A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia.

This study examined the possibility of estimating cardiac output (CO) using a multimodal stacking model that utilizes cardiopulmonary interactions during general anesthesia and outlined a retrospective application of machine learning regression model to a pre-collected dataset. The data of 469 adult patients (obtained from VitalDB) with normal pulmonary function tests who underwent general anesthesia were analyzed. The hemodynamic data in this study included non-invasive blood pressure, plethysmographic heart rate, and SpO2. CO was recorded using Vigileo and EV1000 (pulse contour technique devices). Respiratory data included mechanical ventilation parameters and end-tidal CO2 levels. A generalized linear regression model was used as the metalearner for the multimodal stacking ensemble method. Random forest, generalized linear regression, gradient boosting machine, and XGBoost were used as base learners. A Bland-Altman plot revealed that the multimodal stacked ensemble model for CO prediction from 327 patients had a bias of - 0.001 L/min and - 0.271% when calculating the percentage of difference using the EV1000 device. Agreement of model CO prediction and measured Vigileo CO in 142 patients reported a bias of - 0.01 and - 0.333%. Overall, this model predicts CO compared to data obtained by the pulse contour technique CO monitors with good agreement.

A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.

BACKGROUND: A powerful risk model allows clinicians, at the bedside, to ensure the early identification of and decision-making for patients showing signs of developing physiological instability during treatment. The aim of this study was to enhance the identification of patients at risk for deterioration through an accurate model using electrolyte, metabolite, and acid-base parameters near the end of patients' intensive care unit (ICU) stays. METHODS: This retrospective study included 5157 adult patients during the last 72 hours of their ICU stays. The patients from the MIMIC-III database who had serum lactate, pH, bicarbonate, potassium, calcium, glucose, chloride, and sodium values available, along with the times at which those data were recorded, were selected. Survivor data from the last 24 hours before discharge and four sets of nonsurvivor data from 48-72, 24-48, 8-24, and 0-8 hours before death were analyzed. Deep learning (DL), random forest (RF) and generalized linear model (GLM) analyses were applied for model construction and compared in terms of performance according to the area under the receiver operating characteristic curve (AUC). A DL backcasting approach was used to assess predictors of death vs. discharge up to 72 hours in advance. RESULTS: The DL, RF and GLM models achieved the highest performance for nonsurvivors 0-8 hours before death versus survivors compared with nonsurvivors 8-24, 24-48 and 48-72 hours before death versus survivors. The DL assessment outperformed the RF and GLM assessments and achieved discrimination, with an AUC of 0.982, specificity of 0.947, and sensitivity of 0.935. The DL backcasting approach achieved discrimination with an AUC of 0.898 compared with the DL native model of nonsurvivors from 8-24 hours before death versus survivors with an AUC of 0.894. The DL backcasting approach achieved discrimination with an AUC of 0.871 compared with the DL native model of nonsurvivors from 48-72 hours before death versus survivors with an AUC of 0.846. CONCLUSIONS: The DL backcasting approach could be used to simultaneously monitor changes in the electrolyte, metabolite, and acid-base parameters of patients who develop physiological instability during ICU treatment and predict the risk of death over a period of hours to days.

Fuzzy risk stratification and risk assessment model for clinical monitoring in the ICU.

BACKGROUND: The decisions that clinicians make in intensive care units (ICUs) based on monitored parameters reflecting physiological deterioration are of major medical and biomedical engineering interest. These parameters have been investigated and assessed for their usefulness in risk assessment. METHODS: Totally, 127 ICU adult patients were studied. They were selected from a MIMIC II Waveform Database Matched Subset and had continuous monitoring of heart rate, invasive blood pressure, and oxygen saturation. The monitored data were dimension reduced using deep learning autoencoders and then used to train a support vector machine model (SVM). A combination of methods including fuzzy c-means clustering (FCM), and a random forest (RF) was used to determine the risk levels. RESULTS: When classifying patients into stable or deteriorating groups the main performance parameter was the receiver operating characteristics (ROC). The area under the ROC (AUROC) was 93.2 (95% CI (92.9-93.4)) with sensitivity and specificity values of 0.80 and 0.89, respectively. The suggested fuzzy risk levels using the combined method of the FCM clustering and RF achieved an accuracy of 1 (0.9999, 1), with both sensitivity and specificity values equal to 1. CONCLUSIONS: The potential for using models in risk assessment to estimate a patient's physiological status, stable or deteriorating, within 4 h has been demonstrated. The study was based on retrospective analysis and further studies are needed to evaluate the impact on clinical outcomes using this model.