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Статті в журналах з теми "Delirium prediction"

Автор: Grafiati

Опубліковано: 11 березня 2023

Оновлено: 31 липня 2025

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1

Linkaitė, Gabrielė, Mantas Riauka, Ignė Bunevičiūtė, and Saulius Vosylius. "Evaluation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for the patients in the intensive care unit." Acta medica Lituanica 25, no. 1 (2018): 14–22. http://dx.doi.org/10.6001/actamedica.v25i1.3699.

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Introduction. Delirium not only compromises patient care, but is also associated with poorer outcomes: increased duration of mechanical ventilation, higher mortality, and greater long-term cognitive dysfunction. The PRE-DELIRIC model is a tool used to calculate the risk of the development of delirium. The classification of the patients into groups by risk allows efficient initiation of preventive measures. The goal of this study was to validate the PRE-DELIRIC model using the CAM-ICU (The Confusion Assessment Method for the Intensive Care Unit) method for the diagnosis of delirium. Materials a

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2

Amerongen, Hilde van Nieuw, Sandra Stapel, Jan Jaap Spijkstra, Dagmar Ouweneel, and Jimmy Schenk. "Comparison of Prognostic Accuracy of 3 Delirium Prediction Models." American Journal of Critical Care 32, no. 1 (2023): 43–50. http://dx.doi.org/10.4037/ajcc2023213.

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Background Delirium is a severe complication in critical care patients. Accurate prediction could facilitate determination of which patients are at risk. In the past decade, several delirium prediction models have been developed. Objectives To compare the prognostic accuracy of the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, and to investigate the difference in prognostic accuracy of the PRE-DELIRIC model between patients receiving and patients not receiving mechanical ventilation. Methods This retrospective study involved adult patients admitted to the intensive care unit during a 2-year

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3

Pagali, Sandeep R., Donna Miller, Karen Fischer, et al. "Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool." Mayo Clinic Proceedings 96, no. 5 (2021): 1229–35. http://dx.doi.org/10.1016/j.mayocp.2020.08.049.

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4

Jauk, Stefanie, Diether Kramer, Birgit Großauer, et al. "Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study." Journal of the American Medical Informatics Association 27, no. 9 (2020): 1383–92. http://dx.doi.org/10.1093/jamia/ocaa113.

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Abstract Objective Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest–based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting. Materials and Methods Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 p

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5

Van, den Boogaard M., L. Schoonhoven, E. Maseda, et al. "Recalibration of the delirium prediction model for ICU patients (PRE-DELIRIC): a multinational observational study." Intensive Care med 40, no. 3 (2014): 361–9. https://doi.org/10.1007/s00134-013-3202-7.

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<strong>Purpose: </strong>Recalibration and determining discriminative power, internationally, of the existing delirium prediction model (PRE-DELIRIC) for intensive care patients. <strong>Methods: </strong>A prospective multicenter cohort study was performed in eight intensive care units (ICUs) in six countries. The ten predictors (age, APACHE-II, urgent and admission category, infection, coma, sedation, morphine use, urea level, metabolic acidosis) were collected within 24 h after ICU admission. The confusion assessment method for the intensive care unit (CAM-ICU) was used to identi

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6

Boettger, Soenke, Rafael Meyer, André Richter, et al. "Screening for delirium with the Intensive Care Delirium Screening Checklist (ICDSC): Symptom profile and utility of individual items in the identification of delirium dependent on the level of sedation." Palliative and Supportive Care 17, no. 1 (2018): 74–81. http://dx.doi.org/10.1017/s1478951518000202.

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AbstractObjectiveThe importance of the proper identification of delirium, with its high incidence and adversities in the intensive care setting, has been widely recognized. One common screening instrument is the Intensive Care Delirium Screening Checklist (ICDSC); however, the symptom profile and key features of delirium dependent on the level of sedation have not yet been evaluated.MethodIn this prospective cohort study, the ICDSC was evaluated versus the Diagnostic and Statistical Manual, 4th edition, text revision, diagnosis of delirium set as standard with respect to the symptom profile, a

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7

Arai, *Naohiro, Yuki Sugiura, Shinichiro Nakajima, et al. "PREDICTION OF POSTOPERATIVE DELIRIUM BY BLOOD METABOLOME." International Journal of Neuropsychopharmacology 28, Supplement_1 (2025): i264. https://doi.org/10.1093/ijnp/pyae059.466.

