression equation was: logit(p) = − 2.45 + 0.69 × (Number of Prior Surgeries) − 0.04 × (Weight at Surgery) + 1.01 × (Preop Inotrope) + 1.30 × (RACHS-1 ≥ 4) + 0.013 × (CPB Time). When the trimmed model’s predicted probabilities were used as propensity scores, the LCOS distribution conformed to normality (p = 0.14), whereas the non‑LCOS distribution deviated significantly from normality (p < 0.001) and was left-skewed toward lower scores. Quantile-wise differences were systematic between groups (Mann–Whitney U p < 0.001); details are presented in the shift plot in Fig. 1 (see Fig. 1 ). Discussion In a cohort of 191 postoperative congenital heart disease patients, LCOS occurred in 24% (46/191), at the lower bound of the ≈ 9–71% incidence reported in prior pediatric series using heterogeneous definitions [ 1 , 11 ]. Patients who developed LCOS were younger, had lower weight, more often required preoperative intubation/inotropes, and had higher operative complexity (RACHS-1 ≥ 4, longer CPB). In multivariable analysis, prior surgeries, RACHS-1 ≥ 4, preoperative inotrope use, and longer CPB independently increased LCOS risk, whereas greater operative weight was protective. The trimmed model achieved good discrimination (AUROC 0.879; PRAUC 0.706). Propensity scores were approximately normal in the LCOS group (Shapiro–Wilk p = 0.14) but left-skewed among non-LCOS (p < 0.001), indicating that while low scores help rule out LCOS, some patients with low propensity still developed LCOS. This skewed distribution of LCOS risk is a key finding of our study. Concordance with prior literature and novelty. In prior pediatric series, LCOS has been observed in nearly half of patients following congenital heart surgery and remains the leading cause of postoperative mortality in children[ 11 ]. Our cohort’s 24% incidence is broadly comparable and underscores the clinical burden. Prior published work identified risk factors of LCOS including preoperative hemodynamic reserve and surgical history, include inotrope requirement, intubated status, and prior operations, consistent with our findings that preoperative inotropes and previous surgeries track with higher LCOS risk [ 12 , 13 , 15 ]. In infant-focused studies, longer aortic cross-clamp time and lower body weight are independent predictors[ 20 ]; in our model, higher operative weight was protective and a time-related intraoperative load (longer CPB) conferred incremental risk, aligning with that pattern. These observations complement evidence that LCOS severity captured at the bedside (e.g., LCOSS) relates strongly to morbidity and PCICU resource utilization [ 21 ], and they reinforce the value of combining preoperative/intraoperative features with early postoperative dynamics in risk stratification. Preoperative profile Our finding that preoperative inotrope requirement and preoperative endotracheal intubation independently predict LCOS is consistent with the broader literature that treats preoperative instability as a bedside proxy for limited circulatory reserve and heightened postoperative risk [ 12 , 13 , 15 ]. In large pediatric cohorts and meta-analyses, younger age and lower operative weight repeatedly emerge as core risk factors [ 12 , 13 ], which matches our model’s observation that higher weight is protective. The presence of syndromes (e.g. Down syndrome) highlight compounding pulmonary comorbidities (e.g., pulmonary hypertension) that can worsen hemodynamics and complicate management [ 22 , 23 ]. Overall, our findings are consistent with prior pediatric reports, reinforcing their relevance to contemporary clinical experience [ 12 , 13 , 15 , 22 ]. Intra and postoperative profile Surgical complexity, captured by longer CPB duration and higher RACHS/STAT, likely amplifies circulatory stress and the inflammatory cascade, predisposing to LCOS; these associations are consistently documented across pediatric studies and a recent meta-analysis [ 12 , 13 ]. The classic postoperative time course described by Wernovsky (cardiac index nadir ~ 9–12 h after surgery) contextualizes why LCOS often declares itself hours after the OR, when reperfusion injury and vasoplegia peak [ 24 ]. Targeted pharmacologic prevention has biologic plausibility but risk remains concentrated in high-complexity cases[ 14 ]. Markers of severity used in practice, such as LCOSS and VIS, track morbidity and resource utilization [ 21 ], whereas certain single physiologic readouts may be insufficiently specific for new organ injury [ 17 ]. Consistent with our cohort (higher “open sternum” rates in LCOS), delayed sternal closure is typically deployed for hemodynamic instability and is associated with greater morbidity [ 16 ]. Beyond the OR, fluid overload is a powerful and modifiable risk factor linked to mortality/morbidity in pediatric cardiac surgery [ 25 ]. Looking ahead, converging evidence argues for continuous, multimodal monitoring and analytics-assisted early warning: non-invasive cardiac output (CO) monitoring in CHD and emerging machine-learning models illustrate how integrating pre/intra/postoperative signals (including lactate and blood-pressure dynamics) can improve discrimination and timeliness of LCOS detection [ 1 , 26 , 27 ]. Interpreting physiological markers Interpreting physiologic markers for impending LCOS remains difficult because there is still no uniform definition and single variables perform inconsistently across settings [ 28 ]. Recent reviews emphasize the ongoing lack of consensus definitions, complicating comparisons and surveillance strategies, and argue for multimodal assessment instead of reliance on any single metric [ 29 ]. In practice, scores derived from vasoactive support correlate with adverse outcomes after pediatric cardiac surgery, but they are therapy-dependent surrogates rather than direct measures of oxygen delivery [ 4 , 28 , 30 , 31 ]. Ulate and colleagues proposed the LCOS Score (LCOSS) to fold clinical signs (e.g., tachycardia, oliguria, toe temperature, NIRS, lactate) together with pharmacologic support; in their infant cohort, cumulative LCOSS discriminated composite morbidity well (AUC ≈ 0.83), yet the construct still leaned heavily on treatment intensity and lacked concurrent validation against direct cardiac output or oxygen-delivery measurements [ 21 ]. For example, postoperative cerebral NIRS, while useful for trend monitoring, has not consistently predicted adverse outcomes, underscoring the limitations of relying on any single physiologic marker for LCOS risk stratification [ 17 ]. Taken together, these data support our approach: multivariable, integrative prediction that combines preoperative and intraoperative risk factors with early postoperative physiology offers a more robust path to actionable LCOS risk stratification than reliance on any single marker alone [ 29 ]. Building on this rationale, integrated real-time analytics can fuse multiple physiological and laboratory streams to estimate risk states (e.g., inadequate oxygen delivery) continuously. Model-based indices computed every few seconds from routinely available inputs have been shown to track the probability of dangerously low venous saturation and to correlate with lactate and adverse events; external validations report strong discrimination for detecting low SvO₂ even when some inputs are missing [ 32 , 33 ]. In parallel, pediatrics has seen a broader shift from single-signal monitoring to proactive risk surveillance: collaborative, low-tech prevention bundles in cardiac ICUs achieved ~ 30% reductions in risk-adjusted in-hospital cardiac arrest that were sustained across centers, while machine-learning models using routine electronic health records data can flag