Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms

Wijnberge, Marije; Kim, Yu-Sok; Geerts, Bart F.; Beer, Friso M. de; Beurskens, Charlotte J. P.; Emal, Dina; Hollmann, Markus W.; Vlaar, Alexander P.J.; Veelo, Denise P.

doi:10.1097/eja.0000000000001521

Cited by 26 publications

(31 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A possible explanation is that we applied a different type of analysis, compared to previous trials. As described in detail by Wijnberge et al [ 22 ], the forward analysis also performed in this study, relates better to clinical practice since an increased HPI value is the starting point, rather than the hypotensive event itself. Differences in the analysis could have led us to label certain timeframes differently than we would have in the case of a backward analysis, which presumably yields other results.…”

Section: Discussionmentioning

confidence: 92%

“…To date, performance of the HPI has been externally and clinically validated in five observational studies and three randomized controlled trials (RCTs) [ 28 ]. In non-cardiac surgical patients, external validation has been done on invasive [ 23 ] and non-invasive [ 22 , 24 ] arterial waveform data. All these three trials reported sensitivity and specificity > 85%, based on HPI values 5 min prior to the event (in the case of a backward analysis), or based on an HPI value of 85 (in the case of a forward analysis).…”

Section: Discussionmentioning

confidence: 99%

“…The predictive ability of the HPI was determined using a forward analysis similar to Wijnberge et al [ 22 ], starting with an HPI value exceeding the studied threshold for at least 1 min, after which the next 20 min following the crossing of the threshold were screened for positive (hypotensive) events. Positive events were defined as a MAP < 65 mmHg for at least 1 min.…”

Section: Methodsmentioning

confidence: 99%

“…These measurements would otherwise have been classified as false positives, unjustly reducing the predictive ability of the HPI. Therefore, data with an increase in MAP of ≥ 5 mmHg within 20 s or an increase in MAP of ≥ 8 mmHg within 2 min, starting from a baseline MAP < 70 mmHg, were censored, in accordance with earlier studies [ 17 , 22 – 24 ].…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

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

Ven

Terwindt

Risvanoglu

et al. 2021

J Clin Monit Comput

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

Ven

Terwindt

Risvanoglu

et al. 2021

J Clin Monit Comput

Self Cite

View full text Add to dashboard Cite

show abstract

“…Firstly, we showed that HIF-1α is not significantly up-regulated in both groups and is not dependent on the number and duration of hypotensive events. Subunit α is oxygensensitive, during normoxia is associated with von Hippel-Lindau (VHL) protein, which is responsible for the induction of its proteasome degradation [18,19]. Therefore, in normoxia, the half lifetime of HIF-1α is greatly shorted than in hypoxia conditions [20], as low pressure of oxygen is responsible for blocking the binding of VHL and HIF-1α and its degradation is then inhibited [21].…”

Section: Discussionmentioning

confidence: 99%

Proactive Management of Intraoperative Hypotension Reduces Biomarkers of Organ Injury and Oxidative Stress during Elective Non-Cardiac Surgery: A Pilot Randomized Controlled Trial

Murabito

Astuto

Sanfilippo

et al. 2022

JCM

View full text Add to dashboard Cite

Background: Intraoperative hypotension is associated with increased postoperative morbidity and mortality. Methods: We randomly assigned patients undergoing major general surgery to early warning system (EWS) and hemodynamic algorithm (intervention group, n = 20) or standard care (n = 20). The primary outcome was the difference in hypotension (defined as mean arterial pressure < 65 mmHg) and as secondary outcome surrogate markers of organ injury and oxidative stress. Results: The median number of hypotensive episodes was lower in the intervention group (−5.0 (95% CI: −9.0, −0.5); p < 0.001), with lower time spent in hypotension (−12.8 min (95% CI: −38.0, −2.3 min); p = 0.048), correspondent to −4.8% of total surgery time (95% CI: −12.7, 0.01%; p = 0.048).The median time-weighted average of hypotension was 0.12 mmHg (0.35) in the intervention group and 0.37 mmHg (1.11) in the control group, with a median difference of −0.25 mmHg (95% CI: −0.85, −0.01; p = 0.025). Neutrophil Gelatinase-Associated Lipocalin (NGAL) correlated with time-weighted average of hypotension (R = 0.32; p = 0.038) and S100B with number of hypotensive episodes, absolute time of hypotension, relative time of hypotension and time-weighted average of hypotension (p < 0.001 for all). The intervention group showed lower Neuronal Specific Enolase (NSE) and higher reduced glutathione when compared to the control group. Conclusions: The use of an EWS coupled with a hemodynamic algorithm resulted in reduced intraoperative hypotension, reduced NSE and oxidative stress.

show abstract

Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery

Frassanito

Giuri

Vassalli

et al. 2021

J Clin Monit Comput

View full text Add to dashboard Cite

Intraoperative hypotension (IOH) is common during major surgery and is associated with a poor postoperative outcome. Hypotension Prediction Index (HPI) is an algorithm derived from machine learning that uses the arterial waveform to predict IOH. The aim of this study was to assess the diagnostic ability of HPI working with non-invasive ClearSight system in predicting impending hypotension in patients undergoing major gynaecologic oncologic surgery (GOS). In this retrospective analysis hemodynamic data were downloaded from an Edwards Lifesciences HemoSphere platform and analysed. Receiver operating characteristic curves were constructed to evaluate the performance of HPI working on the ClearSight pressure waveform in predicting hypotensive events, defined as mean arterial pressure < 65 mmHg for > 1 min. Sensitivity, specificity, positive predictive value and negative predictive value were computed at a cutpoint (the value which minimizes the difference between sensitivity and specificity). Thirty-one patients undergoing GOS were included in the analysis, 28 of which had complete data set. The HPI predicted hypotensive events with a sensitivity of 0.85 [95% confidence interval (CI) 0.73–0.94] and specificity of 0.85 (95% CI 0.74–0.95) 15 min before the event [area under the curve (AUC) 0.95 (95% CI 0.89–0.99)]; with a sensitivity of 0.82 (95% CI 0.71–0.92) and specificity of 0.83 (95% CI 0.71–0.93) 10 min before the event [AUC 0.9 (95% CI 0.83–0.97)]; and with a sensitivity of 0.86 (95% CI 0.78–0.93) and specificity 0.86 (95% CI 0.77–0.94) 5 min before the event [AUC 0.93 (95% CI 0.89–0.97)]. HPI provides accurate and continuous prediction of impending IOH before its occurrence in patients undergoing GOS in general anesthesia.

show abstract

Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms

Cited by 26 publications

References 28 publications

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

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

Proactive Management of Intraoperative Hypotension Reduces Biomarkers of Organ Injury and Oxidative Stress during Elective Non-Cardiac Surgery: A Pilot Randomized Controlled Trial

Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery

Contact Info

Product

Resources

About