Identification of a non-linear model as a new method to detect expiratory airflow limitation in mechanically ventilated patients.

Expiratory flow limitation (EFL) can occur in mechanically ventilated patients with chronic obstructive pulmonary disease and other disorders. It leads to dynamic hyperinflation with ensuing deleterious consequences. Detecting EFL is thus clinically relevant. Easily applicable methods however lack this detection being routinely made in intensive care. Using a simple mathematical model, we propose a new method to detect EFL that does not require any intervention or modification of the ongoing therapeutic. The model consists in a monoalveolar representation of the respiratory system, including a collapsible airway that is submitted to periodic changes in pressure at the airway opening: EFL provokes a sharp expiratory increase in the resistance Rc of the collapsible airway. The model parameters were identified via the Levenberg-Marquardt method by fitting simulated data on the airway pressure and the flow signals recorded in 10 mechanically ventilated patients. A sensitivity study demonstrated that only 8/11 parameters needed to be identified, the remaining three being given reasonable physiological values. Flow-volume curves built at different levels of positive expiratory pressure, PEEP, during "PEEP trials" (stepwise increases in positive end-expiratory pressure to optimize ventilator settings) have shown evidence of EFL in three cases. This was concordant with parameter identification (high Rc during expiration for EFL patients). We conclude from these preliminary results that our model is a potential tool for the non-invasive detection of EFL in mechanically ventilated patients.

[Selective activation of blood pressure monitoring alarms: effect of noise pollution in the intensive care unit]. / Activation sélective des alarmes sur le monitoring de la pression artérielle: effet sur la pollution sonore en réanimation.

OBJECTIVE: To evaluate a selective activation of sounding alarms on non-invasive blood pressure (BP) monitoring according to the patient haemodynamic status. STUDY DESIGN: Prospective study. METHODS: Activation of alarms on BP was regulated with a protocol. Sounding alarms were either inactivated when patient's haemodynamic status was stable (group 1), or activated when it was unstable (group 2). The frequency of BP measurement was one every 15 min. For all mean BP value recorded, the following criteria were analyzed: 1) normality of the value compared to ranges 65-115 mmHg in group 1 or compared to alarm thresholds in group 2; 2) consequences on the care and therapeutic; 3) delay when an abnormal value was detected and managed after more than 15 min. RESULTS: 1,674 hours of monitoring from 42 patients, allowed the analysis of 6,695 measurements of mean BP, 3,092 in group 1 and 3,603 in group 2. In group 1, 2,822 measurements were considered as normal and 3,094 measures in group 2. Eight measurements had consequences on therapeutic in group 1, with only one with delay in care giving. 287 measurements had consequences on therapeutic in group 2, 8 with delay in care giving. Six per cent of abnormal measurements in group 2 were managed with delay. This protocol reduced by 52% the production of sounding alarms on BP, without noxious effects for the patients. CONCLUSION: Selective activation of sounding alarms on BP, according to the patient haemodynamic status, reduced noise pollution and could be one solution to improve monitoring efficiency in intensive care unit.

Positive end expiratory pressure and expiratory flow limitation: a model study.

Patients suffering from chronic obstructive pulmonary diseases, frequently exhibit expiratory airflow limitation. We propose a mathematical model describing the mechanical behavior of the ventilated respiratory system. This model has to simulate applied positive end-expiratory pressure (PEEP) effects during expiration, a process used by clinicians to improve airflow. The proposed model consists of a nonlinear two-compartment system. One of the compartments represents the collapsible airways and mimics its dynamic compression, the other represents the lung and chest wall compartment. For all clinical conditions tested (n=16), the mathematical model simulates the removal of expiratory airflow limitation at PEEP lower than 70-80% of intrinsic end-expiratory pressure (PEEPi), i.e. the end-expiratory alveolar pressure (PAet) without PEEP. It also shows the presence of an optimal PEEP. The optimal PEEP contributes to decrease PAet from 7.4+/-0.9 (SD) to 5.4+/-0.9 hPa (p < 0.0001; mild flow limitation) and from 11.8+/-1.1 to 7.8+/-0.7 hPa (p < 0.0001; severe flow limitation). Resistance of the collapsible compartment is decreased from 53+/-7 to 8.2+/-5.9 hPa.L(-1).s (p < 0.0001; mild flow limitation) and from 80+/-11 to 6.9+/-5.4 hPa.L(-1).s (p < 0.0001; severe flow limitation). This simplistic mathematical model gives a plausible explanation of the expiratory airflow limitation removal with PEEP and a rationale to the practice of PEEP application to airflow limited patients.

[Clinical evaluation of alarm efficieny in intensive care]. / Evaluation clinique de la pertinence des alarmes en réanimation.

OBJECTIVE: To evaluate the efficiency of haemodynamic and respiratory monitoring system by a clinical analysis of the alarms. STUDY DESIGN: Observational prospective study. PATIENTS: 25 patients who presented acute respiratory distress syndrome and who were monitored with haemodynamic and respiratory monitoring. METHODS: Each minute, a bedside clinical observer analysed alarms from the monitoring according to detection or absence to clinical events. Four situations were defined to statistical descriptive analysis: a) false positive (FP); b) true positive (TP); c) false negative (FN); and d) true negative (TN). True positive alarm which induced consequences on patients care were also analysed. RESULTS: 15,013 minutes allowed the recordings of 3,665 alarms, 44% from arterial pressure, 17% from SpO2 and 12% from airways maximal pressure. 46% were false positive alarms inducing a noisy pollution. The positive predictive value PPV = TP/(TP + FP) of these alarms were respectively 51% for arterial pressure, 18% for SpO2 and 100% for Paw. Only 5% of true positive alarms induced consequences on patients care. CONCLUSION: This protocol allowed the evaluation of monitoring efficiency. This kind of evaluation may help to improve monitoring capacity with reducing noisy pollution from false positive alarms.