arrest risk hours in advance [ 34 – 36 ]. Taking together, this evidence supports our rationale for multivariable prediction, combining preoperative and intraoperative risk factors with early postoperative physiology is more likely to surface actionable LCOS risk than any single marker alone [ 37 ]. Propensity-score distributions and clinical implications Using the trimmed model’s predicted probabilities as propensity scores, we observed a left-skewed distribution among non-LCOS patients, whereas the LCOS distribution did not deviate from normality (Shapiro–Wilk p = 0.14). Quantile-wise contrasts via a shift function confirmed systematic between-group differences (Mann–Whitney U p < 0.001; see Fig. 1 ). Clinically, a heavy concentration of very low scores in non-LCOS supports strong rule-out performance (reflected in our high NPV), while the presence of LCOS cases even at lower propensities highlights residual risk that static pre/intra-operative models cannot fully capture, consistent with LCOS as a multifactorial, time-evolving syndrome and with meta-analytic evidence that risk reflects disease severity plus intra- and postoperative factors [ 13 , 38 ]. These patterns argue for dynamic updating with early postoperative physiology. Contemporary, model-based indices that continuously estimate the probability of inadequate oxygen delivery (e.g., probability that mixed/central venous saturation falls below a threshold) have shown correlation with measured venous saturations and provide second-to-second risk signals suitable for integration with pre/intra-operative features [ 21 , 39 ]. In parallel, system-level prevention work demonstrates that proactive surveillance frameworks can reduce serious events in cardiac ICUs, underscoring the value of moving from single-marker monitoring to integrated, real-time risk surveillance [ 34 ]. Limitations This study has several limitations. First, it represents a single-center, retrospective analysis, which may limit generalizability and introduce spectrum or case-mix effects. Second, the endpoint definition of LCOS relied on PC4 registry criteria, which incorporate therapy-dependent surrogates such as vasoactive-inotropic score and physician documentation; these measures may vary by practice patterns and carry a risk of misclassification, despite being consensus definitions. Third, although we included a broad range of preoperative and intraoperative variables, by design postoperative physiologic streams (e.g., continuous hemodynamics, lactate kinetics) and direct measures of cardiac output were not incorporated, restricting the model’s ability to capture dynamic recalibration. Finally, the modest sample size and number of LCOS events may constrain statistical power, particularly for multivariable modeling, and external validation in larger, multicenter cohorts will be required to confirm generalizability and calibration. Applications and future work Our findings support clinical use of the LCOS risk–scoring model for (i) rapid risk stratification at OR exit/early PCICU arrival, (ii) resource planning in higher-risk cases (e.g., closer nurse/physician surveillance, earlier access to advanced monitoring), and (iii) triggering hemodynamic-optimization bundles so that patients likely to develop LCOS receive earlier support. Looking ahead, generalizability and time-aware recalibration are essential. We recommend (1) multicenter, prospective external validation with predefined decision thresholds; and (2) dynamic updating of the pre- and intraoperative model using continuous postoperative high-fidelity physiologic data (arterial waveform features, ECG, urine output, lactate kinetics)[ 40 ]. This integration is feasible and clinically relevant: single postoperative signals can be insufficient on their own (e.g., cerebral NIRS not independently associated with new brain injury in infants with critical CHD), whereas model-based, real-time indices that estimate the probability of inadequate oxygen delivery show good discrimination and correlate with venous saturations. In parallel, non-invasive cardiac-output monitoring in CHD continues to broaden the physiologic data available for continuous risk recalibration. Conclusion Among children undergoing surgery for CHD, our model based on pre- and intraoperative variables demonstrated strong discriminatory ability and highlighted clinically plausible predictors of LCOS, including greater surgical complexity, preoperative instability, and longer CPB time, while higher operative weight was protective. These results support early risk stratification at OR exit/PCICU arrival and targeted resource allocation, while motivating prospective multicenter validation and dynamic updating with continuous postoperative physiology. The pattern and effect directions align with contemporary syntheses of LCOS risk in pediatric populations, reinforcing the model’s face validity and potential for bedside utility, while showing unexpected distribution of LCOS incidence across preoperative, operative, and immediately postoperative risk factors. Declarations Competing Interests Author A.T. reports consulting relationships with Siemens Healthineers. Author A.T. reports stock in AMZN; GOOGL; NVDA. NB and KN are an employee of IBM. Ethics approval: The protocol was approved by the Cleveland Clinic Institutional Review Board (IRB #20–390) Consent to participate: Requirement for informed consent was waived due to the study’s retrospective nature and minimal associated risk to patients. Consent to publish: Not applicable Funding: I.T.A., O.B., S.Q.L., B.S.M., and A.T. are supported by the Cleveland Clinic Children’s Center for Artificial Intelligence (C4AI). All authors are supported by the Cleveland Clinic–IBM Discovery Accelerator (SOW50) and Johnson Family Innovation Fund. A.T. and O.B. also report support from the Advanced Research Projects Agency for Health (ARPA-H), award D25AC00206. Author Contribution Conceptualization was contributed by I. T. A., O. B., and A. T. Methodology was developed by I. T. A. and A. C. Formal analysis was conducted by A. C. and I. T. A. Software development was performed by A. C., I. T. A., and G. B. Validation was carried out by I. T. A. Investigation was undertaken by O. B., A. T., and I. T. A. Resource acquisition was managed by B. S. M., S. Q. L., and A. D. Supervision was provided by O. B. and A. T. Data curation was handled by N. M., S. D., S. D., and G. N. Visualization was produced by I. T. A. Project administration was jointly coordinated by A. T. and O. B. The original draft was written by I. T. A. and A. C., with co-writing by O. B. and A. T. Writing – review and editing were contributed by C. K. A., B. S. M., S. Q. L., H. N., A. D., R. N., and N. B. Acknowledgements: None. Data availability: All data produced in the present study are available upon reasonable request to the authors. References Tandon A, Bhattacharya S, Morca A et al (2023) Non-invasive Cardiac Output Monitoring in Congenital Heart Disease. Curr Treat Options Peds 9:247–259. https://doi.org/10.1007/s40746-023-00274-1 Parr GV, Blackstone EH, Kirklin JW (1975) Cardiac performance and mortality early after intracardiac surgery in infants and young children. Circulation 51:867–874. https://doi.org/10.1161/01.cir.51.5.867 Wernovsky G, Wypij D, Jonas RA et al (1995) Postoperative course and hemodynamic profile after the arterial switch operation in neonates and infants. 