Matthias Alexander Zingg

30 years later—Does the ABSI need revision?

In light of changes in patient demographics together with constant developments in burn care, the predictive accuracy of the Abbreviated Burns Severity Index (ABSI) - first described in 1982 - for estimating the mortality of present day burns patients, may be questionable. We reviewed the records of 2813 burns patients treated between January 1968 and December 2008 in the intensive care unit at our institution, aiming to identify emerging discrepancies between the estimated and calculated outcome, based on each of the ABSI variables and the total burn score. The predictive value of each of the defined ABSI variables was confirmed to be highly significant. Univariable and multivariable analysis revealed an exponential increase in odds ratio (OR) for mortality for patients older than 60 years and more than 30% TBSA burned and showed OR values over 10 times higher than other significant variables like inhalation injury. Nevertheless, the ABSI for the estimation of mortality in our entire patient collective was highly accurate and could not be optimised by adapting the point distribution to the increase in OR. Our data indicates that despite significant changes in patient demographics and medical advances over the past 30 years, the ABSI scoring system is still an accurate and valuable tool in the prediction of burn patient mortality.

Master runners dominate 24-h ultramarathons worldwide—a retrospective data analysis from 1998 to 2011

BackgroundThe aims of the present study were to examine (a) participation and performance trends and (b) the age of peak running performance in master athletes competing in 24-h ultra-marathons held worldwide between 1998 and 2011.MethodsChanges in both running speed and the age of peak running speed in 24-h master ultra-marathoners (39,664 finishers, including 8,013 women and 31,651 men) were analyzed.ResultsThe number of 24-h ultra-marathoners increased for both women and men across years (P < 0.01). The age of the annual fastest woman decreased from 48 years in 1998 to 35 years in 2011. The age of peaking running speed remained unchanged across time at 42.5 ± 5.2 years for the annual fastest men (P > 0.05). The age of the annual top ten women decreased from 42.6 ± 5.9 years (1998) to 40.1 ± 7.0 years (2011) (P < 0.01). For the annual top ten men, the age of peak running speed remained unchanged at 42 ± 2 years (P > 0.05). Running speed remained unchanged over time at 11.4 ± 0.4 km h-1 for the annual fastest men and 10.0 ± 0.2 km/h for the annual fastest women, respectively (P > 0.05). For the annual ten fastest women, running speed increased over time by 3.2% from 9.3 ± 0.3 to 9.6 ± 0.3 km/h (P < 0.01). Running speed of the annual top ten men remained unchanged at 10.8 ± 0.3 km/h (P > 0.05). Women in age groups 25–29 (r2 = 0.61, P < 0.01), 30–34 (r2 = 0.48, P < 0.01), 35–39 (r2 = 0.42, P = 0.01), 40–44 (r2 = 0.46, P < 0.01), 55–59 (r2 = 0.41, P = 0.03), and 60–64 (r2 = 0.57, P < 0.01) improved running speed; while women in age groups 45–49 and 50–54 maintained running speed (P > 0.05). Men improved running speed in age groups 25–29 (r2 = 0.48, P = 0.02), 45–49 (r2 = 0.34, P = 0.03), 50–54 (r2 = 0.50, P < 0.01), 55–59 (r2 = 0.70, P < 0.01), and 60–64 (r2 = 0.44, P = 0.03); while runners in age groups 30–34, 35–39, and 40–44 maintained running speed (P > 0.05).ConclusionsFemale and male age group runners improved running speed. Runners aged >40 years achieved the fastest running speeds. By definition, runners aged >35 are master runners. The definition of master runners aged >35 years needs to be questioned for ultra-marathoners competing in 24-h ultra-marathons.

Analysis of participation and performance in athletes by age group in ultramarathons of more than 200 km in length.