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Abstract Background Delirium is a notable risk factor for cognitive dysfunction and poor prognosis. Despite its importance, there is currently no established blood marker that can predict postoperative delirium in the preoperative period. Aims & Objectives We aimed to examine that water-soluble metabolites, lipids, and cytokines in peripheral blood could uniquely classify postoperative delirium. In addition, we investigated whether changes in neuroinflammation-related and water-soluble metabolites in the indoleamine 2,3-dioxygenase (IDO) pathway could predict postoperative delirium. Method

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8

Yan, Pengbo. "Research Progress in the Construction of Delirium Risk Warning Model for ICU Patients Based on Decision Tree: A Review of the Literature." Journal of Modern Nursing Practice and Research 4, no. 4 (2024): 20. http://dx.doi.org/10.53964/jmnpr.2024020.

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Intensive Care Unit (ICU) delirium is a cerebral syndrome characterized by acute disturbance of consciousness, with an incidence of 38%-87%. The occurrence of delirium can lead to prolonged hospital stay, accidental extubation rate, mortality and other adverse consequences. Therefore, early identification of delirium and active correction of reversible causes appear to be particularly important. At present, the risk prediction models for delirium in ICU constructed at home and abroad mainly use logistic regression to build delirium risk prediction models for patients admitted to ICU≥24h. Howev

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9

Green, Cameron, William Bonavia, Candice Toh, and Ravindranath Tiruvoipati. "Prediction of ICU Delirium." Critical Care Medicine 47, no. 3 (2019): 428–35. http://dx.doi.org/10.1097/ccm.0000000000003577.

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10

Matsumoto, Koutarou, Yasunobu Nohara, Mikako Sakaguchi, et al. "Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow." Applied Sciences 13, no. 3 (2023): 1564. http://dx.doi.org/10.3390/app13031564.

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Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients wer

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11

Xiong, Yaxin, Ze Meng, Jiuyue Sun, et al. "Application of a Nomogram Model in Predicting Postoperative Delirium Following Percutaneous Coronary Intervention." Bioengineering 12, no. 6 (2025): 637. https://doi.org/10.3390/bioengineering12060637.

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Background: Postoperative delirium is associated with an increased number of different complications, such as prolonged hospital stay, long-term cognitive impairment, and increased mortality. Therefore, early prediction of delirium after percutaneous coronary intervention (PCI) is necessary, but currently, there is still a lack of reliable and effective prediction models for such patients. Methods: All data used in this study were derived from the MIMIC-IV database. Multivariable Cox regression was employed to analyze the data, and the performance of the newly developed nomogram was evaluated

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12

Matsuoka, Ayaka, Toru Miike, Mariko Miyazaki, et al. "Development of a delirium predictive model for adult trauma patients in an emergency and critical care center: a retrospective study." Trauma Surgery & Acute Care Open 6, no. 1 (2021): e000827. http://dx.doi.org/10.1136/tsaco-2021-000827.

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BackgroundDelirium has been shown to prolong the length of intensive care unit stay, hospitalization, and duration of ventilatory control, in addition to increasing the use of sedatives and increasing the medical costs. Although there have been a number of reports referring to risk factors for the development of delirium, no model has been developed to predict delirium in trauma patients at the time of admission. This study aimed to create a scoring system that predicts delirium in trauma patients.MethodsIn this single-center, retrospective, observational study, trauma patients aged 18 years a

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13

Strating, Tom, Leila Shafiee Hanjani, Ida Tornvall, Ruth Hubbard, and Ian A. Scott. "Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models." BMJ Health & Care Informatics Online 30, no. 1 (2023): e100767. http://dx.doi.org/10.1136/bmjhci-2023-100767.