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Front Cardiovasc Med 9. https://doi.org/10.3389/fcvm.2022.926957 Gaies MG, Jeffries HE, Niebler RA et al Vasoactive-Inotropic Score Is Associated With Outcome After … Pediatric Critical Care Medicine Koponen T, Karttunen J, Musialowicz T et al (2019) Vasoactive-inotropic score and the prediction of morbidity and mortality after cardiac surgery. Br J Anaesth 122:428–436. https://doi.org/10.1016/j.bja.2018.12.019 Abbas Q, Hussain MZH, Shahbaz FF et al (2022) Performance of a Risk Analytic Tool (Index of Tissue Oxygen Delivery IDO2) in Pediatric Cardiac Intensive Care Unit of a Developing Country. Front Pediatr 10. https://doi.org/10.3389/fped.2022.846074 Holman H, Baronov D, McMurray J et al (2024) Validation of the inadequate delivery of oxygen index in an adult cardiovascular intensive care unit. JTCVS Open 22:354–361. https://doi.org/10.1016/j.xjon.2024.09.006 Alten J, Cooper DS, Klugman D et al (2022) Preventing Cardiac Arrest in the Pediatric Cardiac Intensive Care Unit Through Multicenter Collaboration. JAMA Pediatr 176:1027–1036. https://doi.org/10.1001/jamapediatrics.2022.2238 Mueller D, Bailly DK, Banerjee M et al (2024) Sustained Performance of Cardiac Arrest Prevention in Pediatric Cardiac Intensive Care Units. JAMA Netw Open 7:e2432393. https://doi.org/10.1001/jamanetworkopen.2024.32393 Kenet AL, Pemmaraju R, Ghate S et al (2023) A pilot study to predict cardiac arrest in the pediatric intensive care unit. Resuscitation 185:109740. https://doi.org/10.1016/j.resuscitation.2023.109740 Singh Y, Villaescusa JU, da Cruz EM et al (2020) Recommendations for hemodynamic monitoring for critically ill children—expert consensus statement issued by the cardiovascular dynamics section of the European Society of Paediatric and Neonatal Intensive Care (ESPNIC). Crit Care 24:620. https://doi.org/10.1186/s13054-020-03326-2 Alten JA, Cooper DS, Blinder JJ et al (2021) Epidemiology of Acute Kidney Injury After Neonatal Cardiac Surgery: A Report From the Multicenter Neonatal and Pediatric Heart and Renal Outcomes Network. Crit Care Med 49:e941. https://doi.org/10.1097/CCM.0000000000005165 Loomba RS, Villarreal EG, Flores S et al (2025) The Inadequate Oxygen Delivery Index and Its Correlation with Venous Saturation in the Pediatric Cardiac Intensive Care Unit. Pediatr Cardiol 46:39–45. https://doi.org/10.1007/s00246-023-03302-x Loomba RS, Sourour W, Flores S et al (2025) The current state of paediatric publishing utilising high-fidelity physiologic data streaming with sickbay or etiometry: a systematic review. Cardiol Young 35:1809–1814. https://doi.org/10.1017/S1047951125109219 Additional Declarations Competing interest reported. Author A.T. reports consulting relationships with Siemens Healthineers. Author A.T. reports stock in AMZN; GOOGL; NVDA. NB and KN are an employee of IBM. Supplementary Files Table1.docx Table2.docx Table3.docx Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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11:46:04","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167574,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8080625/v1/2de1e1c34c125393476b999d.html"},{"id":96556322,"identity":"dae01ef7-db07-42c5-a36a-693acc68c5f1","added_by":"auto","created_at":"2025-11-23 11:46:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63325,"visible":true,"origin":"","legend":"\u003cp\u003eThe top panel shows propensity-score distributions for LCOS (blue) and no LCOS (gray) with median and interquartile range [IQR, black bars], whiskers (black lines), and decile-matched quantiles (red lines, estimated using the Harrell–Davis quantile estimator); the bottom panel displays LCOS–no LCOS quantile differences (dots) with bootstrap 95% confidence intervals (error bars), where values are expressed in arbitrary units (a.u.) to indicate model-derived risk scores without intrinsic physical units.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-8080625/v1/270da8937de27c6b587aa3fa.png"},{"id":97665156,"identity":"8c7a13a5-8154-4ff6-8cc9-6172f5680d5a","added_by":"auto","created_at":"2025-12-08 09:17:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8080625/v1/a6d9afa4-9a54-48b6-b240-2a0d235648c6.pdf"},{"id":96556321,"identity":"5c1e36af-ddc3-4c77-8d81-7a41d93c9904","added_by":"auto","created_at":"2025-11-23 11:46:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":30604,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8080625/v1/2f943911304c47b02adef3c7.docx"},{"id":96556324,"identity":"8f98714f-62d2-4b4b-8ecd-74ff04ceedfe","added_by":"auto","created_at":"2025-11-23 11:46:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27629,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8080625/v1/8cd0961bdb43517c22e3a032.docx"},{"id":96556326,"identity":"5f00408f-b364-491b-a36a-7864b1f2998e","added_by":"auto","created_at":"2025-11-23 11:46:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28526,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8080625/v1/be446f61efb0dc3d0df0ae63.docx"},{"id":96556333,"identity":"1abfa150-5dc7-4ed8-b1a4-9b3687f91977","added_by":"auto","created_at":"2025-11-23 11:46:04","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":35969,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8080625/v1/242e3061747ca61281d7b7e1.docx"}],"financialInterests":"Competing interest reported. Author A.T. reports consulting relationships with Siemens Healthineers. Author A.T. reports stock in AMZN; GOOGL; NVDA. NB and KN are an employee of IBM.","formattedTitle":"Preoperative, Operative, and Immediate Postoperative Predictors of Onset of Low Cardiac Output Syndrome in Congenital Heart Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLow-cardiac-output syndrome (LCOS) arises in the postoperative period when there is a clinically significant imbalance between systemic oxygen delivery and tissue oxygen consumption [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among infants and children undergoing repair or palliation of congenital heart disease (CHD), LCOS has been documented in 24\u0026ndash;71% of procedures, with the incidence varying according to the diagnostic criteria applied [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Up to one half of early postoperative deaths in CHD are attributed to LCOS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], yet an universally accepted definition is still lacking: a recent review identified 262 distinct sets of diagnostic criteria published before 2020 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In routine practice, clinicians monitor several indicators that consistently feature in most definitions of LCOS, including: a reduced invasively measured cardiac index; elevated serum lactate; widened arterio-venous oxygen difference; a greater core-to-peripheral temperature gradient; oliguria/acute kidney injury; and increasing requirements for vasoactive or inotropic medications as reflected by the vasoactive-inotropic score (VIS); each of these variables is tracked throughout the first 72 h after surgery [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo optimize postoperative management, monitoring, and staffing, it is critical to identify patients at elevated risk of LCOS and to obtain this estimate immediately after surgery. While the literature consistently links LCOS with patient-level and operative factors\u0026mdash;such as younger age, lower preoperative oxygen saturation, bidirectional shunting, longer cardiopulmonary bypass, and residual postoperative shunt\u0026mdash;large-scale assessments of preoperative scoring systems are sparse, and most studies do not provide a risk estimate usable at ICU arrival [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we present the findings of a statistical analysis that integrates preoperative, intraoperative, and immediate postoperative variables to estimate the risk of developing LCOS during the early postoperative intensive care period. By providing transparent risk estimates several hours before overt hemodynamic deterioration, the model is intended to support timely clinical intervention, improve patient outcomes, and facilitate more efficient allocation of intensive-care resources.