Iron deficiency in young basketball players: Is a 100 µg/L ferritin cut-off appropriate for iron supplementation?: Results of a randomized placebo-controlled study.

BACKGROUND: Iron deficiency (ID) is one of the most common factors that may reduce sports performance, supplementation forms and doses are still not standardized in athletes. Our aim was to assess the iron status of young male basketball players and to study the effect of iron supplementation in a randomized placebo-controlled study. HYPOTHESIS: We hypothesized that due to the higher iron demand of athletes, the 100 µg/L ferritin cut-off may be appropriate to determine the non-anemic ID. METHODS: During a sports cardiology screening, questionnaires, laboratory tests, electrocardiograms, echocardiography exams, and cardiopulmonary exercise tests were performed. Athletes with ID (ferritin <100 µg/L) were randomized into iron and placebo groups. Ferrous sulfate (containing 100 mg elemental iron [II] and 60 mg ascorbic acid) or placebo (50 mg vitamin C) was administered for 3 months. All exams were repeated after the supplementation period. RESULTS: We included 65 (age 15.8 ± 1.7 years) basketball players divided into four age groups. Non-anemic ID was observed in 60 (92%) athletes. After supplementation, ferritin levels were higher in the iron group (75.5 ± 25.9 vs. 54.9 ± 10.4 µg/L, p < .01). Ferritin >100 µg/L level was achieved only in 15% of the athletes. There were no differences in performance between the groups (VO2 max: 53.6 ± 4.3 vs. 54.4 ± 5.7 mL/kg/min, p = .46; peak lactate: 9.1 ± 2.2 vs. 9.1 ± 2.6 mmol/L, p = .90). CONCLUSIONS: As a result of the 3-month iron supplementation, the ferritin levels increased; however, only a small portion of the athletes achieved the target ferritin level, while performance improvement was not detectable.

The Impact of COVID-19 on the Preparation for the Tokyo Olympics: A Comprehensive Performance Assessment of Top Swimmers.

BACKGROUND: The Olympic preparation of athletes has been highly influenced by COVID and post-COVID syndrome. As the complex screening of athletes is essential for safe and successful sports, we aimed to repeat the 2019-year sports cardiology screening of the Olympic Swim Team before the Olympics and to compare the results of COVID and non-COVID athletes. METHODS: Patient history, electrocardiogram, laboratory tests, body composition analysis, echocardiography, cardiopulmonary exercise test (CPET) were performed. We used time-ranking points to compare swimming performance. RESULTS: From April 2019, we examined 46 elite swimmers (24 ± 4 years). Fourteen swimmers had COVID infection; all cases were mild. During CPET there was no difference in the performance of COVID (male: VO2 max 55 ± 4 vs. 56.5 ± 5 mL/kg/min, p = 0.53; female: VO2 max 54.6 ± 4 vs. 56 ± 5.5 mL/kg/min, p = 0.86) vs. non-COVID athletes (male VO2 max 56.7 ± 5 vs. 55.5 ± 4.5 mL/kg/min, p = 0.50; female 49.6 ± 3 vs. 50.7 ± 2.6 mL/kg/min, p = 0.47) between 2019 and 2021. When comparing the time results of the National Championships, 54.8% of the athletes showed an improvement (p = 0.75). CONCLUSIONS: COVID infection with short-term detraining did not affect the performance of well-trained swimmers. According to our results, the COVID pandemic did not impair the effectiveness of the preparation for the Tokyo Olympics.

Partitioning the Right Ventricle Into 15 Segments and Decomposing Its Motion Using 3D Echocardiography-Based Models: The Updated ReVISION Method.

Three main mechanisms contribute to global right ventricular (RV) function: longitudinal shortening, radial displacement of the RV free wall (bellows effect), and anteroposterior shortening (as a consequence of left ventricular contraction). Since the importance of these mechanisms may vary in different cardiac conditions, a technology being able to assess their relative influence on the global RV pump function could help to clarify the pathophysiology and the mechanical adaptation of the chamber. Previously, we have introduced our 3D echocardiography (3DE)-based solution-the Right VentrIcular Separate wall motIon quantificatiON (ReVISION) method-for the quantification of the relative contribution of the three aforementioned mechanisms to global RV ejection fraction (EF). Since then, our approach has been applied in several clinical scenarios, and its strengths have been demonstrated in the in-depth characterization of RV mechanical pattern and the prognostication of patients even in the face of maintained RV EF. Recently, various new features have been implemented in our software solution to enable the convenient, standardized, and more comprehensive analysis of RV function. Accordingly, in our current technical paper, we aim to provide a detailed description of the latest version of the ReVISION method with special regards to the volumetric partitioning of the RV and the calculation of longitudinal, circumferential, and area strains using 3DE datasets. We also report the results of the comparison between 3DE- and cardiac magnetic resonance imaging-derived RV parameters, where we found a robust agreement in our advanced 3D metrics between the two modalities. In conclusion, the ReVISION method may provide novel insights into global and also segmental RV function by defining parameters that are potentially more sensitive and predictive compared to conventional echocardiographic measurements in the context of different cardiac diseases.

