@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{SedaghatHamedaniRebsKayvanpouretal.2022,
  author    = {Sedaghat-Hamedani, Farbod and Rebs, Sabine and Kayvanpour, Elham and Zhu, Chenchen and Amr, Ali and M{\"u}ller, Marion and Haas, Jan and Wu, Jingyan and Steinmetz, Lars M. and Ehlermann, Philipp and Streckfuss-B{\"o}meke, Katrin and Frey, Norbert and Meder, Benjamin},
  title     = {Genotype complements the phenotype: identification of the pathogenicity of an LMNA splice variant by nanopore long-read sequencing in a large DCM family},
  series = {International Journal of Molecular Sciences},
  volume    = {23},
  journal   = {International Journal of Molecular Sciences},
  number    = {20},
  issn      = {1422-0067},
  doi       = {10.3390/ijms232012230},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290415},
  year      = {2022},
  abstract  = {Dilated cardiomyopathy (DCM) is a common cause of heart failure (HF) and is of familial origin in 20-40\% of cases. Genetic testing by next-generation sequencing (NGS) has yielded a definite diagnosis in many cases; however, some remain elusive. In this study, we used a combination of NGS, human-induced pluripotent-stem-cell-derived cardiomyocytes (iPSC-CMs) and nanopore long-read sequencing to identify the causal variant in a multi-generational pedigree of DCM. A four-generation family with familial DCM was investigated. Next-generation sequencing (NGS) was performed on 22 family members. Skin biopsies from two affected family members were used to generate iPSCs, which were then differentiated into iPSC-CMs. Short-read RNA sequencing was used for the evaluation of the target gene expression, and long-read RNA nanopore sequencing was used to evaluate the relevance of the splice variants. The pedigree suggested a highly penetrant, autosomal dominant mode of inheritance. The phenotype of the family was suggestive of laminopathy, but previous genetic testing using both Sanger and panel sequencing only yielded conflicting evidence for LMNA p.R644C (rs142000963), which was not fully segregated. By re-sequencing four additional affected family members, further non-coding LMNA variants could be detected: rs149339264, rs199686967, rs201379016, and rs794728589. To explore the roles of these variants, iPSC-CMs were generated. RNA sequencing showed the LMNA expression levels to be significantly lower in the iPSC-CMs of the LMNA variant carriers. We demonstrated a dysregulated sarcomeric structure and altered calcium homeostasis in the iPSC-CMs of the LMNA variant carriers. Using targeted nanopore long-read sequencing, we revealed the biological significance of the variant c.356+1G>A, which generates a novel 5′ splice site in exon 1 of the cardiac isomer of LMNA, causing a nonsense mRNA product with almost complete RNA decay and haploinsufficiency. Using novel molecular analysis and nanopore technology, we demonstrated the pathogenesis of the rs794728589 (c.356+1G>A) splice variant in LMNA. This study highlights the importance of precise diagnostics in the clinical management and workup of cardiomyopathies.},
  language  = {en}
}