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eDesign setting\u003c/p\u003e\u003cp\u003eThis retrospective observational study received approval from the Cleveland Clinic Institutional Review Board (IRB #20\u0026ndash;390). All procedures were conducted in accordance with the ethical standards of the responsible institutional and/or regional review committees and with the principles of the 1975 Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003e Children\u0026thinsp;\u0026le;\u0026thinsp;18 years of age who were admitted to the Pediatric Cardiac Intensive Care Unit (PCICU) at Cleveland Clinic Children\u0026rsquo;s after cardiothoracic surgery for CHD between February 2023 and November 2024 were enrolled in this study. Patients older than 18 years were excluded from study. Reporting followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline (Supplementary Table-1) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source and measurements\u003c/h2\u003e\u003cp\u003eClinical and perioperative data were obtained retrospectively from the institutional electronic medical record (EPIC Systems, Verona, WI) and the Pediatric Cardiac Critical Care Consortium (PC4) registry database. Laboratory values, hemodynamic parameters, and echocardiographic findings were extracted from routinely recorded clinical documentation. Data accuracy was verified by two independent investigators through cross-checking random samples of entries against the source charts.\u003c/p\u003e\u003cp\u003eVariables\u003c/p\u003e\u003cp\u003ePatient characteristics included chronological age, operative weight, prematurity, documented genetic anomalies, single-ventricle physiology, the presence of a bidirectional shunt on echocardiography, preoperative endotracheal intubation, and preoperative vasoactive/inotropic support; together, these variables reflected preoperative physiological status.\u003c/p\u003e\u003cp\u003eIn the operative and postoperative periods, procedural complexity was classified as high risk for the Risk Adjustment for Congenital Heart Surgery (RACHS-1) scores\u0026thinsp;\u0026gt;\u0026thinsp;3 and Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) categories\u0026thinsp;\u0026gt;\u0026thinsp;2. Procedure-specific metrics comprised total cardiopulmonary bypass duration, aortic cross-clamp time, and circulatory-arrest time, as well as the number of prior cardiac operations and whether a heart transplant was performed. Intraoperative transesophageal or epicardial echocardiography findings of residual shunt or lesion and the decision to leave the sternum open postoperatively (delayed sternal closure) were also recorded. Variables for the multivariable analysis were selected based on the univariate results.\u003c/p\u003e\u003cp\u003eIn this study, LCOS was defined according to the PC4 registry criteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]: (1) a vasoactive\u0026ndash;inotropic score (VIS)\u0026thinsp;\u0026gt;\u0026thinsp;15 at any time; (2) a threefold increase in VIS within any 48-hour interval, including postoperative escalation of support, with the resultant VIS\u0026thinsp;\u0026ge;\u0026thinsp;10; (3) an arteriovenous oxygen (AVO₂) difference\u0026thinsp;\u0026gt;\u0026thinsp;40% measured invasively or by near-infrared spectroscopy (NIRS); or (4) documentation of LCOS in a physician note. PC4 prospectively records the earliest qualifying time point for each case.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll eligible patients meeting inclusion criteria during the study period were included using a consecutive sampling approach to minimize selection bias. Continuous variables are summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) according to distribution, and categorical variables as counts. Normality was assessed using the Shapiro\u0026ndash;Wilk test (α\u0026thinsp;=\u0026thinsp;0.05); the null hypothesis of normality was retained when p\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Group comparisons were performed with the χ\u0026sup2; or Fisher exact test for categorical data, the Student \u003cem\u003et\u003c/em\u003e-test for normally distributed continuous data, and the Wilcoxon rank-sum test for non-normal data. Propensity-score distributions between LCOS and non-LCOS were compared using a shift plot and the Mann\u0026ndash;Whitney U test. The shift plot computes a shift function for two (in)dependent groups using the robust Harrell\u0026ndash;Davis quantile estimator with bias-corrected bootstrap confidence intervals [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Univariate screening for candidate predictors of LCOS employed point-biserial, tetrachoric, polychoric or Spearman correlations, with variables retained for multivariable modelling when \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 and each category represented\u0026thinsp;\u0026ge;\u0026thinsp;5 % of the cohort. Independent associations with LCOS were then examined with a multivariable logistic-regression model; non-significant terms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were sequentially removed by backward elimination. Model discrimination is reported with adjusted pseudo-R\u0026sup2; (AOR) and odds ratios with 5 % confidence intervals (CI). All tests were two-sided and a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denoted statistical significance. Analyses were performed in Stata 17 (StataCorp, College Station, TX and SAS 9.3\u0026copy;).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and preoperative characteristics by LCOS status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLCOS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCorrelation\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (65.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient age expressed in days, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1162 (148\u0026ndash;4172)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (8-497)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.36\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient\u0026rsquo;s weight at the time of surgery (kg), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.4 (5.8\u0026ndash;39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.9 (3.1\u0026ndash;8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.36\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrematurity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenetic Anomaly, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle Ventricle, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnostic Group, n (%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-TGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDORV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHLHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreop Intubation n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreop Inotrope, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior Cardiac Surgeries, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreop Bidirectional, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003csup\u003ea\u003c/sup\u003eTetrachoric Correlations unless otherwise specified, \u003csup\u003eb\u003c/sup\u003ePoint-Biseral Correlation, \u003csup\u003ec\u003c/sup\u003eno comparison statistics were run. Abbreviations: Aortic-valve stenosis (AS), Atrial septal defect (ASD), Coarctation of the aorta (CoA), Dextro-transposition of the great arteries (D-TGA), Double-outlet right ventricle (DORV), Hypoplastic left heart syndrome (HLHS), Interrupted aortic arch (IAA), Pulmonary atresia (PA), Tetralogy of Fallot (TOF), Tricuspid-valve atresia (TA), Ventricular septal defect (VSD), Interquartile range (IQR).