scholarly journals Current State of Analgesia and Sedation in the Pediatric Intensive Care Unit

2021 ◽  
Vol 10 (9) ◽  
pp. 1847
Author(s):  
Chinyere Egbuta ◽  
Keira P. Mason
Keyword(s):  
At Risk ◽  
Critically Ill ◽  
Pediatric Patients ◽  
Developmental Stages ◽  
Wide Spectrum ◽  
Pediatric Population ◽  
Pediatric Intensive Care ◽  
Post Traumatic Stress ◽  
Patients At Risk ◽  
Prolonged Icu Stay

Critically ill pediatric patients often require complex medical procedures as well as invasive testing and monitoring which tend to be painful and anxiety-provoking, necessitating the provision of analgesia and sedation to reduce stress response. Achieving the optimal combination of adequate analgesia and appropriate sedation can be quite challenging in a patient population with a wide spectrum of ages, sizes, and developmental stages. The added complexities of critical illness in the pediatric population such as evolving pathophysiology, impaired organ function, as well as altered pharmacodynamics and pharmacokinetics must be considered. Undersedation leaves patients at risk of physical and psychological stress which may have significant long term consequences. Oversedation, on the other hand, leaves the patient at risk of needing prolonged respiratory, specifically mechanical ventilator, support, prolonged ICU stay and hospital admission, and higher risk of untoward effects of analgosedative agents. Both undersedation and oversedation put critically ill pediatric patients at high risk of developing PICU-acquired complications (PACs) like delirium, withdrawal syndrome, neuromuscular atrophy and weakness, post-traumatic stress disorder, and poor rehabilitation. Optimal analgesia and sedation is dependent on continuous patient assessment with appropriately validated tools that help guide the titration of analgosedative agents to effect. Bundled interventions that emphasize minimizing benzodiazepines, screening for delirium frequently, avoiding physical and chemical restraints thereby allowing for greater mobility, and promoting adequate and proper sleep will disrupt the PICU culture of immobility and reduce the incidence of PACs.

Noninvasive hemoglobin monitoring in critically ill pediatric patients at risk of bleeding

2017 ◽  
Vol 41 (4) ◽  
pp. 209-215 ◽  
Author(s):  
P. García-Soler ◽  
J.M. Camacho Alonso ◽  
J.M. González-Gómez ◽  
G. Milano-Manso
Keyword(s):  
At Risk ◽  
Critically Ill ◽  
Pediatric Patients ◽  
Risk Of Bleeding ◽  
Patients At Risk

Peanut-containing Products in Children's Hospitals: Putting Pediatric Patients at Risk

2018 ◽  
Author(s):  
Laura Fletcher ◽  
Tammy Pham ◽  
Maguire Herriman ◽  
Bridget Kiely ◽  
Ruth Milanaik
Keyword(s):  
At Risk ◽  
Pediatric Patients ◽  
Children's Hospitals ◽  
Children’S Hospitals ◽  
Patients At Risk

scholarly journals Fall Reduction Intervention for Hospitalized Pediatric Patients at Risk for falls

2017 ◽  
Vol 2 (1) ◽  
pp. 55-70
Author(s):  
Magda Mohsen ◽  
Omayma OKby ◽  
Reda Elfeshawy
Keyword(s):  
At Risk ◽  
Pediatric Patients ◽  
Patients At Risk

scholarly journals Nutrition support in critically ill COVID-19 patients

2020 ◽  
Vol 4 (1-3) ◽  
pp. 8
Author(s):  
Abdolreza Norouzy
Keyword(s):  
Older Adults ◽  
At Risk ◽  
Critically Ill ◽  
Diagnosis And Treatment ◽  
Nutrition Support ◽  
Patients At Risk ◽  
Short And Long Term ◽  
Long Term Prognosis ◽  
Poor Outcomes

Diagnosis and treatment of malnutrition should be considered in the management of COVID-19 patients to improve both short- and long-term prognosis. Patients at risk for poor outcomes and higher mortality following infection with COVID-19, namely older adults and polymorbid individuals, should be checked for malnutrition through screening and assessment.

scholarly journals NURS-10. IMPROVEMENTS IN A BEHAVIORAL TRAINING AND PHARMACOLOGICAL ANXIOLYSIS ALGORITHM FOR INCREASED COMPLIANCE IN PEDIATRIC PATIENTS IN PREPARATION FOR RADIATION THERAPY: A RETROSPECTIVE ANALYSIS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii422-iii423
Author(s):  
Judy Tran ◽  
Jennifer Holt ◽  
Danielle Crump ◽  
Anita Shea ◽  
Lin Whetzel ◽  
...  
Keyword(s):  
Risk Factors ◽  
Pediatric Patients ◽  
Pediatric Population ◽  
Statistical Significance ◽  
Radiation Risk ◽  
Proton Radiation ◽  
Behavioral Training ◽  
Duration Of Treatment ◽  
Behavioral Compliance ◽  
Patients At Risk

