scholarly journals Quantitative EEG Features of Level of Consciousness in Critically Ill Nonagenarians

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 122-122
Author(s):  
Shawniqua Williams Roberson ◽  
Kevin Haas
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Medical Center ◽  
Clinical Care ◽  
Quantitative Eeg ◽  
Level Of Consciousness ◽  
Anchoring Bias ◽  
History Of ◽  
Automated Processing ◽  
The Relationship

Abstract The standard for monitoring sedation levels in critically ill patients is intermittent bedside evaluation, and is prone to anchoring bias. Quantitative electroencephalography (qEEG) allows automated processing of recorded brain electrical activity and could be used to continuously monitor level of consciousness in critically ill patients. The majority of qEEG studies have included persons 80 years of age or less, and the qEEG profiles of nonagenarians have been incompletely characterized. Knowledge of the qEEG patterns of patients 90 years and older is essential for appropriate interpretation of such metrics in this population. This retrospective cohort study characterized qEEG profiles of acutely ill nonagenarians. We investigated whether the relationship between qEEG and level of consciousness differed between patients with and without a history of dementia. We included patients 90-100 years old admitted to Vanderbilt University Medical Center who underwent EEG and as part of their clinical care. We compared qEEG features to nursing-defined level of arousal as measured by the Richmond Agitation-Sedation Scale (RASS) in patients with and without history of dementia. Between January and December 2019, 26 nonagenarians underwent EEG for clinical purposes. One study was excluded due to excessive artifact. Of the remaining, 6 (24%) were male and 18 (72%) were Caucasian. Among all patients, RASS decreased with increases in EEG theta variability (coefficient -7.7, 95%CI -10.6 to -4.8). This relationship was not significantly modified by history of dementia (coefficient of interaction term -0.36, 95%CI -3.7 to 2.9). Dementia does not impact qEEG features of level of consciousness in nonagenarians.

scholarly journals Etiology, incidence, and outcomes of patient–ventilator asynchrony in critically-ill patients undergoing invasive mechanical ventilation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yongfang Zhou ◽  
Steven R. Holets ◽  
Man Li ◽  
Gustavo A. Cortes-Puentes ◽  
Todd J. Meyer ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mechanical Ventilation ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Invasive Mechanical Ventilation ◽  
Surgical Intensive Care ◽  
Mechanically Ventilated ◽  
History Of ◽  
Training Background ◽  
The Relationship

AbstractPatient–ventilator asynchrony (PVA) is commonly encountered during mechanical ventilation of critically ill patients. Estimates of PVA incidence vary widely. Type, risk factors, and consequences of PVA remain unclear. We aimed to measure the incidence and identify types of PVA, characterize risk factors for development, and explore the relationship between PVA and outcome among critically ill, mechanically ventilated adult patients admitted to medical, surgical, and medical-surgical intensive care units in a large academic institution staffed with varying provider training background. A single center, retrospective cohort study of all adult critically ill patients undergoing invasive mechanical ventilation for ≥ 12 h. A total of 676 patients who underwent 696 episodes of mechanical ventilation were included. Overall PVA occurred in 170 (24%) episodes. Double triggering 92(13%) was most common, followed by flow starvation 73(10%). A history of smoking, and pneumonia, sepsis, or ARDS were risk factors for overall PVA and double triggering (all P < 0.05). Compared with volume targeted ventilation, pressure targeted ventilation decreased the occurrence of events (all P < 0.01). During volume controlled synchronized intermittent mandatory ventilation and pressure targeted ventilation, ventilator settings were associated with the incidence of overall PVA. The number of overall PVA, as well as double triggering and flow starvation specifically, were associated with worse outcomes and fewer hospital-free days (all P < 0.01). Double triggering and flow starvation are the most common PVA among critically ill, mechanically ventilated patients. Overall incidence as well as double triggering and flow starvation PVA specifically, portend worse outcome.

