Mechanism of Dyspnea during Exercise in Children with Corrected Congenital Heart Disease

Abstract

This study will evaluate cardiorespiratory and peripheral muscle function and their relationship with subjective dyspnea threshold after the surgical correction of congenital heart disease in children. Thirteen children with surgically repaired congenital heart disease were recruited. Each participant performed an incremental exercise test on a cycle ergometer until exhaustion. Gas exchanges were continuously sampled to measure the maximal aerobic parameters and ventilatory thresholds. The functional capacity of the subjects was assessed with a 6 min walk test. At the end of the exercise test, isokinetic Cybex Norm was used to evaluate the strength and endurance of the knee extensor muscle in the leg. Dyspnea was subjectively scored with a visual analog scale during the last 15 s of each exercise step. Oxygen consumption measured at the dyspnea score/VO2 relationship located at the dyspnea threshold, at which dyspnea suddenly increased. Results: The maximal and submaximal values of the parameters describing the exercise and the peripheral muscular performances were: VO2 Peak: 33.8 ± 8.9 mL·min−1·kg−1; HR: 174 ± 9 b·min−1; VEmax: 65.68 ± 15.9 L·min−1; P max: 117 ± 27 W; maximal voluntary isometric force MVIF: 120.8 ± 41.9 N/m; and time to exhaustion Tlim: 53 ± 21 s. Oxygen consumption measured at the dyspnea threshold was related to VO2 Peak (R2 = 0.74; p < 0.01), Tlim (R2 = 0.78; p < 0.01), and the distance achieved during the 6MWT (R2 = 0.57; p < 0.05). Compared to the theoretical maximal values for the power output, VO2, and HR, the surgical correction did not repair the exercise performance. After the surgical correction of congenital heart disease, exercise performance was impeded by alterations of the cardiorespiratory function and peripheral local factors. A subjective evaluation of the dyspnea threshold is a reliable criterion that allows the prediction of exercise capacity in subjects suffering from congenital heart disease.

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“…AI expedites the design and execution of clinical trials for neurological drugs [19,[25][26][27][28]. By analyzing historical trial data, patient characteristics, and real-time feedback, AI algorithms optimize trial protocols, identify suitable patient populations, and predict patient responses.…”

“…In section 5, we explored how AI revolutionizes drug discovery for neurological disorders, offering innovative solutions for target identification, drug repurposing, predictive modeling, and expediting clinical trials. These applications collectively contribute to the accelerated development of much-needed therapeutics in the realm of neurological healthcare [19,[25][26][27][28].…”

“…AI expedites the design and execution of clinical trials for neurological drugs by analyzing historical trial data, patient characteristics, and real-time feedback [19,[25][26][27][28]. This accelerates the clinical trial process, reduces costs, and increases the likelihood of bringing effective therapies to market.…”

“…In summary, the integration of AI into drug discovery for neurological disorders revolutionizes the field, offering solutions for target identification, drug repurposing, predictive modeling, and expediting clinical trials. These applications collectively contribute to the accelerated development of much-needed therapeutics in neurological healthcare [19,[25][26][27][28]. The literature review outcome highlights the diverse ways AI is transforming drug discovery, providing a foundation for advancing neurological healthcare.…”

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“…where the meaning of para is a random number in the range of (0, 1), the δ indicates a control parameter (it is set to 2), the iter indicates the current number of iterations, and the iter_max indicates the maximum number of iterations. Subsequently, the EEPS, which combines the balance factor with the original location update mechanism, is shown in Equation (15):…”