Journal of clinical monitoring and computing
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J Clin Monit Comput · Aug 2017
Automated, continuous and non-invasive assessment of pulse pressure variations using CNAP(®) system.
Non-invasive respiratory variations in arterial pulse pressure using infrared-plethysmography (PPVCNAP) are able to predict fluid responsiveness in mechanically ventilated patients. However, they cannot be continuously monitored. The present study evaluated a new algorithm allowing continuous measurements of PPVCNAP (PPVCNAPauto) (CNSystem, Graz, Austria). ⋯ A 15 % baseline PPVCNAPauto threshold discriminated responders with a sensitivity of 76% (95 % CI 53-92 %) and a specificity of 93 % (95 % CI 66-99 %). Area under the ROC curves for PPVCNAPauto was 0.91 (95 % CI 0.76-0.98), which was not different from that for PPVART. When compared with PPVART, PPVCNAPauto performs satisfactorily in assessing fluid responsiveness in hemodynamically stable surgical patients.
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J Clin Monit Comput · Aug 2017
Observational StudyUsing extra systoles to predict fluid responsiveness in cardiothoracic critical care patients.
Fluid responsiveness prediction is an unsettled matter for most critical care patients and new methods relying only on the continuous basic monitoring are desired. It was hypothesized that the post-ectopic beat, which is associated with increased preload, could be analyzed in relation to preceding sinus beats and that the change in cardiac performance (e.g. systolic blood pressure) at the post-ectopic beat could predict fluid responsiveness. Cardiothoracic critical care patients scheduled for a 500 ml volume expansion were observed. ⋯ The change in pre-ejection period predicted fluid responsiveness in 22 patients correctly with 67 % specificity and 83 % sensitivity (optimal threshold: 19 ms pre-ejection period decrease), ROC area: 0.81 (CI [0.66;0.96]). Pulse pressure variation had ROC area of 0.57 (CI [0.39;0.75]). Based on standard critical care monitoring, analysis of the extra systolic post-ectopic beat predicts fluid responsiveness in cardiothoracic critical care patients with good accuracy.
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J Clin Monit Comput · Aug 2017
Reliability of cardiac output measurements using LiDCOrapid™ and FloTrac/Vigileo™ across broad ranges of cardiac output values.
Knowing a patient's cardiac output (CO) could contribute to a safe, optimized hemodynamic control during surgery. Precise CO measurements can serve as a guide for resuscitation therapy, catecholamine use, differential diagnosis, and intervention during a hemodynamic crisis. Despite its invasiveness and intermittent nature, the thermodilution technique via a pulmonary artery catheter (PAC) remains the clinical gold standard for CO measurements. ⋯ An F test revealed no significant difference in the widths of the LoA for both devices when sample sizes capable of detecting a more than two-fold difference were used. We found that both devices tended to underestimate the calculated CIs when the CIs were relatively high. These proportional bias produced large percentage errors in the present study.
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J Clin Monit Comput · Aug 2017
Gradient adjustment method for better discriminating correlating and non-correlating regions of physiological signals: application to the partitioning of impaired and intact zones of cerebral autoregulation.
Cerebral blood flow (CBF) is regulated over a range of systemic blood pressures by the cerebral autoregulation (CA) control mechanism. This range lies within the lower and upper limits of autoregulation (LLA, ULA), beyond which blood pressure drives CBF, and CA function is considered impaired. A standard method to determine autoregulation limits noninvasively using NIRS technology is via the COx measure: a moving correlation index between mean arterial pressure and regional oxygen saturation. ⋯ It is shown that the derived GACOx indices exhibit a mean difference between the intact/impaired regions of 1.54 ± 0.26 (mean ± SD), compared to 0.14 ± 0.10 for the traditional COx method. The GACOx effectively polarizes the COx data in order to better differentiate the intact and impaired zones and, in doing so, makes the determination of the LLA and ULA points a simpler and more consistent task. The method lends itself to the automation of the robust determination of autoregulation zone limits.