scholarly journals Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shubhayu Bhattacharyay ◽  
John Rattray ◽  
Matthew Wang ◽  
Peter H. Dziedzic ◽  
Eusebia Calvillo ◽  
...  
Keyword(s):  
Brain Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
High Frequency ◽  
Prediction Models ◽  
Severe Brain Injury ◽  
Short Term ◽  
Time Frequency ◽  
Yield Information ◽  
Functional States

AbstractOur goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8–25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale–Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53–0.85]) and consistent (observation windows: 12 min–9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2–6 h of observation (AUC: 0.82 [95% CI: 0.75–0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.

scholarly journals Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

2021 ◽  
Author(s):  
Shubhayu Bhattacharyay ◽  
John Rattray ◽  
Matthew Wang ◽  
Peter Dziedzic ◽  
Eusebia Calvillo ◽  
...  
Keyword(s):  
Brain Injury ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Prediction Models ◽  
Severe Brain Injury ◽  
Short Term ◽  
Time Frequency ◽  
Motion Features ◽  
Analysis Of Time Series

The goal of this research is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield important information on underlying neurological states and clinical outcomes. Using wearable microsensors placed on all extremities, we recorded 1,701 hours of continuous, high-frequency accelerometry data from a prospective cohort of patients (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain motion features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at hospital discharge, measured with the Glasgow Outcome Scale—Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53—0.85]) and consistent (observation windows: 12 min — 9 hours) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated SBI patients of upper moderate disability or better (GOSE > 5) with 2—6 hours of observation (AUC: 0.82 [95% CI: 0.75—0.90]). Results suggest that computational analysis of time series motor activity in patients with SBI yields clinically important insights on underlying neurologic states and short-term clinical outcomes.

scholarly journals Author Correction: Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shubhayu Bhattacharyay ◽  
John Rattray ◽  
Matthew Wang ◽  
Peter H. Dziedzic ◽  
Eusebia Calvillo ◽  
...  
Keyword(s):  
Brain Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Severe Brain Injury

scholarly journals Evaluation of development of diabetes insipidus in the early phase following traumatic brain injury in critically ill patients

Critical Care ◽  
2008 ◽  
Vol 12 (Suppl 2) ◽  
pp. P130 ◽  
Author(s):  
V Karali ◽  
E Massa ◽  
G Vassiliadou ◽  
I Chouris ◽  
I Rodin ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Diabetes Insipidus ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Early Phase

Pharmacological Management of Seizures and Status Epilepticus in Critically Ill Patients

2010 ◽  
Vol 23 (5) ◽  
pp. 441-454 ◽  
Author(s):  
Eljim P. Tesoro ◽  
Gretchen M. Brophy
Keyword(s):  
Status Epilepticus ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Neurological Disease ◽  
Medical Emergency ◽  
Significant Morbidity ◽  
First Line ◽  
Short Term ◽  
Pharmacological Management ◽  
First Line Treatment

Seizures are serious complications seen in critically ill patients and can lead to significant morbidity and mortality if the cause is not identified and treated quickly. Uncontrolled seizures can lead to status epilepticus (SE), which is considered a medical emergency. The first-line treatment of seizures is an intravenous (IV) benzodiazepine followed by anticonvulsant therapy. Refractory SE can evolve into a nonconvulsive state requiring IV anesthetics or induction of pharmacological coma. To prevent seizures and further complications in critically ill patients with acute neurological disease or injury, short-term seizure prophylaxis should be considered in certain patients.

scholarly journals Colonization with Extensively Drug-Resistant Acinetobacter Baumannii and Prognosis in Critically Ill Patients: An Observational Cohort Study

2021 ◽  
Author(s):  
Yue Zheng ◽  
Nana Xu ◽  
Jiaojiao Pang ◽  
Hui Han ◽  
Hongna Yang ◽  
...  
Keyword(s):  
Regression Model ◽  
Acinetobacter Baumannii ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Drug Resistant ◽  
Short Term ◽  
Term Survival ◽  
Extensively Drug Resistant ◽  
Kaplan Meier

Abstract Background: Acinetobacter baumannii is one of the most often isolated opportunistic pathogens in intensive care units (ICUs). Extensively drug-resistant A. baumannii (XDR-AB) strains lack susceptibility to almost all antibiotics and pose a heavy burden on healthcare institutions. In this study, we evaluated the impact of XDR-AB colonization on both the short-term and long-term survival of critically ill patients.Methods: We prospectively enrolled patients from two adult ICUs in Qilu Hospital of Shandong University from April 2018 through December 2018. Using nasopharyngeal and perirectal swabs, we evaluated the presence of XDR-AB colonization. Participants were followed up for six months. Primary endpoints were 28-day and six-month mortality after ICU admission. For survival analysis, we used the Kaplan-Meier curve. We identified risk factors associated with 28-day and six-month mortality using the logistic regression model and Cox proportional-hazards survival regression model, respectively. Results: Out of 431 patients, 77 were colonized with XDR-AB. Based on the Kaplan-Meier curve results, the survival before 28 days did not differ by colonization status; however, a significant lower survival rate was obtained at six months in colonized patients. Univariate and multivariate results confirmed that XDR-AB colonization was not associated with 28-day mortality, but was an independent risk factor of lower survival days at six months, resulting in a 1.97 times higher risk of death at six months.Conclusions: XDR-AB colonization has no effect on short-term mortality but is associated with lower long-term survival in critically ill patients.

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