Abstract BACKGROUND In the pediatric population, the probability of compliance with radiation involves multifactorial elements. Younger pediatric patients often require anesthesia to ensure accurate delivery of radiotherapy. The purpose of this analysis was to refine our algorithm in pediatric patients to better identify children who would benefit from behavioral training and/or anxiolyxis intervention with the goal of minimizing anesthesia use. METHOD Retrospective data was collected from electronic medical records from 150 pediatric oncology patients <18 years old, treated with photon and proton radiation at our center from August 2016 to December 2019. We identified potential socio-developmental treatment factors thought to impact behavioral compliance and categorized risk factors based on an algorithm to determine risk for noncompliance with radiotherapy. RESULTS Six categories demonstrated statistical significance (p<0.05) in their influence on behavioral compliance during radiotherapy: age category (specifically age <7: Odds ratio [OR] 3.0, 95% Confidence Interval [CI] 1.0, 9.1), need for sedation with prior imaging studies (p<0.001), parental premonition of requiring anesthesia for successful treatment (p<0.001), duration of treatment, primary language (p<0.001), and use of total body irradiation (OR 3.1, 95% CI 1.1, 9.3). CONCLUSION Identification of pre-radiation risk factors allowed for better recognition of patients at risk for treatment non-compliance and for requiring daily sedation. Future studies should focus on implementing the algorithm prospectively in an effort to identify and direct early intervention with behavioral training and/or anxiolytics to minimize the need for sedation.

Artificial Intelligence in Predicting Cardiac Arrest: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Asma Alamgir ◽  
Osama Mousa 2nd ◽  
Zubair Shah 3rd
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Cardiac Arrest ◽  
Scoping Review ◽  
Pediatric Population ◽  
Data Extraction ◽  
Warning System ◽  
Reference List ◽  
Evaluation Tool ◽  
Patients At Risk

BACKGROUND Cardiac arrest is a life-threatening cessation of heart activity. Early prediction of cardiac arrest is important as it provides an opportunity to take the necessary measures to prevent or intervene during the onset. Artificial intelligence technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS Scoping review was conducted in line with guidelines of PRISMA Extension for Scoping Review (PRISMA-ScR). Scopus, Science Direct, Embase, IEEE, and Google Scholar were searched to identify relevant studies. Backward reference list checking of included studies was also conducted. The study selection and data extraction were conducted independently by two reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. We were able to classify the approach taken by the studies in three different categories - 26 studies predicted cardiac arrest by analyzing specific parameters or variables of the patients while 16 studies developed an AI-based warning system. The rest of the 5 studies focused on distinguishing high-risk cardiac arrest patients from patients, not at risk. 2 studies focused on the pediatric population, and the rest focused on adults (n=45). The majority of the studies used datasets with a size of less than 10,000 (n=32). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (n=38) and the most used algorithm belonged to the neural network (n=23). K-Fold cross-validation was the most used algorithm evaluation tool reported in the studies (n=24). CONCLUSIONS : AI is extensively being used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in changing cardiac medicine for the better. There is a need for more reviews to learn the obstacles of implementing AI technologies in the clinical setting. Moreover, research focusing on how to best provide clinicians support to understand, adapt and implement the technology in their practice is also required.

scholarly journals Cefepime Pharmacokinetics in Critically Ill Pediatric Patients Receiving Continuous Renal Replacement Therapy

2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Gideon Stitt ◽  
Jennifer Morris ◽  
Lindsay Schmees ◽  
Joseph Angelo ◽  
Ayse Akcan Arikan
Keyword(s):  
Intensive Care Unit ◽  
Body Weight ◽  
Intensive Care ◽  
Retrospective Study ◽  
Critically Ill ◽  
Pediatric Patients ◽  
Pediatric Intensive Care ◽  
Free Drug ◽  
Therapeutic Drug ◽  
Free Drug Concentration

ABSTRACT This retrospective study included pediatric intensive care unit patients receiving continuous veno-venous hemodiafiltration (CVVHDF) being treated with cefepime. The free drug concentration above one time the MIC (fT>1×MIC) and four times a presumed MIC (fT>4×MIC) of 8 μg/ml were calculated. Four patients received doses ranging from 48 to 64 mg/kg of body weight every 6 to 12 h. Three patients achieved 100% fT>1×MIC, with the fourth patient achieving 98% fT>1×MIC. Therapeutic drug monitoring should be considered for critically ill patients receiving cefepime on CVVHDF.

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