Differential Pressure Spirometry for Mechanical Ventilation Using Dichotomic Search

Evaluating Airflow Sensor Methods: Precision in Indirect Calorimetry

This study assesses the impact of three volumetric gas flow measurement methods-turbine (fT); pneumotachograph (fP), and Venturi (fV)-on predictive accuracy and precision of expired gas analysis indirect calorimetry (EGAIC) across varying exercise intensities. Six males (Age: 38 ± 8 year; Height: 178.8 ± 4.2 cm;V ̇ O 2 peak $$ \dot{V}{\mathrm{O}}_2\mathrm{peak} $$ : 42 ± 2.8 mL O2 kg-1 min-1) and 14 females (Age = 44.6 ± 9.6 year; Height = 164.6 ± 6.9 cm;V ̇ O 2 peak $$ \dot{V}{\mathrm{O}}_2\mathrm{peak} $$ = 45 ± 8.6 mL O2 kg-1 min-1) were recruited. Participants completed physical exertion on a stationary cycle ergometer for simultaneous pulmonary minute ventilation (V ̇ $$ \dot{V} $$ ) measurements and EGAIC computations. Exercise protocols and subsequent conditions involved a 5-min cycling warm-up at 25 W min-1, incremental exercise to exhaustion (V ̇ O 2 $$ \dot{V}{\mathrm{O}}_2 $$ ramp test), then a steady-state exercise bout induced by a constant Watt load equivalent to 80% ventilatory threshold (80% VT). A linear mixed model revealed that exercise intensity significantly affectedV ̇ O 2 $$ \dot{V}{\mathrm{O}}_2 $$ measurements (p < 0.0001), whereas airflow sensor method (p = 0.97) and its interaction with exercise intensity (p = 0.91) did not. Group analysis of precision yielded aV ̇ O 2 $$ \dot{V}{\mathrm{O}}_2 $$ CV % = 21%; SEM = 5 mL O2 kg-1 min-1. Intra- and interindividual analysis of precision via Bland-Altman revealed a 95% confidence interval (CI) precision benchmark of 3-5 mL kg-1 min-1. Agreement among methods decreased at power outputs elicitingV ̇ $$ \dot{V} $$ up to 150 L min-1, indicating a decrease in precision and highlighting potential challenges in interpreting biological variability, training response heterogeneity, and test-retest comparisons. These findings suggest careful consideration of airflow sensor method variance across metabolic cart configurations.

Sensor technology usage in orthopedic trauma

Medicine in general is quickly transitioning to a digital presence. Orthopaedic surgery is also being impacted by the tenets of digital health but there are also direct efforts with trauma surgery. Sensors are the pen and paper of the next wave of data acquisition. Orthopaedic trauma can and will be part of this new wave of medicine. Early sensor products that are now coming to market, or are in early development, will directly change the way we think about surgical diagnosis and outcomes. Sensor development for biometrics is already here. Wellness devices, pressure, temperature, and other parameters are already being measured. Data acquisition and analysis is going to be a fruitful addition to our research armamentarium with the volume of information now available. A combination of broadband internet, micro electrical machine systems (MEMS), and new wireless communication standards is driving this new wave of medicine. The Internet of Things (IoT) [1] now has a subset which is the Internet of Medical Devices [2-5] permitting a much more in-depth dive into patient procedures and outcomes. IoT devices are now being used to enable remote health monitoring, in hospital treatment, and guide therapies. This article reviews current sensor technology that looks to impact trauma care.