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

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.

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

10.1101/2021.05.19.21257319 ◽

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.

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Controlled normothermia in critically ill patients with traumatic brain injury

EMERGENCY MEDICINE ◽

10.22141/2224-0586.3.82.2017.102326 ◽

2017 ◽

Vol 0 (3.82) ◽

pp. 66-72

Author(s):

A.V. Tsarev

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Critically Ill ◽

Critically Ill Patients

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

Critical Care ◽

10.1186/cc6351 ◽

2008 ◽

Vol 12 (Suppl 2) ◽

pp. P130 ◽

Cited By ~ 5

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

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Increased Levels of Serum S100B Protein in Critically Ill Patients Without Brain Injury

Shock ◽

10.1097/01.shk.0000239757.67464.c6 ◽

2007 ◽

Vol 27 (3) ◽

pp. 339

Author(s):

Christina Routsi ◽

Elizabeth Stamataki ◽

Seraphin Nanas ◽

Charis Roussos

Keyword(s):

Brain Injury ◽

Critically Ill ◽

Critically Ill Patients ◽

S100b Protein ◽

Serum S100b

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The role of non-invasive brain oximetry in adult critically ill patients without primary brain injury

Minerva Anestesiologica ◽

10.23736/s0375-9393.21.15333-7 ◽

2021 ◽

Author(s):

Rafael Badenes ◽

Elisa G. Bogossian ◽

Vicente Chisbert ◽

Chiara Robba ◽

Mauro Oddo ◽

...

Keyword(s):

Brain Injury ◽

Critically Ill ◽

Critically Ill Patients ◽

Non Invasive

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Evaluation of Intravenous Phenobarbital Pharmacokinetics in Critically Ill Patients With Brain Injury

ACTA MEDICA IRANICA ◽

10.18502/acta.v60i1.8323 ◽

2022 ◽

Author(s):

Seyedeh Sana Khezrnia ◽

Bita Shahrami ◽

Mohammad Reza Rouini ◽

Atabak Najafi ◽

Hamid Reza Sharifnia ◽

...

Keyword(s):

Brain Injury ◽

Critically Ill ◽

Critically Ill Patients ◽

Normal Population ◽

Volume Of Distribution ◽

Pharmacokinetic Parameters ◽

Therapeutic Drug ◽

Uv Detector ◽

Therapeutic Goals ◽

The Impact

Phenobarbital is still one of the drugs of choice in managing patients with brain injury in the intensive care unit (ICU). However, the impact of acute physiological changes on phenobarbital pharmacokinetic parameters is not well studied. This study aimed to evaluate the pharmacokinetic parameters of parenteral phenobarbital in critically ill patients with brain injury. Patients with severe traumatic or non-traumatic brain injury at high risk of seizure were included and followed for seven days. All patients initially received phenobarbital as a loading dose of 15 mg/kg over 30-minutes infusion, followed by 2 mg/kg/day divided into three doses. Blood samples were obtained on the first and fourth day of study at 1, 2, 5, 8, and 10 hours after the end of the infusion. Serum concentrations of phenobarbital were measured by high-pressure liquid chromatography (HPLC) with an ultraviolet (UV) detector. Pharmacokinetic parameters, including the volume of distribution (Vd), half-life (t1/2), and the drug clearance (CL), were provided by MonolixSuite 2019R1 software using stochastic approximation expectation-maximization (SAEM) algorithm and compared with previously reported parameters in healthy volunteers. Data from seventeen patients were analyzed. The mean value±standard deviation of pharmacokinetic parameters was calculated as follows: Vd: 0.81±0.15 L/kg; t1/2: 6.16±2.66 days; CL: 4.23±1.51 ml/kg/h. CL and Vd were significantly lower and higher than the normal population with the value of 5.6 ml/kg/h (P=0.002) and 0.7 L/kg (P=0.01), respectively. Pharmacokinetic behavior of phenobarbital may change significantly in critically ill brain-injured patients. This study affirms the value of early phenobarbital therapeutic drug monitoring (TDM) to achieve therapeutic goals.

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