Mechanical Power Ratio and Respiratory Treatment Escalation in COVID-19 Pneumonia: A Secondary Analysis of a Prospectively Enrolled Cohort

BACKGROUND

Under the hypothesis that mechanical power ratio could identify the spontaneously breathing patients with a higher risk of respiratory failure, this study assessed lung mechanics in nonintubated patients with COVID-19 pneumonia, aiming to (1) describe their characteristics; (2) compare lung mechanics between patients who received respiratory treatment escalation and those who did not; and (3) identify variables associated with the need for respiratory treatment escalation.

METHODS

Secondary analysis of prospectively enrolled cohort involving 111 consecutive spontaneously breathing adults receiving continuous positive airway pressure, enrolled from September 2020 to December 2021. Lung mechanics and other previously reported predictive indices were calculated, as well as a novel variable: the mechanical power ratio (the ratio between the actual and the expected baseline mechanical power). Patients were grouped according to the outcome: (1) no-treatment escalation (patient supported in continuous positive airway pressure until improvement) and (2) treatment escalation (escalation of the respiratory support to noninvasive or invasive mechanical ventilation), and the association between lung mechanics/predictive scores and outcome was assessed.

RESULTS

At day 1, patients undergoing treatment escalation had spontaneous tidal volume similar to those of patients who did not (7.1 ± 1.9 vs. 7.1 ± 1.4 ml/kgIBW; P = 0.990). In contrast, they showed higher respiratory rate (20 ± 5 vs. 18 ± 5 breaths/min; P = 0.028), minute ventilation (9.2 ± 3.0 vs. 7.9 ± 2.4 l/min; P = 0.011), tidal pleural pressure (8.1 ± 3.7 vs. 6.0 ± 3.1 cm H2O; P = 0.003), mechanical power ratio (2.4 ± 1.4 vs. 1.7 ± 1.5; P = 0.042), and lower partial pressure of alveolar oxygen/fractional inspired oxygen tension (174 ± 64 vs. 220 ± 95; P = 0.007). The mechanical power (area under the curve, 0.738; 95% CI, 0.636 to 0.839] P < 0.001), the mechanical power ratio (area under the curve, 0.734; 95% CI, 0.625 to 0.844; P < 0.001), and the pressure-rate index (area under the curve, 0.733; 95% CI, 0.631 to 0.835; P < 0.001) showed the highest areas under the curve.

CONCLUSIONS

In this COVID-19 cohort, tidal volume was similar in patients undergoing treatment escalation and in patients who did not; mechanical power, its ratio, and pressure-rate index were the variables presenting the highest association with the clinical outcome.

EDITOR’S PERSPECTIVE

Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions

Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning

PURPOSE

The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique.

METHOD

We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. [Formula: see text] was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer ([Formula: see text]) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km[Formula: see text], treadmill running at 9 and 12  km [Formula: see text] and ergometer cycling at 90 and 110 W).

RESULTS

The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between [Formula: see text] and [Formula: see text] (bias) was higher for the MLR model ([Formula: see text] L) than for the ANN model ([Formula: see text] L).

CONCLUSION

Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.