Advances in Cardiac Pacing: Arrhythmia Prediction, Prevention and Control Strategies

Cardiac arrhythmias constitute a tremendous burden on healthcare and are the leading cause of mortality worldwide. An alarming number of people have been reported to manifest sudden cardiac death as the first symptom of cardiac arrhythmias, accounting for about 20% of all deaths annually. Furthermore, patients prone to atrial tachyarrhythmias such as atrial flutter and fibrillation often have associated comorbidities including hypertension, ischemic heart disease, valvular cardiomyopathy and increased risk of stroke. Technological advances in electrical stimulation and sensing modalities have led to the proliferation of medical devices including pacemakers and implantable defibrillators, aiming to restore normal cardiac rhythm. However, given the complex spatiotemporal dynamics and non-linearity of the human heart, predicting the onset of arrhythmias and preventing the transition from steady state to unstable rhythms has been an extremely challenging task. Defibrillatory shocks still remain the primary clinical intervention for lethal ventricular arrhythmias, yet patients with implantable cardioverter defibrillators often suffer from inappropriate shocks due to false positives and reduced quality of life. Here, we aim to present a comprehensive review of the current advances in cardiac arrhythmia prediction, prevention and control strategies. We provide an overview of traditional clinical arrhythmia management methods and describe promising potential pacing techniques for predicting the onset of abnormal rhythms and effectively suppressing cardiac arrhythmias. We also offer a clinical perspective on bridging the gap between basic and clinical science that would aid in the assimilation of promising anti-arrhythmic pacing strategies.

The Effect of Squad Rotation on Physical Activity at the 2018 World Cup in Russia. Analysis the Most Exploited Players of the 4 Best Teams

The purpose of this study was to examine how the four best teams in the 2018 Football Men's World Cup rotate by squad and how this impact the physical activity of the teams in consecutive rounds. The study sample consisted of the 31 players of the 4 best teams, who played in every tournament match, except for the third game of the group stage. The analysis included 186 observations and was carried out on the most exploited players (MEP) excluding goalkeepers, who played at least 450 mins (5 full matches) in the tournament. The analysis was conducted using data collected by an advanced motion analysis system known as STATS®. The selected physical activity parameters analyzed included: total distance covered (m/min), distance covered at various intensity ranges (m/min), top speed (km/h), and number of sprints performed. It was found that all four teams in the third match of the group stage have performed the largest number of rotations with most exploited players and introduced the highest number of rested players (7.75 ± 2.06). A significant increase was observed between the second and fourth match in the 0–7 km/h distance covered (37.99 ± 3.19–39.23 ± 3.35 m/min) and the top speed (28.12 ± 2.22–29.21 ± 2.64 km/h)—p < 0.05. Furthermore, MEPs in the knockout stage, used pacing strategies by increasing the amount of low-intensity running to maintain high intensity during the game. From a practical point of view, this investigation shows that squad rotation can be a valuable support to a pacing strategy by players.

Win, Draw, or Lose? Global Positioning System-Based Variables’ Effect on the Match Outcome: A Full-Season Study on an Iranian Professional Soccer Team

