Validation of a novel system to assess end-expiratory lung volume and alveolar recruitment in an ARDS model

Background

Personalizing mechanical ventilation requires the development of reliable bedside monitoring techniques. The multiple-breaths nitrogen washin–washout (MBNW) technique is currently available to measure end-expiratory lung volume (EELV_MBNW), but the precision of the technique may be poor, with percentage errors ranging from 28 to 57%. The primary aim of the study was to evaluate the reliability of a novel MBNW bedside system using fast mainstream sensors to assess EELV in an experimental acute respiratory distress syndrome (ARDS) model, using computed tomography (CT) as the gold standard. The secondary aims of the study were: (1) to evaluate trending ability of the novel system to assess EELV; (2) to evaluate the reliability of estimated alveolar recruitment induced by positive end-expiratory pressure (PEEP) changes computed from EELV_MBNW, using CT as the gold standard.

Results

Seven pigs were studied in 6 experimental conditions: at baseline, after experimental ARDS and during a decremental PEEP trial at PEEP 16, 12, 6 and 2 cmH₂O. EELV was computed at each PEEP step by both the MBNW technique (EELV_MBNW) and CT (EELV_CT). Repeatability was assessed by performing replicate measurements. Alveolar recruitment between two consecutive PEEP levels after lung injury was measured with CT (Vrec_CT), and computed from EELV measurements (Vrec_MBNW) as ΔEELV minus the product of ΔPEEP by static compliance.

EELV_MBNW and EELV_CT were significantly correlated (R² = 0.97). An acceptable non-constant bias between methods was identified, slightly decreasing toward more negative values as EELV increased. The conversion equation between EELV_MBNW and EELV_CT was: EELV_MBNW = 0.92 × EELV_CT + 36. The 95% prediction interval of the bias amounted to ± 86 mL and the percentage error between both methods amounted to 13.7%.

The median least significant change between repeated measurements amounted to 8% [CI_95%: 4–10%]. EELV_MBNW adequately tracked EELV_CT changes over time (concordance rate amounting to 100% [CI_95%: 87%–100%] and angular bias amounting to − 2° ± 10°).

Vrec_MBNW and Vrec_CT were significantly correlated (R² = 0.92). A non-constant bias between methods was identified, slightly increasing toward more positive values as Vrec increased.

Conclusions

We report a new bedside MBNW technique that reliably assesses EELV in an experimental ARDS model with high precision and excellent trending ability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40635-021-00410-x.

Imaging in thick samples, a phased Monte Carlo radiation transfer algorithm

SIGNIFICANCE

Optical microscopy is characterized by the ability to get high resolution, below 1  μm, high contrast, functional and quantitative images. The use of shaped illumination, such as with lightsheet microscopy, has led to greater three-dimensional isotropic resolution with low phototoxicity. However, in most complex samples and tissues, optical imaging is limited by scattering. Many solutions to this issue have been proposed, from using passive approaches such as Bessel beam illumination to active methods incorporating aberration correction, but making fair comparisons between different approaches has proven to be challenging.

AIM

We present a phase-encoded Monte Carlo radiation transfer algorithm (φMC) capable of comparing the merits of different illumination strategies or predicting the performance of an individual approach.

APPROACH

We show that φMC is capable of modeling interference phenomena such as Gaussian or Bessel beams and compare the model with experiment.

RESULTS

Using this verified model, we show that, for a sample with homogeneously distributed scatterers, there is no inherent advantage to illuminating a sample with a conical wave (Bessel beam) instead of a spherical wave (Gaussian beam), except for maintaining a greater depth of focus.

CONCLUSION

φMC is adaptable to any illumination geometry, sample property, or beam type (such as fractal or layered scatterer distribution) and as such provides a powerful predictive tool for optical imaging in thick samples.

Validation of NICE diagnostic guidance for rule out of myocardial infarction using high-sensitivity troponin tests

OBJECTIVE

To validate the National Institute for Health and Care Excellence (NICE) recommended algorithms for high-sensitivity troponin (hsTn) assays in adults presenting with chest pain.

METHODS

International post hoc analysis of three prospective, observational studies from tertiary hospital emergency departments. The primary endpoint was cardiac death or acute myocardial infarction (AMI) within 24 hours of presentation, and the secondary endpoint was major adverse cardiac events (MACE) at 30 days.

RESULTS

15% of patients were diagnosed with non-ST elevation myocardial infarction (MI) on admission. The hsTnI algorithm classified 2506/3128 (80.1%) of patients as 'ruled out' with 50 (2.0%) missed MI. 943/3128 (30.1%) of patients had a troponin I level below the limit of detection on admission with 2 (0.2%) missed MI. For the hsTnT algorithm, 1794/3374 (53.1%) of patients were 'ruled out' with 7 (0.4%) missed MI. 490/3374 (14.5%) of patients had a troponin T below the limit of blank on admission with no MI. MACE at 30 days occurred in 10.7% and 8.5% of patients 'ruled out' defined by the hsTnI and hsTnT algorithms, respectively.

CONCLUSIONS

The NICE algorithms could identify patients with low probability of AMI within 2 hours; however, neither strategy performed as predicted by the NICE diagnostic guidance model. Additionally, the rate of MACE at 30 days was sufficiently high that the algorithms should only be used as one component of a more extensive model of risk stratification.

TRIAL REGISTRATION NUMBER

ACTRN12611001069943, NCT00470587; post-results.