Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning

Oh, Jae Young; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jong-In; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae‐Jin; Park, Jin Young; Lee, Boreom

doi:10.1088/1361-6579/aaab07

Cited by 42 publications

(51 citation statements)

References 36 publications

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“…HRV is a simple and non-invasive examination, which can evaluate the activity of autonomic nerve, the energy-saving ability of the body, and the influence of interventions on the function of autonomic nerve in patients [ 48 , 49 ]. Previous studies have shown nearly 70% of PBC patients with autonomic nervous dysfunction [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Transcutaneous Neuromodulation improved inflammation and sympathovagal ratio in patients with primary biliary ssscholangitis and inadequate response to Ursodeoxycholic acid: a pilot study

Yang

Wang

et al. 2020

BMC Complement Med Ther

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Background: At present, ursodeoxycholic acid (UDCA) is internationally recognized as a therapeutic drug in clinic. However, about 40% Primary Biliary Cholangitis (PBC) patients are poor responders to UDCA. It has been demonstrated that Transcutaneous Neuromodulation (TN) can be involved in gut motility, metabolism of bile acids, immune inflammation, and autonomic nerve. Therefore, this study aimed to explore the effect of TN combined with UDCA on PBC and related mechanisms. Methods: According to inclusion and exclusion criteria, 10 healthy volunteers and 15 PBC patients were recruited to control group and TN group, respectively. PBC patients were alternately but blindly assigned to group A (TN combined with UDCA) and group B (sham-TN combined with UDCA), and a crossover design was used. The TN treatment was performed via the posterior tibial nerve and acupoint ST36 (Zusanli) 1 h twice/day for 2 weeks. T test and nonparametric test were used to analyze the data.

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Section: Discussionmentioning

confidence: 99%

Transcutaneous Neuromodulation improved inflammation and sympathovagal ratio in patients with primary biliary ssscholangitis and inadequate response to Ursodeoxycholic acid: a pilot study

Yang

Wang

et al. 2020

BMC Complement Med Ther

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“…Real-time heart rate variability assisted by ML techniques have been investigated in preliminary studies as a method of delirium identification [17]. Excess sympathetic activity associated with delirium can be detected in real-time by continuous monitoring of vital signs.…”

Section: Delirium Identificationmentioning

confidence: 99%

Applying machine learning to continuously monitored physiological data

Rush

Celi

Stone

2018

J Clin Monit Comput

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The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for monitoring within the hospital environment. We present use cases as well as several questions regarding the application of ML to the analysis of the vast amount of complex data that clinicians must interpret in the realm of continuous physiological monitoring. ML, especially employed in bidirectional conjunction with electronic health record data, has the potential to extract much more useful information out of this currently under-analyzed data source from a population level. As a data driven entity, ML is dependent on copious, high quality input data so that error can be introduced by low quality data sources. At present, while ML is being studied in hybrid formulations along with static expert systems for monitoring applications, it is not yet actively incorporated in the formal artificial learning sense of an algorithm constantly learning and updating its rules without external intervention. Finally, innovations in monitoring, including those supported by ML, will pose regulatory and medico-legal challenges, as well as questions regarding precisely how to incorporate these features into clinical care and medical education. Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors. Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development: Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings.

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“…More recent research demonstrates that delirium is often reversible with early detection and treatment. [ 14 – 15 ] Therefore, it is important to identify related risk factors before surgery and make a dedicated perioperative care path. The Cornell assessment of pediatric delirium (CAPD) is a validated screening tool designed for children of all ages and developmental abilities.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a postoperative delirium prediction model for pediatric patients

Lin¹,

Liu²,

Feng³

et al. 2021

Medicine

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Postoperative delirium is a serious complication that relates to poor outcomes. A risk prediction model could help the staff screen for children at high risk for postoperative delirium. Our study aimed to establish a postoperative delirium prediction model for pediatric patients and to verify the sensitivity and specificity of this model. Data were collected from a total of 1134 children (0–16yr) after major elective surgery between February 2020 to June 2020. Demographic and clinical data were collected to explore the risk factors. Multivariate logistic regression analysis was used to develop the model, and we assessed the predictive ability of the model by using the area under the receiver operating characteristics curve (AUROC). Further data were collected from another 100 patients in October 2020 to validate the model. Prevalence of postoperative delirium in this sample was 11.1%. The model consisted of 5 predictors, namely, age, developmental delay, type of surgery, pain, and dexmedetomidine. The AUROC was 0.889 ( P < .001, 95% confidence interval (CI):0.857–0.921), with sensitivity and specificity of 0.754 and 0.867, and the Youden of 0.621. The model verification results showed the sensitivity of 0.667, the specificity of 0.955. Children undergoing surgery are at risk for developing delirium during the postoperative period, young age, developmental delay, otorhinolaryngology surgery, pain, and exposure to dexmedetomidine were associated with increased odds of delirium. Our study established a postoperative delirium prediction model for pediatric patients, which may be a base for development of strategies to prevent and treat postoperative delirium in children.

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Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning

Abstract: Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Cited by 42 publications

References 36 publications

Transcutaneous Neuromodulation improved inflammation and sympathovagal ratio in patients with primary biliary ssscholangitis and inadequate response to Ursodeoxycholic acid: a pilot study

Transcutaneous Neuromodulation improved inflammation and sympathovagal ratio in patients with primary biliary ssscholangitis and inadequate response to Ursodeoxycholic acid: a pilot study

Applying machine learning to continuously monitored physiological data

Development and validation of a postoperative delirium prediction model for pediatric patients

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