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Fig 1.

Patient flowchart.

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Fig 2.

Architecture of the hypotension risk prediction model using multiple waveforms.

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Table 1.

Dataset characteristics.

Overall missing values are below 1%. ASA, American Society of Anesthesiologists classification.

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Table 2.

Data composition.

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Table 3.

Area under the Receiver-operating Characteristic Curve, Area under the Precision-Recall Curve, Sensitivity, and Specificity of our model in predicting intraoperative hypotension.

Value (95% confidence interval); AUROC, area under the receiver operating characteristic; AUPRC, area under the precision-recall curve; ABP, arterial blood pressure; ECG, electrocardiogram; EEG, electroencephalogram.

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Fig 3.

Illustrative patient record demonstrating the trajectory of algorithm outputs from different combinations of inputs (time and signals) and mean arterial pressure.

(a) Trajectory of MAP. Red dash, 65 mmHg. (b) HRI from ABP waveform and (c) a combination of ABP and EEG for predicting event 3-min before, respectively. (d) HRI from ABP waveform, and e a combination of ABP and EEG waveforms for predicting event 15 min before. MAP, mean arterial pressure; HRI, hypotension risk index; ABP, arterial blood pressure; EEG, electroencephalogram.

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Fig 4.

Actual occurrence of hypotensive events according to the Hypotension Risk Index.

(a–d) Actual occurrence of hypotensive events according to the hypotension risk indices to predict the events 3, 5, 10, and 15 min before, respectively. ABP, arterial blood pressure; EEG, electroencephalogram.

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Table 4.

Model performance metrics in post hoc analysis.

AUROC, Area under the Receiver Operating Characteristic Curve; AUPRC, Area under the Precision-Recall Curve.

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