The relationship between sedative drug utilization and outcomes in critically ill patients undergoing mechanical ventilation

2016 ◽  
Vol 30 (5) ◽  
pp. 763-769 ◽  
Author(s):  
Kenshi Hayashida ◽  
Takeshi Umegaki ◽  
Hiroshi Ikai ◽  
Genki Murakami ◽  
Masaji Nishimura ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Critically Ill ◽  
Drug Utilization ◽  
Critically Ill Patients ◽  
Sedative Drug ◽  
The Relationship

Critically Ill Patients with Pandemic Influenza A/H1N1 2009 at a Medical Center in Korea

2011 ◽  
Vol 70 (1) ◽  
pp. 28
Author(s):  
Eun Young Choi ◽  
Jin Won Huh ◽  
Chae-Man Lim ◽  
Younsuck Koh ◽  
Sung-Han Kim ◽  
...  
Keyword(s):  
Critically Ill ◽  
Pandemic Influenza ◽  
Critically Ill Patients ◽  
Influenza A ◽  
Medical Center ◽  
Influenza A H1n1

scholarly journals Pragmatic Disease-Focused Checklist for Use During Rounding on Critically Ill Patients With COVID-19

2021 ◽  
pp. e1-e5
Author(s):  
Somnath Bose ◽  
Akiva Leibowitz
Keyword(s):  
Critically Ill ◽  
Family Communication ◽  
Critically Ill Patients ◽  
Inflammatory Markers ◽  
Medical Center ◽  
Academic Medical Center ◽  
Academic Medical ◽  
Ventilation Strategies ◽  
Novel Coronavirus ◽  
Existing Structure

The sudden surge in cases of novel coronavirus disease 2019 (COVID-19) has presented unprecedented challenges in the care of critically ill patients with the disease. A disease-focused checklist was developed to supplement and streamline the existing structure of rounds during a time of significant resource constraint. A total of 51 critical care consultants across multiple specialties at a tertiary academic medical center were surveyed regarding their preference for a structured checklist. Among the respondents, 82% were in favor of a disease-focused checklist. Mechanical ventilation parameters, rescue ventilation strategies, sedation regimens, inflammatory markers specific to COVID-19, and family communication were the elements most commonly identified as being important for inclusion in such a checklist.

scholarly journals Proximity to the Treating Centre and Outcomes Following Subarachnoid Hemorrhage

2011 ◽  
Vol 38 (1) ◽  
pp. 36-40 ◽  
Author(s):  
Cian J. O'Kelly ◽  
Julian Spears ◽  
David Urbach ◽  
M. Christopher Wallace
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Survival Benefit ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Mortality Rates ◽  
Limited Resources ◽  
Early Complications ◽  
The Impact ◽  
The Relationship

Abstract:Background:In the management of subarachnoid hemorrhage (SAH), the potential for early complications and the centralization of limited resources often challenge the delivery of timely neurosurgical care. We sought to determine the impact of proximity to the accepting neurosurgical centre on outcomes following aneurysmal SAH.Methods:Using administrative data, we analyzed patients undergoing treatment for aneurysmal subarachnoid hemorrhage at neurosurgical centres in Ontario between 1995 and 2004. We compared mortality for patients receiving treatment at a centre in their county (in-county) versus those treated from outside counties (out-of-county). We also examined the impact of distance from the patient's residence to the treating centre.Results:The mortality rates were significantly lower for in-county versus out-of-county patients (23.5% vs. 27.6%, p=0.009). This advantage remained significant after adjusting for potential confounders (HR=0.84, p=0.01). The relationship between distance from the treating centre and mortality was biphasic. Under 300km, mortality increased with increasing distance. Over 300km, a survival benefit was observed.Conclusions:Proximity to the treating neurosurgical centre impacts survival after aneurysmal SAH. These results have significant implications for the triage of these critically ill patients.

