Critical care : the official journal of the Critical Care Forum
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The preferred analysis for studies of mortality among patients treated in an intensive care unit should compare the proportions of patients who died during hospitalization. Studies that look for prognostic covariates should use logistic regression. Survival methods, such as the proportional hazards model, or methods based on competing risk analysis are not appropriate because prolonged survival among patients that die during their hospitalization does not benefit the patient and, therefore, should not be measured in the statistical analysis.
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Most case series suggest that less than half of the patients receiving a mechanical cardiac assist device as a bridge to recovery due to severe post-cardiotomy heart failure survive to hospital discharge. Levosimendan is the only inotropic substance known to improve medium term survival in patients suffering from severe heart failure. ⋯ Levosimendan seems to improve medium term survival in patients failing to wean off cardiopulmonary bypass and requiring cardiac assist devices as a bridge to recovery. This retrospective analysis justifies prospective randomised investigations of levosimendan in this group of patients.
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Kaplan-Meier curves and logistic models are widely used to describe and explain the variability of survival in intensive care unit (ICU) patients. The Kaplan-Meier approach considers that patients discharged alive from hospital are 'non-informatively' censored (for instance, representative of all other individuals who have survived to that time but are still in hospital); this is probably wrong. Logistic models are adapted to this so-called 'competing risks' setting but fail to take into account censoring and differences in exposure time. To address these issues, we exemplified the usefulness of standard competing risks methods; namely, cumulative incidence function (CIF) curves and the Fine and Gray model. ⋯ The Fine and Gray model appears of interest when predicting mortality in ICU patients. It is closely related to the logistic model, through direct modeling of times to death, and can be easily extended to model non-fatal outcomes.
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In the early phase of their disease process, patients with acute lung injury are often ventilated with strategies that control the tidal volume or airway pressure, while modes employing spontaneous breathing are applied later to wean the patient from the ventilator. Spontaneous breathing modes may integrate intrinsic feedback mechanisms that should help prevent ventilator-induced lung injury, and should improve synchrony between the ventilator and the patient's demand. Airway pressure release ventilation with spontaneous breathing was shown to decrease cyclic collapse/recruitment of dependent, juxtadiaphragmatic lung areas compared with airway pressure release ventilation without spontaneous breathing. Combined with previous data demonstrating improved cardiorespiratory variables, airway pressure release ventilation with spontaneous breathing may turn out to be a less injurious ventilatory strategy.
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The ventilator and the endotracheal tube impose additional workload in mechanically ventilated patients breathing spontaneously. The total work of breathing (WOB) includes elastic and resistive work. In a bench test we assessed the imposed WOB using 3100 A/3100 B SensorMedics high-frequency oscillatory ventilators. ⋯ Spontaneous breathing during HFOV resulted in considerable imposed WOB in pediatric and adult simulations, explaining the discomfort seen in those patients breathing spontaneously during HFOV. The level of imposed WOB was lower in the newborn and infant simulations, explaining why these patients tolerate spontaneous breathing during HFOV well. A high fresh gas flow rate reduced the imposed WOB. These findings suggest the need for a demand flow system based on patient need allowing spontaneous breathing during HFOV.