Journal of clinical monitoring and computing
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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
Lag times to steady state drug delivery by continuous intravenous infusion: direct comparison of peristaltic and syringe pump performance identifies contributions from infusion system dead volume and pump startup characteristics.
Time lags between the initiation of a continuous drug infusion and achievement of a steady state delivery rate present an important safety concern. At least 3 factors contribute to these time lags: (1) dead volume size, (2) the ratio between total system flow and dead volume, and (3) startup delay. While clinicians employ both peristaltic pumps and syringe pumps to propel infusions, there has been no head-to-head comparison of drug delivery between commercially available infusion pumps with these distinct propulsion mechanisms. ⋯ Startup delay and dead volume in carrier-based infusion systems cause substantial time lags to reaching intended delivery rates. Peristaltic and syringe pumps are similarly susceptible to dead volume effects. Startup performance differed between peristaltic and syringe pumps; their relative performance may be dependent on flow rate.
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J Clin Monit Comput · Oct 2022
VACuum INtubation (VACcIN) box restricts the exhaled air dispersion generated by simulated cough: description and simulation-based tests of an innovative aerosolization protective prototype.
The COVID-19 pandemic has caused personal protective equipment shortages worldwide and required healthcare workers to develop novel ways of protecting themselves. Anesthesiologists in particular are exposed to increased risks of contamination when performing interventions such as airway manipulations. We developed and tested an aerosolization protective device which contains aerosols around the patient's airway and helps eliminate particles using negative pressure. ⋯ One minute following simulated cough, the mean number of particles per cubic foot in our box with suction on is around 45% that with the suction off (1,462,373 vs 3,272,080, P < 0.0001) in the negative pressure room, and four times lower than with the suction off (760,380 vs 3,088,700, P < 0.0001) in the positive pressure room. After a simulated cough inside the box, particles can be detected in front of the anesthesiologist's face with a non-airtight device, while none are detected when our box is sealed and its suction turned on. The use of our negative pressure intubation box prevents contamination of surroundings and increases particle elimination, regardless of room pressure.
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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.
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J Clin Monit Comput · Aug 2022
Multicenter StudyPrediction of blood lactate values in critically ill patients: a retrospective multi-center cohort study.
Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict subsequent serum lactate changes. ⋯ The LSTM models were able to predict deterioration of serum lactate values of MIMIC-III patients with an AUC of 0.77 (95% CI 0.762-0.771) for the normal group, 0.77 (95% CI 0.768-0.772) for the mild group, and 0.85 (95% CI 0.840-0.851) for the severe group, with only a slightly lower performance in the external validation. The LSTM demonstrated good discrimination of patients who had deterioration in serum lactate levels. Clinical studies are needed to evaluate whether utilization of a clinical decision support tool based on these results could positively impact decision-making and patient outcomes.