Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation

Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation

Objective

To explore the application of the proposed intelligent image processing method in the diagnosis of aortic coarctation computed tomography angiography (CTA) and to clarify its value in the diagnosis of aortic coarctation based on the diagnosis results.

Methods

Fifty-three children with coarctation of the aorta (CoA) and forty children without CoA were selected to constitute the study population. CTA was performed on all subjects. The minimum diameters of the ascending aorta, proximal arch, distal arch, isthmus, and descending aorta were measured using manual and intelligent methods, respectively. The Wilcoxon signed-rank test was used to analyze the differences between the two measurements. The surgical diagnosis results were used as the gold standard, and the diagnostic results obtained by the two measurement methods were compared with the gold standard to quantitatively evaluate the diagnostic results of CoA by the two measurement methods. The Kappa test was used to analyze the consistency of intelligence diagnosis results with the gold standard.

Results

Whether people have CoA or not, there was a significant difference (p < 0.05) in the measurements of the minimum diameter at most sites using the two methods. However, close final diagnoses were made using the intelligent method and the manual. Meanwhile, the intelligent measurement method obtained higher accuracy, specificity, and AUC (area under the curve) compared to manual measurement in diagnosing CoA based on Karl's classification (accuracy = 0.95, specificity = 0.9, and AUC = 0.94). Furthermore, the diagnostic results of the intelligence method applied to the three criteria agreed well with the gold standard (all kappa ≥ 0.8). The results of the comparative analysis showed that Karl's classification had the best diagnostic effect on CoA.

Conclusion

The proposed intelligent method based on image processing can be successfully applied to assist in the diagnosis of CoA.

Echocardiographic findings for improved prenatal diagnosis of aortic coarctation with ventricular septal defect

Accurate prenatal diagnosis of coarctation of the aorta (CoA) associated with ventricular septal defect (VSD) remains challenging. The objective of the study was to identify which Doppler and/or two-dimensional sonographic findings are most useful for predicting fetal CoA/VSD. A retrospective cohort study identified 35 fetuses with suspected CoA/VSD. Prenatal imaging characteristics included the right ventricular/left ventricular, pulmonary artery (PA)/aorta ratio, aortic isthmus (AOI) Z score, diastolic velocity-time integral (VTID), and systolic velocity-time integral (VTIS) at the AOI. The area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were calculated. Significant differences in the PA/AO, VTID, VTID/VTIS, VTID/VTIS, VTID/(VTID + VTIS), and AOI Z score between the true CoA group and false positives were found. When associated with VSD, the VTID/VTIS and VTID/(VTID + VTIS) had the highest AUC (0.97, 95% confidence interval: 0.84-1.00), with 88.46% sensitivity and 100.00% specificity for predicting the true CoA. The AOI Z score had the highest sensitivity (92.31%). Adding the VTID/VTIS to the AOI Z score significantly improved the performance (IDI, 50%; NRI, 82%; P < 0.05), with an improvement in specificity (77.78% vs. 55.56%; non-Event P = 0.008) without sacrificing sensitivity (96.15% vs. 92.31%; Event P = 0.564). In fetuses with suspected CoA associated with VSD, the quantitative spectral Doppler metric aided accurate detection of the fetal CoA, with reduced false positives. The conventional AOI Z score plus spectral Doppler metric may improve the overall diagnostic accuracy of CoA/VSD.

Computed tomography angiography assessment of the degree of simple coarctation of the aorta and its relationship with surgical outcome: A retrospective analysis

OBJECTIVE

To investigate the correlation between the degree of aortic coarctation and surgical prognosis in infants with simple coarctation of the aorta (CoA) using computed tomography angiography (CTA).

METHODS

This study was a retrospective study. Twenty-seven infants with simple CoA who underwent surgical correction from January 2020 to June 2022 were enrolled. Aortic diameters were measured at five different levels and normalized to Z scores based on the square root of body surface area. The relevant data were collected and analyzed, and the predictors associated with surgical outcome were determined.

RESULTS

Patients were divided into the mild CoA group and the severe CoA group according to the severity of coarctation. The mechanical ventilation duration and the length of ICU stay in the mild CoA group were significantly lower than those in the severe CoA group. Multiple linear regression analyses revealed that the degree of aortic coarctation was a significant risk factor for a prolonged postoperative ICU stay. In addition, gestational age and age at operation were risk factors for a prolonged postoperative ICU stay. Correlation analysis showed that the degree of aortic coarctation correlated with the Z scores of the ascending aorta and postcoarctation aorta.

CONCLUSION

The degree of the CoA is an important predictor of surgical outcomes in infants with simple CoA and was significantly correlated with the ascending aorta and postcoarctation aorta Z scores. Therefore, preoperative CTA should be routinely performed to assess the degree of aortic coarctation and better identify risk factors.

Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery

Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative complications and clinical deterioration either due to their underlying pathology or due to the cardiac surgery, contributing significantly to mortality, morbidity, hospital and family costs, and poor quality of life. In current clinical practice, clinical deterioration is detected, in most of the cases, when it has already occurred. Several early warning scores (EWS) have been proposed to assess children at risk of clinical deterioration using vital signs and risk indicators, in order to intervene in a timely manner to reduce the impact of deterioration and risk of death among children. However, EWS are based on measurements performed at a single time point without incorporating trends nor providing information about patient's risk trajectory. Moreover, some of these measurements rely on subjective assessment making them susceptible to different interpretations. All these limitations could explain why the implementation of EWS in high-resource settings failed to show a significant decrease in hospital mortality. By means of machine learning (ML) based algorithms we could integrate heterogeneous and complex data to predict patient's risk of deterioration. In this perspective article, we provide a brief overview of the potential of ML technologies to improve the identification of pediatric CHD patients at high-risk for clinical deterioration after cardiac surgery, and present the CORTEX traffic light, a ML-based predictive system that Sant Joan de Déu Barcelona Children's Hospital is implementing, as an illustration of the application of an ML-based risk stratification system in a relevant hospital setting.