Right ventricular mechanical pattern in patients undergoing mitral valve surgery: a predictor of post-operative dysfunction?

AIMS: The PREPARE-MVR study (PRediction of Early PostoperAtive Right vEntricular failure in Mitral Valve Replacement/Repair patients) sought to investigate the alterations of right ventricular (RV) contraction pattern in patients undergoing mitral valve replacement/repair (MVR) and to explore the associations between pre-operative RV mechanics and early post-operative RV dysfunction (RVD). METHODS AND RESULTS: We prospectively enrolled 42 patients (63 ± 11 years, 69% men) undergoing open-heart MVR. Transthoracic three-dimensional (3D) echocardiography was performed pre-operatively, at intensive care unit discharge, and 6 months after surgery. The 3D model of the RV was reconstructed, and RV ejection fraction (RVEF) was calculated. We decomposed the motion of the ventricle to compute longitudinal ejection fraction (LEF) and radial ejection fraction (REF). Pulmonary artery catheterization was performed to monitor RV stroke work index (RVSWi). RVEF was slightly decreased after MVR [52 (50-55) vs. 51 (46-54)%; P = 0.001], whereas RV contraction pattern changed notably. Before MVR, the longitudinal shortening was the main contributor to global systolic RV function [LEF/RVEF vs. REF/RVEF; 0.53 (0.47-0.58) vs. 0.33 (0.22-0.42); P < 0.001]. Post-operatively, the radial motion became dominant [0.33 (0.28-0.43) vs. 0.46 (0.37-0.51); P = 0.004]. However, this shift was temporary as 6 months later the two components contributed equally to global RV function [0.44 (0.38-0.50) vs. 0.41 (0.36-0.49); P = 0.775]. Pre-operative LEF was an independent predictor of post-operative RVD defined as RVSWi < 300 mmHg⋅mL/m2 [OR = 1.33 (95% CI: 1.08-1.77), P < 0.05]. CONCLUSIONS: MVR induces a significant shift in the RV mechanical pattern. Advanced indices of RV mechanics are associated with invasively measured parameters of RV contractility and may predict post-operative RVD.

Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score.

AIMS: Our aim was to develop a machine learning (ML)-based risk stratification system to predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients undergoing cardiac resynchronization therapy (CRT). METHODS AND RESULTS: Multiple ML models were trained on a retrospective database of 1510 patients undergoing CRT implantation to predict 1- to 5-year all-cause mortality. Thirty-three pre-implant clinical features were selected to train the models. The best performing model [SEMMELWEIS-CRT score (perSonalizEd assessMent of estiMatEd risk of mortaLity With machinE learnIng in patientS undergoing CRT implantation)], along with pre-existing scores (Seattle Heart Failure Model, VALID-CRT, EAARN, ScREEN, and CRT-score), was tested on an independent cohort of 158 patients. There were 805 (53%) deaths in the training cohort and 80 (51%) deaths in the test cohort during the 5-year follow-up period. Among the trained classifiers, random forest demonstrated the best performance. For the prediction of 1-, 2-, 3-, 4-, and 5-year mortality, the areas under the receiver operating characteristic curves of the SEMMELWEIS-CRT score were 0.768 (95% CI: 0.674-0.861; P < 0.001), 0.793 (95% CI: 0.718-0.867; P < 0.001), 0.785 (95% CI: 0.711-0.859; P < 0.001), 0.776 (95% CI: 0.703-0.849; P < 0.001), and 0.803 (95% CI: 0.733-0.872; P < 0.001), respectively. The discriminative ability of our model was superior to other evaluated scores. CONCLUSION: The SEMMELWEIS-CRT score (available at semmelweiscrtscore.com) exhibited good discriminative capabilities for the prediction of all-cause death in CRT patients and outperformed the already existing risk scores. By capturing the non-linear association of predictors, the utilization of ML approaches may facilitate optimal candidate selection and prognostication of patients undergoing CRT implantation.