Medicine (Austin & Northern Health) - Research Publications
Permanent URI for this collection
Search Results
1 - 3 of 3
-
ItemNo Preview AvailableProspective meta-analysis using individual patient data in intensive care medicine(SPRINGER, 2010-01)Meta-analysis is a technique for combining evidence from multiple trials. However, meta-analyses of studies with substantial heterogeneity among patients within trials-common in intensive care-can lead to incorrect conclusions if performed using aggregate data. Use of individual patient data (IPD) can avoid this concern, increase the power of a meta-analysis, and is useful for exploring subgroup effects. Barriers exist to IPD meta-analysis, most of which are overcome if clinical trials are designed to prospectively facilitate the incorporation of their results with other trials. We review the features of prospective IPD meta-analysis and identify those of relevance to intensive care research. We identify three clinical questions, which are the subject of recent or planned randomised controlled trials where IPD MA offers advantages over approaches using aggregate data.
-
ItemDynamic lactate indices as predictors of outcome in critically ill patients(BMC, 2011)INTRODUCTION: Dynamic changes in lactate concentrations in the critically ill may predict patient outcome more accurately than static indices. We aimed to compare the predictive value of dynamic indices of lactatemia in the first 24 hours of intensive care unit (ICU) admission with the value of more commonly used static indices. METHODS: This was a retrospective observational study of a prospectively obtained intensive care database of 5,041 consecutive critically ill patients from four Australian university hospitals. We assessed the relationship between dynamic lactate values collected in the first 24 hours of ICU admission and both ICU and hospital mortality. RESULTS: We obtained 36,673 lactate measurements in 5,041 patients in the first 24 hours of ICU admission. Both the time weighted average lactate (LACTW₂₄) and the change in lactate (LACΔ₂₄) over the first 24 hours were independently predictive of hospital mortality with both relationships appearing to be linear in nature. For every one unit increase in LACTW₂₄ and LACΔ₂₄ the risk of hospital death increased by 37% (OR 1.37, 1.29 to 1.45; P < 0.0001) and by 15% (OR 1.15, 1.10 to 1.20; P < 0.0001) respectively. Such dynamic indices, when combined with Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, improved overall outcome prediction (P < 0.0001) achieving almost 90% accuracy. When all lactate measures in the first 24 hours were considered, the combination of LACTW₂₄ and LACΔ₂₄ significantly outperformed (P < 0.0001) static indices of lactate concentration, such as admission lactate, maximum lactate and minimum lactate. CONCLUSIONS: In the first 24 hours following ICU admission, dynamic indices of hyperlactatemia have significant independent predictive value, improve the performance of illness severity score-based outcome predictions and are superior to simple static indices of lactate concentration.
-
ItemArterial hyperoxia and in-hospital mortality after resuscitation from cardiac arrest(BIOMED CENTRAL LTD, 2011)INTRODUCTION: Hyperoxia has recently been reported as an independent risk factor for mortality in patients resuscitated from cardiac arrest. We examined the independent relationship between hyperoxia and outcomes in such patients. METHODS: We divided patients resuscitated from nontraumatic cardiac arrest from 125 intensive care units (ICUs) into three groups according to worst PaO2 level or alveolar-arterial O2 gradient in the first 24 hours after admission. We defined 'hyperoxia' as PaO2 of 300 mmHg or greater, 'hypoxia/poor O2 transfer' as either PaO2 < 60 mmHg or ratio of PaO2 to fraction of inspired oxygen (FiO2 ) < 300, 'normoxia' as any value between hypoxia and hyperoxia and 'isolated hypoxemia' as PaO2 < 60 mmHg regardless of FiO2. Mortality at hospital discharge was the main outcome measure. RESULTS: Of 12,108 total patients, 1,285 (10.6%) had hyperoxia, 8,904 (73.5%) had hypoxia/poor O2 transfer, 1,919 (15.9%) had normoxia and 1,168 (9.7%) had isolated hypoxemia (PaO2 < 60 mmHg). The hyperoxia group had higher mortality (754 (59%) of 1,285 patients; 95% confidence interval (95% CI), 56% to 61%) than the normoxia group (911 (47%) of 1,919 patients; 95% CI, 45% to 50%) with a proportional difference of 11% (95% CI, 8% to 15%), but not higher than the hypoxia group (5,303 (60%) of 8,904 patients; 95% CI, 59% to 61%). In a multivariable model controlling for some potential confounders, including illness severity, hyperoxia had an odds ratio for hospital death of 1.2 (95% CI, 1.1 to 1.6). However, once we applied Cox proportional hazards modelling of survival, sensitivity analyses using deciles of hypoxemia, time period matching and hyperoxia defined as PaO2 > 400 mmHg, hyperoxia had no independent association with mortality. Importantly, after adjustment for FiO2 and the relevant covariates, PaO2 was no longer predictive of hospital mortality (P = 0.21). CONCLUSIONS: Among patients admitted to the ICU after cardiac arrest, hyperoxia did not have a robust or consistently reproducible association with mortality. We urge caution in implementing policies of deliberate decreases in FiO2 in these patients.