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ObjectivesEarly identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.MethodsWe searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reportin

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14

Silva, Diego Ferreira da, Daniella Rodrigues Alves, Rubens Paulo Alves, et al. "INSTRUMENTOS VALIDADOS DE AVALIAÇÃO DE DELIRIUM NA UNIDADE DE TERAPIA INTENSIVA: REVISÃO SISTEMATIVA." Revista Contemporânea 4, no. 4 (2024): e3931. http://dx.doi.org/10.56083/rcv4n4-051.

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Objetivo: Identificar quais são os instrumentos validados para avaliação de delirium em pacientes adultos na Unidade de Terapia Intensiva, além do CAM-ICU. Método: Estudo de revisão sistemática orientado pela pergunta de pesquisa: ‘’Quais são os instrumentos validados, além do CAM-ICU, para avaliar delirium em pacientes adultos na Unidade de Terapia intensiva?’’. Foram revisadas quatro bases de dados (Medline, Scopus, Embase e Lilacs), utilizando-se os descritores ‘’delirium assessment’’, ‘’validation studies’’, ‘’ICU- Intensive care unit’’. Para a busca, foi utilizada estratégia de busca espe

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15

Cho, Eun Ju, and Myoung Soo Kim. "Comparison of the Validity of the PRE-DELIRIC model and the E-PRE-DELIRIC model for Predicting Delirium in patients after Cardiac Surgery." Journal of Korean Academy of Fundamentals of Nursing 31, no. 3 (2024): 275–85. http://dx.doi.org/10.7739/jkafn.2024.31.3.275.

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Purpose: The purpose of this study was to analyze the validity of the PRE-DELIRIC model and E-PRE-DELIRIC model.Methods: Patients who underwent cardiac surgery at a tertiary hospital between January 2019 and December 2022 were included. The presence or absence of delirium was determined based on risk groups, and the sensitivity, specificity, positive predictive power, and negative predictive power were verified using the Youden index. Receiver operating characteristic curves were derived for the PRE-DELIRIC model and E-PRE-DELIRIC model, the area under the curve was calculated, and the 95% con

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16

Kotfis, Katarzyna, Marta Bott-Olejnik, Aleksandra Szylińska, and Iwona Rotter. "Could Neutrophil-to-Lymphocyte Ratio (NLR) Serve as a Potential Marker for Delirium Prediction in Patients with Acute Ischemic Stroke? A Prospective Observational Study." Journal of Clinical Medicine 8, no. 7 (2019): 1075. http://dx.doi.org/10.3390/jcm8071075.

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Delirium is an acute brain disorder that commonly occurs in patients with acute ischemic stroke (AIS). Pathomechanism of delirium is related to the neuroinflammatory process and oxidative stress. Search for readily available diagnostic marker that will aid clinicians in early identification of delirium is ongoing. The aim of this study was to investigate whether neutrophil-to-lymphocyte ratio (NLR) could serve as a potential marker for delirium prediction in patients with AIS and to find an easy diagnostic tool using laboratory and clinical parameters to predict delirium. Prospective observati

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17

Carson, Richard C. "Preoperative Prediction of Postoperative Delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (1994): 1573. http://dx.doi.org/10.1001/jama.1994.03510440033015.

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O'Hara, Dorene A. "Preoperative Prediction of Postoperative Delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (1994): 1573. http://dx.doi.org/10.1001/jama.1994.03510440033016.

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19

Rozner, Marc A. "Preoperative Prediction of Postoperative Delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (1994): 1573. http://dx.doi.org/10.1001/jama.1994.03510440033017.

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Rozner, M. A. "Preoperative prediction of postoperative delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (1994): 1573a—1574. http://dx.doi.org/10.1001/jama.271.20.1573a.

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Carson, R. C. "Preoperative prediction of postoperative delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (1994): 1573b—1573. http://dx.doi.org/10.1001/jama.271.20.1573b.

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O'Hara, D. A. "Preoperative prediction of postoperative delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (1994): 1573c—1573. http://dx.doi.org/10.1001/jama.271.20.1573c.

