Use of the Hypotension Prediction Index During Cardiac Surgery

Shin, Brian; Maler, Steven A.; Reddy, K. R. Madhusudan; Fleming, Neal

doi:10.1053/j.jvca.2020.12.025

Cited by 38 publications

(30 citation statements)

References 15 publications

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“…This is supported by the hypotension statistics reported in this study. In our cohort, relatively little hypotension was reported (median 3% of case time, versus 6–11% in surgical patients) [ 17 , 23 , 27 ], which probably results in fewer FN predictions of the HPI, thus leading to a high sensitivity for almost all studied thresholds. The optimal HPI threshold is therefore mainly determined by the reported specificity, which gradually increased towards higher HPI values, due to fewer FP predictions of the HPI in those regions.…”

Section: Discussionmentioning

confidence: 98%

“…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). Furthermore, HPI performance has been evaluated during cardiac surgery, where it performed moderate to good, with sensitivity of 0.62–0.84 and specificity of 0.78–0.84 [ 27 , 29 ]. These promising results have provided a basis for three randomized clinical trials, to study whether prediction could result in a (clinically relevant) reduction of intra-operative hypotension.…”

Section: Discussionmentioning

confidence: 99%

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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

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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.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

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

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“…Future research should focus on these real-time applications of ML exploring patterns in complex datasets. Then promising results can be achieved, as demonstrated by the effective hypotension early warning system by Hatib et al (48,49) and the automation of echocardiography in two other studies (51,52). Not only aimed at the development and validation of such models but also their clinical effectiveness in randomized controlled trials should be addressed.…”

Section: Discussionmentioning

confidence: 99%

“…The model was developed using large datasets, including cardiac surgery patients. A recently published study demonstrated the high predictive capability of the HPI solely in cardiac surgery (49). ML can also be used to identify relationships between risks, as demonstrated © Journal of Thoracic Disease.…”

Section: Predictions Of Hemodynamic Instabilitymentioning

confidence: 96%

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Rellum¹,

Schuurmans²,

Ven³

et al. 2021

J Thorac Dis

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Background: Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. Methods: We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. Results: Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. Conclusions: ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.

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“…There is growing evidence regarding the performance of the HPI applied to invasive arterial waveforms in large cohort of patients [21,22]. More recently, this technology has been applied to non-invasive arterial pressure waveforms derived from ClearSight [18,19].…”

Section: Hpi Applied To Invasive and Non-invasive Arterial Waveform: Any Difference?mentioning

confidence: 99%

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

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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.

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Use of the Hypotension Prediction Index During Cardiac Surgery

Cited by 38 publications

References 15 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

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

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

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