Performance of the Hypotension Prediction Index with non-invasive arterial pressure waveforms in non-cardiac surgical patients
| Autor: | Barak Cohen, Jos J. Settels, Sai Buddi, Zhongping Jian, Daniel I. Sessler, Tetsuya Shimada, Feras Hatib, Kamal Maheshwari |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Adult
Mean arterial pressure medicine.medical_specialty Hemodynamics Health Informatics Critical Care and Intensive Care Medicine Sensitivity and Specificity Machine Learning 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests 030202 anesthesiology Anesthesiology Internal medicine medicine Humans Waveform Arterial Pressure Intraoperative hypotension Original Research Receiver operating characteristic business.industry Hypotension prediction 030208 emergency & critical care medicine Non-invasive blood pressure Middle Aged Confidence interval Anesthesiology and Pain Medicine Blood pressure Cardiology Hypotension Prediction Index Hypotension business Surgical patients |
| Zdroj: | Journal of Clinical Monitoring and Computing |
| ISSN: | 1573-2614 1387-1307 |
| Popis: | An algorithm derived from machine learning uses the arterial waveform to predict intraoperative hypotension some minutes before episodes, possibly giving clinician’s time to intervene and prevent hypotension. Whether the Hypotension Prediction Index works well with noninvasive arterial pressure waveforms remains unknown. We therefore evaluated sensitivity, specificity, and positive predictive value of the Index based on non-invasive arterial waveform estimates. We used continuous hemodynamic data measured from ClearSight (formerly Nexfin) noninvasive finger blood pressure monitors in surgical patients. We re-evaluated data from a trial that included 320 adults ≥ 45 years old designated ASA physical status 3 or 4 who had moderate-to-high-risk non-cardiac surgery with general anesthesia. We calculated sensitivity and specificity for predicting hypotension, defined as mean arterial pressure ≤ 65 mmHg for at least 1 min, and characterized the relationship with receiver operating characteristics curves. We also evaluated the number of hypotensive events at various ranges of the Hypotension Prediction Index. And finally, we calculated the positive predictive value for hypotension episodes when the Prediction Index threshold was 85. The algorithm predicted hypotension 5 min in advance, with a sensitivity of 0.86 [95% confidence interval 0.82, 0.89] and specificity 0.86 [0.82, 0.89]. At 10 min, the sensitivity was 0.83 [0.79, 0.86] and the specificity was 0.83 [0.79, 0.86]. And at 15 min, the sensitivity was 0.75 [0.71, 0.80] and the specificity was 0.75 [0.71, 0.80]. The positive predictive value of the algorithm prediction at an Index threshold of 85 was 0.83 [0.79, 0.87]. A Hypotension Prediction Index of 80–89 provided a median of 6.0 [95% confidence interval 5.3, 6.7] minutes warning before mean arterial pressure decreased to |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink