Background

Maintaining spontaneous breathing effort in mechanically ventilated patients limits respiratory muscle disuse and atrophy [1,2,3,4]. Too high respiratory effort may lead to excessive lung stress and strain causing lung injury on one hand. On the other hand, it may lead to excessive muscle loading causing muscle injury (mainly diaphragm injury) leading to muscle dysfunction [5]. High respiratory drive and effort frequently exist in critically ill patients, mainly due to insufficient ventilator assistance and sedation, but evidence also suggests biological predisposition (e.g., pulmonary and systemic inflammation, lung mechanical heterogeneity) plays a role as well. Therefore, it is important to have a reliable estimate of respiratory effort to enable lung and diaphragm protective mechanical ventilation [6,7,8].

The gold standard to obtain respiratory effort is esophageal manometry. This technique is minimally invasive, requires appropriate equipment and expertise, and can be time consuming. Other monitoring techniques or parameters only reflect respiratory drive (P0.1 and electrical activity of the diaphragm) or muscle loading (diaphragm ultrasound) and provide only limited information about lung stress and strain (plateau pressure and driving pressure) [7]. Recently, Bertoni et al. [9] demonstrated that dynamic transpulmonary driving pressure (∆PL) and respiratory muscle pressure (Pmus) can be estimated from the maximal decline in airway pressure (Paw) from positive end-expiratory pressure (PEEP) during an expiratory occlusion maneuver (∆Pocc). Direct estimates of ∆PL and Pmus were unreliable, excessive ∆PL and Pmus, however, could be predicted with reasonable accuracy.

Coronavirus disease 2019 (COVID-19) is a new type of lung disease [10,11,12] originating from Wuhan, China, in December 2019. Because of the sheer number of mechanically ventilated patients with severe lung disease, it was impossible to measure esophageal pressure to assess respiratory mechanics in every patient. Therefore, we estimated ∆PL and Pmus according to Bertoni et al. [9] in every COVID-19 patient with spontaneous breathing effort as part of standard patient care. If computed ∆PL and/or Pmus were excessive (i.e., higher than Pmus 13–15 cmH2O and ∆PL 16–17 cmH2O), or if patients received prolonged mechanical ventilation with no progress (i.e., ≥14 days) or if patients remained hypercapnic (PaCO2 ≥ 60 mmHg), respiratory mechanics was assessed by esophageal manometry for clinical purposes.

The aim of this paper is to describe respiratory mechanics in mechanically ventilated COVID-19 patients with spontaneous breathing effort, to compute ∆PL and Pmus from ∆Pocc and assess the discriminative performance for excessive ∆PL and Pmus, and to assess the relation between ∆Pocc and respiratory effort.

Methods

Study population

Dynamic transpulmonary driving pressure and respiratory muscle pressure were assessed in COVID-19 patients admitted to the Intensive Care Unit of the Radboud University Medical Center according to Bertoni et al. [9] as follows:

  1. (1)

    computed ∆PL = (peak Paw − PEEP)—2/3 × ∆Pocc.

  2. (2)

    computed Pmus = − 3/4 × ∆Pocc.

If patients had high respiratory effort and/or high dynamic transpulmonary driving pressure (i.e., computed Pmus 13–15 cmH2O and ∆PL 16–17 cmH2O or higher), prolonged mechanical ventilation without clinical progress (i.e.,  ≥ 14 days) or remained hypercapnic (PaCO2 ≥ 60 mmHg), esophageal manometry was obtained as part of our standard clinical protocol. Patients or their legal representatives were informed about the measurements.

Study protocol

This was an observational study. All patients were ventilated with a Servo-i/u ventilator (Getinge, Sölna, Sweden). Ventilator settings were set by the treating intensivist. Patients received a nasogastric catheter with esophageal balloon [Cooper (Cooper Surgical, Trumbull, USA) or Neurovent (NeuroVent Research Inc, Toronto, Canada)] to obtain esophageal pressure (Pes). Catheter position was validated using the dynamic occlusion test [13]. A total of 3–4 manual expiratory occlusions (lasting ~ 1–2 s) were performed during a 10–15 min recording per patient. After the recordings, ventilator settings or sedation strategies were adjusted, if deemed necessary, in accordance with the treating intensivist. Being an observational study, the effect of different ventilator settings or sedatives was not investigated.

