KI-augmentierte perioperative klinische Entscheidungsunterstützung (KIPeriOP) – Studiendesign und erste Zwischenergebnisse

Translated title of the contribution: AI-augmented perioperative clinical decision support (KIPeriOP) – study design and initial interim results

S. Hottenrott, P. Bendz, P. Meybohm, E. Bauer, S. Schmee, T. Haas, P. Kranke, F. Rumpf, P. Helmer, A. Hennemuth, M. Westphal, R. Alpers, M. Hüllebrand, P. Börm, N. Blanck, K. Zacharowski, L. Vo, P. Booms, A. Ghanem, C. WolframC. Wagner, L. Milz, F. Yürek, E. Salgado, C. Spies, F. Balzer, A. Flothow, L. Sundmacher, A. König, W. Schüttig, F. von Dincklage, S. K. Nagel, J. C. Heilinger

Research output: Contribution to journalArticlepeer-review

Abstract

The perioperative mortality rate in western industrialised nations is up to 4 %, with cardiovascular, pulmonary, infectious and coagulation-related complications being the most common causes. Findings and measures for preoperative risk evaluation are actually described in detail in multiple medical guidelines of different societies. In clinical practice, these are often not sufficiently followed for reasons of complexity, time pressure or incorrect risk assessment. To address this problem, a clinical decision support system (CDS) has been developed. Method and results The CE-certified KIPeriOP CDS system comprises numerous CDS tools that allow for a structured recording of the patient‘s medical history, examination and laboratory values and support physicians in making, step-by-step, evidence- and guideline-based decisions on pre-, intra- and postoperative care. A central element is the guideline-based CDS tool, which addresses various aspects of preoperative risk evaluation, anaemia management and prevention of delirium. The development of the KIPeriOP CDS system has also created a data model of over 2,500 coded, interoperable patient data items that can ideally be processed in perioperative medicine. As part of a prospective, randomised controlled study, the usability and clinical impact of the KIPeriOP CDS will be tested in 480 patients at four hospitals in Germany. The primary hypothesis was to increase guideline adherence by reducing unnecessary and increasing indicated additional preoperative examinations (e. g., ECG, echocardiography, cardiac stress tests, chest X-ray, pulmonary function tests, carotid Doppler) compared to standard care without the CDS system. Secondary endpoints include perioperative complications, documentation quality, health economic and ethical aspects. Based on real patient data, new prediction models for perioperative complications will also be developed using machine learning and AI algorithms and compared with established risk scores. Conclusion KIPeriOP aims to improve the precision and efficiency of preoperative risk evaluation, support clinical decision-making and improve the quality of patient care.

Translated title of the contributionAI-augmented perioperative clinical decision support (KIPeriOP) – study design and initial interim results
Original languageGerman
Pages (from-to)156-172
Number of pages17
JournalAnasthesiologie und Intensivmedizin
Volume65
Issue number4
DOIs
StatePublished - Apr 2024

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