Background Participation and performance trends for athletes by age group have been investigated for marathoners and ultramarathoners competing in races up to 161 km, but not for longer distances of more than 200 km. Methods Participation and performance trends in athletes by age group in the Badwater (217 km) and Spartathlon (246 km) races were compared from 2000 to 2012. Results The number of female and male finishers increased in both races across years (P < 0.05). The age of the annual five fastest men decreased in Badwater from 42.4 ± 4.2 years to 39.8 ± 5.7 years (r2 = 0.33, P = 0.04). For women, the age remained unchanged at 42.3 ± 3.8 years in Badwater (P > 0.05). In Spartathlon, the age of the annual five fastest finishers was unchanged at 39.7 ± 2.4 years for men and 44.6 ± 3.2 years for women (P > 0.05). In Badwater, running speed increased in men from 7.9 ± 0.7 km/hour to 8.7 ± 0.6 km/hour (r2 = 0.51, P < 0.01) and in women from 5.4 ± 1.1 km/hour to 6.6 ± 0.5 km/hour (r2 = 0.61, P < 0.01). In Spartathlon, running speed remained unchanged at 10.8 ± 0.7 km/hour in men and 8.7 ± 0.5 km/hour in women (P > 0.05). In Badwater, the number of men in age groups 30–34 years (r2 = 0.37, P = 0.03) and 40–44 years (r2 = 0.75, P < 0.01) increased. In Spartathlon, the number of men increased in the age group 40–44 years (r2 = 0.33, P = 0.04). Men in age groups 30–34 (r2 = 0.64, P < 0.01), 35–39 (r2 = 0.33, P = 0.04), 40–44 (r2 = 0.34, P = 0.04), and 55–59 years (r2 = 0.40, P = 0.02) improved running speed in Badwater. In Spartathlon, no change in running speed was observed. Conclusion The fastest finishers in ultramarathons more than 200 km in distance were 40–45 years old and have to be classified as “master runners” by definition. In contrast to reports of marathoners and ultramarathoners competing in races of 161 km in distance, the increase in participation and the improvement in performance by age group were less pronounced in ultramarathoners competing in races of more than 200 km.

Attempted Suicide by Self-Immolation is a Powerful Predictive Variable for Survival of Burn Injuries

Up to 9% of all burn victims in western countries are reported to have been caused by self-immolation with suicidal intent and usually involve extensive injuries. The authors sought to identify differences between suicide burn victims as opposed to those who sustained their injuries accidentally with regard to injury severity and mortality and determine the possible impact of suicide as a prognostic variable in the context of a scoring system such as the Abbreviated Burns Severity Index (ABSI). The data of all burns patients treated at the Specialist Burns Intensive Care Unit, University Hospital Zürich, between 1968 and 2008 were analyzed retrospectively. Of the 2813 patients included in the study, 191 were identified as attempted suicides, most commonly involving the use of accelerants. Thirty percent of all suicide victims had preexisting psychiatric diagnoses. Suicide victims presented with significantly more extensive burns (53.7%, ±0.98 SEM vs 21.4 %, ±0.36 SEM, P < .0001), had higher total ABSI scores (8.4, ±0.23 SEM vs 6.6, ±0.05 SEM, P < .0001), and had higher mortality rates (42.9% [83/191] vs 16.3% [426/2622]) than accident victims. Furthermore, logistic regression revealed suicide to be a significant predictor of mortality as inhalation injury (odds ratio 2.2, 95% confidence interval 1.4–3.5, P < .0003 and odds ratio 2.4, 95% confidence interval 1.4–4.0, P < .0009, respectively). The odds of dying from an attempted suicide are twice as high compared with those of accident patients in the same ABSI category, making suicide a powerful predictor of mortality. The authors therefore suggest including it as a fixed variable in scoring systems for estimating a patient’s mortality after burn injuries such as the widely used ABSI.

