Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: feasibility of using a computer algorithm.
OBJECTIVE
Ineffective triggering (IT) is the most common manifestation of patient-ventilator asynchrony in mechanically ventilated patients. IT in the expiratory phase (ITE) accounts for the majority of IT and is associated with characteristic features of flow and airway pressure deflection, caused by ineffective effort from the patient. The purpose of this study was to quantify the characteristics of flow and airway pressure deflections of ITE and, using a computerized algorithm, to evaluate their usefulness in the detection of ITEs.


METHODS
Prospective, clinical study.


METHODS
Medical intensive care unit in a 1,000-bed university hospital.


METHODS
A total of 14 mechanically ventilated adult patients with patient-ventilator asynchrony.


METHODS
None.


RESULTS
We analyzed 5,899 breaths and found that 1,831 were ITEs. The average values for maximum flow deflection (F(def)) and maximum airway pressure deflection (P(def)) in ITEs were 13.94 +/- 8.0 L/min and 1.91 +/- 0.97 cm H2O. With a starting value of 0.1 L/min for F(def) and 0.01 cm H2O for P(def), the area under the receiver operating characteristics curve of F(def) and P(def) for the detection of ITEs was 0.98 and 0.97, respectively. Sensitivity and specificity for the detection of ITEs were 91.5% and 96.2% for F(def), respectively, for a cutoff value of 5.45 L/min, and 93.3% and 92.9% for Pdef, for a cutoff value of 0.45 cm H2O.


CONCLUSIONS
We conclude that accurately detecting and quantifying ITEs is feasible using a computerized algorithm based on F(def) and P(def). Such a computerized estimation of patient-ventilator interaction might be helpful for adjusting ventilator settings in an intensive care unit.
Chen
Chang-Wen
CW
Medical Intensive Care Unit, Department of Internal Medicine, National Cheng Kung University Medical Center, Tainan, Taiwan.
Lin
Wei-Chieh
WC
Hsu
Chih-Hsin
CH
Cheng
Kuo-Sheng
KS
Lo
Chien-Shun
CS
D000368:Aged; D000369:Aged, 80 and over; D000403:Airway Resistance / Q000502:physiology*; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D004868:Equipment Failure; D045853:Exhalation / Q000502:physiology*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012123:Pulmonary Ventilation / Q000502:physiology*; D012121:Respiration, Artificial / Q000009:adverse effects* / Q000295:instrumentation; D012131:Respiratory Insufficiency / Q000175:diagnosis / Q000503:physiopathology* / Q000628:therapy; D012122:Ventilators, Mechanical