Aims We aimed to identify a ‘frequent admitter’ phenotype among patients admitted for acute decompensated heart failure (HF). Methods and results We studied 10 363 patients in a population‐based prospective HF registry (2008–2012), segregated into clusters based on their 3‐year HF readmission frequency trajectories. Using receiver‐operating characteristic analysis, we identified the index year readmission frequency threshold that most accurately predicts HF admission frequency clusters. Two clusters of HF patients were identified: a high frequency cluster (90.9%, mean 2.35 ± 3.68 admissions/year) and a low frequency cluster (9.1%, mean 0.50 ± 0.81 admission/year). An index year threshold of two admissions was optimal for distinguishing between clusters. Based on this threshold, ‘frequent admitters’, defined as patients with ≥ 2 HF admissions in the index year (n = 2587), were of younger age (68 ± 13 vs 69 ± 13 years), more often male (58% vs. 54%), smokers (38.4% vs. 34.4%) and had lower left ventricular ejection fraction (37 ± 17 vs. 41 ± 17%) compared to ‘non‐frequent admitters’ (< 2 HF admissions in the index year; n = 7776) (all P < 0.001). Despite similar rates of advanced care utilization, frequent admitters had longer length of stay (median 4.3 vs. 4.0 days), higher annual inpatient costs (€ 7015 vs. € 2967) and higher all‐cause mortality at 3 years compared to the non‐frequent admitters (adjusted odds ratio 2.33, 95% confidence interval 2.11–2.58; P < 0.001). Conclusion ‘Frequent admitters’ have distinct clinical characteristics and worse outcomes compared to non‐frequent admitters. This study may provide a means of anticipating the HF readmission burden and thereby aid in healthcare resource distribution relative to the HF admission frequency phenotype.

Being a small well‐organized city state, Singapore appears to be an ideal place to establish a fully electric road transport system. To analyze the challenges and potential of electric mobility (or “electromobility”), TUMCREATE Ltd., a company funded by Singapore's National Research Foundation, was launched in 2011 as “Centre for Electromobility in Megacities”. During the first five years, the research at TUMCREATE covered everything “from the molecule to the megacity”, that is, from fundamental research on new materials for energy storage systems to battery cells, battery packs, vehicle technology, in‐vehicle electronics to road infrastructure and the power system. One outcome was a prototype for an electric taxi for tropical megacities called EVA with a battery capacity of 50 kWh and fast charging capability of 160 kW. This paper presents a review of past and ongoing activities in the field of integration of electromobility into Singapore's power system. The focus of this publication lies on charging of electric vehicles (EVs). Results show that the integration of EVs into the power system is feasible, leads to lower emissions, and can even offer new services and support integration of renewable energies.

For the transportation sector, electromobility presents a chance both to abolish oil dependency and to open the possibility of using sustainable energy sources. In the case of taxis, the substitution of electric vehicles for conventional vehicles with an internal combustion engine may be especially favorable because the driving patterns involve several waiting periods, which can be used for recharging the battery. An infrastructure consisting of charging stations at taxi stands needs to be developed to ensure the energy supply of these taxis. Designing a charging infrastructure requires the development of an optimization method to maximize the economic benefit of the whole system, consisting of electric taxis (e-taxis) and charging stations (CSs). The number of charging stations should be kept as minimal as possible to reduce costs. Simultaneously, the energy supply for these taxis has to be ensured to enable high mileage and earnings. This study introduces an event-based simulation of the e-taxis’ mileage and an economic analysis that evaluates the benefit of possible configurations of numbers of e-taxis and numbers of CSs per taxi stand. Because the number of possible configurations is high, an optimization algorithm is presented to reduce the calculation time and to find configurations with the highest economic efficiency. One result of the infrastructure optimization indicates that 445 CSs would be economically ideal for a taxi fleet in Munich, Germany, with 3,402 vehicles with a battery capacity of 20 kW-h.

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