Development of a delirium predictive model for adult trauma patients in an emergency and critical care center: a retrospective study

Matsuoka, Ayako; Miike, Toru; Miyazaki, Mariko; Goto, Taku; Sasaki, Akira; Yamazaki, Hirotaka; Komaki, Moe; Higuchi, Masahiro; Mori, Kosuke; Shinada, Kota; Nakayama, Kento; Sakurai, Ryota; Asahi, Miho; Futami, Akiko; Yoshitake, K; Narumi, Shougo; Koba, Mayuko; Koami, Hiroyuki; Kawaguchi, Atsushı; Murakawa, Toru; Monji, Akira; Sakamoto, Yuichirou

doi:10.1136/tsaco-2021-000827

Cited by 6 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with the existing literatures in this research area, our study has breakthroughs in terms of sample size, model selection, and model visualization methods. For example, our sample size was larger compared to previous studies ( N = 518 compared to N = 308–380) 57–59 . Meanwhile, many previous studies only build a single prediction model, while this study builds nine prediction models to select the best performing prediction model by comparison 57–59 .…”

Section: Discussionmentioning

confidence: 96%

“…For example, our sample size was larger compared to previous studies ( N = 518 compared to N = 308–380). 57 , 58 , 59 Meanwhile, many previous studies only build a single prediction model, while this study builds nine prediction models to select the best performing prediction model by comparison. 57 , 58 , 59 In addition, unlike other studies that transformed the model into a nomogram, we opted for a simpler and faster online tool.…”

Section: Discussionmentioning

confidence: 99%

“… 57 , 58 , 59 Meanwhile, many previous studies only build a single prediction model, while this study builds nine prediction models to select the best performing prediction model by comparison. 57 , 58 , 59 In addition, unlike other studies that transformed the model into a nomogram, we opted for a simpler and faster online tool. 31 Currently, most prediction models are built and only internally validated, the fact that external validation was carried out in this study is another major advantage.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning for prediction of delirium in patients with extensive burns after surgery

et al. 2023

View full text Add to dashboard Cite

AimsMachine learning‐based identification of key variables and prediction of postoperative delirium in patients with extensive burns.MethodsFive hundred and eighteen patients with extensive burns who underwent surgery were included and randomly divided into a training set, a validation set, and a testing set. Multifactorial logistic regression analysis was used to screen for significant variables. Nine prediction models were constructed in the training and validation sets (80% of dataset). The testing set (20% of dataset) was used to further evaluate the model. The area under the receiver operating curve (AUROC) was used to compare model performance. SHapley Additive exPlanations (SHAP) was used to interpret the best one and to externally validate it in another large tertiary hospital.ResultsSeven variables were used in the development of nine prediction models: physical restraint, diabetes, sex, preoperative hemoglobin, acute physiological and chronic health assessment, time in the Burn Intensive Care Unit and total body surface area. Random Forest (RF) outperformed the other eight models in terms of predictive performance (ROC:84.00%) When external validation was performed, RF performed well (accuracy: 77.12%, sensitivity: 67.74% and specificity: 80.46%).ConclusionThe first machine learning‐based delirium prediction model for patients with extensive burns was successfully developed and validated. High‐risk patients for delirium can be effectively identified and targeted interventions can be made to reduce the incidence of delirium.

show abstract

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for prediction of delirium in patients with extensive burns after surgery

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The Prediction of Delirium in ICU patients (PRE-DELIRIC) score includes 10 common delirium risk factors and shown to be highly predictive for delirium, significantly outperforming clinicians in risk assessment and delirium prediction [28]. Recently, a trauma-specific prediction model was developed by Matsuoka et al [29 ▪▪ ] in 2021 using five risk factors available on admission (clinical frailty score, fibrin/fibrinogen degradation products, BMI, lactate, GCS) and was able to predict delirium in their validation cohort with an area under the curve of 0.86. Although this model is promising in its specificity for the trauma ICU population, it needs further validation.…”

