@article{SommerAmrBavendieketal.2022,
  author    = {Sommer, Kim K. and Amr, Ali and Bavendiek, Udo and Beierle, Felix and Brunecker, Peter and Dathe, Henning and Eils, J{\"u}rgen and Ertl, Maximilian and Fette, Georg and Gietzelt, Matthias and Heidecker, Bettina and Hellenkamp, Kristian and Heuschmann, Peter and Hoos, Jennifer D. E. and Keszty{\"u}s, Tibor and Kerwagen, Fabian and Kindermann, Aljoscha and Krefting, Dagmar and Landmesser, Ulf and Marschollek, Michael and Meder, Benjamin and Merzweiler, Angela and Prasser, Fabian and Pryss, R{\"u}diger and Richter, Jendrik and Schneider, Philipp and St{\"o}rk, Stefan and Dieterich, Christoph},
  title     = {Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores},
  series = {Life},
  volume    = {12},
  journal   = {Life},
  number    = {5},
  issn      = {2075-1729},
  doi       = {10.3390/life12050749},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275239},
  year      = {2022},
  abstract  = {Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.},
  language  = {en}
}
@article{PilgramEberweinWilleetal.2021,
  author    = {Pilgram, Lisa and Eberwein, Lukas and Wille, Kai and Koehler, Felix C. and Stecher, Melanie and Rieg, Siegbert and Kielstein, Jan T. and Jakob, Carolin E. M. and R{\"u}thrich, Maria and Burst, Volker and Prasser, Fabian and Borgmann, Stefan and M{\"u}ller, Roman-Ulrich and Lanznaster, Julia and Isberner, Nora and Tometten, Lukas and Dolff, Sebastian},
  title     = {Clinical course and predictive risk factors for fatal outcome of SARS-CoV-2 infection in patients with chronic kidney disease},
  series = {Infection},
  volume    = {49},
  journal   = {Infection},
  number    = {4},
  organization    = {LEOSS Study group},
  issn      = {0300-8126},
  doi       = {10.1007/s15010-021-01597-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-308957},
  pages     = {725-737},
  year      = {2021},
  abstract  = {Purpose The ongoing pandemic caused by the novel severe acute respiratory coronavirus 2 (SARS-CoV-2) has stressed health systems worldwide. Patients with chronic kidney disease (CKD) seem to be more prone to a severe course of coronavirus disease (COVID-19) due to comorbidities and an altered immune system. The study's aim was to identify factors predicting mortality among SARS-CoV-2-infected patients with CKD. Methods We analyzed 2817 SARS-CoV-2-infected patients enrolled in the Lean European Open Survey on SARS-CoV-2-infected patients and identified 426 patients with pre-existing CKD. Group comparisons were performed via Chi-squared test. Using univariate and multivariable logistic regression, predictive factors for mortality were identified. Results Comparative analyses to patients without CKD revealed a higher mortality (140/426, 32.9\% versus 354/2391, 14.8\%). Higher age could be confirmed as a demographic predictor for mortality in CKD patients (> 85 years compared to 15-65 years, adjusted odds ratio (aOR) 6.49, 95\% CI 1.27-33.20, p = 0.025). We further identified markedly elevated lactate dehydrogenase (> 2 × upper limit of normal, aOR 23.21, 95\% CI 3.66-147.11, p < 0.001), thrombocytopenia (< 120,000/µl, aOR 11.66, 95\% CI 2.49-54.70, p = 0.002), anemia (Hb < 10 g/dl, aOR 3.21, 95\% CI 1.17-8.82, p = 0.024), and C-reactive protein (≥ 30 mg/l, aOR 3.44, 95\% CI 1.13-10.45, p = 0.029) as predictors, while renal replacement therapy was not related to mortality (aOR 1.15, 95\% CI 0.68-1.93, p = 0.611). Conclusion The identified predictors include routinely measured and universally available parameters. Their assessment might facilitate risk stratification in this highly vulnerable cohort as early as at initial medical evaluation for SARS-CoV-2.},
  language  = {en}
}