Pulse wave analysis as a tool for the evaluation of resuscitation therapy in septic shock

Objective. Pulse wave analysis (PWA) can provide insights into cardiovascular biomechanical properties. The use of PWA in critically ill patients, such as septic shock patients, is still limited, but it can provide complementary information on the cardiovascular effects of treatment when compared to standard indices outlined in international guidelines. Previous works have highlighted how sepsis induces severe cardiovascular derangement with altered arterial blood pressure waveform morphology and how resuscitation according to standard haemodynamic targets is not able to restore the physiological functioning of the cardiovascular system. The aim of this work is to test the effectiveness of PWA in characterizing arterial waveforms obtained from a swine experiment involving polymicrobial septic shock and resuscitation with different drugs.Methods. During the experiment, morphological aortic waveform features, such as indices related to the dicrotic notch and inflection point, were extracted by means of PWA techniques. Finally, all the PWA indices were used to compute a clustering classification (mini batch K-means) of the pigs according to the different phases of the experiment. This analysis aimed to test if PWA features alone could be used to distinguish between the different responses to the administered therapies.Results. The PWA indices highlighted different cardiovascular conditions of the pigs in response to different treatments, despite the mean haemodynamic values typically used to guide therapy administration being similar in all animals. The clustering algorithm was able to distinguish between the different phases of the experiment and the different responses of the animals based on the unique information derived from the aortic PWA.Conclusion. Even when used alone, PWA indices were highly informative when assessing therapy responses in cases of septic shock.Significance. A complex pathological condition like septic shock requires extensive monitoring without neglecting important information from commonly measured signals such as arterial blood pressure. Future studies are needed to understand how individual differences in the response to therapy are associated with different cardiovascular conditions that may become specific therapy targets.

Fully convolutional neural network and PPG signal for arterial blood pressure waveform estimation

Objective. The quality of the arterial blood pressure (ABP) waveform is crucial for predicting the value of blood pressure. The ABP waveform is predicted through experiments, and then Systolic blood pressure (SBP), Diastolic blood pressure, (DBP), and Mean arterial pressure (MAP) information are estimated from the ABP waveform.Approach. To ensure the quality of the predicted ABP waveform, this paper carefully designs the network structure, input signal, loss function, and structural parameters. A fully convolutional neural network (CNN) MultiResUNet3+ is used as the core architecture of ABP-MultiNet3+. In addition to performing Kalman filtering on the original photoplethysmogram (PPG) signal, its first-order derivative and second-order derivative signals are used as ABP-MultiNet3+ enter. The model's loss function uses a combination of mean absolute error (MAE) and means square error (MSE) loss to ensure that the predicted ABP waveform matches the reference waveform.Main results. The proposed ABP-MultiNet3+ model was tested on the public MIMIC II databases, MAE of MAP, DBP, and SBP was 1.88 mmHg, 3.11 mmHg, and 4.45 mmHg, respectively, indicating a small model error. It experiment fully meets the standards of the AAMI standard and obtains level A in the DBP and MAP prediction standard test under the BHS standard. For SBP prediction, it obtains level B in the BHS standard test. Although it does not reach level A, it has a certain improvement compared with the existing methods.Significance. The results show that this algorithm can achieve sleeveless blood pressure estimation, which may enable mobile medical devices to continuously monitor blood pressure and greatly reduce the harm caused by Cardiovascular disease (CVD).

Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation

Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R² of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.