Outcomes in Children Who Undergo Post-cardiotomy ECMO: A Report from the STS-CHSD.

BACKGROUND: Children who undergo cardiac surgery may require post-cardiotomy extracorporeal membrane oxygenation (ECMO). Although morbidities are considerable, our understanding of outcome determinants is limited. We evaluated associations between patient and peri-operative factors with outcomes. METHODS: The STS Congenital Heart Surgery Database was queried for patients <18yo who underwent post-cardiotomy ECMO from 1/2016-6/2021. PRIMARY OUTCOME: survival to hospital discharge. SECONDARY OUTCOME: survival without neurologic injury. Logistic regression for binary outcomes and competing risk analysis for survival were used to identify the most important predictors. Variables were selected by stepwise procedure using entry level p=0.35. Those with p≤0.1 were kept in the final model. RESULTS: A total of 3,181 patients were supported with post-cardiotomy ECMO during the same hospitalization as cardiac surgery: (A) intra-operative initiation of ECMO, n=1206; (B) early post-operative (≤48 hours), n=936; (C) late post-operative (>48 hours), n=1039. Most common primary procedure of index operation was the Norwood procedure. 57% intra-op survived to discharge, versus 59% early post-op and 42% late post-op group (χ2 (2) = 64, p<0.0001, V = 0.14). In all groups, post-op septicemia, cardiac arrest, and new neurologic injury had the strongest association with mortality, while post-operative reintubation and unplanned non-cardiac reoperation were associated with higher survival. CONCLUSIONS: Multiple risk factors impact survival in children who undergo cardiac surgery and post-cardiotomy ECMO. ECMO initiated >48 hours after surgery is associated with the poorest outcomes. This is the first step in creating a predictive tool to educate clinicians and families regarding expectations in this high-risk population.

Outcomes of Intracorporeal Continuous and Paracorporeal Pulsatile Ventricular Assist Devices in Pediatric Patients 10-30 kg.

Ventricular assist devices (VADs) have been increasingly implanted in pediatric patients. Paracorporeal VADs are generally chosen when intracorporeal continuous (IC) devices are too large. Superiority between IC and paracorporeal pulsatile (PP) devices remains unclear in smaller pediatric patients. Our study analyzes outcomes of IC and PP VADs in pediatric patients who could be considered for either of these options. Using the Advanced Cardiac Therapies Improving Outcomes Network (ACTION) database, we identified children between 10 and 30 kg who received a VAD between June 2018 and September 2021. Survival and stroke outcomes were analyzed based on VAD type. There were 41 patients in the IC group and 54 patients in the PP group. Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) profile at the time of implant was higher in the PP cohort ( p < 0.02). The PP cohort was younger ( p < 0.001) and smaller ( p < 0.001) than the IC cohort. The diagnosis was similar between cohorts. Overall survival was similar between groups. Stroke was more common in the PP cohort, but did not reach statistical significance ( p = 0.07). Discharge was possible only in the IC group, but the discharge rate was low (9.5%). Direct comparisons remain challenging given differences in INTERMACS profiles, age, and size.

Machine learning trained poly (3,4-ethylenedioxythiophene) functionalized carbon matrix suspended Cu nanoparticles for precise monitoring of nitrite from pickled vegetables.

Precise monitoring of nitrite from real samples has gained significant attention due to its detrimental impact on human health. Herein, we have fabricated poly(3,4-ethylenedioxythiophene) functionalized carbon matrix suspended Cu nanoparticles (PEDOT-C@Cu-NPs) through a facile green synthesis approach. Additionally, we have used machine learning (ML) to optimize experimental parameters such as pH, drying time, and concentrations to predict current of the designed electrochemical sensor. The ML optimized concentration of fabricated C@Cu-NPs was further functionalized by PEDOT (π-electron mediator). The designed PEDOT functionalized C@Cu-NPs (PEDOT-C@Cu-NPs) electrode has shown excellent electro-oxidation capability towards NO2- ions due to highly exposed Cu facets, defects rich graphitic C and high π-electron density. Additionally, the designed material has shown low detection limit (3.91 µM), high sensitivity (0.6372 µA/µM/cm2), and wide linear range (5-580 µM). Additionally, the designed electrode has shown higher electrochemical sensing efficacy against real time monitoring from pickled vegetables extract.

Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring.

The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat quality because, athletes might have positive test for CLN even after consuming very low quantity of CLN contaminated meat. Numerous materials applied for CLN monitoring faced potential challenges like sluggish ion transport, non-uniform ion/molecule movement, and inadequate electrode surface binding. To overcome these shortcomings, herein we engineered bimetallic zeolitic imidazole framework (BM-ZIF) derived N-doped porous carbon embedded Co nanoparticles (CN-CoNPs), dispersed on conductive cellulose acetate-polyaniline (CP) electrospun nanofibers for sensitive electrochemical monitoring of CLN. Interestingly, the smartly designed CN-CoNPs wrapped CP (CN-CoNPs-CP) electrospun nanofibers offers rapid diffusion of CLN molecules to the sensing interface through amine and imine groups of CP, thus minimizing the inhomogeneous ion transportation and inadequate electrode surface binding. Additionally, to synchronize experiments, machine learning (ML) algorithms were applied to optimize, predict, and validate voltametric current responses. The ML-trained sensor demonstrated high selectivity, even amidst interfering substances, with notable sensitivity (4.7527 µA/µM/cm2), a broad linear range (0.002-8 µM), and a low limit of detection (1.14 nM). Furthermore, the electrode exhibited robust stability, retaining 98.07% of its initial current over a 12-h period. This ML-powered sensing approach was successfully employed to evaluate meat quality in terms of CLN level. To the best of our knowledge, this is the first study of using ML powered system for electrochemical sensing of CLN.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.

Mechanical Ventilation and Outcomes of Children Who Undergo Ventricular Assist Device Placement: 2014-2020 Linked Analysis From the Advanced Cardiac Therapies Improving Outcomes Network and Pediatric Cardiac Critical Care Consortium Registries.

OBJECTIVES: Placement of a ventricular assist device (VAD) improves outcomes in children with advanced heart failure, but adverse events remain important consequences. Preoperative mechanical ventilation (MV) increases mortality, but it is unknown what impact prolonged postoperative MV has. DESIGN: Advanced Cardiac Therapies Improving Outcomes Network (ACTION) and Pediatric Cardiac Critical Care Consortium (PC 4 ) registries were used to identify and link children with initial VAD placement admitted to the cardiac ICU (CICU) from August 2014 to July 2020. Demographics, cardiac diagnosis, preoperative and postoperative CICU courses, and outcomes were compiled. Univariable and multivariable statistics assessed association of patient factors with prolonged postoperative MV. Multivariable logistic regression sought independent associations with outcomes. SETTING: Thirty-five pediatric CICUs across the United States and Canada. PATIENTS: Children on VADs included in both registries. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Two hundred forty-eight ACTION subjects were linked to a matching patient in PC 4 . Median (interquartile) age 7.7 years (1.5-15.5 yr), weight 21.3 kg (9.1-58 kg), and 56% male. Primary diagnosis was congenital heart disease (CHD) in 35%. Pre-VAD explanatory variables independently associated with prolonged postoperative MV included: age (incidence rate ratio [IRR], 0.95; 95% CI, 0.93-0.96; p < 0.01); preoperative MV within 48 hours (IRR, 2.76; 95% CI, 1.59-4.79; p < 0.01), 2-7 days (IRR, 1.82; 95% CI, 1.15-2.89; p = 0.011), and greater than 7 days before VAD implant (IRR, 2.35; 95% CI, 1.62-3.4; p < 0.01); and CHD (IRR, 1.96; 95% CI, 1.48-2.59; p < 0.01). Each additional day of postoperative MV was associated with greater odds of mortality (odds ratio [OR], 1.09 per day; p < 0.01) in the full cohort. We identified an associated greater odds of mortality in the 102 patients with intracorporeal devices (OR, 1.24; 95% CI, 1.04-1.48; p = 0.014), but not paracorporeal devices (77 patients; OR, 1.04; 95% CI, 0.99-1.09; p = 0.115). CONCLUSIONS: Prolonged MV after VAD placement is associated with greater odds of mortality in intracorporeal devices, which may indicate inadequacy of cardiopulmonary support in this group. This linkage provides a platform for future analyses in this population.

