Dynamic isolation forest for anomaly detection in post-PCI myocardial infarction patients
1Medical Laboratory, The Second People's Hospital of Gansu Province (Affiliated Hospital of Northwest Minzu University), Lanzhou, 743000, China. 01585@xbmu.edu.cn.
Insights
This study introduces Dynamic Isolation Forest (DIF) to objectively monitor patients after percutaneous coronary intervention (PCI). DIF effectively identifies abnormal recovery patterns, aiding in risk stratification for myocardial infarction patients.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Biomedical Data Science
Background:
- Post-percutaneous coronary intervention (PCI) care for myocardial infarction (MI) patients necessitates continuous laboratory monitoring.
- Current assessment of dynamic laboratory changes relies heavily on clinical expertise, lacking objective methods.
- Identifying deviations from typical recovery trajectories is crucial for patient management.
Purpose of the Study:
- To develop and validate an objective method for assessing post-PCI recovery trajectories in MI patients.
- To compare the performance of Dynamic Isolation Forest (DIF) against other unsupervised methods for anomaly detection.
- To evaluate the prognostic value of DIF in an external dataset.
Main Methods:
- Inclusion of 183 MI patients who underwent PCI.
- Construction of fixed three-time-point windows for analysis.
- Comparison of Dynamic Isolation Forest (DIF) with other unsupervised anomaly detection techniques.
- Validation using blinded expert review and an external prognostic analysis in the MIMIC-IV database.
Main Results:
- DIF demonstrated the highest agreement with expert ratings (Spearman's ρ = 0.585, Kendall's τ = 0.452).
- DIF achieved an Area Under the Curve (AUC) of 0.859, outperforming other methods.
- Higher DIF anomaly scores in the MIMIC-IV cohort correlated with an increased risk of mortality.
Conclusions:
- Dynamic Isolation Forest (DIF) can objectively identify abnormal recovery windows post-PCI, deviating from typical trajectories.
- DIF shows potential for risk stratification in myocardial infarction patients.
- Further clinical validation is required to establish the full clinical utility of DIF.
Abstract:
Patients with myocardial infarction require continuous monitoring of multiple laboratory parameters after percutaneous coronary intervention (PCI), but assessment of their dynamic changes and of whether they deviate from the typical recovery trajectory still relies largely on clinical experience, and objective methods remain lacking. This study included 183 patients with myocardial infarction who underwent PCI, constructed fixed three-time-point windows, used blinded expert review as the reference, compared dynamic isolation forest (DIF) with other unsupervised methods, and conducted an external supportive prognostic analysis in MIMIC-IV. The results showed that DIF had the best agreement with expert ratings (Spearman's ρ = 0.585; Kendall's τ = 0.452), with an AUC of 0.859, and overall outperformed the other methods; the MIMIC-IV analysis showed that higher DIF anomaly scores were associated with an increased risk of death. DIF can be used to identify abnormal recovery windows after PCI that deviate from the typical recovery trajectory and may have potential for risk stratification, although its clinical utility still requires further validation.
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