The Recovery Room: Transition from a Sleepy Postoperative Unit to a Vibrant and Cost-Effective Multipurpose Perioperative Care Unit.

The anesthesiologist, who traditionally was solely responsible for the intra- and postoperative care of patients, has undergone a transformation over the last decades and has emerged as a specialist for perioperative medicine. This includes preoperative assessment, preoperative stabilization of emergent cases, pre- or postoperative initiation of regional blocks, postoperative recovery and if needed postoperative intensive care outside the intensive care unit. A traditional recovery room, designated to take care of patients emerging from anesthesia only, no longer matches the modern anesthesiologist's demands. However, a traditional recovery room can easily be transformed into a vibrant multi-purpose perioperative care unit. Especially in smaller hospitals, this serves to match the anesthesiologist's demands without the financial burden of separate units for each task. On the contrary, it allows to transform the recovery room from a mandatory, but costly postoperative unit into a highly productive and demanding perioperative unit, allowing for extra revenues without corresponding costs. Worldwide, operating rooms are linked to an adjacent recovery room allowing patients to emerge from anesthesia until they fulfill the criteria to be transferred either to the regular ward or, in case of outpatient surgery, to be discharged home. Running these recovery rooms, however, is expensive due to the required technical equipment and the monthly costs of highly qualified anesthesia personnel. Despite these financial burdens, such recovery rooms are still mandatory to ensure full recovery after anesthesia and surgery. In most countries, there is no (full) reimbursement for providing recovery rooms, turning them into fiscally deficient units in most hospitals. However, recovery rooms can be further developed allowing hospitals to improve their caseloads, reduce turnover times in the operating room, and even help to manage a shortage of beds in the intensive care unit. In this paper, we describe the potential transformation from a traditional recovery room to a multi-purpose perioperative high-tech unit.

Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model.

BACKGROUND: Cyclic recruitment and de-recruitment of atelectasis (c-R/D) is a contributor to ventilator-induced lung injury (VILI). Bedside detection of this dynamic process could improve ventilator management. This study investigated the potential of automated lung sound analysis to detect c-R/D as compared to four-dimensional computed tomography (4DCT). METHODS: In ten piglets (25 ± 2 kg), acoustic measurements from 34 thoracic piezoelectric sensors (Meditron ASA, Norway) were performed, time synchronized to 4DCT scans, at positive end-expiratory pressures of 0, 5, 10, and 15 cmH2O during mechanical ventilation, before and after induction of c-R/D by surfactant washout. 4DCT was post-processed for within-breath variation in atelectatic volume (Δ atelectasis) as a measure of c-R/D. Sound waveforms were evaluated for: 1) dynamic crackle energy (dCE): filtered crackle sounds (600-700 Hz); 2) fast Fourier transform area (FFT area): spectral content above 500 Hz in frequency and above -70 dB in amplitude in proportion to the total amount of sound above -70 dB amplitude; and 3) dynamic spectral coherence (dSC): variation in acoustical homogeneity over time. Parameters were analyzed for global, nondependent, central, and dependent lung areas. RESULTS: In healthy lungs, negligible values of Δ atelectasis, dCE, and FFT area occurred. In lavage lung injury, the novel dCE parameter showed the best correlation to Δ atelectasis in dependent lung areas (R2 = 0.88) where c-R/D took place. dCE was superior to FFT area analysis for each lung region examined. The analysis of dSC could predict the lung regions where c-R/D originated. CONCLUSIONS: c-R/D is associated with the occurrence of fine crackle sounds as demonstrated by dCE analysis. Standardized computer-assisted analysis of dCE and dSC seems to be a promising method for depicting c-R/D.

Influence of inspiration to expiration ratio on cyclic recruitment and derecruitment of atelectasis in a saline lavage model of acute respiratory distress syndrome.