Outcomes of Intracorporeal Continuous and Paracorporeal Pulsatile Ventricular Assist Devices in Pediatric Patients 10-30 kg.

Ventricular assist devices (VADs) have been increasingly implanted in pediatric patients. Paracorporeal VADs are generally chosen when intracorporeal continuous (IC) devices are too large. Superiority between IC and paracorporeal pulsatile (PP) devices remains unclear in smaller pediatric patients. Our study analyzes outcomes of IC and PP VADs in pediatric patients who could be considered for either of these options. Using the Advanced Cardiac Therapies Improving Outcomes Network (ACTION) database, we identified children between 10 and 30 kg who received a VAD between June 2018 and September 2021. Survival and stroke outcomes were analyzed based on VAD type. There were 41 patients in the IC group and 54 patients in the PP group. Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) profile at the time of implant was higher in the PP cohort ( p < 0.02). The PP cohort was younger ( p < 0.001) and smaller ( p < 0.001) than the IC cohort. The diagnosis was similar between cohorts. Overall survival was similar between groups. Stroke was more common in the PP cohort, but did not reach statistical significance ( p = 0.07). Discharge was possible only in the IC group, but the discharge rate was low (9.5%). Direct comparisons remain challenging given differences in INTERMACS profiles, age, and size.

Contemporary Trends in Cardiac Surgical Care for Trisomy 13 and 18 Patients Admitted to Hospitals in the United States.

OBJECTIVE: To assess rates of cardiac surgery and the clinical and demographic features that influence surgical vs nonsurgical treatment of congenital heart disease (CHD) in patients with trisomy 13 (T13) and trisomy 18 (T18) in the United States. STUDY DESIGN: A retrospective study was performed using the Pediatric Health Information System. All hospital admissions of children (<18 years of age) with T13 and T18 in the United States were identified from 2003 through 2022. International Classifications of Disease (ICD) codes were used to identify presence of CHD, extracardiac comorbidities/malformations, and performance of cardiac surgery. RESULTS: Seven thousand one hundred thirteen patients were identified. CHD was present in 62% (1625/2610) of patients with T13 and 73% (3288/4503) of patients with T18. The most common CHD morphologies were isolated atrial/ventricular septal defects (T13 40%, T18 42%) and aortic hypoplasia/coarctation (T13 21%, T18 23%). Single-ventricle morphologies comprised 6% (100/1625) of the T13 and 5% (167/3288) of the T18 CHD cohorts. Surgery was performed in 12% of patients with T13 plus CHD and 17% of patients with T18 plus CHD. For all cardiac diagnoses, <50% of patients received surgery. Nonsurgical patients were more likely to be born prematurely (P < .05 for T13 and T18). The number of extracardiac comorbidities was similar between surgical/nonsurgical patients with T13 (median 2 vs 2, P = .215) and greater in surgical vs nonsurgical patients with T18 (median 3 vs 2, P < .001). Hospital mortality was <10% for both surgical cohorts. CONCLUSIONS: Patients with T13 or T18 and CHD receive surgical palliation, but at a low prevalence (≤17%) nationally. Given operative mortality <10%, opportunity exists perhaps for quality improvement in the performance of cardiac surgery for these vulnerable patient populations.

Stabilization and tracking control of underactuated ball and beam system using metaheuristic optimization based TID-F and PIDD2-PI control schemes.