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBetween February 2023 and November 2024, 191 patients (80 female) admitted to the pediatric cardiac intensive care unit (PCICU) who met the study criteria were included; their demographic and baseline clinical characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the PC4 registry, 46 of these 191 patients (24%) developed LCOS. Children who developed LCOS were younger at surgery (123 days [8\u0026ndash;497] vs 1162 [148\u0026ndash;4172], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had lower weight at time of cardiac surgery (4.9 kg [3.1\u0026ndash;8.9] vs 13.4 kg [5.8\u0026ndash;39.1], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Single-ventricle physiology was more common (37% vs 8.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as were preoperative intubation (37% vs 4.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and preoperative inotrope use (32.6% vs 9.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Prematurity (17.4% vs 10.3%), genetic anomalies (30.4% vs 26.2%), prior cardiac surgery (41.3% vs 30.3%), and male sex (65.2% vs 55.8%) did not distinguish the groups at a statistically significant level (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Diagnostic patterns suggested enrichment of Hypoplastic left heart syndrome (HLHS) (26.1% vs 3.5%) and Double-outlet right ventricle (DORV) (6.5% vs 0.7%) in LCOS, with no LCOS cases among Aortic-valve stenosis (AS) or Atrial septal defect (ASD); Ventricular septal defect (VSD) remained common in both groups (26.1% vs 34.5%). Preoperative bidirectional shunt was somewhat more frequent in LCOS (26.1% vs 16.6%).\u003c/p\u003e\u003cp\u003eOperative and intraoperative features also diverged between groups. Heart transplantation was more frequent in LCOS (8.7% vs 0.7%). High-risk procedure categories were overrepresented (RACHS-1\u0026thinsp;\u0026gt;\u0026thinsp;4: 52.2% vs 10.3%; STAT\u0026thinsp;\u0026gt;\u0026thinsp;3: 65.2% vs 29.7%). Open sternum postoperatively was notably more common (37% vs 1.4%), while residual shunt rates were similar (17.4% vs 15.2%). Procedural durations are reported as mean minutes\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and were longer in LCOS: cardiopulmonary bypass 122.2\u0026thinsp;\u0026plusmn;\u0026thinsp;51.0 vs 82.5\u0026thinsp;\u0026plusmn;\u0026thinsp;41.9, aortic cross-clamp 67.7\u0026thinsp;\u0026plusmn;\u0026thinsp;43.2 vs 56.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.6, and circulatory arrest 4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 vs 0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOperative and Immediate postoperative characteristics by LCOS status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLCOS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCorrelation\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Transplant, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRACHS1\u0026thinsp;\u0026gt;\u0026thinsp;4 (high), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAT\u0026thinsp;\u0026gt;\u0026thinsp;3 (high), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (65.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen Sternum, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual Shunt, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cardiopulmonary bypass duration, in minutes, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (56\u0026ndash;108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (88\u0026ndash;153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAortic cross-clamp time, in minutes, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (32\u0026ndash;82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (47\u0026ndash;89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of circulatory arrest during bypass, in minutes, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003csup\u003ea\u003c/sup\u003eTetrachoric Correlations unless otherwise specified; \u003csup\u003eb\u003c/sup\u003ePoint-Biseral Correlation; \u003csup\u003ec\u003c/sup\u003e90% of both groups had zero duration\u003c/p\u003e\u003cp\u003eThere were 6 deaths (3.1%) in our cohort. 33 patients (17.2%) experienced at least one major complication. Seven patients (3.6%) sustained a cardiopulmonary arrest; 2 (1.0%) underwent extracorporeal cardiopulmonary resuscitation (E-CPR), and 2 (1.0%) progressed to brain death. 7 patients (3.6%) required extracorporeal life support (ECLS). Twenty-two patients (11.5%) developed a clinically significant arrhythmia necessitating treatment. Three patients (1.6%) developed renal dysfunction requiring continuous renal replacement treatment (CRRT). Ten patients (5.2%) developed sepsis. Twenty-six patients (13.6%) required a prolonged duration of mechanical ventilation; the overall MV duration was a median of 63 hours [IQR 13\u0026ndash;171]. The median PCICU length of stay (LOS) was 2 days [IQR 1\u0026ndash;9] and the median hospital LOS was 6 days [IQR 3.0\u0026ndash;20.5].\u003c/p\u003e\u003cp\u003eAll covariates associated with LCOS at p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analyses and present in \u0026ge;\u0026thinsp;5% of patients were entered into a logistic regression; the full model (pseudo-R\u0026sup2;=0.33) and the backward-selected trimmed model are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the trimmed model, markers of operative complexity and preoperative instability independently increased LCOS risk, whereas higher operative weight was protective. Each additional prior cardiac surgery doubled the odds of LCOS (AOR 2.00, 95% CI 1.34\u0026ndash;2.97); procedures with RACHS-1\u0026thinsp;\u0026ge;\u0026thinsp;4 carried\u0026thinsp;~\u0026thinsp;3.7-fold higher odds (AOR 3.69, 1.38\u0026ndash;9.81); preoperative inotrope use was associated with higher odds (AOR 2.75, 1.02\u0026ndash;7.43); and longer CPB duration conferred incremental risk (AOR 1.01 per minute, 1.00\u0026ndash;1.02). Each additional kilogram at surgery was associated with lower odds (AOR 0.92, 0.87\u0026ndash;0.97). Model performance was good (AUROC 0.879; PRAUC 0.706); accuracy 0.817, positive predictive value (PPV) 0.657, recall 0.50, F1 0.568, and negative predictive value (NPV) 0.853, indicating strong discrimination with moderate sensitivity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable model predicting Low Cardiac Output Syndrome.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAOR (CL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFull Model\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatural-logarithm transform of patient age in days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79 (0.56\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrematurity (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.44 (0.4\u0026ndash;5.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle Ventricle (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.64 (0.43\u0026ndash;6.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Prior Cardiac Surgeries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (1.15\u0026ndash;3.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient\u0026rsquo;s weight at the time of surgery (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.9\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreop Inotrope, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.7 (0.92\u0026ndash;7.