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23

Oosterhoff, Jacobien H. F., Aditya V. Karhade, Tarandeep Oberai, Esteban Franco-Garcia, Job N. Doornberg, and Joseph H. Schwab. "Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms." Geriatric Orthopaedic Surgery & Rehabilitation 12 (January 2021): 215145932110622. http://dx.doi.org/10.1177/21514593211062277.

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Introduction Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. Materials & Methods Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Su

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Neefjes, Elisabeth, Maurice Van Der Vorst, Bertha Verdegaal, Aartjan TF Beekman, Johannes Berkhof, and Henk M. W. Verheul. "Identification of patients at risk for delirium on a medical oncology hospital ward." Journal of Clinical Oncology 32, no. 31_suppl (2014): 130. http://dx.doi.org/10.1200/jco.2014.32.31_suppl.130.

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130 Background: Delirium is a distressing experience for patients with cancer. Incidence rates of delirium vary between 5 and 88 percent. We studied the incidence of delirium on our medical oncology ward, along with its predisposing and precipitating factors, in order to identify patients who may benefit from screening and early interventions. Methods: We evaluated patients admitted to our medical oncology ward between January 2011 and June 2012 for delirium. In this period a screening program with the Delirium Observation Screening Scale was initiated. Risk factors for delirium were extracted

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Heesakkers, Hidde, John W. Devlin, Arjen J. C. Slooter, and Mark van den Boogaard. "Association between delirium prediction scores and days spent with delirium." Journal of Critical Care 58 (August 2020): 6–9. http://dx.doi.org/10.1016/j.jcrc.2020.03.008.

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26

Cheng, Yong-Chao. "Construction of a risk prediction model to identify potential risk factors for the development of delirium after total hip arthroplasty." Medicine 103, no. 52 (2024): e41054. https://doi.org/10.1097/md.0000000000041054.

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The aim was to investigate the factors associated with the development of delirium after total hip arthroplasty and to develop a predictive model. The clinical data of 320 patients who underwent total hip arthroplasty between January 2021 and December 2023 were retrospectively analyzed, and 72 cases were classified as the delirium group and 248 cases were classified as the no delirium group based on the occurrence of delirium after surgery. One-way ANOVA was used to compare the differences in basic information between the 2 groups; statistically significant indicators were included in the bina

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27

Boogaard, M. v. d., P. Pickkers, A. J. C. Slooter, et al. "Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study." BMJ 344, feb09 3 (2012): e420-e420. http://dx.doi.org/10.1136/bmj.e420.

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28

Ho, Mu-Hsing, Kee-Hsin Chen, Jed Montayre, et al. "Diagnostic test accuracy meta-analysis of PRE-DELIRIC (PREdiction of DELIRium in ICu patients): A delirium prediction model in intensive care practice." Intensive and Critical Care Nursing 57 (April 2020): 102784. http://dx.doi.org/10.1016/j.iccn.2019.102784.

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Miyazawa, Yusuke, Narimasa Katsuta, Tamaki Nara, et al. "Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records." PLOS ONE 19, no. 1 (2024): e0296760. http://dx.doi.org/10.1371/journal.pone.0296760.

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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 c

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Marcantonio, Edward R. "Preoperative Prediction of Postoperative Delirium-Reply." JAMA: The Journal of the American Medical Association 271, no. 20 (1994): 1574. http://dx.doi.org/10.1001/jama.1994.03510440033018.

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Rosenberg, Karen. "Prognostic Accuracy of Delirium Prediction Models." AJN, American Journal of Nursing 123, no. 4 (2023): 55. http://dx.doi.org/10.1097/01.naj.0000925520.21363.23.

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McCusker, Jane, Martin G. Cole, Philippe Voyer, et al. "Use of nurse-observed symptoms of delirium in long-term care: effects on prevalence and outcomes of delirium." International Psychogeriatrics 23, no. 4 (2010): 602–8. http://dx.doi.org/10.1017/s1041610210001900.

