Journal of clinical monitoring and computing
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J Clin Monit Comput · Aug 2019
Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.
A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients' physiology when analysed in detail can however be predictive of acute deterioration leading to cardiac or respiratory arrests. ⋯ A positive predictive value of 11% and negative predictive value of 98% was obtained with a prevalence of 5% by our method of prediction. While clinicians predicted 4 out of the 69 cardiac arrests (6%), the prediction system predicted 63 (91%) cardiac arrests. Prospective validation of the automated system remains.
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J Clin Monit Comput · Aug 2019
Selection of cuffed endotracheal tube for children with congenital heart disease based on an ultrasound-based linear regression formula.
It remains to be discovered whether a formula predicting the subglottic transverse diameter measured by ultrasound (SGDformula) for the selection of an appropriate endotracheal tube (ETT) for children without congenital heart disease (CHD) is useful for children with CHD. A formula for predicting SGD was established after assessing 60 children ≤ 8 years without CHD and validated on 60 children with CHD. We selected the cuffed ETT size based on the SGD by ultrasound (SGDultra). ⋯ And the mean bias (SGDformula-ETT size and SGDultra-ETT size) was 0.21 mm (95% confidence interval, - 0.59 to 1.01 mm) and 0.00 mm (- 0.79 to 0.84 mm). For the CHD group, the ultrasound-based method yielded a 78% success rate of ETT size choice, while the formula-based method permitted an appropriate ETT size in only 32% of subjects (P < 0.001). Our analysis showed that measuring the SGDultra was more accurate in predicting the correct OD of the ETT in children with CHD undergoing cardiovascular surgery, based on the correlation and agreement with ETT OD.
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J Clin Monit Comput · Aug 2019
Comparative Study Observational StudyA comparison of propofol-to-BIS post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods: an observational, open-label pilot study.
We evaluated the feasibility and robustness of three methods for propofol-to-bispectral index (BIS) post-operative intensive care sedation, a manually-adapted target controlled infusion protocol (HUMAN), a computer-controlled predictive control strategy (EPSAC) and a computer-controlled Bayesian rule-based optimized control strategy (BAYES). ⋯ Both computer-based control systems are feasible to be used during ICU sedation with overall tighter control than HUMAN and even with lower required CePROP. EPSAC control required higher CeREMI than BAYES or HUMAN to maintain stable control. Clinical trial number: NCT00735631.
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J Clin Monit Comput · Aug 2019
Letter Case ReportsLimitations of near infrared spectroscopy (NIRS) in neurosurgical setting: our case experience.
One of the primary goals of anaesthesia in neurosurgical procedures is prevention of cerebral hypoxia leading to secondary neurological injury. Cerebral oximetry detects periods of cerebral hypoxemia and allows intervention for prevention of secondary brain injury and its sequelae. This can be achieved by the use of Near Infrared Spectroscopy (NIRS). ⋯ In a neurosurgical setting, the erroneous values on the operative side could be attributed to altered tissue boundary conditions resulting in a changed optical path, which is normally held as a constant in NIRS measurements. The altered tissue boundary conditions could be due to the presence of air or blood between the myocutaneous flapskull, skull-dura, dura-brain interphases. It could also be that the sensors' penetrating depth was inadequate to compensate for the increased distance between sensor and brain tissue, thereby resulting in inaccurately higher values (> 80%).
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J Clin Monit Comput · Aug 2019
Mathematical arterialisation of peripheral venous blood gas for obtainment of arterial blood gas values: a methodological validation study in the clinical setting.
Arterial blood gas (ABG) analysis is an essential tool in the clinical assessment of acutely ill patients. Venous to arterial conversion (v-TAC), a mathematical method, has been developed recently to convert peripheral venous blood gas (VBG) values to arterialized VBG (aVBG) values. The aim of this study was to test the validity of aVBG compared to ABG in an emergency department (ED) setting. ⋯ Bland-Altman plot revealed clinically acceptable mean difference and limits-of-agreement intervals between ABG and aVBG pH and pCO2, but not between ABG and aVBG pO2. Arterialization of VBG using v-TAC is a valid method for measuring pH and pCO2, but not for pO2. Larger clinical studies are required to evaluate the applicability of v-TAC in different patient subpopulations.