A decision support system for suggesting ventilator settings: retrospective evaluation in cardiac surgery patients ventilated in the ICU.

Selecting appropriate ventilator settings decreases the risk of ventilator-induced lung injury. A decision support system (DSS) has been developed based on physiological models, which can advise on setting of tidal volume (Vt), respiratory frequency (f) and fraction of inspired oxygen (FiO2). The aim of this study is to assess the feasibility of the DSS by comparing its advice with the values used in clinical practice. Data from 20 patients following uncomplicated coronary artery bypass grafting (CABG) with cardiopulmonary bypass was used to test the DSS. Ventilator settings suggested by the DSS were compared to the settings selected by the clinician. When compared to the clinician the DSS suggested: lowering FiO2 (by median 7%, range 2-17%) at high SpO2 and increasing FiO2 (by median 2%, range 1-5%) at low SpO2; lowering ventilation volume (by median 0.57 l min(-1), range 0.2-1.1 l min(-1)) at high pHa and increasing ventilation volume (by median 0.4 l min(-1), range 0.1-0.9 l min(-1)) at low pHa. Suggested changes in ventilation volume were such that simulated values of PIP were < or = 22.9 cmH2O and respiratory frequency < or = 18 breaths min(-1). In all cases, computer suggested values of FiO2, Vt or f were consistent with maintaining sufficient oxygenation, normalising pH and obtaining low values of PIP.

Variation in the PaO2/FiO2 ratio with FiO2: mathematical and experimental description, and clinical relevance.

INTRODUCTION: Previous studies have shown through theoretical analyses that the ratio of the partial pressure of oxygen in arterial blood (PaO2) to the inspired oxygen fraction (FiO2) varies with the FiO2 level. The aim of the present study was to evaluate the relevance of this variation both theoretically and experimentally using mathematical model simulations, comparing these ratio simulations with PaO2/FiO2 ratios measured in a range of different patients. METHODS: The study was designed as a retrospective study using data from 36 mechanically ventilated patients and 57 spontaneously breathing patients studied on one or more occasions. Patients were classified into four disease groups (normal, mild hypoxemia, acute lung injury and acute respiratory distress syndrome) according to their PaO2/FiO2 ratio. On each occasion the patients were studied using four to eight different FiO2 values, achieving arterial oxygen saturations in the range 85-100%. At each FiO2 level, measurements were taken of ventilation, of arterial acid-base and of oxygenation status. Two mathematical models were fitted to the data: a one-parameter 'effective shunt' model, and a two-parameter shunt and ventilation/perfusion model. These models and patient data were used to investigate the variation in the PaO2/FiO2 ratio with FiO2, and to quantify how many patients changed disease classification due to variation in the PaO2/FiO2 ratio. An F test was used to assess the statistical difference between the two models' fit to the data. A confusion matrix was used to quantify the number of patients changing disease classification. RESULTS: The two-parameter model gave a statistically better fit to patient data (P < 0.005). When using this model to simulate variation in the PaO2/FiO2 ratio, disease classification changed in 30% of the patients when changing the FiO2 level. CONCLUSION: The PaO2/FiO2 ratio depends on both the FiO2 level and the arterial oxygen saturation level. As a minimum, the FiO2 level at which the PaO2/FiO2 ratio is measured should be defined when quantifying the effects of therapeutic interventions or when specifying diagnostic criteria for acute lung injury and acute respiratory distress syndrome. Alternatively, oxygenation problems could be described using parameters describing shunt and ventilation/perfusion mismatch.

Retrospective evaluation of a decision support system for controlled mechanical ventilation.

Management of mechanical ventilation in intensive care patients is complicated by conflicting clinical goals. Decision support systems (DSS) may support clinicians in finding the correct balance. The objective of this study was to evaluate a computerized model-based DSS for its advice on inspired oxygen fraction, tidal volume and respiratory frequency. The DSS was retrospectively evaluated in 16 intensive care patient cases, with physiological models fitted to the retrospective data and then used to simulate patient response to changes in therapy. Sensitivity of the DSS's advice to variations in cardiac output (CO) was evaluated. Compared to the baseline ventilator settings set as part of routine clinical care, the system suggested lower tidal volumes and inspired oxygen fraction, but higher frequency, with all suggestions and the model simulated outcome comparing well with the respiratory goals of the Acute Respiratory Distress Syndrome Network from 2000. Changes in advice with CO variation of about 20% were negligible except in cases of high oxygen consumption. Results suggest that the DSS provides clinically relevant and rational advice on therapy in agreement with current 'best practice', and that the advice is robust to variation in CO.

Variability of preference toward mechanical ventilator settings: a model-based behavioral analysis.

PURPOSE: The purpose of this study was to evaluate Danish clinicians' opinions toward ventilator settings using standardized model-simulated patients. The models ensured that all clinicians received identical presentations of data and anticipated responses to changes in patient state, enabling opinions on the same patient cases to be obtained from different clinicians. MATERIALS AND METHODS: Ten Danish intensive care clinicians' and a computerized decision support system each provided suggestions for respiratory frequency (f), tidal volume (Vt) and insoired oxygen fraction (FiO2) in the same 10 model-simulated patient cases. The 110 suggestions were then evaluated by the 10 clinicians in a ranking and classification procedure. RESULTS: Clinicians' preferences toward ventilator settings (Fio(2), Vt, and f) and the resulting simulated values of arterial oxygen saturation, peak inspiratory pressure, and pH were significantly different (P < .005). The results of the classification showed that clinicians generally had poor opinion of the advice provided by other clinicians and the decision support system, considering this advice to be unacceptable in 33% of cases and good only in 21%. The ranking procedure also showed that clinicians did not agree on the best and worst advice. CONCLUSION: The present study shows significant difference in opinion on appropriate settings of f, Vt, and Fio(2) in the same computerized decision support system model-simulated patient cases.

Prospective evaluation of a decision support system for setting inspired oxygen in intensive care patients.

PURPOSE: The aim of the study was to prospectively evaluate a decision support system for its ability to provide appropriate suggestions of inspired oxygen fraction in intensive care patients comparing with levels used by clinicians in attendance. MATERIALS AND METHODS: Thirteen mechanically ventilated patients were studied in an intensive care unit where up to 4 experiments were performed during 2 consecutive days. Inspired oxygen fraction was selected in each experiment by both the decision support system and attending clinicians, and each selection was evaluated by measuring arterial oxygen saturation. RESULTS: Median (interquartile range [range]) changes in inspired oxygen fraction from baseline level by attending clinicians and the decision support system were 0.00 (-0.05 to 0.00 [-0.10 to 0.05]) and -0.03 (-0.07 to 0.01 [-0.16 to 0.12]), respectively. Clinician ranges of inspired oxygen fraction and arterial oxygen saturation were 0.25 to 0.70 and 0.92 to 0.99, respectively. Decision support system ranges of inspired oxygen fraction and arterial oxygen saturation were 0.26 to 0.54 and 0.94 to 0.99, respectively. CONCLUSIONS: The decision support system selects appropriate levels of inspired oxygen fraction in intensive care patients and could be used for automatic frequent assessment of patients, freeing the focus of clinicians to concentrate on more challenging therapy.