Heidelberg Perioperative Deep Data Study (HeiPoDD): study protocol for a prospective cohort study for prediction of complications after high-risk non-cardiac surgery in a German university hospital
Introduction Perioperative adverse events increase morbidity and mortality. The rate and severity of complications and the risk for subsequent mortality are increased after high-risk procedures and in elevated-risk patients. Over the past decades, a multitude of prognostic studies identified periope...
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 September 2025
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| In: |
BMJ open
Year: 2025, Jahrgang: 15, Heft: 9, Pages: 1-7 |
| ISSN: | 2044-6055 |
| DOI: | 10.1136/bmjopen-2024-098590 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1136/bmjopen-2024-098590 Verlag, kostenfrei, Volltext: https://bmjopen.bmj.com/content/15/9/e098590 
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| Verfasserangaben: | Mona Jung-König, Hans Thomas Hölzer, Solveig Tenckhoff, Sachin Kumar, Melanie Etheredge, Dominik Schaack, Tobias Hölle, Laura Christine Schuster, Christian Nusshag, Mathias Rath, Christian Uhl, Frank Pianka, Christoph W. Michalski, Markus Hohenfellner, Rawa Arif, Matthias Karck, Dittmar Böckler, Pascal Probst, Markus Diener, Markus A. Weigand, Martin Dugas, Christian Niklas, Manuel Feißt, Rosa Klotz, Jan Larmann |
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| 245 | 1 | 0 | |a Heidelberg Perioperative Deep Data Study (HeiPoDD) |b study protocol for a prospective cohort study for prediction of complications after high-risk non-cardiac surgery in a German university hospital |c Mona Jung-König, Hans Thomas Hölzer, Solveig Tenckhoff, Sachin Kumar, Melanie Etheredge, Dominik Schaack, Tobias Hölle, Laura Christine Schuster, Christian Nusshag, Mathias Rath, Christian Uhl, Frank Pianka, Christoph W. Michalski, Markus Hohenfellner, Rawa Arif, Matthias Karck, Dittmar Böckler, Pascal Probst, Markus Diener, Markus A. Weigand, Martin Dugas, Christian Niklas, Manuel Feißt, Rosa Klotz, Jan Larmann |
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| 520 | |a Introduction Perioperative adverse events increase morbidity and mortality. The rate and severity of complications and the risk for subsequent mortality are increased after high-risk procedures and in elevated-risk patients. Over the past decades, a multitude of prognostic studies identified perioperative risk factors at the population level. However, to allow for the advancement of precision surgery strategies, improved risk prediction on the individual patient level is warranted. Comprehensive, consecutive, multisource, structured, high-quality patient-related and procedure-related data sets, together with thorough follow-up and combined with state-of-the-art machine-learning analyses, are needed to facilitate precise prediction of perioperative complications. Therefore, we designed and currently conduct the Heidelberg Perioperative Deep Data study (HeiPoDD). Here, we report the rationale and design of the HeiPoDD study. - Methods and analysis HeiPoDD is a prospective, single-centre, exploratory cohort study aiming to build up a large-scale deep-data base and corresponding biomaterial collection. 1040 adult patients planned for elective high-risk, non-cardiac surgery for any indication at Heidelberg University Hospital, Germany will be included. The obtained study-specific data set includes clinical data, lab values, genome- and proteome analysis as well as plasma, serum and peripheral blood mononuclear cells (PBMC) collected before and at days 1, 3 and 7 postsurgery. Urine samples are collected before and at day 1 postsurgery. Structured follow-up for perioperative complications such as redo-surgery, length of intensive care stay or length of hospital stay is conducted at days 30, 90 and 1 year postsurgery and for disease progression and survival after 3 and 5 years postsurgery. All study data will be transferred to the HeiPoDD registry to allow merging with all available routine clinical data from the hospital information system including imaging studies as well as haemodynamic and respiratory biosignals. Biomaterials will be stored in the HeiPoDD biomaterial bank to allow further analyses. - Ethics and dissemination The trial protocol and amendments were approved by the ethics committee of the University of Heidelberg (S-758/2021). The protocol is registered with the German Clinical Trial Register (DRKS00024625). Participating patients’ data will be recorded only in pseudonymised form. After completion of the study, data collected during the study will be kept on file for up to 30 years. Biomedical samples collected during the study and entered into the biobank will be held for the same amount of time. The findings will be disseminated in peer-reviewed academic journals. | ||
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| 650 | 4 | |a Artificial Intelligence | |
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| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Risk Factors | |
| 650 | 4 | |a Surgery | |
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