Section: Elderly Patients With Rib Fracturesmentioning

confidence: 99%

Delirium in trauma ICUs: a review of incidence, risk factors, outcomes, and management

Williams

Estime

Kuza

2023

Current Opinion in Anaesthesiology

View full text Add to dashboard Cite

Purpose of reviewThis article reviews the impact and importance of delirium on patients admitted to the ICU after trauma, including the latest work on prevention and treatment of this condition. As the population ages, the incidence of geriatric trauma will continue to increase with a concomitant rise in the patient and healthcare costs of delirium in this population.Recent findingsRecent studies have further defined the risk factors for delirium in the trauma ICU patient population, as well as better demonstrated the poor outcomes associated with the diagnosis of delirium in these patients. Recent trials and meta-analysis offer some new evidence for the use of dexmedetomidine and quetiapine as preferred agents for prevention and treatment of delirium and add music interventions as a promising part of nonpharmacologic bundles.SummaryTrauma patients requiring admission to the ICU are at significant risk of developing delirium, an acute neuropsychiatric disorder associated with increased healthcare costs and worse outcomes including increased mortality. Ideal methods for prevention and treatment of delirium are not well established, especially in this population, but recent research helps to clarify optimal prevention and treatment strategies.

show abstract

“…39 The use of prediction models has shown promise in predicting several types of delirium, including postoperative and subsyndromal delirium as well as delirium in the ICU. 40 Frailty status has been identified as an important predictor in the surgical setting 41,42 and among trauma patients, 43 however, not in the general intensive care population. An important innovation in the intensive care setting appears to be the use of machine learning and the use of dynamic prediction models.…”

Section: Nomograms To Estimate the Absolute Risk Of An Acute Episode ...mentioning

confidence: 99%

Frailty in the prediction of delirium in the intensive care unit: A secondary analysis of the Deli study

Frost,

Brennan,

Sanchez

et al. 2023

Acta Anaesthesiol Scand

View full text Add to dashboard Cite

BackgroundDelirium is an acute disorder of attention and cognition with an incidence of up to 70% in the adult intensive care setting. Due to the association with significantly increased morbidity and mortality, it is important to identify who is at the greatest risk of an acute episode of delirium while being cared for in the intensive care. The objective of this study was to determine the ability of the cumulative deficit frailty index and clinical frailty scale to predict an acute episode of delirium among adults admitted to the intensive care.MethodsThis study is a secondary analysis of the Deli intervention study, a hybrid stepped‐wedge cluster randomized controlled trial to assess the effectiveness of a nurse‐led intervention to reduce the incidence and duration of delirium among adults admitted to the four adult intensive care units in the south‐west of Sydney, Australia. Important predictors of delirium were identified using a bootstrap approach and the absolute risks, based on the cumulative deficit frailty index and the clinical frailty scale are presented.ResultsDuring the 10‐mth data collection period (May 2019 and February 2020) 2566 patients were included in the study. Both the cumulative deficit frailty index and the clinical frailty scale on admission, plus age, sex, and APACHE III (AP III) score were able to discriminate between patients who did and did not experience an acute episode of delirium while in the intensive care, with AUC of 0.701 and 0.703 (moderate discriminatory ability), respectively. The addition of a frailty index to a prediction model based on age, sex, and APACHE III score, resulted in net reclassified of risk. Nomograms to individualize the absolute risk of delirium using these predictors are also presented.ConclusionWe have been able to show that both the cumulative deficits frailty index and clinical frailty scale predict an acute episode of delirium among adults admitted to intensive care.

show abstract

Development of a delirium predictive model for adult trauma patients in an emergency and critical care center: a retrospective study

Cited by 6 publications

References 34 publications

Machine learning for prediction of delirium in patients with extensive burns after surgery

Machine learning for prediction of delirium in patients with extensive burns after surgery

Delirium in trauma ICUs: a review of incidence, risk factors, outcomes, and management

Frailty in the prediction of delirium in the intensive care unit: A secondary analysis of the Deli study

Contact Info

Product

Resources

About