Evidence map›Paper›PMID 40935419›Full record

ArticleBMJ open2025

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.

Mona Jung-König, Hans Thomas Hölzer, Solveig Tenckhoff, Sachin Kumar, Melanie Etheredge, Dominik Schaack, Tobias Hölle, Laura Christine Schuster, Christian Nusshag, Mathias Rath and 15 more

Abstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Mona Jung-KönigDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Hans Thomas HölzerDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.ORCID http://orcid.org/0000-0003-2414-2727
Solveig TenckhoffThe Study Center of the German Surgical Society, Heidelberg University, Heidelberg, Germany.
Sachin KumarDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Melanie EtheredgeDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Dominik SchaackDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Tobias HölleDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Laura Christine SchusterDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Christian NusshagDepartment of Nephrology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Mathias RathDepartment of Urology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Christian UhlDepartment of Vascular and Endovascular Surgery, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Frank PiankaDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Christoph W MichalskiDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Clinical Study Center, Heidelberg, Germany.
Markus HohenfellnerDepartment of Urology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Rawa ArifDepartment of Cardiac Surgery, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Matthias KarckDepartment of Cardiac Surgery, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Dittmar BöcklerDepartment of Vascular and Endovascular Surgery, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Pascal ProbstDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.ORCID http://orcid.org/0000-0002-0895-4015
Markus DienerDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Markus A WeigandDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Martin DugasHeidelberg University, Medical Faculty Heidelberg, Institute of Medical Informatics, Heidelberg, Germany.ORCID http://orcid.org/0000-0001-9740-0788
Christian NiklasHeidelberg University, Medical Faculty Heidelberg, Institute of Medical Informatics, Heidelberg, Germany.
Manuel FeißtInstitute of Medical Biometry, Heidelberg University, Heidelberg, Germany.
Rosa KlotzThe Study Center of the German Surgical Society, Heidelberg University, Heidelberg, Germany.
Jan LarmannDepartment of Anesthesiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany an-direktion@ukaachen.de.ORCID http://orcid.org/0000-0003-3365-4572

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPerioperative 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.

Indexed as

Elective Surgical ProceduresPostoperative ComplicationsAdultFemaleGermanyHospitals, UniversityHumansMachine LearningProspective StudiesResearch DesignRisk AssessmentRisk FactorsAdult anaesthesiaArtificial IntelligenceClinical Decision-MakingMachine LearningRisk FactorsSurgery

Identifiers

PMID40935419
PMCPMC12519369

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.