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaving a RACHS-1 score of 4 or above (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.61 (1.09\u0026ndash;11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAT score of 3 or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53 (0.16\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreop Bidirectional (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.33 (0.46\u0026ndash;3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cardiopulmonary bypass duration, in minutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.02 (1-1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAortic cross-clamp time, in minutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R2\u0026thinsp;=\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTrimmed Model\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Prior Cardiac Surgeries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.34\u0026ndash;2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient\u0026rsquo;s weight at the time of surgery (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.87\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOn a Preoperative Inotrope (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.75 (1.02\u0026ndash;7.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaving a RACHS-1 score of 4 or above (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.69 (1.38\u0026ndash;9.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cardiopulmonary bypass duration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Multivariable logistic regression models predicting low cardiac output syndrome (LCOS). The Full model includes all candidate predictors meeting inclusion criteria, while the Trimmed model presents the most parsimonious set of independent predictors. Significant variables in the final model included number of prior cardiac surgeries, lower body weight, preoperative inotrope use, higher RACHS-1 category, and longer cardiopulmonary bypass duration. The final regression equation was: \u003cem\u003elogit(p)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.45\u0026thinsp;+\u0026thinsp;0.69 \u0026times; (Number of Prior Surgeries)\u0026thinsp;\u0026minus;\u0026thinsp;0.04 \u0026times; (Weight at Surgery)\u0026thinsp;+\u0026thinsp;1.01 \u0026times; (Preop Inotrope)\u0026thinsp;+\u0026thinsp;1.30 \u0026times; (RACHS-1\u0026thinsp;\u0026ge;\u0026thinsp;4)\u0026thinsp;+\u0026thinsp;0.013 \u0026times; (CPB Time).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhen the trimmed model\u0026rsquo;s predicted probabilities were used as propensity scores, the LCOS distribution conformed to normality (p\u0026thinsp;=\u0026thinsp;0.14), whereas the non‑LCOS distribution deviated significantly from normality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and was left-skewed toward lower scores. Quantile-wise differences were systematic between groups (Mann\u0026ndash;Whitney U p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); details are presented in the shift plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn a cohort of 191 postoperative congenital heart disease patients, LCOS occurred in 24% (46/191), at the lower bound of the \u0026asymp;\u0026thinsp;9\u0026ndash;71% incidence reported in prior pediatric series using heterogeneous definitions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Patients who developed LCOS were younger, had lower weight, more often required preoperative intubation/inotropes, and had higher operative complexity (RACHS-1\u0026thinsp;\u0026ge;\u0026thinsp;4, longer CPB). In multivariable analysis, prior surgeries, RACHS-1\u0026thinsp;\u0026ge;\u0026thinsp;4, preoperative inotrope use, and longer CPB independently increased LCOS risk, whereas greater operative weight was protective. The trimmed model achieved good discrimination (AUROC 0.879; PRAUC 0.706). Propensity scores were approximately normal in the LCOS group (Shapiro\u0026ndash;Wilk p\u0026thinsp;=\u0026thinsp;0.14) but left-skewed among non-LCOS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that while low scores help rule out LCOS, some patients with low propensity still developed LCOS. This skewed distribution of LCOS risk is a key finding of our study.\u003c/p\u003e\u003cp\u003eConcordance with prior literature and novelty.\u003c/p\u003e\u003cp\u003eIn prior pediatric series, LCOS has been observed in nearly half of patients following congenital heart surgery and remains the leading cause of postoperative mortality in children[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our cohort\u0026rsquo;s 24% incidence is broadly comparable and underscores the clinical burden. Prior published work identified risk factors of LCOS including preoperative hemodynamic reserve and surgical history, include inotrope requirement, intubated status, and prior operations, consistent with our findings that preoperative inotropes and previous surgeries track with higher LCOS risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In infant-focused studies, longer aortic cross-clamp time and lower body weight are independent predictors[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; in our model, higher operative weight was protective and a time-related intraoperative load (longer CPB) conferred incremental risk, aligning with that pattern. These observations complement evidence that LCOS severity captured at the bedside (e.g., LCOSS) relates strongly to morbidity and PCICU resource utilization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and they reinforce the value of combining preoperative/intraoperative features with early postoperative dynamics in risk stratification.\u003c/p\u003e\u003cp\u003ePreoperative profile\u003c/p\u003e\u003cp\u003eOur finding that preoperative inotrope requirement and preoperative endotracheal intubation independently predict LCOS is consistent with the broader literature that treats preoperative instability as a bedside proxy for limited circulatory reserve and heightened postoperative risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In large pediatric cohorts and meta-analyses, younger age and lower operative weight repeatedly emerge as core risk factors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which matches our model\u0026rsquo;s observation that higher weight is protective. The presence of syndromes (e.g. Down syndrome) highlight compounding pulmonary comorbidities (e.g., pulmonary hypertension) that can worsen hemodynamics and complicate management [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Overall, our findings are consistent with prior pediatric reports, reinforcing their relevance to contemporary clinical experience [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIntra and postoperative profile\u003c/p\u003e\u003cp\u003eSurgical complexity, captured by longer CPB duration and higher RACHS/STAT, likely amplifies circulatory stress and the inflammatory cascade, predisposing to LCOS; these associations are consistently documented across pediatric studies and a recent meta-analysis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The classic postoperative time course described by Wernovsky (cardiac index nadir\u0026thinsp;~\u0026thinsp;9\u0026ndash;12 h after surgery) contextualizes why LCOS often declares itself hours after the OR, when reperfusion injury and vasoplegia peak [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Targeted pharmacologic prevention has biologic plausibility but risk remains concentrated in high-complexity cases[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Markers of severity used in practice, such as LCOSS and VIS, track morbidity and resource