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      Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage

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          Abstract

          Objective

          Delirium is associated with worse outcomes in patients with stroke and neurocritical illness, but delirium detection in these patients can be challenging with existing screening tools. To address this gap, we aimed to develop and evaluate machine learning models that detect episodes of post-stroke delirium based on data from wearable activity monitors in conjunction with stroke-related clinical features.

          Design

          Prospective observational cohort study.

          Setting

          Neurocritical Care and Stroke Units at an academic medical center.

          Patients

          We recruited 39 patients with moderate-to-severe acute intracerebral hemorrhage (ICH) and hemiparesis over a 1-year period [mean (SD) age 71.3 (12.20), 54% male, median (IQR) initial NIH Stroke Scale 14.5 (6), median (IQR) ICH score 2 (1)].

          Measurements and main results

          Each patient received daily assessments for delirium by an attending neurologist, while activity data were recorded throughout each patient's hospitalization using wrist-worn actigraph devices (on both paretic and non-paretic arms). We compared the predictive accuracy of Random Forest, SVM and XGBoost machine learning methods in classifying daily delirium status using clinical information alone and combined with actigraph data. Among our study cohort, 85% of patients ( n = 33) had at least one delirium episode, while 71% of monitoring days ( n = 209) were rated as days with delirium. Clinical information alone had a low accuracy in detecting delirium on a day-to-day basis [accuracy mean (SD) 62% (18%), F1 score mean (SD) 50% (17%)]. Prediction performance improved significantly ( p < 0.001) with the addition of actigraph data [accuracy mean (SD) 74% (10%), F1 score 65% (10%)]. Among actigraphy features, night-time actigraph data were especially relevant for classification accuracy.

          Conclusions

          We found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.

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          Most cited references28

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            The REDCap consortium: Building an international community of software platform partners

            The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
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              Monitoring sedation status over time in ICU patients: reliability and validity of the Richmond Agitation-Sedation Scale (RASS).

              Goal-directed delivery of sedative and analgesic medications is recommended as standard care in intensive care units (ICUs) because of the impact these medications have on ventilator weaning and ICU length of stay, but few of the available sedation scales have been appropriately tested for reliability and validity. To test the reliability and validity of the Richmond Agitation-Sedation Scale (RASS). Prospective cohort study. Adult medical and coronary ICUs of a university-based medical center. Thirty-eight medical ICU patients enrolled for reliability testing (46% receiving mechanical ventilation) from July 21, 1999, to September 7, 1999, and an independent cohort of 275 patients receiving mechanical ventilation were enrolled for validity testing from February 1, 2000, to May 3, 2001. Interrater reliability of the RASS, Glasgow Coma Scale (GCS), and Ramsay Scale (RS); validity of the RASS correlated with reference standard ratings, assessments of content of consciousness, GCS scores, doses of sedatives and analgesics, and bispectral electroencephalography. In 290-paired observations by nurses, results of both the RASS and RS demonstrated excellent interrater reliability (weighted kappa, 0.91 and 0.94, respectively), which were both superior to the GCS (weighted kappa, 0.64; P<.001 for both comparisons). Criterion validity was tested in 411-paired observations in the first 96 patients of the validation cohort, in whom the RASS showed significant differences between levels of consciousness (P<.001 for all) and correctly identified fluctuations within patients over time (P<.001). In addition, 5 methods were used to test the construct validity of the RASS, including correlation with an attention screening examination (r = 0.78, P<.001), GCS scores (r = 0.91, P<.001), quantity of different psychoactive medication dosages 8 hours prior to assessment (eg, lorazepam: r = - 0.31, P<.001), successful extubation (P =.07), and bispectral electroencephalography (r = 0.63, P<.001). Face validity was demonstrated via a survey of 26 critical care nurses, which the results showed that 92% agreed or strongly agreed with the RASS scoring scheme, and 81% agreed or strongly agreed that the instrument provided a consensus for goal-directed delivery of medications. The RASS demonstrated excellent interrater reliability and criterion, construct, and face validity. This is the first sedation scale to be validated for its ability to detect changes in sedation status over consecutive days of ICU care, against constructs of level of consciousness and delirium, and correlated with the administered dose of sedative and analgesic medications.
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                Author and article information

                Contributors
                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                09 June 2023
                2023
                : 14
                : 1135472
                Affiliations
                [1] 1Brown Center for Biomedical Informatics, Brown University , Providence, RI, United States
                [2] 2IMDEA Networks Institute , Madrid, Spain
                [3] 3Department of Neurology, Brown University , Providence, RI, United States
                [4] 4Department of Psychiatry, Brown University , Providence, RI, United States
                Author notes

                Edited by: Sim Kuan Goh, Xiamen University Malaysia, Malaysia

                Reviewed by: Hipólito Nzwalo, University of Algarve, Portugal; Chow Khuen Chan, University of Malaya, Malaysia

                *Correspondence: Carsten Eickhoff carsten@ 123456brown.edu

                †These authors have contributed equally to this work

                ‡These authors have contributed equally to this work and share senior authorship

                Article
                10.3389/fneur.2023.1135472
                10288850
                37360342
                317ed693-f1c6-45a7-a449-96b12cbcab57
                Copyright © 2023 Ahmed, Garcia-Agundez, Petrovic, Radaei, Fife, Zhou, Karas, Moody, Drake, Jones, Eickhoff and Reznik.

                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.

                History
                : 31 December 2022
                : 24 April 2023
                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 28, Pages: 7, Words: 4925
                Funding
                Funded by: European Commission, doi 10.13039/501100000780;
                Funded by: Rhode Island Foundation, doi 10.13039/100014082;
                Funded by: Brown University, doi 10.13039/100006418;
                This study was supported by the Rhode Island Foundation, Brown University's Office of the Vice President for Research (OVPR) via a Big Data Collaborative Seed Award, and the Global Individual Fellowship Marie Skłodowska-Curie Action H2020-MSCA-IF-2020 MAESTRO (Grant Number: 101027770).
                Categories
                Neurology
                Original Research
                Custom metadata
                Neurocritical and Neurohospitalist Care

                Neurology
                delirium,neurocritical care,stroke,intracerebral hemorrhage,actigraphy,machine learning,wearable electronic devices

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