Anesthesiology
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The Hypotension Prediction Index is designed to timely predict intraoperative hypotension and is based on arterial waveform analysis using machine learning. It has recently been suggested that this algorithm is highly correlated with the mean arterial pressure (MAP) itself. Therefore, the aim of this study was to compare the Index with MAP based prediction methods and it is hypothesized that their ability to predict hypotension is comparable. ⋯ In clinical practice, the Hypotension Prediction Index alarms are highly similar to those derived from MAP, which implies that the machine learning algorithm could be substituted by an alarm based on a MAP threshold set at 72 or 73 mmHg. Further research on intraoperative hypotension prediction should therefore include comparison with MAP based alarms and related effects on patient outcome.
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The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiological changes that may lead to hypotension. The original validation used a case control (backwards) analysis that has been suggested to be biased. We therefore conducted a cohort (forwards) analysis and compared this to the original validation technique. ⋯ Using an updated methodology, we found the utility of the HPI index to predict future hypotensive events is high, with an area under the receiver-operating-characteristics curve similar to that of the original validation method.
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Little is known about the pharmacodynamic characteristics of liposomal bupivacaine. Hypothesizing that we would not identify pharmacodynamic differences from plain bupivacaine during the initial period after administration, but would find better long-term pharmacodynamic characteristics, we designed a randomized, controlled, triple-blinded, single-center study in volunteers. ⋯ Our results show that liposomal bupivacaine is not a suitable 'sole' drug for intraoperative regional anesthesia. Findings of its limited long-term efficacy add to existing evidence that a moderate effect, at best, should be expected on postoperative pain therapy.
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Pain that accompanies deafferentation is one of the most mysterious and misunderstood medical conditions. Prevalence rates for the assorted conditions vary considerably but the most reliable estimates are greater than 50% for strokes involving the somatosensory system, brachial plexus avulsions, spinal cord injury, and limb amputation, with controversy surrounding the mechanistic contributions of deafferentation to ensuing neuropathic pain syndromes. Deafferentation pain has also been described for loss of other body parts (e.g., eyes and breasts) and may contribute to between 10% and upwards of 30% of neuropathic symptoms in peripheral neuropathies. ⋯ Due in part to the concurrent morbidities, the physical, psychologic, and by extension socioeconomic costs of disorders associated with deafferentation are higher than for other chronic pain conditions. Treatment is symptom-based, with evidence supporting first-line antineuropathic medications such as gabapentinoids and antidepressants. Studies examining noninvasive neuromodulation and virtual reality have yielded mixed results.