Runners in their forties dominate ultra-marathons from 50 to 3,100 miles

OBJECTIVES: This study investigated performance trends and the age of peak running speed in ultra-marathons from 50 to 3,100 miles. METHODS: The running speed and age of the fastest competitors in 50-, 100-, 200-, 1,000- and 3,100-mile events held worldwide from 1971 to 2012 were analyzed using single- and multi-level regression analyses. RESULTS: The number of events and competitors increased exponentially in 50- and 100-mile events. For the annual fastest runners, women improved in 50-mile events, but not men. In 100-mile events, both women and men improved their performance. In 1,000-mile events, men became slower. For the annual top ten runners, women improved in 50- and 100-mile events, whereas the performance of men remained unchanged in 50- and 3,100-mile events but improved in 100-mile events. The age of the annual fastest runners was approximately 35 years for both women and men in 50-mile events and approximately 35 years for women in 100-mile events. For men, the age of the annual fastest runners in 100-mile events was higher at 38 years. For the annual fastest runners of 1,000-mile events, the women were approximately 43 years of age, whereas for men, the age increased to 48 years of age. For the annual fastest runners of 3,100-mile events, the age in women decreased to 35 years and was approximately 39 years in men. CONCLUSION: The running speed of the fastest competitors increased for both women and men in 100-mile events but only for women in 50-mile events. The age of peak running speed increased in men with increasing race distance to approximately 45 years in 1,000-mile events, whereas it decreased to approximately 39 years in 3,100-mile events. In women, the upper age of peak running speed increased to approximately 51 years in 3,100-mile events.

Age and gender difference in non-drafting ultra-endurance cycling performance - the ‘Swiss Cycling Marathon’

BackgroundIn recent years, there was an increased interest in investigating the gender difference in performance and the age of peak performance in ultra-endurance performances such as ultra-triathlon, ultra-running, and ultra-swimming, but not in ultra-cycling. The aim of the present study was to analyze the gender difference in ultra-cycling performance and the age of peak ultra-cycling performance in the 720-km ‘Swiss Cycling Marathon’, the largest European qualifier for the ‘Race Across America’.MethodsChanges in the cycling speed and age of 985 finishers including 38 women and 947 men competing in the Swiss Cycling Marathon from 2001 to 2012 covering a distance of 720 km with a change of altitude of 4,993 m were analyzed using linear regression.ResultsThe gender difference in performance was 13.6% for the fastest cyclists ever, 13.9% ± 0.5% for the three fastest cyclists ever and 19.1% ± 3.7% for the ten fastest cyclists ever. The gender difference in performance for the annual top three women and men decreased from 35.0% ± 9.5% in 2001 to 20.4% ± 7.7% in 2012 (r2 = 0.72, p = 0.01). The annual top three women improved cycling speed from 20.3 ± 3.1 km h−1 in 2003 to 24.8 ± 2.4 km h−1 in 2012 (r2 = 0.79, p < 0.01). The cycling speed of the annual top three men remained unchanged at 30.2 ± 0.6 km h−1 (p > 0.05). The age of peak performance for the ten fastest finishers ever was 35.9 ± 9.6 years for men and 38.7 ± 7.8 years for women, respectively (p = 0.47).ConclusionsThe gender difference in ultra-cycling performance decreased over the 2001 to 2012 period in the 720-km Swiss Cycling Marathon for the annual top three cyclists and reached approximately 14%. Both women and men achieved peak performance at the age of approximately 36 to 39 years. Women might close the gender gap in ultra-endurance cycling in longer cycling distances. Future studies need to investigate the gender difference in performance in the Race Across America, the longest nonstop and non-drafting ultra-cycling race in the world.

Variables that influence Ironman triathlon performance - what changed in the last 35 years?

Objective This narrative review summarizes findings for Ironman triathlon performance and intends to determine potential predictor variables for Ironman race performance in female and male triathletes. Methods A literature search was performed in PubMed using the terms “Ironman”, “triathlon”, and “performance”. All resulting articles were searched for related citations. Results Age, previous experience, sex, training, origin, anthropometric and physiological characteristics, pacing, and performance in split disciplines were predictive. Differences exist between the sexes for anthropometric characteristics. The most important predictive variables for a fast Ironman race time were age of 30–35 years (women and men), a fast personal best time in Olympic distance triathlon (women and men), a fast personal best time in marathon (women and men), high volume and high speed in training where high volume was more important than high speed (women and men), low body fat, low skin-fold thicknesses and low circumference of upper arm (only men), and origin from the United States of America (women and men). Conclusion These findings may help athletes and coaches to plan an Ironman triathlon career. Age and previous experience are important to find the right point in the life of a triathlete to switch from the shorter triathlon distances to the Ironman distance. Future studies need to correlate physiological characteristics such as maximum oxygen uptake with Ironman race time to investigate their potential predictive value and to investigate socio-economic aspects in Ironman triathlon.