Data acquisition

Ventilator flow and airway pressure (Paw) were obtained (sample frequency 100 Hz) by connecting a RS-232 cable via the serial port of the Servo-i/u to a dedicated measurement set-up using Servotracker software (Servotracker release 4.2, Getinge, Sölna, Sweden). The esophageal balloon (i.e., Pes) and a T-piece connected to the expiration port of Servo-i/u (i.e., Paw) were coupled to pressure transducers and acquired (sample frequency 100 Hz) using a dedicated measurement set-up (Biopac MP160, BIOPAC Inc., USA). Signals were synchronized offline based on Paw tracings that were acquired using both software programs. Brief manual expiratory occlusions (lasting ~ 1–2 s) were performed to enable offline synchronization. Data were processed and analyzed offline using Matlab R2018a (Mathworks, Natick, MA, USA).

Signal analysis

The occlusion pressure (∆Pocc) was defined as the maximal deflection in Paw from positive end-expiratory pressure (PEEP) during an expiratory occlusion maneuver (Fig. 1). The decrease in Pes during the first 100 ms of this maneuver was computed as P0.1. Transpulmonary pressure (PL) was determined by subtracting Pes from Paw. Dynamic transpulmonary driving pressure (∆PL) was computed from onset to peak during inspiration. Dynamic lung compliance (Cdyn) was calculated as tidal volume divided by the increase in PL between points of zero flow. Chest wall elastance (Ecw) was estimated based on predicted vital capacity [9, 14], from this chest wall elastic recoil pressure (Pcw) was computed as the product of tidal volume and Ecw. The pressure generated by the respiratory muscles (Pmus) was calculated as Pcw minus Pes. The integral of the product of Pmus and tidal volume represents work of breathing (WOB), calculated per liter and per minute. The integral of Pmus over time is defined as esophageal pressure–time product (PTPes), calculated per breath and per minute [14, 15].

Fig. 1
figure 1

Flow and pressure tracings showing an expiratory occlusion maneuver. From top to bottom: flow (in mL/s), airway pressure (Paw), esophageal pressure (Pes), transpulmonary pressure (PL) (Paw – Pes), chest wall elastic recoil pressure (Pcw) (tidal volume × estimated chest wall elastance) and respiratory muscle pressure (Pmus) (Pcw – Pes) (pressures in cmH2O). During an expiratory occlusion maneuver the patient inhales against a closed valve, resulting in a decrease in airway pressure. The maximal deflection in Paw from positive end-expiratory pressure is defined as occlusion pressure (∆Pocc). From this ∆PL and Pmus were computed and compared with true dynamic lung stress (increase in PL from onset to peak during inspiration) and true respiratory effort (peak Pmus during inspiration)

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Data obtained during expiratory occlusion maneuvers were averaged. Data were analyzed on a breath-by-breath basis and averaged over at least a 4-min period free of artifacts or esophageal contractions. Only recordings where ∆Pes/∆Pocc was between 0.8 and 1.2 were included in the analysis.

Statistical analysis

Normality was tested and data are presented accordingly as mean ± standard deviation (SD) or as median [interquartile range (IQR)]. Measured and computed ∆PL and Pmus were compared using Bland–Altman analysis. Receiver operating characteristic curve analysis was performed and sensitivity and specificity were computed to assess the accuracy of computed ∆PL and Pmus to detect excessive ∆PL > 20 cmH2O and Pmus > 10 and > 15 cmH2O. Linear regression analysis was performed to assess the relationship between ∆Pocc and respiratory effort. For all tests a two-tailed P < 0.05 was considered statistically significant. Statistical analyses were performed with Prism 5 (Graphad software, San Diego, USA).

Results

Patient characteristics

Esophageal manometry was obtained in 15 COVID-19 patients between April and July 2020. Two patients were excluded from analysis due to incorrect ∆Pes/∆Pocc. Patient characteristics at time of measurement are shown in Table 1. In general, patients were 61 ± 9 years old, had high PaCO2 (63 ± 17 mmHg) and received prolonged mechanical ventilation (41 ± 32 days). Respiratory failure was the main problem.

Table 1 Patient characteristics
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Respiratory parameters are shown in Table 2. Only in patient 7 it was not possible to analyze a 4-min period due to continuous esophageal contractions. Patients had a low Cdyn [24 (20–31) mL/cmH2O], high ∆PL (25 ± 6 cmH2O) and high Pmus (16 ± 7 cmH2O).

