Gastrointestinal Impedance Spectroscopy to Detect Hypoperfusion During Hemorrhage

Comparison of gastric reactance with commonly used perfusion markers in a swine hypovolemic shock model

Background

The gut has been hypothesized to be a protagonist tissue in multiple organ dysfunction syndrome (MODS) for the past three decades. Gastric reactance (XL) is a potential perfusion marker derived from gastric impedance spectroscopy (GIS), which is an emerging tool through which living tissue can be continuously measured to determine its pathophysiological evolution. This study aimed to compare the performance of XL [positive predictive values (PPV), negative predictive values (NPV), and area under the curve (AUC)] against commonly used perfusion markers before and during hypovolemic shock in swine subjects.

Methods

Prospective, controlled animal trial with two groups, control group (CG) N = 5 and shock (MAP ≤ 48 mmHg) group (SG) N = 16. Comparison time points were defined as T-2 (2 h before shock), T-1 (1 h before shock), T0 (shock), T1 (1 h after shock), and T2 (2 h after shock). Shock severity was assessed through blood gases, systemic and hemodynamic variables, and via histological examination for assessing inflammation-edema and detachment in the gastric mucosa. Macroscopic assessment of the gastric mucosa was defined in five levels (0—normal mucosa, 1—stippling or epithelial hemorrhage, 2—pale mucosa, 3—violet mucosa, and 4—marmoreal mucosa). Receiver Operating Characteristic (ROC) curves of perfusion markers and XL were calculated to identify optimal cutoff values and their individual ability to predict hypovolemic shock.

Results

Comparison among the CG and the SG showed statistically significant differences in XL measurements at T-1, T0, T1, and T2, while lactate showed statistically significant differences until T1 and T2. Statistically significant differences were detected in mucosa class (p < 0.001) and in inflammation-edema in the gastric body and the fundus (p = 0.021 and p = 0.043). The performance of the minimum XL value per subject  per event (XL_Min) was better (0.81 ≤ AUC ≤ 0.96, 0.93 ≤ PPV ≤ 1.00, 0.45 ≤ NPV ≤ 0.83) than maximum lactate value (Lac_Max) per subject per event (0.29 ≤ AUC ≤ 0.82, 0.82 ≤ PPV ≤ 0.91, 0.24 ≤ NPV ≤ 0.82). Cutoff values for XL_Min show progressive increases at each time point, while cutoff values for Lac_Max increase only at T2.

Conclusions

XL proved to be an indirect and consistent marker of inadequate gastric mucosal perfusion, which shows significant and detectable changes before commonly used markers of global perfusion under the hypovolemic shock conditions outlined in this work.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40635-022-00476-1.

Correlation of optic nerve and optic nerve sheath diameter with intracranial pressure in pigs

Objective

Several studies have shown an association between intracranial pressure and the diameter of the optic nerve sheath measured by transbulbar ultrasonography. To understand the pathophysiology of this phenomenon, we aimed to measure the changes of the optic nerve, optic nerve sheath and perineural space separately with increasing intracranial pressure in a porcine model.

Methods

An external ventricular drain was placed into the third ventricle through a right paramedian burrhole in eight anesthesized pigs. The diameters of the optic nerve and the optic nerve sheath were measured while the intracranial pressure (ICP) was increased in steps of 10mmHg from baseline up to 60 mmHg.

Results

The median diameters of the optic nerve (ON) increased from 0.36 cm (baseline– 95% confidence interval (CI) 0.33 cm to 0.45 cm) to 0.68 cm (95% CI 0.57 cm to 0.82 cm) at ICP of 60 mmHg (p<0.0001) and optic nerve sheath (ONS) from 0.88 cm (95% CI 0.79 cm to 0.98 cm) to 1.24 cm (95% CI 1.02 cm to 1.38 cm) (p< 0.002) while the median diameter of the perineural space (PNS) (baseline diameter 95% CI 0.40 cm to 0.59 cm to diameters at ICP 60 95% CI 0.38 cm to 0.62 cm) did not change significantly (p = 0.399). Multiple comparisons allowed differentiation between baseline and values ≥40 mmHg for ON (p = 0.017) and between baseline and values ≥ 50mmHg for ONS (p = 0.006). A linear correlation between ON (R² = 0.513, p<0.0001) and ONS (R² = 0.364, p<0.0001) with ICP was found. The median coefficient of variation for intra- and inter-investigator variability was 8% respectively 2.3%.

Conclusion

Unexpectedly, the increase in ONS diameter with increasing ICP is exclusively related to the increase of the diameter of the ON. Further studies should explore the reasons for this behaviour.

Machine learning for intraoperative prediction of viability in ischemic small intestine

OBJECTIVE

Evaluation of intestinal viability is essential in surgical decision-making in patients with acute intestinal ischemia. There has been no substantial change in the mortality rate (30%-93%) of patients with acute mesenteric ischemia (AMI) since the 1980s. As the accuracy from the first laparotomy alone is 50%, the gold standard is a second-look laparotomy, increasing the accuracy to 87%-89%. This study investigates the use of machine learning to classify intestinal viability and histological grading in pig jejunum, based on multivariate time-series of bioimpedance sensor data.

APPROACH

We have previously used a bioimpedance sensor system to acquire electrical parameters from perfused, ischemic and reperfused pig jejunum (7  +  15 pigs) over 1-16 h of ischemia and 1-8 h of reperfusion following selected durations of ischemia. In this study we compare the accuracy of using end-point bioimpedance measurements with a feedforward neural network (FNN), versus the accuracy when using a recurrent neural network with long short-term memory units (LSTM-RNN) with bioimpedance data history over different periods of time.

MAIN RESULTS

Accuracies in the range of what has been reported clinically can be achieved using FNN's on a single bioimpedance measurement, and higher accuracies can be achieved when employing LSTM-RNN on a sequence of data history.

SIGNIFICANCE

Intraoperative bioimpedance measurements on intestine of suspect viability combined with machine learning can increase the accuracy of intraoperative assessment of intestinal viability. Increased accuracy in intraoperative assessment of intestinal viability has the potential to reduce the high mortality and morbidity rate of the patients.