Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning

Panyawut Sri-Iesaranusorn; Ryoichi Sadahiro; Syo Murakami; Saho Wada; Ken Shimizu; Teruhiko Yoshida; Kazunori Aoki; Yasuhito Uezono; Hiromichi Matsuoka; Kazushi Ikeda; Junichiro Yoshimoto

doi:10.3389/fpsyt.2023.1205605

Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning

Front Psychiatry. 2023 Jun 27:14:1205605. doi: 10.3389/fpsyt.2023.1205605. eCollection 2023.

Authors

Panyawut Sri-Iesaranusorn¹, Ryoichi Sadahiro^{2

3}, Syo Murakami³, Saho Wada^{3

4}, Ken Shimizu⁵, Teruhiko Yoshida⁶, Kazunori Aoki², Yasuhito Uezono⁷, Hiromichi Matsuoka³, Kazushi Ikeda¹, Junichiro Yoshimoto^{1

8}

Affiliations

¹ Division of Information Science, Nara Institute of Science and Technology, Nara, Japan.
² Department of Immune Medicine, National Cancer Center Research Institute, Tokyo, Japan.
³ Department of Psycho-Oncology, National Cancer Center Hospital, Tokyo, Japan.
⁴ Department of Neuropsychiatry, Nippon Medical School, Tama Nagayama Hospital, Tokyo, Japan.
⁵ Department of Psycho-Oncology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan.
⁶ Department of Clinical Genomics, National Cancer Center Research Institute, Tokyo, Japan.
⁷ Department of Pain Control Research, The Jikei University School of Medicine, Tokyo, Japan.
⁸ Department of Biomedical Data Science, Fujita Health University School of Medicine, Aichi, Japan.

Abstract

Background: Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.

Methods: We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms.

Results: Participants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep-wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit.

Conclusion: We found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.

Keywords: K-means clustering; cancer surgery; delirium rating scale-revised-98; hypothesis-free categorization; phenotype; postoperative delirium.