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BACKGROUND: The American Statistical Association has highlighted problems with null hypothesis significance testing and outlined alternative approaches that may 'supplement or even replace P-values'. One alternative is to report the false positive risk (FPR), which quantifies the chance the null hypothesis is true when the result is statistically significant. METHODS: We reviewed single-centre, randomised trials in 10 anaesthesia journals over 6 yr where differences in a primary binary outcome were statistically significant. We calculated a Bayes factor by two methods (Gunel, Kass). From the Bayes factor we calculated the FPR for different prior beliefs for a real treatment effect. Prior beliefs were quantified by assigning pretest probabilities to the null and alternative hypotheses. RESULTS: For equal pretest probabilities of 0.5, the median (inter-quartile range [IQR]) FPR was 6% (1-22%) by the Gunel method and 6% (1-19%) by the Kass method. One in five trials had an FPR ≥20%. For trials reporting P-values 0.01-0.05, the median (IQR) FPR was 25% (16-30%) by the Gunel method and 20% (16-25%) by the Kass method. More than 90% of trials reporting P-values 0.01-0.05 required a pretest probability >0.5 to achieve an FPR of 5%. The median (IQR) difference in the FPR calculated by the two methods was 0% (0-2%). CONCLUSIONS: Our findings suggest that a substantial proportion of single-centre trials in anaesthesia reporting statistically significant differences provide limited evidence of real treatment effects, or, alternatively, required an implausibly high prior belief in a real treatment effect. CLINICAL TRIAL REGISTRATION: PROSPERO (CRD42023350783).

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OBJECTIVE: Intervention for repair of secondary mitral valve disease is frequently associated with recurrent regurgitation. We sought to determine if there was sufficient evidence to support inclusion of anatomic indices of leaflet dysfunction in the management of secondary mitral valve disease. METHODS: We performed a systematic review and meta-analysis of published reports comparing anatomic indices of leaflet dysfunction with the complexity of valve repair and the outcome from intervention. Patients were stratified by the severity of leaflet dysfunction. A secondary analysis was performed comparing outcomes when procedural complexity was optimally matched to severity of leaflet dysfunction and when intervention was not matched to dysfunction. RESULTS: We identified 6864 publications, of which 65 met inclusion criteria. An association between the severity of leaflet dysfunction and the procedural complexity was highly predictive of satisfactory freedom from recurrent regurgitation. Patients were categorized into 4 groups based on stratification of leaflet dysfunction. Satisfactory results were achieved in 93.7% of patients in whom repair complexity was appropriately matched to severity of leaflet dysfunction and in 68.8% in whom repair was not matched to dysfunction (odds ratio, 0.148; 95% confidence interval, 0.119-0.184; P < .0001). CONCLUSIONS: For patients with secondary mitral valve disease, satisfactory outcome from valve repair improves when procedural complexity is matched to anatomic indices of leaflet dysfunction. Anatomic indices of leaflet dysfunction should be considered when planning interventions for secondary mitral regurgitation. Routine inclusion of anatomic indices in trial design and reporting should facilitate comparison of results and strengthen guidelines. There are sufficient data to support anatomic staging of secondary mitral valve disease.

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Randomized controlled trials are one of the best ways of quantifying the effectiveness of medical interventions. Therefore, when the authors of a randomized superiority trial report that differences in the primary outcome between the intervention group and the control group are "significant" (i.e., P ≤ 0.05), we might assume that the intervention has an effect on the outcome. Similarly, when differences between the groups are "not significant," we might assume that the intervention does not have an effect on the outcome. Nevertheless, both assumptions are frequently incorrect.In this article, we explore the relationship that exists between real treatment effects and declarations of statistical significance based on P values and confidence intervals. We explain why, in some circumstances, the chance an intervention is ineffective when P ≤ 0.05 exceeds 25% and the chance an intervention is effective when P > 0.05 exceeds 50%.Over the last decade, there has been increasing interest in Bayesian methods as an alternative to frequentist hypothesis testing. We provide a robust but nontechnical introduction to Bayesian inference and explain why a Bayesian posterior distribution overcomes many of the problems associated with frequentist hypothesis testing.Notwithstanding the current interest in Bayesian methods, frequentist hypothesis testing remains the default method for statistical inference in medical research. Therefore, we propose an interim solution to the "significance problem" based on simplified Bayesian metrics (e.g., Bayes factor, false positive risk) that can be reported along with traditional P values and confidence intervals. We calculate these metrics for four well-known multicentre trials. We provide links to online calculators so readers can easily estimate these metrics for published trials. In this way, we hope decisions on incorporating the results of randomized trials into clinical practice can be enhanced, minimizing the chance that useful treatments are discarded or that ineffective treatments are adopted.