The aim of the study was to determine the between-match and between-halves match variability of various Global Positioning System (GPS) variables and metabolic power average (MPA) in competitions, based on the match results obtained by professional soccer players over a full season. Observations on individual match performance measures were undertaken on thirteen outfield players competing in the Iranian Premier League. The measures selected for analysis included total duration, accelerations in zones (AccZ1, 2, and 3), decelerations in zones (DecZ1, 2, and 3), and MPA collected by the Wearable Inertial Measurement Unit (WIMU). The GPS manufacturer set the thresholds for the variables analyzed as follows: AccZ1 (<2 m.s−2); AccZ2 (2 to 4 m.s−2); AccZ3 (>4 m.s−2); DecZ1 (<−2 m.s−2); DecZ2 (−2 to −4 m.s−2); DecZ3 (>−4 m.s−2). The results revealed significant differences between wins and draws for the duration of the match and draws compared to wins for the first- half duration (p ≤ 0.05; ES = 0.36 [−0.43,1.12]), (p ≤ 0.05; ES = −7.0 [−8.78, −4.78], respectively. There were significant differences on AccZ1 during the first-half between draws and defeats (p ≤ 0.05; ES = −0.43 [−1.32,0.46]), for AccZ3 in the second-half between draws and defeats (p ≤ 0.05; ES = 1.37 [0.48,2.25]). In addition, there were significant differences between wins and draws (p ≤ 0.05; ES = 0.22 [−0.62,1.10]), and wins and defeats for MPA in the first- half (p ≤ 0.05; ES = 0.34 [−0.65,1.22]). MPA showed further differences between draws and defeats in the second- half (p ≤ 0.05; ES = 0.57 [−0.22,1.35]). Descriptive analysis revealed differences between the first and second half for wins in AccZ2 (p = 0.005), DecZ2 (p = 0.029), and MPA (p = 0.048). In addition, draws showed significant differences between the first and second half in duration, AccZ1, AccZ2, and DecZ2 (p = 0.008), (p = 0.017), (p = 0.040), and (p = 0.037) respectively. Defeats showed differences between the first and second half in AccZ1, AccZ3, and MPA (p = 0.001), (p = 0.018), and (p = 0.003) respectively. In summary, the study reveals large variations between the match duration, accelerometer variables, and MPA both within and between matches. Regardless of the match outcome, the first half seems to produce greater outputs. The results should be considered when performing a half-time re-warm-up, as this may be an additional factor influencing the drop in the intensity markers in the second half in conjunction with factors such as fatigue, pacing strategies, and other contextual variables that may influence the results.

Influence of the Cyclist’s Characteristics on the Optimal Pacing Strategy for an Ascending Road

Pacing strategies are used in cycling to optimize the power delivered by the cyclist during a race. Gains in race time have been obtained when using these strategies compared to self-paced approaches. For this reason, this study is focused on revising the effect that the variation of the cyclist’s parameters has on the pacing strategy and its results. A numeric method was used to propose pacing strategies for a cyclist riding on an ascending 3.7 km route with a constant 6.26% road grade. The method was validated and then implemented to study the effect of aerobic and anaerobic power delivery capacity, mass, and drag area on the pacing strategies and their corresponding estimated race times. The results showed that modifying 1% of the aerobic capacity or cyclist mass value led to a change of 1% on the race time. Modifying 1% the anaerobic capacity and the drag area led to changes of 0.03% and 0.02% on the race time, respectively. These results are strongly dependent on the route characteristics. It was concluded that for the studied route (constantly ascending), the variation of the cyclist’s aerobic capacity influences the pacing strategy (i.e., the power delivery over the distance). The anaerobic capacity and mass of the cyclist also influence the pacing strategy to a lesser extent.

Performance and Thermal Perceptions of Runners Competing in the London Marathon: Impact of Environmental Conditions

The 2018 Virgin Money London Marathon (2018 VMLM) was the hottest in the race’s 37-year history. The aims of this research were to (1) survey novice mass participation marathoners to examine the perceptual thermal demands of this extreme weather event and (2) investigate the effect of the air temperature on finish times. A mixed-methods design involving the collection of survey data (n = 364; male = 63, female = 294) and secondary analysis of environmental and marathon performance (676,456 finishers) between 2001 and 2019 was used. The 2018 VMLM mean finishing time was slower than the mean of all other London marathons; there were positive correlations between maximum race day temperature and finish time for mass-start participants, and the difference in maximum race day temperature and mean maximum daily temperature for the 60 days before the London Marathon (p < 0.05). Of the surveyed participants, 23% classified their thermal sensation as ‘warm’, ‘hot’ or ‘very hot’ and 68% ‘thermally comfortable’ during training, compared with a peak of 95% feeling ‘warm’, ‘hot’ or ‘very hot’ and 77% ‘uncomfortable’ or ‘very uncomfortable’ during the 2018VMLM. Organisers should use temperature forecasting and plan countermeasures such as adjusting the start time of the event to avoid high temperatures, help runners predict finish time and adjust pacing strategies accordingly and provide safety recommendations for participants at high-risk time points as well as cooling strategies.

