Automatic recruitment maneuvers in porcine acute lung injury based on online PaO2 measurements

cc4354 CCJ Poster presentation Automatic recruitment maneuvers in porcine acute lung injury based on online PaO2 measurements Luepschen H Meier T Großherr M Leibecke T Leonhardt S

Medical Information Technology, RWTH Aachen University, Aachen, Germany

Department of Anesthesiology, University of Lübeck, Germany

Department of Radiology, University of Lübeck, Germany

Critical Care 26th International Symposium on Intensive Care and Emergency Medicine Meeting abstracts 26th International Symposium on Intensive Care and Emergency Medicine Brussels, Belgium

21–24 March 2006

http://www.intensive.org 1364-8535 2006 10 Suppl 1 P7 http://ccforum.com/supplements/10/S1 10.1186/cc4354 21 3 2006

Introduction

The individualization of lung protective ventilation strategies (e.g. recruitment maneuvers [RM] and PEEP titration to keep the lungs open) requires careful bedside observations. Many parameters must be monitored, which calls for computer aid. A fuzzy-logic ventilation expert system has been tested regarding its ability to automatically conduct RMs based on the open lung concept (OLC).

Methods

Three pigs received lavages to induce ARDS and baseline ventilation of 8 ml/kg Vt, RR = 25, I:E = 1:1 and FiO₂= 1.0. The block diagram of the ventilation setup is depicted in Fig. 1a. It is capable of conducting automatic RMs while continuously recording pulmonary parameters. Fuzzy controllers handle the four phases of OLC-RM. They were fed with medical knowledge from experienced physicians. An electrical impedance tomograph (EIT) provided images of the ventilation distribution and CT scans were made. During phase 1 of RM, the controller increased the PEEP level (PCV, Pdelta = 8 cmH₂O) until the lung was supposed to be open according to online PaO₂measurements (Paratrend 7). In the closing phase 2, PEEP was automatically titrated until PaO₂started to decrease. After re-opening, steady-state ventilation (phase 4) was established at a PEEP = 2 cmH₂O above the closing pressure.

Figure 1

(a) Setup featuring sensor fusion and automatic ventilation control

(a) Setup featuring sensor fusion and automatic ventilation control. (b) Pulmonary parameters during all phases of an automatic RM (FiO₂= 1.0). (c) CT scans and end-inspiratory EIT images before and after automatic RM.

Results

The pulmonary parameters of one pig during an RM cycle can be seen in Fig. 1b. After 20 min, PaO₂, Vt and compliance Crs (= Pdelta/Vt) were significantly increased in all animals and PaCO₂ reduced to normal values. Phases 1–3 of the RM process lasted approximately 5 min, partially due to the dynamic latency (15 s) of the measurement system. An optimization of the fuzzy PaO₂controller and additional sensors will shorten the execution time and reduce the heart's pressure load. Figure 1c shows the CT and EIT images before and after RM. Atelectases are removed and ventilation is increased, more evenly distributed and shifted from ventral to dorsal regions.

Conclusion

Automatic RM with a sustained improvement of PaO₂ and Crs could be achieved. The EIT showed a high potential to visualize and assess RM with a high temporal resolution.