Prediction of half-marathon race time in recreational female and male runners

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r2 = 0.42, adjusted r2 = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) – 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r2 = 0.68, adjusted r2 = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) – 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the models.

Pacing strategy in male elite and age group 100 km ultra-marathoners

Pacing strategy has been investigated in elite 100 km and elite 161 km (100 mile) ultra-marathoners, but not in age group ultra-marathoners. This study investigated changes in running speed over segments in male elite and age group 100 km ultra-marathoners with the assumption that running speed would decrease over segments with increasing age of the athlete. Running speed during segments in male elite and age group finishers for 5-year age groups (ie, 18–24 to 65–69 years) in the 100 km Lauf Biel in Switzerland was investigated during the 2000–2009 period. Average running speed over segment time station (TS) TS1–TS2 (56.1 km) was compared with running speed Start–TS1 (38 km) and Start–TS3 (76.7 km) and running speed TS2–TS3 was compared with running speed Start–Finish. For the top ten athletes in each edition, running speed decreased from 2000 to 2009 for TS1–TS2 and TS2–TS3 (P<0.0001) but not in TS3–Finish (P>0.05). During TS1–TS2, athletes were running at 98.0%±2.1% of the running speed of Start–TS1. In TS2–TS3, they were running at 94.6%±3.4% of the running speed of TS1–TS2. In TS3–Finish, they were running at 95.5%±3.8% of running speed in TS2–TS3. For age group athletes, running speed decreased in TS1–TS2 and TS2–TS3. In TS3–Finish, running speed remained unchanged with the exception of the age group 40–44 years for which running speed increased. Running speed showed the largest decrease in the age group 18–24 years. To summarize, the top ten athletes in each edition maintained their running speed in the last segment (TS3–Finish) although running speed decreased over the first two segments (TS1–TS2 and TS2–TS3). The best pacers were athletes in the age group 40–44 years, who were able to achieve negative pacing in the last segment (TS3–Finish) of the race. The negative pacing in the last segment (TS3–Finish) was likely due to environmental conditions, such as early dawn and the flat circuit in segment TS3–Finish of the race.

What predicts performance in ultra-triathlon races? - a comparison between Ironman distance triathlon and ultra-triathlon.

Objective This narrative review summarizes recent intentions to find potential predictor variables for ultra-triathlon race performance (ie, triathlon races longer than the Ironman distance covering 3.8 km swimming, 180 km cycling, and 42.195 km running). Results from studies on ultra-triathletes were compared to results on studies on Ironman triathletes. Methods A literature search was performed in PubMed using the terms “ultra”, “triathlon”, and “performance” for the aspects of “ultra-triathlon”, and “Ironman”, “triathlon”, and “performance” for the aspects of “Ironman triathlon”. All resulting papers were searched for related citations. Results for ultra-triathlons were compared to results for Ironman-distance triathlons to find potential differences. Results Athletes competing in Ironman and ultra-triathlon differed in anthropometric and training characteristics, where both Ironmen and ultra-triathletes profited from low body fat, but ultra-triathletes relied more on training volume, whereas speed during training was related to Ironman race time. The most important predictive variables for a fast race time in an ultra-triathlon from Double Iron (ie, 7.6 km swimming, 360 km cycling, and 84.4 km running) and longer were male sex, low body fat, age of 35–40 years, extensive previous experience, a fast time in cycling and running but not in swimming, and origins in Central Europe. Conclusion Any athlete intending to compete in an ultra-triathlon should be aware that low body fat and high training volumes are highly predictive for overall race time. Little is known about the physiological characteristics of these athletes and about female ultra-triathletes. Future studies need to investigate anthropometric and training characteristics of female ultra-triathletes and what motivates women to compete in these races. Future studies need to correlate physiological characteristics such as maximum oxygen uptake (VO2max) with ultra-triathlon race performance in order to investigate whether these characteristics are also predictive for ultra-triathlon race performance.