Robotic process automation to identify patients at high risk for perioperative myocardial infarction or injury: a prospective, blinded, paired reader-controlled single-centre study

Koray Durak; Katrin Burri-Winkler; Gabrielle Huré; Ivo Strebel; Julia Reinhardt; Vanessa Thommen; Mirjam Pargger; Noemi Glarner; Fisnik Haziri; Esther Seeberger; Nadine Doyle; Daniel Bolliger; Luzius A Steiner; Edin Mujagic; Didier Lardinois; Stefan Schären; Andreas Mueller; Felix Mahfoud; Danielle Menosi Gualandro; Christian Puelacher; Christian Mueller

doi:10.1016/j.bja.2025.07.035

Robotic process automation to identify patients at high risk for perioperative myocardial infarction or injury: a prospective, blinded, paired reader-controlled single-centre study

Br J Anaesth. 2025 Nov;135(5):1153-1160. doi: 10.1016/j.bja.2025.07.035. Epub 2025 Jul 31.

Authors

Koray Durak¹, Katrin Burri-Winkler², Gabrielle Huré¹, Ivo Strebel¹, Julia Reinhardt¹, Vanessa Thommen¹, Mirjam Pargger¹, Noemi Glarner¹, Fisnik Haziri¹, Esther Seeberger¹, Nadine Doyle¹, Daniel Bolliger³, Luzius A Steiner⁴, Edin Mujagic⁵, Didier Lardinois⁶, Stefan Schären⁷, Andreas Mueller⁸, Felix Mahfoud¹, Danielle Menosi Gualandro¹, Christian Puelacher⁹, Christian Mueller¹⁰

Affiliations

¹ Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland.
² Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland; Department of Anaesthesia, University Hospital Basel, University of Basel, Basel, Switzerland.
³ Department of Anaesthesia, University Hospital Basel, University of Basel, Basel, Switzerland.
⁴ Department of Anaesthesia, University Hospital Basel, University of Basel, Basel, Switzerland; Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.
⁵ Department of Vascular Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
⁶ Department of Thoracic Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
⁷ Department of Spinal Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
⁸ Department of Orthopedics and Trauma Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
⁹ Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland; Department of Internal Medicine III, Cardiology and Angiology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: christian.puelacher@i-med.ac.at.
¹⁰ Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: christian.mueller@usb.ch.

PMID: 40750466
DOI: 10.1016/j.bja.2025.07.035

Abstract

Background: Although current guidelines recommend active surveillance for perioperative myocardial infarction, injury, or both in high-risk patients, implementation remains limited in most institutions worldwide because of a lack of resources.

Methods: We hypothesised that robotic process automation (RPA), a software technology that enables virtual bots to replicate human tasks within digital systems, could accurately replace experienced clinical staff. Manual screening by experienced clinical staff and RPA screening were carried out simultaneously and blinded to identify high-risk patients eligible for active surveillance for myocardial infarction/injury, according to predefined screening criteria. Discrepant identification was reviewed by an independent clinician blinded to the origin of the identification, generating a reference standard classification of paired reader-controlled patients to investigate the primary diagnostic endpoint: relative true positive fraction.

Results: In 660 participants (median age 60 yr, interquartile range 42-73 yr, 54.8% female), 77/660 (12%) were eligible for active surveillance for perioperative myocardial infarction or injury according to the reference standard classification. RPA screening achieved 75 (97%) true positive identifications, compared with 63 (82%) identified from manual screening (relative true positive fraction: 1.19, 95% confidence interval 1.08-1.32, P=0.004). The number needed to screen to identify one additional true positive using RPA screening was 6. RPA screening had a sensitivity of 0.97 (0.91-0.99), compared with 0.82 (0.72-0.89) for. Both approaches had high specificity (RPA screening: 0.98 [0.97-0.99], compared with manual screening: 1.0 [0.99-1.00]). The estimated annual cost of RPA screening was 81% lower compared with manual screening.

Conclusions: RPA screening was superior to standard-of-care manual screening by experienced clinical staff in identifying patients at high risk for perioperative myocardial infarction or injury.

Clinical trial registration: NCT02573532.

Keywords: active surveillance; myocardial injury after noncardiac surgery; perioperative care; perioperative myocardial infarction; perioperative myocardial injury; robotic process automation.

MeSH terms

Adult
Aged
Automation
Female
Humans
Male
Middle Aged
Myocardial Infarction* / diagnosis
Postoperative Complications* / diagnosis
Prospective Studies
Risk Assessment / methods
Robotic Surgical Procedures* / methods
Robotics* / methods
Single-Blind Method

Associated data

ClinicalTrials.gov/NCT02573532