In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional integrator PIDD2-PI. The second control strategy uses the tilt integral derivative with filter (TID-F). The designed controllers employ two distinct metaheuristic computation techniques: grey wolf optimization (GWO) and whale optimization algorithm (WOA) for the parameter tuning. We evaluated the dynamic and steady-state performance of the proposed control strategies using four performance indices. In addition, to analyze the robustness of proposed control strategies, a comprehensive comparison has been performed with a variety of controllers, including tilt integral-derivative (TID), fractional order proportional integral derivative (FOPID), integral-proportional derivative (I-PD), proportional integral-derivative (PI-D), and proportional integral proportional derivative (PI-PD). By comparing different test cases, including the variation in the parameters of the BBS with disturbance, we examine step response, set point tracking, disturbance rejection analysis, and robustness of proposed control strategies. The comprehensive comparison of results shows that WOA-PIDD2-PI-ISE and GWO-TID-F- ISE perform superior. Moreover, the proposed control strategies yield oscillation-free, stable, and quick response, which confirms the robustness of the proposed control strategies to the disturbance, parameter variation of BBS, and tracking performance. The practical implementation of the proposed controllers can be in the field of under actuated mechanical systems (UMS), robotics and industrial automation. The proposed control strategies are successfully tested in MATLAB simulation.

Composite Biosynthetic Graft for Repair of Long-Segment Tracheal Stenosis: A Pilot In Vivo and In Vitro Feasibility Study.

Pediatric patients who undergo surgery for long-segment congenital tracheal stenosis (LSCTS) have suboptimal outcomes and postsurgical complications. To address this, we propose a biosynthetic graft comprising (1) a porcine small intestinal submucosa extracellular matrix (SIS-ECM) patch for tracheal repair, and (2) a resorbable polymeric exostent for biomechanical support. The SIS-ECM patch was evaluated in vivo in an ovine trachea model over an 8 month period. Concurrently, the biosynthetic graft was evaluated in a benchtop lamb trachea model for biomechanical stability. In vivo results show that SIS-ECM performs better than bovine pericardium (control) by preventing granulation tissue/restenosis, restoring tracheal architecture, blood vessels, matrix components, pseudostratified columnar and stratified epithelium, ciliary structures, mucin production, and goblet cells. In vitro tests show that the biosynthetic graft can provide the desired axial and flexural stability, and biomechanical function approaching that of native trachea. These results encourage future studies to evaluate safety and efficacy, including biomechanics and collapse risk, biodegradation, and in vivo response enabling a stable long-term tracheal repair option for pediatric patients with LSCTS and other tracheal defects.

Lung Transplantation for Pulmonary Vascular Disease in Children: A United Network for Organ Sharing Analysis.

Pulmonary vascular disease (PVD) represents an important clinical indication for lung transplant (LTx) in infants, children, and adolescents. There is limited information on LTx outcomes in these patients. We explored LTx volumes and post-LTx survival in children with PVD compared to other diagnoses. The UNOS Registry was queried from 1989 to 2020 to identify first-time pediatric LTx recipients (< 18 yo). PVD was categorized as idiopathic pulmonary arterial hypertension (IPAH) and non-idiopathic arterial hypertension (non-IPAH) and compared to all other patients as other diagnoses. Univariate and multivariate regression models were performed. 984 pediatric LTx patients (593 before 2010 and 391 during/after 2010) were identified, of which 145 (14.7%) had PVD. There has been no significant change in annual rate of all LTxs over comparative eras. However, there has been a decrease in rate of LTxs for PVD patients. Children with PVD had similar survival to other LTx groups in the early era (p = 0.2) and the latter era (p = 0.9). Univariate Cox models, showed that LTx in patients with PVD was associated with a significantly less risk of mortality for children aged 6-11 years compared to younger and older cohorts (HR = 0.4 [0.17-0.98]; p = 0.045), whereas multivariate analysis showed a trend toward higher mortality in 11-17-year-olds (HR = 1.54 [0.97-2.45]; p = 0.06). For PVD patients, oxygen supplementation and ventilator support at LTx were associated with worse post-transplant survival (p = 0.029 and p = 0.01). There has been a decrease in LTx volume for pediatric patients with PVD in the modern era. Post-LTx outcomes for children with PVD are similar to those of other diagnoses in both eras, with children aged 6-11 years having the best survival. Given these findings, LTx should be considered for this patient population.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Background: Total Cardiac Volume (TCV) based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. Objective: This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Materials and Methods: Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a Dense-Net architecture in combination with residual blocks of ResNet architecture. Results: The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). Conclusion: A deep learning based 3D-CNN model can provide accurate automatic measurement of TCV from CT images.