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ABSTRACTBackground: Previous studies have reported that nurse detection of delirium has low sensitivity compared to a research diagnosis. As yet, no study has examined the use of nurse-observed delirium symptoms combined with research-observed delirium symptoms to diagnose delirium. Our specific aims were: (1) to describe the effect of using nurse-observed symptoms on the prevalence of delirium symptoms and diagnoses in long-term care (LTC) facilities, and (2) to compare the predictive validity of delirium diagnoses based on the use of research-observed symptoms alone with those based on resea

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Park, Chanmin, Changho Han, Su Kyeong Jang, et al. "Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study." Journal of Medical Internet Research 27 (April 2, 2025): e59520. https://doi.org/10.2196/59520.

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Background Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical care, addressing needs for timely intervention and resource optimization in ICUs. Objective We aimed to create a novel machine learning model for delirium prediction in ICU patients using only continuous physiological data. Methods We developed models integrating routinely available clinical data, such as age, sex, and patient monitoring device outputs

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Khan, Ariba, Kayla Heslin, Michelle Simpson, and Michael Malone. "Electronic Health Record Data Can be Used at the Bedside to Identify Older Hospitalized Patients With Delirium." Innovation in Aging 4, Supplement_1 (2020): 136. http://dx.doi.org/10.1093/geroni/igaa057.447.

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Abstract Delirium is a serious condition that is often underrecognized. Several delirium predictive rules can assist in early detection. The coupling of prediction rules with features of the EHR are in their infancy but hold potential. This study aimed to determine variables within the EHR that can be used to identify older hospitalized patients with delirium. This is a prospective study among patients &gt;=65 years admitted to the hospital. Researchers screened daily for delirium using the 3-D CAM. Predictive variables were extracted from the EHR. Basic descriptive statistics were conduct

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Mufti, Hani Nabeel, Gregory Marshal Hirsch, Samina Raza Abidi, and Syed Sibte Raza Abidi. "Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study." JMIR Medical Informatics 7, no. 4 (2019): e14993. http://dx.doi.org/10.2196/14993.

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Background Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. Howeve

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Mestres Gonzalvo, Carlota, Hugo A. J. M. de Wit, Brigit P. C. van Oijen, et al. "Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study." BMJ Open 7, no. 11 (2017): e016654. http://dx.doi.org/10.1136/bmjopen-2017-016654.

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ObjectivesDelirium is an underdiagnosed, severe and costly disorder, and 30%–40% of cases can be prevented. A fully automated model to predict delirium (DEMO) in older people has been developed, and the objective of this study is to validate the model in a hospital setting.SettingSecondary care, one hospital with two locations.DesignObservational study.ParticipantsThe study included 450 randomly selected patients over 60 years of age admitted to Zuyderland Medical Centre. Patients who presented with delirium on admission were excluded.Primary outcome measuresDevelopment of delirium through cha

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Ling, Yu-Ting, Qian-Qian Guo, Si-Min Wang, et al. "Nomogram for Prediction of Postoperative Delirium after Deep Brain Stimulation of Subthalamic Nucleus in Parkinson’s Disease under General Anesthesia." Parkinson's Disease 2022 (November 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/6915627.

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Introduction. Postoperative delirium can increase cognitive impairment and mortality in patients with Parkinson’s disease. The purpose of this study was to develop and internally validate a clinical prediction model of delirium after deep brain stimulation of the subthalamic nucleus in Parkinson’s disease under general anesthesia. Methods. We conducted a retrospective observational cohort study on the data of 240 patients with Parkinson’s disease who underwent deep brain stimulation of the subthalamic nucleus under general anesthesia. Demographic characteristics, clinical evaluation, imaging d

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Wang, Song, Shujun Yu, Chen Li, et al. "Evaluating the relationship between inflammatory markers and preoperative delirium in elderly hip fracture patients: A retrospective observational study." Medicine 104, no. 10 (2025): e41569. https://doi.org/10.1097/md.0000000000041569.

