Automatic Algorithm for Monitoring Systolic Pressure Variation and Difference in Pulse Pressure

Evaluation of intraosseous pressure in a hypovolemic animal model

BACKGROUND

In emergent situations, access to the vascular bed is frequently required for fluid and medication administration. Central venous catheter placement is associated with risk and may slow resuscitation in the unstable patient. The purpose of this study was to determine whether intraosseous pressure (IOP) could be consistently recorded and how similar this pressure was to central venous and arterial pressure in a porcine hemorrhagic shock model.

MATERIALS AND METHODS

After sedation, eight female swine had catheters placed in the femoral vein, aorta via femoral artery, and superior vena cava. IOP lines were placed in the proximal humerus, distal femur, and proximal tibia. Pressure readings were recorded continuously through the five stages of progressive hypovolemia. Pressure data were descriptively summarized, with the percent of change of IOP at each stage compared with arterial pressure using a multilevel mixed effects linear model with log transformation.

RESULTS

The IOP baseline values were between 16 and 18 mm Hg, approximately 22% of baseline arterial pressure. The intraosseous (IO) waveform mostly closely resembled the arterial pressure waveform, including the presence of a dichroitic notch. Pressure variations caused by ventilation (respiratory variability) were also identified in all the tracings. The rate of pressure change in the humeral IO most closely matched the change in arterial pressure rate. IO blood gas analysis showed gas composition to most closely match venous blood.

CONCLUSIONS

IOP was reliably obtained in this porcine model and suggests potential for clinical application in humans.

Using arterial pressure waveform analysis for the assessment of fluid responsiveness

Predicting the effects of volume expansion on cardiac output and oxygen delivery is of major importance in different clinical scenarios. Functional hemodynamic parameters based on pulse waveform analysis, which are relying on the effects of mechanical ventilation on stroke volume and its surrogates, have been shown to be reliable predictors of fluid responsiveness during anesthesia and intensive care unit treatment, as demonstrated by several clinical studies and meta-analyses. However, different limitations of these parameters have to be considered when they are used in clinical practice. Today, they can be continuously and automatically monitored by a variety of commercially available devices. These parameters have been introduced into the concept of perioperative fluid management and hemodynamic optimization - an approach that may positively impact postoperative patients' outcomes. In this article, technical aspects of the assessment of the functional hemodynamic parameters derived from pulse waveform analysis are summarized, emphasizing their advantages, limitations and potential applications, primarily in a perioperative setting in order to improve patient outcome.

Anaesthesia monitoring using fuzzy logic

OBJECTIVE

Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration.

METHOD

The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients' data.

RESULTS

The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures.

CONCLUSION

The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.