Andrea Kraus

Rationale: Information on lung volume characteristics in infants with clinical signs of respiratory distress immediately after birth is limited. Objectives: To assess changes in respiratory dynamics and physiological parameters in infants with and without respiratory distress in the delivery room and to determine the effect of continuous positive airway pressure (CPAP) support. Methods: Electrical impedance tomography data were obtained from late preterm and term infants, born via caesarean section in a tertiary referral centre. Changes in the ratio of inspiratory to expiratory time (Ti/Te-ratio), end-expiratory lung impedance (∆EELI), oxygen saturation (SpO ₂ ) and heart rate (HR) over the first 10 min as well as corresponding changes after CPAP application were assessed. Measurements and main results: Of 73 infants, 18 (25%) received CPAP after birth (11 not admitted ( CPAP group) and 7 admitted to the neonatal intensive care unit ( CPAP/NICU )). Ti/Te ratio differed significantly between groups with the highest values in the CPAP/NICU group, mostly due to a reduced Te. There was no difference in ∆EELI. Similarly, infants in the CPAP/NICU group had lower SpO ₂ and HR trajectories over time than the other two groups. After CPAP application, ΔEELI increased significantly. There were no changes in Ti/Te ratio, SpO ₂ and HR after CPAP initiation. Conclusions: In infants with more severe respiratory distress, Ti/Te ratio was increased, and SpO ₂ and HR were reduced, suggesting that these parameters may serve as early predictors of respiratory failure. Application of CPAP resulted in an immediate increase in EELI, highlighting the importance of early CPAP initiation for infants with respiratory distress.

Background: Aortic valve replacement (AVR) is the definitive therapy for patients with severe aortic valve stenosis (AoS). The aim of this work is to compare the effect of a mechanical prosthesis (MP) and a bioprosthesis (BP) on the survival of patients aged 50–65 years after AVR. Methods: The retrospective analysis included 276 patients aged 50 to 65 years who had undergone isolated AVR for AoS; 161 patients were implanted with an MP and 115 with a BP. Patient survival, adjusted for age, gender and risk parameters affecting survival, was assessed. A subgroup analysis was performed on the 208 patients with a modern valve (prosthesis models that are no longer used in clinical practice were removed from the sample). Results: After adjusting for risk factors for overall survival as well as for age and sex, the implantation of an MP did not have a significant effect on overall survival in comparison to a BP, at a median follow-up of 10.3 years (p = 0.477). The size of the MP had no significant effect on overall survival either (HR: 1.29; 95%CI: 0.16–10.21; p = 0.812). However, the indexed effective orifice area of the BP had a positive effect on overall survival (HR: 0.09; 95%CI: 0.01–0.78; p = 0.029). Conclusions: The estimated survival of patients aged between 50 and 65 years after implantation of a BP with a sufficiently large indexed effective orifice area may exceed that of patients with an MP.

Background Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported from a standardised source over the next one to four weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results Over 52 weeks we collected and combined up to 28 forecast models for 32 countries. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 84% of participating models’ forecasts of incident cases (with a total N=862), and 92% of participating models’ forecasts of deaths (N=746). Across a one to four week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over four weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than two weeks. Code and data availability All data and code are publicly available on Github: covid19-forecast-hub-europe/euro-hub-ensemble.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Birth‐and‐death processes are widely used to model the development of biological populations. Although they are relatively simple models, their parameters can be challenging to estimate, as the likelihood can become numerically unstable when data arise from the most common sampling schemes, such as annual population censuses. A further difficulty arises when the discrete observations are not equi‐spaced, for example, when census data are unavailable for some years. We present two approaches to estimating the birth, death, and growth rates of a discretely observed linear birth‐and‐death process: via an embedded Galton‐Watson process and by maximizing a saddlepoint approximation to the likelihood. We study asymptotic properties of the estimators, compare them on numerical examples, and apply the methodology to data on monitored populations.

Goals: We aimed to assess the impact of very cold days on inflammatory bowel disease (IBD) flares and infectious gastroenteritis (IG). We defined a cold day using the World Meteorological definition of an ice day, which is a day with a maximum temperature below 0°C. Background: Recently, we have shown that heat waves increase the risk for IG and IBD flares. Study: We retrospectively collected data from 738 IBD and 786 IG patients admitted to the University Hospital of Zurich between 2001 and 2005 and from 506 patients with other noninfectious chronic intestinal inflammations as controls. Climate data were received by the Swiss Federal Office for Meteorology and Climatology. Results: There was no evidence for an increased risk of IBD flares (relative risk, RR = 0.99, 95% confidence interval, CI: 0.72-1.33, p = 0.94) or IG flares (RR = 1.16, 95% CI: 087-1.52, p = 0.30) on very cold days. This negative finding was confirmed in alternative formulations with lagged or cumulative (possibly lagged) effects. Conclusion: In this retrospective controlled observational study, no evidence for an increase in hospital admissions due to flares of IBD and IG during cold days was observed. This may be attributed to not relevantly altered bacterial growth conditions during cold days compared to heat waves.

