Evaluation of pre-induction dynamic arterial elastance as an adjustable predictor of post-induction hypotension: A prospective observational study.

STUDY OBJECTIVE: Dynamic arterial elastance (Eadyn) has been suggested as a functional measure of arterial load. We aimed to evaluate whether pre-induction Eadyn can predict post-induction hypotension. DESIGN: Prospective observational study. PATIENTS: Adult patients undergoing general anesthesia with invasive and non-invasive arterial pressure monitoring systems. MEASUREMENTS: We collected invasive and non-invasive Eadyns (n = 38 in each), respectively. In both invasive and non-invasive Eadyns, pre-induction Eadyns were obtained during one-minute tidal and deep breathing in each patient before anesthetic induction. Post-induction hypotension was defined as a decrease of >30% in mean blood pressure from the baseline value or any absolute mean blood pressure value of <65 mmHg for 10 min after anesthetic induction. The predictabilities of Eadyns for the development of post-induction hypotension were tested using receiver-operating characteristic curve analysis. MAIN RESULTS: Invasive Eadyn during deep breathing showed significant predictability with an area under the curve (AUC) of 0.78 (95% Confidence interval [CI], 0.61-0.90, P = 0.001). But non-invasive Eadyn during tidal breathing (AUC = 0.66, 95% CI, 0.49-0.81, P = 0.096) and deep breathing (AUC = 0.53, 95% CI, 0.36-0.70, P = 0.75), and invasive Eadyn during tidal breathing (AUC = 0.66, 95% CI, 0.41-0.74, P = 0.095) failed to predict post-induction hypotension. CONCLUSION: In our study, invasive pre-induction Eadyn during deep breathing -could predict post-induction hypotension. Despite its invasiveness, future studies will be needed to evaluate the usefulness of Eadyn as a predictor of post-induction hypotension because it is an adjustable parameter.

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. Predictive ability was evaluated at HPI thresholds from 0 to 100, at incremental intervals of 5. After exceeding the studied threshold, the next 20 min were screened for positive (mean arterial pressure (MAP) < 65 mmHg for at least 1 min) or negative (absence of MAP < 65 mmHg for at least 1 min) events. Subsequently, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and time to event were determined for every threshold. Almost all patients (93%) experienced at least one hypotensive event. Median number of events was 21 [7-54] and time spent in hypotension was 114 min [20-303]. 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.