utilization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], whereas certain single physiologic readouts may be insufficiently specific for new organ injury [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consistent with our cohort (higher \u0026ldquo;open sternum\u0026rdquo; rates in LCOS), delayed sternal closure is typically deployed for hemodynamic instability and is associated with greater morbidity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Beyond the OR, fluid overload is a powerful and modifiable risk factor linked to mortality/morbidity in pediatric cardiac surgery [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Looking ahead, converging evidence argues for continuous, multimodal monitoring and analytics-assisted early warning: non-invasive cardiac output (CO) monitoring in CHD and emerging machine-learning models illustrate how integrating pre/intra/postoperative signals (including lactate and blood-pressure dynamics) can improve discrimination and timeliness of LCOS detection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInterpreting physiological markers\u003c/p\u003e\u003cp\u003eInterpreting physiologic markers for impending LCOS remains difficult because there is still no uniform definition and single variables perform inconsistently across settings [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Recent reviews emphasize the ongoing lack of consensus definitions, complicating comparisons and surveillance strategies, and argue for multimodal assessment instead of reliance on any single metric [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In practice, scores derived from vasoactive support correlate with adverse outcomes after pediatric cardiac surgery, but they are therapy-dependent surrogates rather than direct measures of oxygen delivery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Ulate and colleagues proposed the LCOS Score (LCOSS) to fold clinical signs (e.g., tachycardia, oliguria, toe temperature, NIRS, lactate) together with pharmacologic support; in their infant cohort, cumulative LCOSS discriminated composite morbidity well (AUC\u0026thinsp;\u0026asymp;\u0026thinsp;0.83), yet the construct still leaned heavily on treatment intensity and lacked concurrent validation against direct cardiac output or oxygen-delivery measurements [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For example, postoperative cerebral NIRS, while useful for trend monitoring, has not consistently predicted adverse outcomes, underscoring the limitations of relying on any single physiologic marker for LCOS risk stratification [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Taken together, these data support our approach: multivariable, integrative prediction that combines preoperative and intraoperative risk factors with early postoperative physiology offers a more robust path to actionable LCOS risk stratification than reliance on any single marker alone [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding on this rationale, integrated real-time analytics can fuse multiple physiological and laboratory streams to estimate risk states (e.g., inadequate oxygen delivery) continuously. Model-based indices computed every few seconds from routinely available inputs have been shown to track the probability of dangerously low venous saturation and to correlate with lactate and adverse events; external validations report strong discrimination for detecting low SvO₂ even when some inputs are missing [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In parallel, pediatrics has seen a broader shift from single-signal monitoring to proactive risk surveillance: collaborative, low-tech prevention bundles in cardiac ICUs achieved\u0026thinsp;~\u0026thinsp;30% reductions in risk-adjusted in-hospital cardiac arrest that were sustained across centers, while machine-learning models using routine electronic health records data can flag arrest risk hours in advance [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Taking together, this evidence supports our rationale for multivariable prediction, combining preoperative and intraoperative risk factors with early postoperative physiology is more likely to surface actionable LCOS risk than any single marker alone [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePropensity-score distributions and clinical implications\u003c/p\u003e\u003cp\u003eUsing the trimmed model\u0026rsquo;s predicted probabilities as propensity scores, we observed a left-skewed distribution among non-LCOS patients, whereas the LCOS distribution did not deviate from normality (Shapiro\u0026ndash;Wilk p\u0026thinsp;=\u0026thinsp;0.14). Quantile-wise contrasts via a shift function confirmed systematic between-group differences (Mann\u0026ndash;Whitney U p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Clinically, a heavy concentration of very low scores in non-LCOS supports strong rule-out performance (reflected in our high NPV), while the presence of LCOS cases even at lower propensities highlights residual risk that static pre/intra-operative models cannot fully capture, consistent with LCOS as a multifactorial, time-evolving syndrome and with meta-analytic evidence that risk reflects disease severity plus intra- and postoperative factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These patterns argue for dynamic updating with early postoperative physiology. Contemporary, model-based indices that continuously estimate the probability of inadequate oxygen delivery (e.g., probability that mixed/central venous saturation falls below a threshold) have shown correlation with measured venous saturations and provide second-to-second risk signals suitable for integration with pre/intra-operative features [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In parallel, system-level prevention work demonstrates that proactive surveillance frameworks can reduce serious events in cardiac ICUs, underscoring the value of moving from single-marker monitoring to integrated, real-time risk surveillance [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, it represents a single-center, retrospective analysis, which may limit generalizability and introduce spectrum or case-mix effects. Second, the endpoint definition of LCOS relied on PC4 registry criteria, which incorporate therapy-dependent surrogates such as vasoactive-inotropic score and physician documentation; these measures may vary by practice patterns and carry a risk of misclassification, despite being consensus definitions. Third, although we included a broad range of preoperative and intraoperative variables, by design postoperative physiologic streams (e.g., continuous hemodynamics, lactate kinetics) and direct measures of cardiac output were not incorporated, restricting the model\u0026rsquo;s ability to capture dynamic recalibration. Finally, the modest sample size and number of LCOS events may constrain statistical power, particularly for multivariable modeling, and external validation in larger, multicenter cohorts will be required to confirm generalizability and calibration.