@misc{SeibertMerzApel2017,
  author    = {Seibert, Mathias and Merz, Bruno and Apel, Heiko},
  title     = {Seasonal forecasting of hydrological drought in the Limpopo Basin},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {626},
  doi       = {10.25932/publishup-41844},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-418442},
  pages     = {1611 -- 1629},
  year      = {2017},
  abstract  = {The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Nino and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42\% explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts.},
  language  = {en}
}
@article{ReibisKuehlSalzwedeletal.2017,
  author    = {Reibis, Rona Katharina and K{\"u}hl, Uwe and Salzwedel, Annett and Rasawieh, Mortesa and Eichler, Sarah and Wegscheider, Karl and V{\"o}ller, Heinz},
  title     = {Return to work in heart failure patients with suspected viral myocarditis},
  series = {SAGE Open Medicine},
  volume    = {5},
  journal   = {SAGE Open Medicine},
  publisher = {Sage},
  address   = {Thousand Oaks, Calif.},
  issn      = {2050-3121},
  doi       = {10.1177/2050312117744978},
  year      = {2017},
  abstract  = {Background: Endomyocardial biopsy is considered as the gold standard in patients with suspected myocarditis. We aimed to evaluate the impact of bioptic findings on prediction of successful return to work. Methods: In 1153 patients (48.9 ± 12.4 years, 66.2\% male), who were hospitalized due to symptoms of left heart failure between 2005 and 2012, an endomyocardial biopsy was performed. Routine clinical and laboratory data, sociodemographic parameters, and noninvasive and invasive cardiac variables including endomyocardial biopsy were registered. Data were linked with return to work data from the German statutory pension insurance program and analyzed by Cox regression. Results: A total of 220 patients had a complete data set of hospital and insurance information. Three quarters of patients were virus-positive (54.2\% parvovirus B19, other or mixed infection 16.7\%). Mean invasive left ventricular ejection fraction was 47.1\% ± 18.6\% (left ventricular ejection fraction <45\% in 46.3\%). Return to work was achieved after a mean interval of 168.8 ± 347.7 days in 220 patients (after 6, 12, and 24 months in 61.3\%, 72.2\%, and 76.4\%). In multivariate regression analysis, only age (per 10 years, hazard ratio, 1.27; 95\% confidence interval, 1.10-1.46; p = 0.001) and left ventricular ejection fraction (per 5\% increase, hazard ratio, 1.07; 95\% confidence interval, 1.03-1.12; p = 0.002) were associated with increased, elevated work intensity (heavy vs light, congestive heart failure, 0.58; 95\% confidence interval, 0.34-0.99; p < 0.049) with decreased probability of return to work. None of the endomyocardial biopsy-derived parameters was significantly associated with return to work in the total group as well as in the subgroup of patients with biopsy-proven myocarditis. Conclusion: Added to established predictors, bioptic data demonstrated no additional impact for return to work probability. Thus, socio-medical evaluation of patients with suspected myocarditis furthermore remains an individually oriented process based primarily on clinical and functional parameters.},
  language  = {en}
}