Trigger performance of mid-level ICU mechanical ventilators during assisted ventilation: a bench study

Objective: To compare the triggering performance of mid-level ICU mechanical ventilators with a standard ICU mechanical ventilator. Design: Experimental bench study. Setting: The respiratory care laboratory of a university-affiliated teaching hospital. Subject: A computerized mechanical lung model, the IngMar ASL5000. Interventions: Ten mid-level ICU ventilators were compared to an ICU ventilator at two levels of lung model effort, three combinations of respiratory mechanics (normal, COPD and ARDS) and two modes of ventilation, volume and pressure assist/control. A total of 12 conditions were compared. Measurements and main results: Performance varied widely among ventilators. Mean inspiratory trigger time was < 100 ms for only half of the tested ventilators. The mean inspiratory delay time (time from initiation of the breath to return of airway pressure to baseline) was longer than that for the ICU ventilator for all tested ventilators except one. The pressure drop during triggering (Ptrig) was comparable with that of the ICU ventilator for only two ventilators. Expiratory Settling Time (time for pressure to return to baseline) had the greatest variability among ventilators. Conclusions: Triggering differences among these mid-level ICU ventilators and with the ICU ventilator were identified. Some of these ventilators had a much poorer triggering response with high inspiratory effort than the ICU ventilator. These ventilators do not perform as well as ICU ventilators in patients with high ventilatory demand.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Effects of low level laser therapy (808 nm) on physical strength training in humans

Recent studies have investigated whether low level laser therapy (LLLT) can optimize human muscle performance in physical exercise. This study tested the effect of LLLT on muscle performance in physical strength training in humans compared with strength training only. The study involved 36 men (20.8 +/- 2.2 years old), clinically healthy, with a beginner and/or moderate physical activity training pattern. The subjects were randomly distributed into three groups: TLG (training with LLLT), TG (training only) and CG (control). The training for TG and TLG subjects involved the leg-press exercise with a load equal to 80% of one repetition maximum (1RM) in the leg-press test over 12 consecutive weeks. The LLLT was applied to the quadriceps muscle of both lower limbs of the TLG subjects immediately after the end of each training session. Using an infrared laser device (808 nm) with six diodes of 60 mW each a total energy of 50.4 J of LLLT was administered over 140 s. Muscle strength was assessed using the 1RM leg-press test and the isokinetic dynamometer test. The muscle volume of the thigh of the dominant limb was assessed by thigh perimetry. The TLG subjects showed an increase of 55% in the 1RM leg-press test, which was significantly higher than the increases in the TG subjects (26%, P = 0.033) and in the CG subjects (0.27%, P < 0.001). The TLG was the only group to show an increase in muscle performance in the isokinetic dynamometry test compared with baseline. The increases in thigh perimeter in the TLG subjects and TG subjects were not significantly different (4.52% and 2.75%, respectively; P = 0.775). Strength training associated with LLLT can increase muscle performance compared with strength training only.

Performance impacts of appraisal and coping with stress in workplace settings: The role of affect and emotional intelligence

We review the literature on stress in organizational settings and, based on a model of job insecurity and emotional intelligence by Jordan, Ashkanasy and Härtel (2002), present a new model where affective responses associated with stress mediate the impact of workplace stressors on individual and organizational performance outcomes. Consistent with Jordan et al., emotional intelligence is a key moderating variable. In our model, however, the components of emotional intelligence are incorporated into the process of stress appraisal and coping. The chapter concludes with a discussion of the implications of these theoretical developments for understanding emotional and behavioral responses to workplace.