Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records

• Published in: PloS one Vol. 19; no. 1; p. e0296760
• Main Authors: Miyazawa, Yusuke, Katsuta, Narimasa, Nara, Tamaki, Nojiri, Shuko, Naito, Toshio, Hiki, Makoto, Ichikawa, Masako, Takeshita, Yoshihide, Kato, Tadafumi, Okumura, Manabu, Tobita, Morikuni
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.01.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Computational linguistics; Coronaviruses; COVID-19; COVID-19 - complications; COVID-19 - epidemiology; Critical Care - methods; Data mining; Delirium; Delirium - diagnosis; Delirium - epidemiology; Delirium - etiology; Disease prevention; Hospital patients; Hospitals; Humans; Infections; Intensive Care Units; Japanese language; Language processing; Machine learning; Medical colleges; Medical personnel; Medical records; Medical research; Medicine, Experimental; Mental disorders; Methods; Mineral industry; Mines and mineral resources; Mining industry; Natural language; Natural language interfaces; Natural language processing; Nervous system; Nurses; Nursing; Nursing Records; Pandemics; Patients; Prevention; Psychiatrists; Retrospective Studies; Risk factors; Risk taking; Sensitivity; Support vector machines; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0296760

COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.