Journal of clinical monitoring and computing
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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.
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J Clin Monit Comput · Feb 2019
Real-time, spectral analysis of the arterial pressure waveform using a wirelessly-connected, tablet computer: a pilot study.
Spectral analysis of the arterial pressure waveform, using specialized hardware, has been used for the retrospective calculation of the 'Spectral Peak Ratio' (SPeR) of the respiratory and cardiac arterial spectral peaks. The metric can quantify the cardiovascular response to volume loading by analysing the effect of changing tidal volume (indexed to body weight) (VTI) on pulse pressure variability. In this pilot study, the feasibility of real-time SPeR calculation, using a mobile computer which was wirelessly connected to the patient monitor, was evaluated by examining the determinants of SPeR in 60 cardiac-surgical patients. ⋯ Real-time spectral analysis of the arterial waveform was easily accomplished. The regression of SPeR on VTI was linear. β appeared to represent the slope of the cardiac response curve at the venous return curve equilibrium point. Measurements were possible at a significantly lower VTI than the equivalent time domain metrics.
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J Clin Monit Comput · Feb 2019
Multicenter StudyForewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.
Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this study was to get proof of the concept that advanced statistical techniques (machine learning) are methods that are able to provide early warning of impending hypotensive events before they occur during neuro-critical care. ⋯ With a decision threshold of 0.3, > 15 min warning of patient instability can be achieved. We have shown, using advanced machine learning techniques running in a live neuro-critical care environment, that it would be possible to give neurointensive teams early warning of potential hypotensive events before they emerge, allowing closer monitoring and earlier clinical assessment in an attempt to prevent the onset of hypotension. The multi-centre clinical infrastructure developed to support the clinical studies provides a solid base for further collaborative research on data quality, false positive correction and the display of early warning data in a clinical setting.
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J Clin Monit Comput · Feb 2019
Observational StudyQuantitative computed tomography in comparison with transpulmonary thermodilution for the estimation of pulmonary fluid status: a clinical study in critically ill patients.
Extravascular lung water (index) (EVLW(I)) can be estimated using transpulmonary thermodilution (TPTD). Computed tomography (CT) with quantitative analysis of lung tissue density has been proposed to quantify pulmonary edema. We compared variables of pulmonary fluid status assessed using quantitative CT and TPTD in critically ill patients. ⋯ There was no significant correlation between TVI and EVLWI before CT, EVLWI after CT, or mean EVLWI. CT-derived variables did not predict elevated TPTD-derived EVLWI values. In unselected critically ill patients, variables of pulmonary fluid status assessed using quantitative CT cannot be used to predict EVLWI.
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J Clin Monit Comput · Feb 2019
Finger and forehead photoplethysmography-derived pulse-pressure variation and the benefits of baseline correction.
To non-invasively predict fluid responsiveness, respiration-induced pulse amplitude variation (PAV) in the photoplethysmographic (PPG) signal has been proposed as an alternative to pulse pressure variation (PPV) in the arterial blood pressure (ABP) signal. However, it is still unclear how the performance of the PPG-derived PAV is site-dependent during surgery. The aim of this study is to compare finger- and forehead-PPG derived PAV in their ability to approach the value and trend of ABP-derived PPV. ⋯ By correcting for the baseline variation, improved agreements were obtained for both the finger and forehead, and the difference between these two agreements was diminished. The tracking abilities for both finger- and forehead-derived PAV still warrant improvement for wide use in clinical practice. Overall, our results show that baseline-corrected finger- and forehead-derived PAV may provide a non-invasive alternative for PPV.