Mean Arterial Pressure Time Series Research Articles

A flexible framework for coding and predicting acute hypotensive episodes using Markov chains

Over the past decade, the prediction of acute hypotensive episodes (AHEs) using computational models has gained significant attention. AHEs in patients admitted to intensive care units can lead to severe or life-threatening conditions. AHEs are defined as a drop in the mean arterial pressure (MAP) below a threshold during an observation period. However, adaptable AHE modeling approaches are required to account for patient-specific definitions caused by inter-patient variability. This study proposes a flexible three-stage framework for the AHE prediction problem capable of modeling different hypotension states. The initial stage involves encoding the MAP time series using the proposed scheme. A clustering process is then performed to determine the k≥2 hypotension states and transition probabilities between them. The proposed AHE prediction model is built using a cohort of 453 MAP waveforms from MIMIC-II. In the third stage, we use 173 MAP waveforms to validate the proposed prediction method. Given a patient’s MAP time series, we compute the initial probability distribution by considering (a) an encoded observation interval and (b) a similarity measure to determine the closest kth cluster (state) to the encoded interval. Based on the properties of the Markov process, we predict the most probable state for look-ahead times h ranging from 1 to 30 min. The proposed framework offers competitive performance for h=7 with an accuracy of 0.965, which is clinically relevant for medical practitioners. We also provide a comparative analysis to validate the effectiveness of the proposed approach, which exhibits a significant statistical difference (α=0.05) compared with other state-of-the-art methods.

Preoperative Blood Pressure Complexity Indices as a Marker for Frailty in Patients Undergoing Cardiac Surgery

Frailty, a state of decreased physiological reserve, increases the risk of adverse outcomes. There is no standard tool for frailty during perioperative period. Autonomic dysfunction, an underlying process in frailty, could result in hemodynamic fluctuations. Complexity, the physiological adaptability of a system can quantify these fluctuations. The authors hypothesized that complexity could be a marker for frailty and explored their relationship in cardiac surgical patients. Prospective, observational study. Single-center teaching hospital. Three hundred and sixty-four adult patients undergoing cardiac surgery. None. Preoperative beat-to-beat systolic arterial pressure (SAP) and mean arterial pressure (MAP) time series were obtained. Complexity indices were calculated using multiscale entropy (MSE) analysis. Frailty was assessed from: age >70 years, body mass index <18.5, hematocrit <35%, albumin <3.4 g/dL, and creatinine >2.0 mg/dL. The association between complexity indices and frailty was explored by logistic regression and predictive ability by C-statistics. In total, 190 (52%) patients had frailty. The complexity index (MSEΣ) median (quartile 1, quartile 3) of SAP and MAP time series decreased significantly in frail patients (SAP: 8.32 [7.27, 9.24] v 9.13 [8.00, 9.72], p < 0.001 and MAP: 8.56 [7.56; 9.27] v 9.18 [8.26; 9.83], p < 0.001). MSE Σ demonstrated a fair predictive ability of frailty (C-statistic: SAP 0.62 and MAP 0.64). Preoperative BP complexity indices correlate and predict frailty. Impaired autonomic control is the underlying mechanism to explain this finding. A simple automated measure of preoperative BP complexity in the surgeon's office has the potential to reliably assess frailty.

Prediction of acute hypotensive episodes using EMD, statistical method and multi GP

Acute hypotensive episodes (AHE) is a critical event in the ICU as it can cause multiple organ failure and thus has severe implications on the mortality risk. Timely and rapid intervention is necessary to save a patient’s life. For the sake of this purpose, this paper presents a methodology to predict AHE for ICU patients based on mean arterial pressure time series. First, the empirical mode decomposition method is used to calculate complex MAP series. Then the original and intrinsic mode functions of MAP series are transformed for the feature extraction with the methods of probabilistic distribution and statistics. Finally, the multi genetic programming is presented to build the classification models for the prediction of AHE. The methodology is applied to the dataset from Multi-parameter intelligent monitoring for intensive care. The achieved accuracy of the proposed methodology is 82.92 % in the training set and 79.93 % in the testing set with the sixfold cross-validation in the total 2866 patient’s records. Simulation results revealed the reasonable forecasting accuracy in prediction of AHE in 1 h forecasting window.

An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier

Acute hypotensive episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presents a methodology to predict AHE for ICU patients based on big data time series. The experimental data we used is mean arterial pressure (MAP), which is transformed from arterial blood pressure (ABP) data. Then, the Hilbert-Huang transform method was used to calculate patient's MAP time series and some features, which are the bandwidth of the amplitude modulation, the frequency modulation, and the power of intrinsic mode function (IMF), were extracted. Finally, the multiple genetic programming (Multi-GP) is used to build the classification models for detection of AHE. The methodology is applied in the datasets of the 10th PhysioNet and Computers Cardiology Challenge in 2009 and Multiparameter Intelligent Monitoring for Intensive Care (MIMIC-II). We achieve the accuracy of 83.33% in the training set and 91.89% in the testing set of the 2009 challenge's dataset and the 84.13% in the training set and 82.41% in the testing set of the MIMIC-II dataset.