Medicine (Austin & Northern Health) - Research Publications
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ItemDuration of platelet storage and outcomes of critically ill patients(WILEY, 2017-03)BACKGROUND: The storage duration of platelet (PLT) units is limited to 5 to 7 days. This study investigates whether PLT storage duration is associated with patient outcomes in critically ill patients. STUDY DESIGN AND METHODS: This study was a retrospective analysis of critically ill patients admitted to the intensive care unit (ICU) of two hospitals in Australia who received one or more PLT transfusions from 2008 to 2014. Storage duration was approached in several different ways. Outcome variables were hospital mortality and ICU-acquired infection. Associations between PLT storage duration and outcomes were evaluated using multiple logistic regression and also by Cox regression. RESULTS: Among 2250 patients who received one or more PLT transfusions while in the ICU, the storage duration of PLTs was available for 64% of patients (1430). In-hospital mortality was 22.1% and ICU infection rate 7.2%. When comparing patients who received PLTs of a maximum storage duration of not more than 3, 4, or 5 days, there were no significant differences in baseline characteristics. After confounders were adjusted for, the storage duration of PLTs was not independently associated with mortality (4 days vs. ≤3 days, odds ratio [OR] 0.88, 95% confidence interval [CI] 0.59-1.30; 5 days vs. ≤3 days, OR 0.97, 95% CI 0.68-1.37) or infection (4 days vs. ≤3 days, OR 0.71, 95% CI 0.39-1.29; 5 days vs. ≤3 days, OR 1.11, 95% CI 0.67-1.83). Similar results were obtained regardless of how storage duration of PLTs was approached. CONCLUSIONS: In this large observational study in a heterogeneous ICU population, storage duration of PLTs was not associated with an increased risk of mortality or infection.
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ItemPreoperative identification of cardiac surgery patients at risk of receiving a platelet transfusion: The Australian Cardiac Surgery Platelet Transfusion (ACSePT) risk prediction tool(WILEY, 2020-10)UNLABELLED: Platelet (PLT) transfusions are limited and costly resources. Accurately predicting clinical demand while limiting product wastage remains difficult. A PLT transfusion prediction score was developed for use in cardiac surgery patients who commonly require PLT transfusions. STUDY DESIGN AND METHODS: Using the Australian and New Zealand Society of Cardiac and Thoracic Surgeons National Cardiac Surgery Database, significant predictors for PLT transfusion were identified by multivariate logistic regression. Using a development data set containing 2005 to 2016 data, the Australian Cardiac Surgery Platelet Transfusion (ACSePT) risk prediction tool was developed by assigning weights to each significant predictor that corresponded to a probability of PLT transfusion. The predicted probability for each score was compared to actual PLT transfusion occurrence in a validation (2017) data set. RESULTS: The development data set contained 38 independent variables and 91 521 observations. The validation data set contained 12 529 observations. The optimal model contained 23 variables significant at P < .001 and an area under the receiver operating characteristic (ROC) curve of 0.69 (95% confidence interval [CI], 0.68-0.69). ACSePT contained nine variables and had an area under the ROC curve of 0.66 (95% CI, 0.65-0.66) and overall predicted probability of PLT transfusion of 19.8% for the validation data set compared to an observed risk of 20.3%. CONCLUSION: The ACSePT risk prediction tool is the first scoring system to predict a cardiac surgery patient's risk of receiving a PLT transfusion. It can be used to identify patients at higher risk of receiving PLT transfusions for inclusion in clinical trials and by PLT inventory managers to predict PLT demand.
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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.
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ItemNo Preview AvailableBench-to-bedside review: Avoiding pitfalls in critical care meta-analysis-funnel plots, risk estimates, types of heterogeneity, baseline risk and the ecologic fallacy(BMC, 2008)Meta-analysis can be a powerful tool for demonstrating the applicability of a concept beyond the context of individual clinical trials and observational studies, including exploration of effects across different subgroups. Meta-analysis avoids Simpson's paradox, in which a consistent effect in constituent trials is reversed when results are simply pooled. Meta-analysis in critical care medicine is made more complicated, however, by the heterogeneous nature of critically ill patients and the contexts within which they are treated. Failure to properly adjust for this heterogeneity risks missing important subgroup effects in, for example, the interaction of treatment with varying levels of baseline risk. When subgroups are defined by characteristics that vary within constituent trials (such as age) rather than features constant within each trial (such as drug dose), there is the additional risk of incorrect conclusions due to the ecological fallacy. The present review explains these problems and the strategies by which they are overcome.
