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

Ward H van der Ven, Lotte E Terwindt, Nurseda Risvanoglu, Evy L K Ie, Marije Wijnberge, Denise P Veelo, Bart F Geerts, Alexander P J Vlaar, Björn J P van der Ster, Ward H van der Ven, Lotte E Terwindt, Nurseda Risvanoglu, Evy L K Ie, Marije Wijnberge, Denise P Veelo, Bart F Geerts, Alexander P J Vlaar, Björn J P van der Ster

Abstract

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.

Keywords: COVID-19; Hemodynamics; Hypotension; Intensive care unit; Machine-learning; Validation.

Conflict of interest statement

MW reports receipt of consultancy fees from Edwards Lifesciences outside the submitted work. DPV reports receipt of personal fees and other from Edwards Lifesciences and consultancy fees and grants from Philips and Hemologic outside the submitted work. BFG reports receipt of grants from Edwards Lifesciences and consultancy fees and grants from Philips outside the submitted work. APJV reports receipt of grants from Edwards Lifesciences and Philips outside the submitted work. WHV, LET, NR, ELKI, and BJPS declare that they have no competing interests.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Receiver operating characteristic curve with 95% CI of the HPI for prediction of hypotensive events. Hypotension is defined as a MAP AUC area under the curve, CI confidence interval, HPI Hypotension Prediction Index, MAP mean arterial pressure
Fig. 2
Fig. 2
Event rate with 95% CI in relation to incremental HPI thresholds. Dashed line represents the line of identity. CI confidence interval, HPI Hypotension Prediction Index

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