Pacing Strategies of 1500 m Freestyle Swimmers in the World Championships According to Their Final Position

In 1500 m freestyle swimming races, pacing is generally represented by a parabolic or U-shaped curve indicating that swimming velocity is greatest at the start and the last laps of the race while swimmers maintain an even pace through the middle section of the race. However, there is no information to determine if 1500 m race winners select pacing different to other, less successful swimmers within the same competition. Therefore, this investigation aimed to describe the pacing strategies adopted by 1500 m freestyle competitive swimmers in World Championships (long course), from 2003 to 2019 to determine the most effective pacing to obtain victory or a medal. The official overall and split times for 1500 m freestyle races of the Fédération Internationale de Natation (FINA) were obtained from the website of this organization. In total, data of 143 swimming performances (71 male and 72 female) were extracted. With the split times, lap times, and position were calculated across the race. To determine differences in the pacing between best- and worst-ranked finalist, swimmers in each race were divided into four groups based on the final position (1st vs. 2nd vs. 3rd vs. 4–8th). All the lap times of the winners of the race were faster than those of participants classified as 4–8th position for men and women races (p < 0.05). However, there were no differences in lap velocity among the different positions achieved at the end of the race when it was normalized by average race velocity. Additionally, there were no differences in the lap-to-lap variability among swimmers with different positions at the end of the race. In summary, both men and women elite swimmers selected parabolic pacing consisting of a fast start in the first lap, an even pace close to their average race velocity in the mid-section of the race (from 50 to 1400 m), followed by an end spurt in the final lap(s). This pattern was very similar in all finalists irrespective of the final position in the race. Hence, the obtaining of a medal in the World Championships was associated to possessing a faster average race velocity rather than a specific pacing profile through the race.

Stroking Rates of Open Water Swimmers during the 2019 FINA World Swimming Championships

The aim of the present research was to examine the stroking rate (SR) values of successful and non-successful swimmers in the 10 km and 25 km races of the FINA 2019 World Swimming Championships. Data from 175 participants (95 men and 80 female) were classified according to their finishing positions. There were no meaningful differences in the overall SR values displayed by successful or non-successful participants during the 10 km and 25 km open water races of the FINA 2019 World Swimming Championships. However, there were changes in the SR throughout the races that depended on the swimmer’s performance group and gender. Successful swimmers in the 10 km event typically displayed even SR in the first 5 km but, unlike the remaining performance groups, increased their SR at some point in the second 5 km of the race. In the 25 km race, successful female swimmers presented an even SR profile for most of the race, whereas successful males presented a more variable profile. Nevertheless, no relationships between partial or average SR and finishing positions occurred, either in the 10 km or in the 25 km race. Changes in the SR values should be included in the race plan of open water swimmers according to tactical and pacing strategies.

Temporal Incidence and Predictors of High‐Grade Atrioventricular Block After Transcatheter Aortic Valve Replacement

Background The temporal incidence of high‐grade atrioventricular block (HAVB) after transcatheter aortic valve replacement (TAVR) is uncertain. As a result, periprocedural monitoring and pacing strategies remain controversial. This study aimed to describe the temporal incidence of initial episode of HAVB stratified by pre‐ and post‐TAVR conduction and identify predictors of delayed events. Methods and Results Consecutive patients undergoing TAVR at a single center between February 2012 and June 2019 were retrospectively assessed for HAVB within 30 days. Patients with prior aortic valve replacement, permanent pacemaker (PPM), or conversion to surgical replacement were excluded. Multivariable logistic regression was performed to assess predictors of delayed HAVB (initial event >24 hours post‐TAVR). A total of 953 patients were included in this study. HAVB occurred in 153 (16.1%). After exclusion of those with prophylactic PPM placed post‐TAVR, the incidence of delayed HAVB was 33/882 (3.7%). Variables independently associated with delayed HAVB included baseline first‐degree atrioventricular block or right bundle‐branch block, self‐expanding valve, and new left bundle‐branch block. Forty patients had intraprocedural transient HAVB, including 16 who developed HAVB recurrence and 6 who had PPM implantation without recurrence. PPM was placed for HAVB in 130 (13.6%) (self‐expanding valve, 23.7% versus balloon‐expandable valve, 11.9%; P <0.001). Eight (0.8%) patients died by 30 days, including 1 unexplained without PPM present. Conclusions Delayed HAVB occurs with higher frequency in patients with baseline first‐degree atrioventricular block or right bundle‐branch block, new left bundle‐branch block, and self‐expanding valve. These findings provide insight into optimal monitoring and pacing strategies based on periprocedural ECG findings.