Table 2 Respiratory parameters
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Computed ∆P L and P mus

Bland–Altman analysis showed low bias, but wide limits of agreement between measured and computed ∆PL [− 1.1 ± 5.9 cmH2O (bias ± 95% limits of agreement)] (Fig. 2a). Bias between measured and computed Pmus was higher and limits of agreement were equally wide (2.3 ± 6.0 cmH2O) (Fig. 2b). This means there is poor agreement between measured and computed ∆PL and Pmus.

Fig. 2
figure 2

Bland–Altman plots with regression lines in which measured and computed dynamic transpulmonary driving pressure (∆PL) (a) and respiratory muscle pressure (Pmus) (b) are compared. Computed ∆PL overestimates measured ∆PL (a) (− 1.1 ± 5.9 cmH2O (bias ± 95% limits of agreement), while computed Pmus underestimates measured Pmus (b) (2.3 ± 6.0 cmH2O). Limits of agreement are large for both parameters. There was no significant trend in differences (for ∆PL r2 = 0.27; P = 0.06 and for Pmus r2 = 0.18; P = 0.15)

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Receiver operating characteristic curve analysis was performed to assess the discriminative performance to predict excessive dynamic lung stress and respiratory effort (Fig. 3; Table 3). Excessive ∆PL > 20 cmH2O was accurately predicted by computed ∆PL > 19 cmH2O [with area under the receiver operating curve (AUROC) 1.00 (95% confidence interval (CI), 1.00–1.00), sensitivity 100% (95% CI, 72–100%) and specificity 100% (95% CI, 16–100%)]. Discriminative performance for Pmus > 10 cmH2O was only moderate, but was acceptable for Pmus > 15 cmH2O with computed Pmus > 13 cmH2O [with AUROC 0.98 (95% CI, 0.90–1.00), sensitivity 100% (95% CI, 54–100%) and specificity 86% (95% CI, 42–100%)] (Fig. 3).

Fig. 3
figure 3

Receiver operating characteristic curves (ROC) showing the discriminative performance of computed transpulmonary driving pressure (∆PL) to detect measured excessive (> 20 cmH2O) ∆PL [area under the ROC (AUROC) 1.00 (95% confidence interval (CI), 1.00–1.00)] (a) and computed respiratory muscle pressure (Pmus) to detect measured Pmus > 10 cmH2O [AUROC 0.94 (95% CI, 0.81–1.00)] (b) and 15 cmH2O (AUROC 0.98 (95% CI, 0.90–1.00)] (c). Threshold values are shown as points on the curves

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Table 3 Discriminative performance
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∆P occ and respiratory effort

∆Pocc was correlated with respiratory effort (Fig. 4). Only moderate correlations were found between ∆Pocc and PTPes breath (r2 = 0.51; P = 0.0060) and WOB (calculated per liter) (r2 = 0.68; P = 0.0005). Respiratory effort calculated per minute showed much better correlations with ∆Pocc (for PTPes/min r2 = 0.73; P = 0.0002 and WOB r2 = 0.85; P < 0.0001).

Fig. 4
figure 4

Relation between occlusion pressure (∆Pocc) and respiratory effort. ∆Pocc is moderately correlated with work of breathing (WOB) calculated per liter (a) and pressure time product of esophageal pressure per breath (PTPes breath) (c). Higher correlations were found between ∆Pocc and respiratory effort when respiratory effort was calculated per minute (b, d)

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Discussion

We demonstrate that the mechanically ventilated COVID-19 patients with spontaneous breathing effort included in this study received prolonged mechanical ventilation, had a low dynamic lung compliance, high dynamic transpulmonary driving pressures and high respiratory effort. Dynamic transpulmonary driving pressure and respiratory muscle pressure were estimated from the maximal decline in airway pressure from PEEP during an expiratory occlusion maneuver. Computed ∆PL and Pmus are unreliable for direct estimates of ∆PL and Pmus derived from esophageal manometry, as analysis showed poor agreement between computed and measured values. However, they can predict excessive ∆PL (> 20 cmH2O) and Pmus (> 15 cmH2O) with high sensitivity and specificity. The occlusion pressure is highly correlated with respiratory effort per minute.

