Clinical implementation of an AI-based prediction model for decision support for patients undergoing colorectal cancer surgery

Adverse outcomes after elective cancer surgery are a main contributor to decreased survival, poorer oncological outcomes and increased healthcare costs. Identifying high-risk patients and selecting interventions according to individual risk profiles in the perioperative period in cancer surgery is a challenge. Using real-world data on 18,403 patients with colorectal cancer from Danish national registries and consecutive patients from a single center, we developed, validated and implemented an artificial-intelligence-based risk prediction model in clinical practice as a decision support tool for personalized perioperative treatment. Personalized treatment pathways were designed according to the predicted risk of 1-year mortality with the intensity of interventions increasing with the predicted risk. The developed model had an area under the receiver operating characteristic curve of 0.79 in the validation set. Results from the nonrandomized before/after cohort study showed an incidence proportion of the comprehensive complication index >20 of 19.1% in the personalized treatment group versus 28.0% in the standard-of-care group, adjusted odds ratio of 0.63 (95% confidence interval, 0.42-0.92; P = 0.02). The incidence of any medical complication was 23.7% in the personalized treatment group and 37.3% in the standard-of-care group; odds ratio of 0.53 (95% confidence interval, 0.36-0.76; P < 0.001). According to the short-term health economic modeling, personalized perioperative treatment was cost effective. The study demonstrates a fully scalable registry-based approach for using readily available data in an artificial-intelligence-based decision support pipeline in clinical practice. Our results indicate that this specific approach can be a cost-effective strategy to improve key surgical clinical outcomes.

Authors

Institutions

Publication Details

Journal
Nature Medicine
Published
2025-09-18
DOI
https://doi.org/10.1038/s41591-025-03942-x
Citations
8
Primary Topic
Cardiac, Anesthesia and Surgical Outcomes
Type
article
Field-Weighted Citation Impact
11.11

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical implementation of an AI-based prediction model for decision support for patients undergoing colorectal cancer surgery

Steffen Brisling, Stine Ydegaard Turiño, Ellen Holm, Rasmus Peuliche Vogelsang et al.
8 citations
Nature Medicine
Cardiac, Anesthesia and Surgical Outcomes
11.11
article

Clinical implementation of an AI-based prediction model for decision support for patients undergoing colorectal cancer surgery

Steffen Brisling, Stine Ydegaard Turiño, Ellen Holm, Rasmus Peuliche Vogelsang, Xenia Lea Schefte, Lars Peter Kloster Andersen, Karoline Mortensen, Ilze Ose, Oliver Wedel Fischer, Egemen Ayhan, Trine Kjær, Martin Høyer Rose, Andreas Weinberger Rosen, Mikail Gögenur, Søren Thorgaard Skou, Lasse Bremholm Hansen, Jens Eriksen, Michael Seiersen, Nina Abild Frederiksen, A. Nielsen, Rasmus Dahlin Bojesen, Christina Alexandersen, Dilara Seyma Gögenur, S Eraslan, L. Löffler, Ismail Gögenur, M. Thomsen, Tina Fransgård, Enise Gögenur, Angelina Astrid Righult, Ida Kolukisa Saqi, Helle Skadborg Spuur, Ines Raben, Andinoos Eros Tsouchnikas, Philip Wallentin Steenfos
article en
8 citations

Abstract

Adverse outcomes after elective cancer surgery are a main contributor to decreased survival, poorer oncological outcomes and increased healthcare costs. Identifying high-risk patients and selecting interventions according to individual risk profiles in the perioperative period in cancer surgery is a challenge. Using real-world data on 18,403 patients with colorectal cancer from Danish national registries and consecutive patients from a single center, we developed, validated and implemented an artificial-intelligence-based risk prediction model in clinical practice as a decision support tool for personalized perioperative treatment. Personalized treatment pathways were designed according to the predicted risk of 1-year mortality with the intensity of interventions increasing with the predicted risk. The developed model had an area under the receiver operating characteristic curve of 0.79 in the validation set. Results from the nonrandomized before/after cohort study showed an incidence proportion of the comprehensive complication index >20 of 19.1% in the personalized treatment group versus 28.0% in the standard-of-care group, adjusted odds ratio of 0.63 (95% confidence interval, 0.42-0.92; P = 0.02). The incidence of any medical complication was 23.7% in the personalized treatment group and 37.3% in the standard-of-care group; odds ratio of 0.53 (95% confidence interval, 0.36-0.76; P < 0.001). According to the short-term health economic modeling, personalized perioperative treatment was cost effective. The study demonstrates a fully scalable registry-based approach for using readily available data in an artificial-intelligence-based decision support pipeline in clinical practice. Our results indicate that this specific approach can be a cost-effective strategy to improve key surgical clinical outcomes.

Nature MedicineVol. 31(11)
University of Copenhagen (DK), University of Southern Denmark (DK), Zealand University Hospital (DK), Psychiatry Region Zealand (DK), Slagelse Hospital (DK), Nordic Lymphoma Group (DK), Ringsted Sygehus (DK), Zealand University Hospital Køge (DK)
Novo Nordisk, Novo Nordisk Fonden, Sjællands Universitetshospital, Interreg
Openalex Percentile: Top 1%
Cardiac, Anesthesia and Surgical Outcomes
11.11
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.