Venous thromboembolism in critically ill patients

2009 ◽  
Vol 101 (01) ◽  
pp. 139-144 ◽  
Author(s):  
Mark Williams ◽  
Andrew Shorr
Keyword(s):  
Severe Sepsis ◽  
Venous Thromboembolism ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Controlled Trial ◽  
Double Blind ◽  
Vte Prophylaxis ◽  
Drotrecogin Alfa Activated ◽  
History Of ◽  
Sepsis And Septic Shock

SummaryVenous thromboembolism (VTE) is a central concern in the intensive care unit (ICU). However, little is known about both current practices for VTE prevention in the ICU and the risk for VTE in persons with severe sepsis and septic shock. XPRESS was a randomized, double-blind, placebo-controlled trial of prophylactic heparin in patients with severe sepsis and higher disease severity who were treated with drotrecogin alfa (activated) (DAA). Subjects were randomized to unfractionated heparin, low-molecular-weight heparin, or placebo during the DAA infusion period. All patients underwent ultrasonography between days 4-6 to screen for VTE. We assessed baseline utilization of VTE prophylaxis along with application of these methods after completion of the DAA infusion. The study included 1,935 subjects and, prior to enrollment approximately half were given no form of prophylaxis. By day 6, 5% of subjects developed a VTE, and the rate of VTE did not vary based on type of heparin administered. The vast majority of VTE detected by day 6 were clinically silent. Of factors analyzed, history of VTE was the only variable independently associated with development of a VTE (odds ratio, 3.66, 95% confidence interval 1.77–7.56, p=0.005). Strikingly, patients who were initially receiving heparin prophylaxis prior to enrollment but who then had this discontinued because of randomization to placebo suffered more VTE that persons continuing on some form of heparin. Despite multiple guidelines, physicians do not uniformly prescribe VTE prophylaxis. Nonetheless, early VTE occurs even in persons given DAA. Most VTE in critically ill patients are clinically silent.

scholarly journals Using machine learning tools to predict outcomes for emergency department intensive care unit patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Learning Tools ◽  
Critical Patients

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

Evaluation of the Safety and Efficacy of Metformin Use in Hospitalized, Non–Critically Ill Patients

2020 ◽  
Vol 36 (3) ◽  
pp. 102-109
Author(s):  
Tahnia Alauddin ◽  
Sarah E. Petite
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Medical Center ◽  
Academic Medical Center ◽  
Kidney Injury ◽  
Hospitalized Patients ◽  
Adverse Outcomes ◽  
Safety And Efficacy ◽  
Antihyperglycemic Therapy ◽  
Heart Failure Diagnosis

Background: Contraindications and precautions to metformin have limited inpatient use, and limited evidence exists evaluating metformin in hospitalized patients. Objective: This study aimed to determine the safety and efficacy of inpatient metformin use. Methods: This study was an observational, retrospective, cohort study at an academic medical center between June 1, 2016, and May 31, 2018. Hospitalized adults with type 2 diabetes mellitus receiving at least 1 metformin dose were included. The primary endpoint was to identify hospitalized patients using metformin with at least 1 contraindication or precautionary warning against use. Secondary endpoints included assessing metformin efficacy with glycemic control, characterizing adverse outcomes of inpatient metformin, and comparing the efficacy of metformin-containing regimens. Results: Two hundred patients were included. There were 126 incidences of potentially unsafe use identified in 111 patients (55.5%). The most common reasons were age ≥65 years (47%), heart failure diagnosis (7.5%), and metformin within 48 hours of contrast (6%). Metformin was contraindicated in 2 patients (1%) with an estimated glomerular filtration rate ≤30 mL/min/1.73 m2. The overall median daily blood glucose was 146 mg/dL (interquartile range [IQR] = 122-181). Patients were divided into 3 groups: metformin monotherapy, metformin plus oral antihyperglycemic therapy, and metformin plus insulin. The median daily blood glucoses were 129 mg/dL (IQR = 110-152), 154 mg/dL (IQR = 133-178), and 174 mg/dL (IQR = 142-203; P < .001), respectively. Two patients (1%) developed acute kidney injury, and no patients developed lactic acidosis. Conclusions: Metformin was associated with goal glycemic levels in hospitalized patients with no adverse outcomes. These results suggest the potential for metformin use in hospitalized, non–critically ill patients.

Sign in / Sign up

Export Citation Format

Share Document