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Preoperative delirium is common and associated with poor clinical outcomes in elderly hip fracture patients. Although inflammatory markers have shown potential in predicting postoperative delirium, their relevance to preoperative delirium remains unclear. This study aimed to investigate the relationship between inflammatory markers and preoperative delirium to improve risk prediction and management strategies. We retrospectively studied 548 elderly hip fracture patients aged 70 years or older. The primary outcome was preoperative delirium diagnosed using the Confusion Assessment Method (CAM).

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Hanison, J., S. Umar, K. Acharya, and D. Conway. "Evaluation of the PRE-DELIRIC delirium prediction tool on a general ICU." Critical Care 19, Suppl 1 (2015): P479. http://dx.doi.org/10.1186/cc14559.

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Panteleev, O. O., and V. V. Ryabov. "Delirium in a patient with myocardial infarction." Siberian Journal of Clinical and Experimental Medicine 37, no. 3 (2022): 49–55. http://dx.doi.org/10.29001/2073-8552-2022-37-3-49-55.

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Delirium is a predictor of poor outcome in both myocardial infarction and other nosologies. Despite the growing interest in this problem, no eff ective methods for prediction, prevention, and treatment of delirium have been found. This literature review highlights the current ideas about delirium etiology, pathogenesis, approaches to prevention and treatment, and features of delirium in patients with myocardial infarction. The review presents the analysis of clinical trials and meta-analyses with the identifi cation of causes for clinical trials failures and the search for future promising dir

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Hur, Sujeong, Ryoung-Eun Ko, Junsang Yoo, Juhyung Ha, Won Chul Cha, and Chi Ryang Chung. "A Machine Learning–Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study." JMIR Medical Informatics 9, no. 7 (2021): e23401. http://dx.doi.org/10.2196/23401.

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Background Delirium frequently occurs among patients admitted to the intensive care unit (ICU). There is limited evidence to support interventions to treat or resolve delirium in patients who have already developed delirium. Therefore, the early recognition and prevention of delirium are important in the management of critically ill patients. Objective This study aims to develop and validate a delirium prediction model within 24 hours of admission to the ICU using electronic health record data. The algorithm was named the Prediction of ICU Delirium (PRIDE). Methods This is a retrospective coho

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Ruppert, Matthew M., Jessica Lipori, Sandip Patel, et al. "ICU Delirium-Prediction Models: A Systematic Review." Critical Care Explorations 2, no. 12 (2020): e0296. http://dx.doi.org/10.1097/cce.0000000000000296.

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SPLETE, HEIDI. "Tool Boosts Prediction Of Delirium in Adult." Hospitalist News 5, no. 7 (2012): 10. http://dx.doi.org/10.1016/s1875-9122(12)70147-5.

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Biju, Ashok, Babar Khan, and Heidi Lindroth. "VALIDATION OF A POSTOPERATIVE DELIRIUM PREDICTION MODEL." American Journal of Geriatric Psychiatry 28, no. 4 (2020): S96—S97. http://dx.doi.org/10.1016/j.jagp.2020.01.122.

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Wang, Gang, Lei Zhang, Ying Qi, et al. "Development and Validation of a Postoperative Delirium Prediction Model for Elderly Orthopedic Patients in the Intensive Care Unit." Journal of Healthcare Engineering 2021 (June 8, 2021): 1–6. http://dx.doi.org/10.1155/2021/9959077.

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We developed a prediction model for delirium in elderly patients in the intensive care unit who underwent orthopedic surgery and then temporally validated its predictive power in the same hospital. In the development stage, we designed a prospective cohort study, and 319 consecutive patients aged over 65 years from January 2018 to December 2019 were screened. Demographic characteristics and clinical variables were evaluated, and a final prediction model was developed using the multivariate logistic regression analysis. In the validation stage, 108 patients were included for temporal validation

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Kaźmierski, J., P. Miler, A. Pawlak, et al. "Raised preoperative monocyte chemoattractant protein-1 as the independent predictor of delirium after cardiac surgery. A prospective cohort study." European Psychiatry 64, S1 (2021): S252. http://dx.doi.org/10.1192/j.eurpsy.2021.676.

