Computational fluid dynamics: a primer for congenital heart disease clinicians

Gerrah, Rabin; Haller, Stephen J.

doi:10.1177/0218492320957163

Cited by 12 publications

(22 citation statements)

References 50 publications

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“…One factor to explore may be the altered haemodynamics of the CHD patients who may be unable to increase cardiac output to meet surgical demand, suffer from right ventricular dysfunction or have elevated pulmonary arterial pressure 18. These haemodynamic changes may cause patients to be fluid unresponsive, limiting the options for interoperative resuscitation 19. Therefore, optimising the interoperative management of CHD with measures such as invasive or multimodal haemodynamic monitoring may be advantageous.…”

Section: Discussionmentioning

confidence: 99%

Major elective non-cardiac operations in adults with congenital heart disease

Williamson

Ebrahimian

Ascandar

et al. 2022

Heart

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ObjectiveTo assess the impact of congenital heart disease (CHD) on resource utilisation and clinical outcomes in patients undergoing major elective non-cardiac operations.BackgroundDue to advances in congenital cardiac management in recent years, more patients with CHD are living into adulthood and are requiring non-cardiac operations.MethodsThe 2010–2018 Nationwide Readmissions Database was used to identify all adults undergoing major elective operations (pneumonectomy, hepatectomy, hip replacement, pancreatectomy, abdominal aortic aneurysm repair, colectomy, gastrectomy and oesophagectomy). Multivariable regression models were used to categorise key clinical outcomes.ResultsOf an estimated 4 941 203 adults meeting inclusion criteria, 5234 (0.11%) had a previous diagnosis of CHD. Over the study period, the incidence of CHD increased from 0.06% to 0.17%, p<0.001. CHD patients were on average younger (63.3±14.8 vs 64.4±12.5 years, p=0.004), had a higher Elixhauser Comorbidity Index (3.3±2.2 vs 2.3±1.8, p<0.001) and received operations at high volume centres more frequently (66.6% vs 62.0%, p=0.003). Following risk adjustment, these patients had increased risk of in-hospital mortality (adjusted risk ratio (ARR): 1.76, 95% CI 1.25 to 2.47), experienced longer hospitalisation durations (+1.6 days, 95% CI 1.3 to 2.0) and cost more (+$8370, 95% CI $6686 to $10 055). Furthermore, they were more at risk for in-hospital complications (ARR: 1.24 95% CI 1.17 to 1.31) and endured higher adjusted risk of readmission at 30 days (ARR: 1.32 95% CI 1.13 to 1.54).ConclusionsAdults with CHD are more frequently comprising the major elective operative cohort for non-cardiac cases. Due to the inferior clinical and financial outcomes suffered by this population, perioperative risk stratification may benefit from the inclusion of CHD as a factor that portends unfavourable outcomes.

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Section: Discussionmentioning

confidence: 99%

Major elective non-cardiac operations in adults with congenital heart disease

Williamson

Ebrahimian

Ascandar

et al. 2022

Heart

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“…e hemodynamics of ACS patients can also be evaluated by computational fluid dynamics (CFD) methods [21]. When the CFD method is adopted, it is necessary to solve the fluid dynamics control equation and then establish a hemodynamic model.…”

Section: Hemodynamics Of Vulnerable Plaquesmentioning

confidence: 99%

Analysis of Predictive Model of Coronary Vulnerable Plaque under Hemodynamic Numerical Simulation

Song

Chen

Shang

et al. 2022

Journal of Healthcare Engineering

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Objective. Vulnerable plaque is considered to be the cause of most clinical coronary arteries, and linear cytokines are an important factor causing plaque instability. Early prediction of vulnerable plaque is of great significance in the treatment of cardiovascular diseases. Methods. Computational fluid dynamics (CFD) was used to simulate the hemodynamics around plaques, and the serum biochemical markers in 224 patients with low-risk acute coronary syndrome (ACS) were analyzed. Vulnerable plaques were predicted according to the distribution of biochemical markers in serum. Results. CFD can accurately capture the hemodynamic characteristics around the plaque. The patient’s age, history of hyperlipidemia, apolipoprotein B (apoB), adiponectin (ADP), and sE-Selection were risk factors for vulnerable plaque. Area under curve (AUC) values corresponding to the five biochemical markers were 0.601, 0.523, 0.562, 0.519, 0.539, and the AUC value after the combination of the five indicators was 0.826. Conclusion. The combination of multiple biochemical markers to predict vulnerable plaque was of high diagnostic value, and this method was convenient and noninvasive, which was worthy of clinical promotion.

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“…The main application of using CFD in CHD lies in preoperative planning and optimization of surgical outcomes. 94 Despite availability of powerful computers nowadays, studies of modelling complex CHD problems are still limited by computer power. Another challenge of incorporating CHD into clinical practice is that clinicians are not familiar with CFD techniques.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%