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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.
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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.
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ItemIs platelet transfusion associated with hospital-acquired infections in critically ill patients?(BMC, 2017-01-06)BACKGROUND: Platelets are commonly transfused to critically ill patients. Reports suggest an association between platelet transfusion and infection. However, there is no large study to have determined whether platelet transfusion in critically ill patients is associated with hospital-acquired infection. METHODS: We conducted a multi-centre study using prospectively maintained databases of two large academic intensive care units (ICUs) in Australia. Characteristics of patients who received platelets in ICUs between 2008 and 2014 were compared to those of patients who did not receive platelets. Association between platelet administration and infection (bacteraemia and/or bacteriuria) was modelled using multiple logistic regression and Cox regression, with blood components as time-varying covariates. A propensity covariate adjustment was also performed to verify results. RESULTS: Of the 18,965 patients included, 2250 (11.9%) received platelets in ICU with a median number of 1 platelet unit (IQR 1-3) administered. Patients who received platelets were more severely ill at ICU admission (mean Acute Physiology and Chronic Health Evaluation III score 65 (SD 29) vs 52 (SD 25), p < 0.01) and had more comorbidities (31% vs 19%, p < 0.01) than patients without platelet transfusion. Invasive mechanical ventilation (87% vs 57%, p < 0.01) and renal replacement therapy (20% vs 4%, p < 0.01) were more frequently administered in patients receiving platelets than in patients without platelets. On univariate analysis, platelet transfusion was associated with hospital-acquired infection in the ICU (7.7% vs 1.4%, p < 0.01). After adjusting for confounders, including other blood components administered, patient severity, centre, year, and diagnosis category, platelet transfusions were independently associated with infection (adjusted OR 2.56 95% CI 1.98-3.31, p < 0.001). This association was also found in survival analysis with blood components as time-varying covariates (adjusted HR 1.85, 95% CI 1.41-2.41, p < 0.001) and when only bacteraemia was considered (adjusted OR 3.30, 95% CI 2.30-4.74, p <0.001). Platelet transfusions remained associated with infection after propensity covariate adjustment. CONCLUSIONS: After adjustment for confounders, including patient severity and other blood components, platelet transfusion was independently associated with ICU-acquired infection. Further research aiming to better understand this association and to prevent this complication is warranted.
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ItemThe effect of dexmedetomidine on vasopressor requirements in patients with septic shock: a subgroup analysis of the Sedation Practice in Intensive Care Evaluation [SPICE III] Trial(BMC, 2020-07-16)BACKGROUND: Septic shock is associated with decreased vasopressor responsiveness. Experimental data suggest that central alpha2-agonists like dexmedetomidine (DEX) increase vasopressor responsiveness and reduce catecholamine requirements in septic shock. However, DEX may also cause hypotension and bradycardia. Thus, it remains unclear whether DEX is hemodynamically safe or helpful in this setting. METHODS: In this post hoc subgroup analysis of the Sedation Practice in Intensive Care Evaluation (SPICE III) trial, an international randomized trial comparing early sedation with dexmedetomidine to usual care in critically patients receiving mechanical ventilation, we studied patients with septic shock admitted to two tertiary ICUs in Australia and Switzerland. The primary outcome was vasopressor requirements in the first 48 h after randomization, expressed as noradrenaline equivalent dose (NEq [μg/kg/min] = noradrenaline + adrenaline + vasopressin/0.4). RESULTS: Between November 2013 and February 2018, 417 patients were recruited into the SPICE III trial at both sites. Eighty-three patients with septic shock were included in this subgroup analysis. Of these, 44 (53%) received DEX and 39 (47%) usual care. Vasopressor requirements in the first 48 h were similar between the two groups. Median NEq dose was 0.03 [0.01, 0.07] μg/kg/min in the DEX group and 0.04 [0.01, 0.16] μg/kg/min in the usual care group (p = 0.17). However, patients in the DEX group had a lower NEq/MAP ratio, indicating lower vasopressor requirements to maintain the target MAP. Moreover, on adjusted multivariable analysis, higher dexmedetomidine dose was associated with a lower NEq/MAP ratio. CONCLUSIONS: In critically ill patients with septic shock, patients in the DEX group received similar vasopressor doses in the first 48 h compared to the usual care group. On multivariable adjusted analysis, dexmedetomidine appeared to be associated with lower vasopressor requirements to maintain the target MAP. TRIAL REGISTRATION: The SPICE III trial was registered at ClinicalTrials.gov ( NCT01728558 ).