Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2019
Observational StudyComputerized tests to evaluate recovery of cognitive function after deep sedation with propofol and remifentanil for colonoscopy.
The use of sedation for diagnostic procedures including gastrointestinal endoscopy is rapidly growing. Recovery of cognitive function after sedation is important because it would be important for most patients to resume safe, normal life soon after the procedure. Computerized tests have shown being accurate descriptors of cognitive function. ⋯ Cognitive impairment in attention and psychomotor function after propofol and remifentanil sedation was significant and large and could be easily detected by computerized cognitive tests. Even though, patients were fully recovered 40 min after ending the procedure. From a cognitive recovery point of view, larger studies should be undertaken to propose adequate criteria for discharge after sedation.
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J Clin Monit Comput · Feb 2019
Observational StudyPractical impact of a decision support for goal-directed fluid therapy on protocol adherence: a clinical implementation study in patients undergoing major abdominal surgery.
The purpose of this study was to assess the effects of using a real time clinical decision-support system, "Assisted Fluid Management" (AFM), to guide goal-directed fluid therapy (GDFT) during major abdominal surgery. We compared a group of patients managed using the AFM system with a historical cohort of patients (control group) who had been managed using a manual GDFT strategy. Adherence to the protocol was defined as the relative intraoperative time spent with a stroke volume variation (SVV) < 13%. ⋯ The incidence of postoperative complications was comparable in the two groups. Implementation of a decision support system for GDFT guidance resulted in a significantly longer period during surgery with a SVV < 13% with a reduced total amount of fluid administered. Trial registration: Clinical Trials.gov (NCT03141411).
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J Clin Monit Comput · Feb 2019
Can postoperative deltoid weakness after cervical laminoplasty be prevented by using intraoperative neurophysiological monitoring?
Laminoplasty, frequently performed in patients with cervical myelopathy, is safe and provides relatively good results. However, motor palsy of the upper extremities, which occurs after decompression surgery for cervical myelopathy, often reduces muscle strength of the deltoid muscle, mainly in the C5 myotome. The aim of this study was to investigate prospectively whether postoperative deltoid weakness (DW) can be predicted by performing intraoperative neurophysiological monitoring (IONM) during cervical laminoplasty and to clarify whether it is possible to prevent palsy using IONM. ⋯ Persistent Br(E)-MsEP alerts of the deltoid muscle had a 100% sensitivity and specificity for predicting a postoperative acute deficit. IONM was unable to predict delayed-onset DW. In only 1 patient were we able to prevent postoperative DW by performing a foraminotomy.
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J Clin Monit Comput · Feb 2019
The intracranial pressure curve correlates to the pulsatile component of cerebral blood flow.
Current methods to measure cerebral blood flow (CBF) in the neuro critical care setting cannot monitor the CBF continuously. In contrast, continuous measurement of intracranial pressure (ICP) is readily accomplished, and there is a component of ICP that correlates with arterial inflow of blood into the cranial cavity. This property may have utility in using continuous ICP curve analysis to continuously estimate CBF. ⋯ In contrast there was a correlation between the AUCICP and ccCBFMRpuls (R2 = 0.440 P = 0.013). The AUCΔV correlated more appropriately with the ccCBFMRpuls. (R2 = 0.688 P < 0.001). Our findings suggests that the pulsatile part of the intracranial pressure curve, especially when transformed into a volume curve, correlates to the pulsatile part of the CBF.
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J Clin Monit Comput · Feb 2019
Predicting delayed cerebral ischemia after subarachnoid hemorrhage using physiological time series data.
To develop and validate a prediction model for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) using a temporal unsupervised feature engineering approach, demonstrating improved precision over standard features. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Baseline information and standard grading scales were evaluated: age, sex, Hunt Hess grade, modified Fisher Scale (mFS), and Glasgow Coma Scale (GCS). ⋯ Combined baseline and physiologic features with redundant feature reduction: AUC 0.77. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that our models achieve higher classification accuracy.