Placebo effects emerging from the expectations of relatives, also known as placebo by proxy, have seldom been explored. The aim of this study was to investigate whether in a randomized controlled trial (RCT) there is a clinically relevant difference in long-term outcome between very preterm infants whose parents assume that verum (PAV) had been administered and very preterm infants whose parents assume that placebo (PAP) had been administered. The difference between the PAV and PAP infants with respect to the primary outcome–IQ at 5 years of age–was considered clinically irrelevant if the confidence interval (CI) for the mean difference resided within our pre-specified ±5-point equivalence margins. When adjusted for the effects of verum/placebo, socioeconomic status (SES), head circumference and sepsis, the CI was [−3.04, 5.67] points in favor of the PAV group. Consequently, our study did not show equivalence between the PAV and PAP groups, with respect to the pre-specified margins of equivalence. Therefore, our findings suggest that there is a small, but clinically irrelevant degree to which a preterm infant’s response to therapy is affected by its parents’ expectations, however, additional large-scale studies are needed to confirm this conjecture.

Although under discussion, induced hypothermia (IH) is an established therapy for patients with cardiac arrest or traumatic brain injuries. The influences on coagulopathy and bleeding tendency in severely injured patients (SIP) with concomitant traumatic brain injury are most widely unclear. Therefore, the aim of this study was to quantify the effect of mild IH in SIP with concomitant severe traumatic brain injuries on transfusion rate and mortality. In this retrospective multi-centre study, SIP from three European level-1 trauma centres with an ISS ≥16 between 2009 and 2011 were included. At hospital A, patients qualified for IH with age ≤70 years and a severe head injury with an abbreviated injury scale (AISHead) of ≥3. IH was defined as target core body temperature of 35 °C. Hypothermic patients were matched with two patients, one from hospital B and one from hospital C using age and AISHead. The effect of IH on the transfusion rate, complications and mortality was quantified with 95 % confidence intervals (CI). Patients not treated with IH in hospital A and those from hospital B and C, who were not matched, were used to adjust the CI for the effect of inter-hospital therapy protocol differences. Mean age of patients in the IH-group (n = 43) was 35.7 years, mean ISS 30 points and sex distribution showed 83.7 % male. Mean age of matched patients in the normotherm-group (n = 86) was 36.7 years, mean ISS 33 points and there were 75.6 % males. For the hypothermic patients, we pointed out an estimate of mean difference for the number of transfused units of packed red blood cells as well as for mortality which does not indicate a decrease in the benefit gained by hypothermia. It is suggested that hypothermic patients tend to a higher rate of lung failure and thromboembolisms. Though tending to an increased rate of complications, there is no evidence for a difference in both; rate of transfusion and mortality in SIP. Mild IH as an option for severe head injuries seems as well-being practicable in the presence of multiple severe injuries. Further, clinical studies regarding the side effects are necessary.

Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is considered a progressive cardiomyopathy. However, data on the clinical features of disease progression are limited. The aim of this study was to assess 12-lead surface electrocardiographic (ECG) changes during long-term follow-up, and to compare these findings with echocardiographic data in our large cohort of patients with ARVC/D. Baseline and follow-up ECGs of 111 patients from three tertiary care centers in Switzerland were systematically analyzed with digital calipers by two blinded observers, and correlated with findings from transthoracic echocardiography. The median follow-up was 4 years (IQR 1.9–9.2 years). ECG progression was significant for epsilon waves (baseline 14% vs. follow-up 31%, p = 0.01) and QRS duration (111 ms vs. 114 ms, p = 0.04). Six patients with repolarization abnormalities according to the 2010 Task Force Criteria at baseline did not display these criteria at follow-up, whereas in all patients with epsilon waves at baseline these depolarization abnormalities also remained at follow-up. T wave inversions in inferior leads were common (36% of patients at baseline), and were significantly associated with major repolarization abnormalities (p = 0.02), extensive echocardiographic right ventricular involvement (p = 0.04), T wave inversions in lateral precordial leads (p = 0.05), and definite ARVC/D (p = 0.05). Our data supports the concept that ARVC/D is generally progressive, which can be detected by 12-lead surface ECG. Repolarization abnormalities may disappear during the course of the disease. Furthermore, the presence of T wave inversions in inferior leads is common in ARVC/D.