In critical care trials reporting statistically significant effects on mortality, the findings often depend on a small number of events. Critical care clinicians should be wary of basing decisions on trials with a low fragility index. We advocate the reporting of fragility index for future trials in critical care to aid interpretation and decision making by clinicians.
The influence of variables that might affect the accuracy of pulse oximetry (SpO 2 ) recordings in critically ill patients is not well established. We sought to describe the relationship between paired SpO 2 /SaO 2 (oxygen saturation via arterial blood gas analysis) in adult intensive care unit (ICU) patients and to describe the diagnostic performance of SpO 2 in detecting low SaO 2 and PaO 2 . A paired SpO 2 /SaO 2 measurement was obtained from 404 adults in ICU. Measurements were used to calculate bias, precision, and limits of agreement. Associations between bias and variables including vasopressor and inotrope use, capillary refill time, hand temperature, pulse pressure, body temperature, oximeter model, and skin colour were estimated. There was no overall statistically significant bias in paired SpO 2 /SaO 2 measurements; observed limits of agreement were +/-4.4%. However, body temperature, oximeter model, and skin colour, were statistically significantly associated with the degree of bias. SpO 2 <89% had a sensitivity of 3/7 (42.9%; 95% confidence intervals, CI, 9.9% to 81.6%) and a specificity of 344/384 (89.6%; 95% CI 86.1% to 92.5%) for detecting SaO 2 <89%. The absence of statistically significant bias in paired SpO 2 /SaO 2 in adult ICU patients provides support for the use of pulse oximetry to titrate oxygen therapy. However, SpO 2 recordings alone should be used cautiously when SaO 2 recordings of 4.4% higher or lower than the observed SpO 2 would be of concern. A range of variables relevant to the critically ill had little or no effect on bias.
BackgroundAn important limitation of many critical care trial designs is that they hypothesize large, and potentially implausible, reductions in mortality. Interpretation of trial results could be improved by systematic assessment of the plausibility of trial hypotheses; however, such assessment has not been attempted in the field of critical care medicine. The purpose of this study was to determine clinicians’ views about prior probabilities and plausible effect sizes for ongoing critical care trials where the primary endpoint is landmark mortality.MethodsWe conducted a systematic review of clinical trial registries in September 2015 to identify ongoing critical care medicine trials where landmark mortality was the primary outcome, followed by a clinician survey to obtain opinions about ten large trials. Clinicians were asked to estimate the probability that each trial would demonstrate a mortality effect equal to or larger than that used in its sample size calculations.ResultsEstimates provided by individual clinicians varied from 0% to 100% for most trials, with a median estimate of 15% (IQR 10–20%). The median largest absolute mortality reduction considered plausible was 4.5% (IQR 3.5–5%), compared with a median absolute mortality reduction used in sample size calculations of 5% (IQR 3.6–10%) (P = 0.27).ConclusionsFor some of the largest ongoing critical care trials, many clinicians regard prior probabilities as low and consider that plausible effects on absolute mortality are less than 5%. Further work is needed to determine whether pooled estimates obtained by surveying clinicians are replicable and accurate or whether other methods of estimating prior probability are preferred.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1726-x) contains supplementary material, which is available to authorized users.
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We suggest that the practice of obtaining routine radiographs of the cervical spine following ACDF should be abandoned, unless there is a clear clinical indication.
Increasing demand for surgery and anaesthesia has created an imperative to manage anaesthetic workforce and caseload. This may include changes to distribution of cases amongst anaesthetists of different grades, including non-physician anaesthetists. To achieve this safely, an assessment of case complexity is essential. We present a novel system for scoring complexity of cases in anaesthesia, the Oxford Anaesthetic Complexity score. This integrates patient, anaesthetic, surgical and systems factors, and is different from assessments of risk. We adopted an end-user development approach to the design of the score, and validated it using a dataset of anaesthetic cases. Across 688 cases, the median (IQR [range]) complexity score was 19 (17-22 [15-33]). Cases requiring a consultant anaesthetist had a significantly higher median (IQR [range]) score than those requiring a senior trainee at 22 (20-25 [15-33]) vs. 19 (17-21 [15-28]), p < 0.001. Cases undertaken in a tertiary acute hospital had a significantly higher score than those in a district general hospital, the median (IQR [range]) scores being 20 (17-22 [15-33]) vs. 17 (16-19 [17-28]), p < 0.001. Receiver-operating characteristic analysis showed good prediction of complexity sufficient to require a consultant anaesthetist, with area under the curve of 0.84. Any rise in complexity above baseline (score > 15) was strongly predictive of a case too complex for a junior trainee (positive predictive value 0.93). The Oxford Anaesthetic Complexity score can be used to match cases to different grades of anaesthetist, and can help in defining cases appropriate for the expanding nonphysician anaesthetist workforce.
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