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Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning
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 understan...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333495/ https://www.ncbi.nlm.nih.gov/pubmed/37441147 http://dx.doi.org/10.3389/fpsyt.2023.1205605 |
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author | Sri-iesaranusorn, Panyawut Sadahiro, Ryoichi Murakami, Syo Wada, Saho Shimizu, Ken Yoshida, Teruhiko Aoki, Kazunori Uezono, Yasuhito Matsuoka, Hiromichi Ikeda, Kazushi Yoshimoto, Junichiro |
author_facet | Sri-iesaranusorn, Panyawut Sadahiro, Ryoichi Murakami, Syo Wada, Saho Shimizu, Ken Yoshida, Teruhiko Aoki, Kazunori Uezono, Yasuhito Matsuoka, Hiromichi Ikeda, Kazushi Yoshimoto, Junichiro |
author_sort | Sri-iesaranusorn, Panyawut |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10333495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103334952023-07-12 Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning Sri-iesaranusorn, Panyawut Sadahiro, Ryoichi Murakami, Syo Wada, Saho Shimizu, Ken Yoshida, Teruhiko Aoki, Kazunori Uezono, Yasuhito Matsuoka, Hiromichi Ikeda, Kazushi Yoshimoto, Junichiro Front Psychiatry Psychiatry 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. Frontiers Media S.A. 2023-06-27 /pmc/articles/PMC10333495/ /pubmed/37441147 http://dx.doi.org/10.3389/fpsyt.2023.1205605 Text en Copyright © 2023 Sri-iesaranusorn, Sadahiro, Murakami, Wada, Shimizu, Yoshida, Aoki, Uezono, Matsuoka, Ikeda and Yoshimoto. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Sri-iesaranusorn, Panyawut Sadahiro, Ryoichi Murakami, Syo Wada, Saho Shimizu, Ken Yoshida, Teruhiko Aoki, Kazunori Uezono, Yasuhito Matsuoka, Hiromichi Ikeda, Kazushi Yoshimoto, Junichiro Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning |
title | Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning |
title_full | Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning |
title_fullStr | Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning |
title_full_unstemmed | Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning |
title_short | Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning |
title_sort | data-driven categorization of postoperative delirium symptoms using unsupervised machine learning |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333495/ https://www.ncbi.nlm.nih.gov/pubmed/37441147 http://dx.doi.org/10.3389/fpsyt.2023.1205605 |
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