OBJECTIVE: Cyclic recruitment and derecruitment of atelectasis can occur during mechanical ventilation, especially in injured lungs. Experimentally, cyclic recruitment and derecruitment can be quantified by respiration-dependent changes in PaO2 (ΔPaO2), reflecting the varying intrapulmonary shunt fraction within the respiratory cycle. This study investigated the effect of inspiration to expiration ratio upon ΔPaO2 and Horowitz index. DESIGN: Prospective randomized study. SETTING: Laboratory investigation. SUBJECTS: Piglets, average weight 30 ± 2 kg. INTERVENTIONS: At respiratory rate 6 breaths/min, end-inspiratory pressure (Pendinsp) 40 cm H2O, positive end-expiratory pressure 5 cm H2O, and FIO2 1.0, measurements were performed at randomly set inspiration to expiration ratios during baseline healthy and mild surfactant depletion injury. Lung damage was titrated by repetitive surfactant washout to induce maximal cyclic recruitment and derecruitment as measured by multifrequency phase fluorimetry. Regional ventilation distribution was evaluated by electrical impedance tomography. Step changes in airway pressure from 5 to 40 cm H2O and vice versa were performed after lavage to calculate PO2-based recruitment and derecruitment time constants (TAU). MEASUREMENTS AND MAIN RESULTS: In baseline healthy, cyclic recruitment and derecruitment could not be provoked, whereas in model acute respiratory distress syndrome, the highest ΔPaO2 were routinely detected at an inspiration to expiration ratio of 1:4 (range, 52-277 torr [6.9-36.9 kPa]). Shorter expiration time reduced cyclic recruitment and derecruitment significantly (158 ± 85 torr [21.1 ± 11.3 kPa] [inspiration to expiration ratio, 1:4]; 25 ± 12 torr [3.3 ± 1.6 kPa] [inspiration to expiration ratio, 4:1]; p < 0.0001), whereas the PaO2/FIO2 ratio increased (267 ± 50 [inspiration to expiration ratio, 1:4]; 424 ± 53 [inspiration to expiration ratio, 4:1]; p < 0.0001). Correspondingly, regional ventilation redistributed toward dependent lung regions (p < 0.0001). Recruitment was much faster (TAU: fast 1.6 s [78%]; slow 9.2 s) than derecruitment (TAU: fast 3.1 s [87%]; slow 17.7 s) (p = 0.0078). CONCLUSIONS: Inverse ratio ventilation minimizes cyclic recruitment and derecruitment of atelectasis in an experimental model of surfactant-depleted pigs. Time constants for recruitment and derecruitment, and regional ventilation distribution, reflect these findings and highlight the time dependency of cyclic recruitment and derecruitment.

Assessment of regional ventilation distribution: comparison of vibration response imaging (VRI) with electrical impedance tomography (EIT).

BACKGROUND: Vibration response imaging (VRI) is a bedside technology to monitor ventilation by detecting lung sound vibrations. It is currently unknown whether VRI is able to accurately monitor the local distribution of ventilation within the lungs. We therefore compared VRI to electrical impedance tomography (EIT), an established technique used for the assessment of regional ventilation. METHODOLOGY/PRINCIPAL FINDINGS: Simultaneous EIT and VRI measurements were performed in the healthy and injured lungs (ALI; induced by saline lavage) at different PEEP levels (0, 5, 10, 15 mbar) in nine piglets. Vibration energy amplitude (VEA) by VRI, and amplitudes of relative impedance changes (rel.ΔZ) by EIT, were evaluated in seven regions of interest (ROIs). To assess the distribution of tidal volume (VT) by VRI and EIT, absolute values were normalized to the VT obtained by simultaneous spirometry measurements. Redistribution of ventilation by ALI and PEEP was detected by VRI and EIT. The linear correlation between pooled VT by VEA and rel.ΔZ was R(2) = 0.96. Bland-Altman analysis showed a bias of -1.07±24.71 ml and limits of agreement of -49.05 to +47.36 ml. Within the different ROIs, correlations of VT-distribution by EIT and VRI ranged between R(2) values of 0.29 and 0.96. ALI and PEEP did not alter the agreement of VT between VRI and EIT. CONCLUSIONS/SIGNIFICANCE: Measurements of regional ventilation distribution by VRI are comparable to those obtained by EIT.