\u003c/p\u003e\u003cp\u003eApplications and future work\u003c/p\u003e\u003cp\u003eOur findings support clinical use of the LCOS risk\u0026ndash;scoring model for (i) rapid risk stratification at OR exit/early PCICU arrival, (ii) resource planning in higher-risk cases (e.g., closer nurse/physician surveillance, earlier access to advanced monitoring), and (iii) triggering hemodynamic-optimization bundles so that patients likely to develop LCOS receive earlier support. Looking ahead, generalizability and time-aware recalibration are essential. We recommend (1) multicenter, prospective external validation with predefined decision thresholds; and (2) dynamic updating of the pre- and intraoperative model using continuous postoperative high-fidelity physiologic data (arterial waveform features, ECG, urine output, lactate kinetics)[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This integration is feasible and clinically relevant: single postoperative signals can be insufficient on their own (e.g., cerebral NIRS not independently associated with new brain injury in infants with critical CHD), whereas model-based, real-time indices that estimate the probability of inadequate oxygen delivery show good discrimination and correlate with venous saturations. In parallel, non-invasive cardiac-output monitoring in CHD continues to broaden the physiologic data available for continuous risk recalibration.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong children undergoing surgery for CHD, our model based on pre- and intraoperative variables demonstrated strong discriminatory ability and highlighted clinically plausible predictors of LCOS, including greater surgical complexity, preoperative instability, and longer CPB time, while higher operative weight was protective. These results support early risk stratification at OR exit/PCICU arrival and targeted resource allocation, while motivating prospective multicenter validation and dynamic updating with continuous postoperative physiology. The pattern and effect directions align with contemporary syntheses of LCOS risk in pediatric populations, reinforcing the model\u0026rsquo;s face validity and potential for bedside utility, while showing unexpected distribution of LCOS incidence across preoperative, operative, and immediately postoperative risk factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eAuthor A.T. reports consulting relationships with Siemens Healthineers. Author A.T. reports stock in AMZN; GOOGL; NVDA. NB and KN are an employee of IBM.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval:\u003c/h2\u003e\u003cp\u003e The protocol was approved by the Cleveland Clinic Institutional Review Board (IRB #20\u0026ndash;390)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003cp\u003e Requirement for informed consent was waived due to the study\u0026rsquo;s retrospective nature and minimal associated risk to patients.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eI.T.A., O.B., S.Q.L., B.S.M., and A.T. are supported by the Cleveland Clinic Children\u0026rsquo;s Center for Artificial Intelligence (C4AI). All authors are supported by the Cleveland Clinic\u0026ndash;IBM Discovery Accelerator (SOW50) and Johnson Family Innovation Fund. A.T. and O.B. also report support from the Advanced Research Projects Agency for Health (ARPA-H), award D25AC00206.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization was contributed by I. T. A., O. B., and A. T. Methodology was developed by I. T. A. and A. C. Formal analysis was conducted by A. C. and I. T. A. Software development was performed by A. C., I. T. A., and G. B. Validation was carried out by I. T. A. Investigation was undertaken by O. B., A. T., and I. T. A. Resource acquisition was managed by B. S. M., S. Q. L., and A. D. Supervision was provided by O. B. and A. T. Data curation was handled by N. M., S. D., S. D., and G. N. Visualization was produced by I. T. A. Project administration was jointly coordinated by A. T. and O. B. The original draft was written by I. T. A. and A. C., with co-writing by O. B. and A. T. Writing \u0026ndash; review and editing were contributed by C. K. A., B. S. M., S. Q. L., H. N., A. D., R. N., and N. B.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e\u003cp\u003eAll data produced in the present study are available upon reasonable request to the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTandon A, Bhattacharya S, Morca A et al (2023) Non-invasive Cardiac Output Monitoring in Congenital Heart Disease. Curr Treat Options Peds 9:247\u0026ndash;259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40746-023-00274-1\u003c/span\u003e\u003cspan address=\"10.1007/s40746-023-00274-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParr GV, Blackstone EH, Kirklin JW (1975) Cardiac performance and mortality early after intracardiac surgery in infants and young children. 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Cardiol Young 35:1809\u0026ndash;1814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1047951125109219\u003c/span\u003e\u003cspan address=\"10.1017/S1047951125109219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Congenital heart disease, Low cardiac output syndrome, Pediatric cardiac surgery; Risk prediction, Logistic regression, Intensive care, Propensity score","lastPublishedDoi":"10.21203/rs.3.rs-8080625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8080625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLow cardiac output syndrome (LCOS) is a leading cause of morbidity and mortality after congenital heart disease (CHD) surgery. Early, transparent risk estimation at pediatric cardiac intensive care unit admission could guide monitoring and resource allocation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo develop and evaluate a multivariable model that estimates the risk of LCOS using routinely available preoperative, intraoperative, and immediate postoperative variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this single-center retrospective observational cohort, children ≤ 18 years undergoing CHD surgery between February 2023 and November 2024 were included. LCOS was defined using prespecified criteria from the Pediatric Cardiac Critical Care Consortium (PC4). Candidate predictors were screened in univariate analyses; independent associations were estimated with multivariable logistic regression and backward elimination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 191 patients, 46 (24%) developed LCOS. Independent predictors were higher surgical complexity (Risk Adjustment for Congenital Heart Surgery [RACHS-1] ≥ 4; adjusted odds ratio [AOR] 3.69, 95% CI 1.38–9.81), preoperative inotrope use (AOR 2.75, 1.02–7.43), longer cardiopulmonary bypass duration (AOR 1.01 per minute, 1.00–1.02), and a greater number of prior cardiac operations (AOR 2.00 per operation, 1.34–2.97); higher operative weight was protective (AOR 0.92 per kilogram, 0.87–0.97). Model performance metrics were area under the receiver-operating characteristic curve (AUROC) 0.879 and area under the precision–recall curve (AUPRC) 0.706; at a prespecified decision threshold, accuracy 0.817, positive predictive value (PPV) 0.657, sensitivity (recall) 0.50, F1 score 0.568, and negative predictive value (NPV) 0.853.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA parsimonious, interpretable model derived from routinely collected data identifies children at increased risk of LCOS at ICU arrival and can inform early intervention and staffing. Prospective multicenter validation and dynamic updating with continuous postoperative physiology are warranted.\u003c/p\u003e","manuscriptTitle":"Preoperative, Operative, and Immediate Postoperative Predictors of Onset of Low Cardiac Output Syndrome in Congenital Heart Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:45:59","doi":"10.21203/rs.3.rs-8080625/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09284427-9eaa-4b8e-af6f-38cbe317385d","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T17:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-23 11:45:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8080625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8080625","identity":"rs-8080625","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}