Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2022
In-silico analysis of closed-loop vasopressor control of phenylephrine versus norepinephrine.
We have previously demonstrated in in-silico, pre-clinical animal models, and finally human clinical studies the ability of a novel closed-loop vasopressor titration system to manage norepinephrine infusion rates to keep mean arterial blood pressure in a very tight range, reduce hypotension time and severity, and reduce overtreatment. We hypothesized that the same controller could, with modification for pharmacologic differences, suitably titrate a lower-potency longer duration of action agent like phenylephrine. Using the same physiologic simulation model as was used previously for in-silico testing of our controller for norepinephrine, we first updated the model to include a new vasopressor agent modeled after phenylephrine. ⋯ The controller kept the simulated patients' MAP in target for 94% of management time in the simple scenarios and more than 85% of time in the most challenging scenarios. Varvel criteria were all under 1% error for expected pharmacologic responses and were consistent with those established for norepinephrine in our previous studies. The controller was able to acceptably titrate phenylephrine in this simulated patient model consistent with performance previously seen for norepinephrine after adjusting for the anticipated differences between the two agents.
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J Clin Monit Comput · Oct 2022
Evaluation of a new smartphone optical blood pressure application (OptiBP™) in the post-anesthesia care unit: a method comparison study against the non-invasive automatic oscillometric brachial cuff as the reference method.
We compared blood pressure (BP) values obtained with a new optical smartphone application (OptiBP™) with BP values obtained using a non-invasive automatic oscillometric brachial cuff (reference method) during the first 2 h of surveillance in a post-anesthesia care unit in patients after non-cardiac surgery. Three simultaneous BP measurements of both methods were recorded every 30 min over a 2-h period. The agreement between measurements was investigated using Bland-Altman and error grid analyses. ⋯ We observed a good agreement between BP values obtained by the OptiBP™ system and BP values obtained with the reference method. The OptiBP™ system fulfilled the AAMI validation requirements for MAP and DAP and error grid analysis indicated that the vast majority of measurement pairs (≥ 99%) were in risk zones A and B. Trial Registration ClinicalTrials.gov Identifier: NCT04262323.
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J Clin Monit Comput · Oct 2022
Accuracy of pulse pressure variations for fluid responsiveness prediction in mechanically ventilated patients with biphasic positive airway pressure mode.
The accuracy of pulse pressure variation (PPV) to predict fluid responsiveness using pressure-controlled (PC) instead of volume-controlled modes is under debate. To specifically address this issue, we designed a study to evaluate the accuracy of PPV to predict fluid responsiveness in severe septic patients who were mechanically ventilated with biphasic positive airway pressure (BIPAP) PC-ventilation mode. 45 patients with sepsis or septic shock and who were mechanically ventilated with BIPAP mode and a target tidal volume of 7-8 ml/kg were included. PPV was automatically assessed at baseline and after a standard fluid challenge (Ringer's lactate 500 ml). ⋯ Using a gray zone approach, we identified that PPV values comprised between 5 and 15% do not allow a reliable fluid responsiveness prediction. In critically ill septic patients ventilated under BIPAP mode, PPV appears to be an accurate method for fluid responsiveness prediction. However, PPV values comprised between 5 and 15% constitute a gray zone that does not allow a reliable fluid responsiveness prediction.
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J Clin Monit Comput · Oct 2022
Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study.
The Hypotension Prediction Index (HPI) is a commercially available machine-learning algorithm that provides warnings for impending hypotension, based on real-time arterial waveform analysis. The HPI was developed with arterial waveform data of surgical and intensive care unit (ICU) patients, but has never been externally validated in the latter group. In this study, we evaluated diagnostic ability of the HPI with invasively collected arterial blood pressure data in 41 patients with COVID-19 admitted to the ICU for mechanical ventilation. ⋯ The optimal threshold was 90, with a sensitivity of 0.91 (95% confidence interval 0.81-0.98), specificity of 0.87 (0.81-0.92), PPV of 0.69 (0.61-0.77), NPV of 0.99 (0.97-1.00), and median time to event of 3.93 min (3.72-4.15). Discrimination ability of the HPI was excellent, with an area under the curve of 0.95 (0.93-0.97). This validation study shows that the HPI correctly predicts hypotension in mechanically ventilated COVID-19 patients in the ICU, and provides a basis for future studies to assess whether hypotension can be reduced in ICU patients using this algorithm.