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

Luciano Frassanito, Pietro Paolo Giuri, Francesco Vassalli, Alessandra Piersanti, Alessia Longo, Bruno Antonio Zanfini, Stefano Catarci, Anna Fagotti, Giovanni Scambia, Gaetano Draisci, Luciano Frassanito, Pietro Paolo Giuri, Francesco Vassalli, Alessandra Piersanti, Alessia Longo, Bruno Antonio Zanfini, Stefano Catarci, Anna Fagotti, Giovanni Scambia, Gaetano Draisci

Abstract

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.

Keywords: Gynaecologic Oncologic Surgery; Hemodynamic monitoring; Hypotension prediction; Intraoperative hypotension; Machine learning; Volume clamp method.

Conflict of interest statement

The authors declare that they have no competing interests in this section.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
A The non-invasive ClearSight finger cuff. B Volume clamp—vascular unloading technique. The inflatable finger cuff measures the diameter of the finger artery with an integrated infrared transmission plethysmograph. This leads to high-frequent adjusts of the cuff pressure to keep the blood volume in the finger artery constant throughout the cardiac cycle (C). From the pressure adjustments needed to maintain a constant blood volume in the finger artery the arterial blood pressure waveform can be derived and analysed to estimate arterial blood pressure and cardiac output. D Example of HemoSphere hemodynamic monitor screen
Fig. 2
Fig. 2
Flow of participants through the study. The study time frame is from the beginning of hemodynamic monitoring until the end of surgery. HPI Hypotension Prediction Index, ROC receiver operating characteristic
Fig. 3
Fig. 3
Receiver operating characteristic curves for HPI (Hypotension Prediction Index) and ΔMAP (changes in mean arterial pressure) over the preceding 15 min for predicting hypotension 5, 10 and 15 min before its occurrence. ROC is a plot of true positive rate (sensitivity) and false positive rate (1—specificity) at HPI values from 0 to 100

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Source: PubMed

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