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IntroductionDelirium is a frequent and serious complication of cardiac surgery. However, the knowledge regarding pathogenesis of postoperative delirium is limited.ObjectivesTo investigate whether increased levels of monocyte chemoattractant protein-1 (MCP-1) and hyper-sensitive C-Reactive Protein (hsCRP) are associated with postoperative delirium in cardiac surgery patients.MethodsPatients were examined and screened for major depressive disorder (MDD) and cognitive impairment one day preoperatively, using the Mini International Neuropsychiatric Interview and The Mini-Mental State Examination T

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Park, Woo Rhim, Hye Rim Kim, Jin Young Park, Hesun Erin Kim, Jaehwa Cho, and Jooyoung Oh. "Potential Usefulness of Blood Urea Nitrogen to Creatinine Ratio in the Prediction and Early Detection of Delirium Motor Subtype in the Intensive Care Unit." Journal of Clinical Medicine 11, no. 17 (2022): 5073. http://dx.doi.org/10.3390/jcm11175073.

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Prediction and early detection of delirium can improve patient outcomes. A high blood urea nitrogen to creatinine ratio (BCR), which reflects dehydration, has been reported as a risk factor for delirium. Additionally, BCR represents skeletal muscle loss in intensive care unit (ICU) patients, which can have critical implications for clinical outcomes. We investigated whether BCR could be used to predict the occurrence and motor subtype of delirium in ICU patients through a retrospective cohort study that included 7167 patients (50 years or older) admitted to the ICU. Patients were assessed dail

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Alibhai, Shabbir M. H., Patrick Jung, Zuhair Alam, et al. "Delirium incidence, risk factors, and treatment in older adults receiving chemotherapy: A scoping review." Journal of Clinical Oncology 37, no. 15_suppl (2019): e23025-e23025. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e23025.

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e23025 Background: Older adults with cancer are at increased risk of delirium given their advanced age, multiple comorbidities and medications, prevalence of cognitive impairment, and possibly cancer treatment. Awareness of such risks and interventions to prevent or treat delirium is important to clinicians and to provide high quality care. However, there is scant published information on the risks of delirium with chemotherapy or evidence-based approaches to prevent or treat it. We performed a scoping review to summarize the available evidence. Methods: We conducted a scoping review using the

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Denk, Alexander, Karolina Müller, Sophie Schlosser, et al. "Liver diseases as a novel risk factor for delirium in the ICU–Delirium and hepatic encephalopathy are two distinct entities." PLOS ONE 17, no. 11 (2022): e0276914. http://dx.doi.org/10.1371/journal.pone.0276914.

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Background Delirium prevalence is high in critical care settings. We examined the incidence, risk factors, and outcome of delirium in a medical intensive care unit (MICU) with a particular focus on liver diseases. We analyzed this patient population in terms of delirium risk prediction and differentiation between delirium and hepatic encephalopathy. Methods We conducted an observational study and included 164 consecutive patients admitted to an MICU of a university hospital. Patients were assessed for delirium using the Confusion Assessment Method for ICUs and the Richmond Agitation-Sedation S

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Evensen, Sigurd, Anette Hylen Ranhoff, Stian Lydersen, and Ingvild Saltvedt. "The delirium screening tool 4AT in routine clinical practice: prediction of mortality, sensitivity and specificity." European Geriatric Medicine 12, no. 4 (2021): 793–800. http://dx.doi.org/10.1007/s41999-021-00489-1.

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Abstract Purpose Delirium is common and associated with poor outcomes, partly due to underdetection. We investigated if the delirium screening tool 4 A’s test (4AT) score predicts 1 year mortality and explored the sensitivity and specificity of the 4AT when applied as part of a clinical routine. Methods Secondary analyses of a prospective study of 228 patients acutely admitted to a Medical Geriatric Ward. Physicians without formal training conducted the index test (the 4AT); a predefined cut-off ≥ 4 suggested delirium. Reference standard was delirium diagnosed by two geriatricians using the Di

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