Prediction of intraoperative hypotension from the linear extrapolation of mean arterial pressure

BACKGROUND Hypotension prediction index (HPI) software is a proprietary machine learning-based algorithm used to predict intraoperative hypotension (IOH). HPI has shown superiority in predicting IOH when compared to the predictive value of changes in mean arterial pressure (Delta MAP) alone. However, the predictive value of Delta MAP alone, with no reference to the absolute level of MAP, is counterintuitive and poor at predicting IOH. A simple linear extrapolation of mean arterial pressure (LepMAP) is closer to the clinical approach. OBJECTIVES Our primary objective was to investigate whether LepMAP better predicts IOH than Delta MAP alone. DESIGN Retrospective diagnostic accuracy study. SETTING Two tertiary University Hospitals between May 2019 and December 2019. PATIENTS A total of 83 adult patients undergoing high risk non-cardiac surgery. DATA SOURCES Arterial pressure data were automatically extracted from the anaesthesia data collection software (one value per minute). IOH was defined as MAP ANALYSIS Correlations for repeated measurements and the area under the curve (AUC) from receiver operating characteristics (ROC) were determined for the ability of LepMAP and Delta MAP to predict IOH at 1, 2 and 5 min before its occurrence (A-analysis, using the whole dataset). Data were also analysed after exclusion of MAP values between 65 and 75 mmHg (B-analysis). RESULTS A total of 24 318 segments of ten minutes duration were analysed. In the A-analysis, ROC AUCs to predict IOH at 1, 2 and 5 min before its occurrence by LepMAP were 0.87 (95% confidence interval, CI, 0.86 to 0.88), 0.81 (95% CI, 0.79 to 0.83) and 0.69 (95% CI, 0.66 to 0.71) and for Delta MAP alone 0.59 (95% CI, 0.57 to 0.62), 0.61 (95% CI, 0.59 to 0.64), 0.57 (95% CI, 0.54 to 0.69), respectively. In the B analysis for LepMAP these were 0.97 (95% CI, 0.9 to 0.98), 0.93 (95% CI, 0.92 to 0.95) and 0.86 (95% CI, 0.84 to 0.88), respectively, and for Delta MAP alone 0.59 (95% CI, 0.53 to 0.58), 0.56 (95% CI, 0.54 to 0.59), 0.54 (95% CI, 0.51 to 0.57), respectively. LepMAP ROC AUCs were significantly higher than Delta MAP ROC AUCs in all cases. CONCLUSIONS LepMAP provides reliable real-time and continuous prediction of IOH 1 and 2 min before its occurrence. LepMAP offers better discrimination than Delta MAP at 1, 2 and 5 min before its occurrence. Future studies evaluating machine learning algorithms to predict IOH should be compared with LepMAP rather than Delta MAP.