Using near-infrared reflectance spectroscopy (NIRS) to assess distal-limb perfusion on venoarterial (V-A) extracorporeal membrane oxygenation (ECMO) patients with femoral cannulation

Perfusion ◽  
2018 ◽  
Vol 33 (8) ◽  
pp. 618-623 ◽  
Author(s):  
Killian Patton-Rivera ◽  
James Beck ◽  
Kenmond Fung ◽  
Christine Chan ◽  
Matthew Beck ◽  
...  

Background: Patients requiring V-A ECMO who receive femoral cannulation have an associated risk of distal, lower-limb hypoperfusion and ischemia of the cannulated leg. This pilot study evaluated the usefulness of non-invasive lower-limb oximetry, using near-infrared reflectance spectroscopy (NIRS) to detect limb ischemia. Methods: Between June 2016 and January 2017, 25 patients receiving femoral V-A ECMO were continuously monitored using the CASMED Fore-Sight Elite (CAS Medical Systems Inc., Branford, CT) tissue oximeter. A retrospective pilot study was conducted to review the correlation between NIRS tissue saturations (StO2) and clinical indications of limb ischemia. Patients were monitored for StO2s less than 50% for more than four minutes or StO2 differentials between the cannulated and non-cannulated legs greater than 15%. Results: Twenty-five patients (age 22-78) were monitored with NIRS. Six patients had clinical indications of lower-limb ischemia: cold limb, mottled skin and pulseless Doppler ultrasound. All six patients had StO2s below 50% that persisted for longer than four minutes. Of the 25 patients, one patient had a false-positive indication of hypoperfusion with StO2 below 50% for more than four minutes due to a venous saturation below 30%. Another patient had a false-positive pulseless Doppler ultrasound caused by high doses of pressor support. The StO2 was greater than 60%, which confirmed the clinical determination of adequate perfusion. Five patients had StO2s below 50% for less than four minutes and none of these patients had clinical indications of lower-limb hypoperfusion. All patients with cannula-related obstruction of flow to the distal portion of the leg had StO2 differentials greater than 15%. No patients without cannula-related obstruction to flow had StO2 differentials greater than 15%. Conclusion: Advancements in NIRS technology seem to have improved its accuracy for continuous, non-invasive monitoring of regional tissue and may provide clinicians with an additional metric to protect the distal portion of the cannulated leg.

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.


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