Background: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. Methods: Forty-eight post-operative children, median age 4 months (2.9–11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33–197), mean lactate level at admission of 22.3 mg/dL (range 6.3–71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance. Results: The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw. Conclusions: The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.
Introduction: Despite improvements in management for children after cardiac surgery, a non-negligible proportion of patients suffer from cardiac arrest, having a poor prognosis. Although serum lactate levels are widely accepted markers of hemodynamic instability, measuring lactate requires discrete blood sampling. An alternative method to evaluate hemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. Hypothesis: We hypothesize that blood lactate in PICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. Methods: Forty-eight children, who underwent heart surgery, were included. Patient characteristics and physiological measurements were acquired and analyzed using specialized software/hardware, including heart rate, lactate level, arterial waveform sharpness, and area under the curve. Predicting a patient’s blood lactate levels was accomplished using regression-based supervised learning algorithms, including regression decision trees, tuned decision trees, random forest regressor, tuned random forest, AdaBoost regressor, and hypertuned AdaBoost. All algorithms were compared with hold-out cross validation. Two approaches were considered: basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. The second approach supports updating the learning system’s predictive capacity whenever a patient has a new ground truth blood lactate reading acquired. Results: In both approaches, the best performing machine learning method was the tuned random forest, which yielded a mean absolute error of 5.60 mg/dL in the first approach, and 4.62 mg/dL when predicting blood lactate with updated ground truth. Conclusions: In conclusion, the tuned random forest is capable of predicting the level of serum lactate by analyzing perioperative variables, including the arterial pressure waveform. Machine learning can predict the patient’s hemodynamics non-invasively, continuously, and with accuracy that may demonstrate clinical utility.
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