scholarly journals When it is oportune to avoid cesarean myomectomy? An analysis of possible factors influencing duration of treatment in the intensive care unit

2015 ◽  
Vol 62 (2) ◽  
pp. 71-76 ◽  
Author(s):  
Radmila Sparic ◽  
Jelena Stamenkovic ◽  
Lazar Nejkovic ◽  
Andrea Tinelli
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Postoperative Hemorrhage ◽  
Treatment Results ◽  
Duration Of Treatment ◽  
Factors Affecting ◽  
Duration Of Surgery ◽  
Cesarean Myomectomy ◽  
Perioperative Hemorrhage ◽  
Icu Treatment

Background: Cesarean myomectomy (CM) is a controversial issue, even relatively contraindicated in obstetric practice. Recent reports showed that CM is not associated with increased morbidity, but those are lacking the data about risks of intensive care unit (ICU) treatment. Aim: The authors evaluated the factors affecting the duration of ICU treatment in patients after CM. Material and Methods: The study included 57 women who underwent CM and were postoperatively admitted and treated in ICU. Correlation analysis was used to estimate the effect of various parameters on the duration of ICU treatment. Results: There was a highly significant correlation between duration of ICU treatment and number of postoperative transfusions (p=0.001), duration of surgery (p=0.007), intraoprative hemorrhage (p=0.008) and myoma diameter (p=0.009). Duration of ICU treatment was also correlated with gestational age, hypertensive syndrome in pregnancy, preoperative hematocrit and hemoglobin values, number of intraoperative transfusions, postoperative hemorrhage and repeated myomectomy. Conclusions: In our report, longer ICU tretment was required in cases of perioperative hemorrhage, prolonged surgeries and those requiring perioperative transfusion. Patients who have had previous myomectomy, with lower preoperative hemoglobine and hematocrit vaues and bigger myomas are at risk of prolonged ICU treatment.

Prevalence and Factors Affecting Postoperative Delirium in a Neurosurgical Intensive Care Unit

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Gulsah Kose ◽  
Keziban Şirin ◽  
Mehtap Balin Inel ◽  
Sevcan Mertoglu ◽  
Raziye Aksakal ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Postoperative Delirium ◽  
Factors Affecting ◽  
Neurosurgical Intensive Care ◽  
Neurosurgical Intensive Care Unit

Factors affecting outcomes in geriatric traumatic subdural hematoma in a neurosurgical intensive care unit

2021 ◽  
Author(s):  
Kunal P. Kanakia ◽  
Anne Marie Wells ◽  
Missak Tchoulhakian ◽  
Brian S. Iskra ◽  
Christian Kaculini ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Subdural Hematoma ◽  
Factors Affecting ◽  
Neurosurgical Intensive Care ◽  
Neurosurgical Intensive Care Unit

scholarly journals Residual Neuromuscular Blockade in Critical Care

2012 ◽  
Vol 32 (3) ◽  
pp. e1-e10 ◽  
Author(s):  
Jason Wilson ◽  
Angela S. Collins ◽  
Brea O. Rowan
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Critical Care ◽  
Intensive Care ◽  
Neuromuscular Blockade ◽  
Upper Airway ◽  
Surgical Interventions ◽  
Residual Neuromuscular Blockade ◽  
Factors Affecting ◽  
Anesthesia Time

Neuromuscular blockade is a pharmacological adjunct for anesthesia and for surgical interventions. Neuromuscular blockers can facilitate ease of instrumentation and reduce complications associated with intubation. An undesirable sequela of these agents is residual neuromuscular blockade. Residual neuromuscular blockade is linked to aspiration, diminished response to hypoxia, and obstruction of the upper airway that may occur soon after extubation. If an operation is particularly complex or requires a long anesthesia time, residual neuromuscular blockade can contribute to longer stays in the intensive care unit and more hours of mechanical ventilation. Given the risks of this medication class, it is essential to have an understanding of the mechanism of action of, assessment of, and factors affecting blockade and to be able to identify factors that affect pharmacokinetics.

Prediction Score for Postoperative Neurologic Complications after Brain Tumor Craniotomy

2018 ◽  
Vol 129 (6) ◽  
pp. 1111-1120 ◽  
Author(s):  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Brain Tumor ◽  
Predictive Value ◽  
Prediction Score ◽  
Motor Deficit ◽  
Outcome Variable ◽  
Neurologic Complications ◽  
University Hospitals ◽  
Duration Of Surgery

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Craniotomy for brain tumor displays significant morbidity and mortality, and no score is available to discriminate high-risk patients. Our objective was to validate a prediction score for postoperative neurosurgical complications in this setting. Methods Creation of a score in a learning cohort from a prospective specific database of 1,094 patients undergoing elective brain tumor craniotomy in one center from 2008 to 2012. The validation cohort was validated in a prospective multicenter independent cohort of 830 patients from 2013 to 2015 in six university hospitals in France. The primary outcome variable was postoperative neurologic complications requiring in–intensive care unit management (intracranial hypertension, intracranial bleeding, status epilepticus, respiratory failure, impaired consciousness, unexpected motor deficit). The least absolute shrinkage and selection operator method was used for potential risk factor selection with logistic regression. Results Severe complications occurred in 125 (11.4%) and 90 (10.8%) patients in the learning and validation cohorts, respectively. The independent risk factors for severe complications were related to the patient (Glasgow Coma Score before surgery at or below 14, history of brain tumor surgery), tumor characteristics (greatest diameter, cerebral midline shift at least 3 mm), and perioperative management (transfusion of blood products, maximum and minimal systolic arterial pressure, duration of surgery). The positive predictive value of the score at or below 3% was 12.1%, and the negative predictive value was 100% in the learning cohort. In–intensive care unit mortality was observed in eight (0.7%) and six (0.7%) patients in the learning and validation cohorts, respectively. Conclusions The validation of prediction scores is the first step toward on-demand intensive care unit admission. Further research is needed to improve the score’s performance before routine use.

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