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. 2024 Nov 5;14(1):26741.
doi: 10.1038/s41598-024-78482-4.

Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery

Tomohisa Seki  1 , Toru Takiguchi  2 , Yu Akagi  3 , Hiromasa Ito  2 , Kazumi Kubota  2 , Kana Miyake  2 , Masafumi Okada  2 , Yoshimasa Kawazoe  2   4
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Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery

Tomohisa Seki et al. Sci Rep. .

Abstract

Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying iterative random forest to analyze combinations of factors that could potentially be clinically valuable in identifying these high-risk populations. To this end, we used the Japan Medical Data Center database, which includes claims data from Japan between January 2005 and April 2021, and employed iterative random forests to extract factor combinations that influence outcomes. The analysis demonstrated that a combination of a prior history of stroke and extremely low LDL-C levels was associated with a high non-cardiac postoperative risk. The incidence of major adverse cardiovascular events in the population characterized by the incidence of previous stroke and extremely low LDL-C levels was 15.43 events per 100 person-30 days [95% confidence interval, 6.66-30.41] in the test data. At this stage, the results only show correlation rather than causation; however, these findings may offer valuable insights for preoperative risk assessment in non-cardiac surgery.

Keywords: Iterative random forests; Machine learning; Non-cardiac surgery; Perioperative risk.

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Conflict of interest statement

YK is affiliated with the Artificial Intelligence and Digital Twin Development in Healthcare, Graduate School of Medicine, The University of Tokyo which is an endowment department. However, the sponsors had no influence over the interpretation, writing, or publication of this work. TS, TT, YA, HI, KK, KM, and MO have no conflicts of interest directly relevant to the content of this article.

Figures

Fig. 1
Fig. 1
Selection scheme of the study patients.
Fig. 2
Fig. 2
Cumulative incidence plots using the Kaplan–Meier method. The plots show the cumulative incidence of postoperative MACE occurrence stratified by a combination of history of cerebrovascular disease and LDL-C < 51.34 mg/dL. The light colors at the top and bottom of the plot indicate confidence intervals. MACEs, major adverse cardiac events.
See this image and copyright information in PMC

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