Dynamic lung stress and respiratory effort

Maintaining spontaneous breathing effort during mechanical ventilation has become increasingly important in recent years, due to accumulating evidence for over-assistance myotrauma not only during controlled mechanical ventilation, but also during high levels of pressure support ventilation [1,2,3,4,5]. Too high respiratory effort, however, can also cause lung and/or diaphragm injury. This might not be that obvious when relying only on plateau and driving pressures on the ventilator screen. The pressure generated by the respiratory muscles (i.e., Pmus) might in fact be quite high and thus the pleural pressure (i.e., Pes) quite negative, despite high levels of pressure support. Indirect evidence suggests that high Pmus may cause load-induced muscle injury and dysfunction [5, 6]. Negative pleural pressures in an already injured lung increase transpulmonary pressures and thus lung stress and strain and worsen vascular leakage [i.e., patient-self inflicted lung injury (P-SILI)] [16]. In our study, patients had a relatively high Pmus and PaCO2. Apparently, they were not able to increase Pmus to achieve a normal PaCO2. Patients had a high respiratory frequency, but this was insufficient in most patients to meet ventilatory demands as they had high dead space ventilation reflecting severe gas exchange disorders (Additional file 1: Table S1) [17]. ∆Pocc was only moderately correlated with PTPes breath and WOB (J/L), but highly correlated when respiratory effort was multiplied with respiratory frequency [i.e., PTPes/min and WOB (J/min)]. Telias et al. [18] observed something similar for P0.1, which correlated better with respiratory effort per minute as compared to respiratory effort per breath. Together, the data from our study and the study of Telias et al. [18] suggest that in response to high respiratory drive critically ill patients increase respiratory frequency rather than tidal volume, probably due to a combination of respiratory muscle weakness and decreased lung compliance, limiting the ability to increase effort per breath [7, 19, 20].

Clinical implications

Bertoni et al. [9] provided a novel non-invasive method to compute ∆PL and Pmus from ∆Pocc in mechanically ventilated patients with spontaneous breathing effort. We demonstrated that this novel method can also be applied in COVID-19 patients. In accordance with Bertoni et al. [9], computed ∆PL and Pmus cannot directly replace ∆PL and Pmus derived from esophageal manometry. In the external validation cohort they found reasonable discriminative performance for ∆PL > 15 cmH2O and Pmus > 10 cmH2O. In this study, we were able to show that computed values can also be used to predict excessive ∆PL (> 20 cmH2O) and Pmus (> 15 cmH2O). This is very useful when it is not feasible to perform esophageal manometry for various reasons.

COVID-19 patients have severely injured lungs and are prone to high respiratory effort, necessitating close monitoring to enable lung and diaphragm protective ventilation [6, 8]. If computed ∆PL and/or Pmus are/is excessively high, one can decide to measure esophageal pressures. If that is not feasible, ventilator settings should be changed followed by appropriate sedation to keep computed ∆PL and Pmus within the clinically acceptable range based on most recent studies and reviews [6, 8, 9]. Excessive sedation, however, can lead to insufficient respiratory effort (i.e., diminished ∆Pocc) and increased patient ventilator asynchronies [8].

Limitations

This study has some limitations. First, the relatively small sample size. However, many physiological studies with critically ill patients are limited in sample size. For example, the external validation cohort in the study by Bertoni et al. [9] only included 12 patients. Second, there is a selection bias. Only patients with computed high respiratory effort and/or high dynamic transpulmonary driving pressure, prolonged mechanical ventilation and/or who were hypercapnic, were included in the study. Therefore, we found relatively high measured ∆PL and Pmus. Third, limitations in measured ∆PL and Pmus. ∆PL is the dynamic transpulmonary driving pressure, therefore it may overestimate lung stress due to the resistance component. Some studies suggest to perform an end-inspiratory occlusion maneuver in the presence of spontaneous breathing activity to obtain semi-static pressure measurements [21, 22]. For Pmus calculations the chest wall elastance was estimated based on predicted vital capacity. Bertoni et al. [9] demonstrated that predicted values approximated measured values of chest wall elastance.

Conclusions

In mechanically ventilated COVID-19 patients with spontaneous breathing effort ∆PL and Pmus can be computed from an expiratory occlusion maneuver. Computed ∆PL and Pmus cannot replace ∆PL and Pmus derived from esophageal manometry, but they can predict excessive ∆PL and Pmus with high accuracy. The occlusion pressure is highly correlated with respiratory effort per minute.