RéSUMé: Les études randomisées contrôlées constituent l'un des meilleurs moyens de quantifier l'efficacité des interventions médicales. Par conséquent, lorsque les autrices et auteurs d'une étude randomisée superiorité signalent que les différences dans le critère d'évaluation principal entre le groupe d'intervention et le groupe témoin sont « significatives ¼ (c.-à-d. P ≤ 0,05), nous pourrions supposer que l'intervention a un effet sur le critère d'évaluation. De même, lorsque les différences entre les groupes ne sont « pas significatives ¼, nous pourrions supposer que l'intervention n'a pas d'effet sur le critère d'évaluation. Pourtant, ces deux hypothèses s'avèrent souvent incorrectes.Dans cet article, nous explorons la relation qui existe entre les effets réels d'un traitement et les déclarations de signification statistique fondées sur les valeurs P et les intervalles de confiance. Nous expliquons pourquoi, dans certaines circonstances, la probabilité qu'une intervention soit inefficace lorsque P ≤ 0,05 dépasse 25 % et la probabilité qu'une intervention soit efficace lorsque P > 0,05 dépasse 50 %.Au cours de la dernière décennie, nous avons assisté à un intérêt croissant pour les méthodes bayésiennes comme alternative aux tests d'hypothèses fréquentistes. Nous proposons une introduction robuste mais non technique à l'inférence bayésienne et expliquons pourquoi une distribution postérieure bayésienne surmonte bon nombre des problèmes associés aux tests d'hypothèses fréquentistes.Malgré l'intérêt actuel pour les méthodes bayésiennes, les tests d'hypothèses fréquentistes restent la méthode par défaut pour l'inférence statistique en recherche médicale. Par conséquent, nous proposons une solution provisoire au « problème de signification ¼ basée sur des mesures bayésiennes simplifiées (par exemple, facteur de Bayes, risque de faux positifs) qui peuvent être rapportées en même temps que les mesures traditionnelles des valeurs P et des intervalles de confiance. Nous calculons ces paramètres pour quatre études multicentriques bien connues. Nous fournissons des liens vers des calculatrices en ligne afin que les lectrices et lecteurs puissent facilement estimer ces mesures pour les études publiées. De cette façon, nous espérons que les décisions sur l'intégration des résultats des études randomisées dans la pratique clinique pourront être améliorées, minimisant ainsi le risque que des traitements utiles soient rejetés ou que des traitements inefficaces soient adoptés.

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BACKGROUND: The sample size calculation is an important step in designing randomised controlled trials. For a trial comparing a control and an intervention group, where the outcome is binary, the sample size calculation requires choosing values for the anticipated event rates in both the control and intervention groups (the effect size), and the error rates. The Difference ELicitation in TriAls guidance recommends that the effect size should be both realistic, and clinically important to stakeholder groups. Overestimating the effect size leads to sample sizes that are too small to reliably detect the true population effect size, which in turn results in low achieved power. In this study, we use the Delphi approach to gain consensus on what the minimum clinically important effect size is for Balanced-2, a randomised controlled trial comparing processed electroencephalogram-guided 'light' to 'deep' general anaesthesia on the incidence of postoperative delirium in older adults undergoing major surgery. METHODS: Delphi rounds were conducted using electronic surveys. Surveys were administered to two stakeholder groups: specialist anaesthetists from a general adult department in Auckland City Hospital, New Zealand (Group 1), and specialist anaesthetists with expertise in clinical research, identified from the Australian and New Zealand College of Anaesthetist's Clinical Trials Network (Group 2). A total of 187 anaesthetists were invited to participate (81 from Group 1 and 106 from Group 2). Results from each Delphi round were summarised and presented in subsequent rounds until consensus was reached (>70% agreement). RESULTS: The overall response rate for the first Delphi survey was 47% (88/187). The median minimum clinically important effect size was 5.0% (interquartile range: 5.0-10.0) for both stakeholder groups. The overall response rate for the second Delphi survey was 51% (95/187). Consensus was reached after the second round, as 74% of respondents in Group 1 and 82% of respondents in Group 2 agreed with the median effect size. The combined minimum clinically important effect size across both groups was 5.0% (interquartile range: 3.0-6.5). CONCLUSIONS: This study demonstrates that surveying stakeholder groups using a Delphi process is a simple way of defining a minimum clinically important effect size, which aids the sample size calculation and determines whether a randomised study is feasible.

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OBJECTIVES: Acute respiratory distress syndrome (ARDS) is associated with high ventilation-perfusion heterogeneity and dead-space ventilation. However, whether the degree of dead-space ventilation is associated with outcomes is uncertain. In this systematic review and meta-analysis, we evaluated the ability of dead-space ventilation measures to predict mortality in patients with ARDS. DATA SOURCES: MEDLINE, CENTRAL, and Google Scholar from inception to November 2022. STUDY SELECTION: Studies including adults with ARDS reporting a dead-space ventilation index and mortality. DATA EXTRACTION: Two reviewers independently identified eligible studies and extracted data. We calculated pooled effect estimates using a random effects model for both adjusted and unadjusted results. The quality and strength of evidence were assessed using the Quality in Prognostic Studies and Grading of Recommendations, Assessment, Development, and Evaluation, respectively. DATA SYNTHESIS: We included 28 studies in our review, 21 of which were included in our meta-analysis. All studies had a low risk of bias. A high pulmonary dead-space fraction was associated with increased mortality (odds ratio [OR], 3.52; 95% CI, 2.22-5.58; p < 0.001; I2 = 84%). After adjusting for other confounding variables, every 0.05 increase in pulmonary-dead space fraction was associated with an increased odds of death (OR, 1.23; 95% CI, 1.13-1.34; p < 0.001; I2 = 57%). A high ventilatory ratio was also associated with increased mortality (OR, 1.55; 95% CI, 1.33-1.80; p < 0.001; I2 = 48%). This association was independent of common confounding variables (OR, 1.33; 95% CI, 1.12-1.58; p = 0.001; I2 = 66%). CONCLUSIONS: Dead-space ventilation indices were independently associated with mortality in adults with ARDS. These indices could be incorporated into clinical trials and used to identify patients who could benefit from early institution of adjunctive therapies. The cut-offs identified in this study should be prospectively validated.

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In this article, I discuss the potential pitfalls of interpreting p values, confidence intervals, and declarations of statistical significance. To illustrate the issues, I discuss the LOVIT trial, which compared high-dose vitamin C with placebo in mechanically ventilated patients with sepsis. The primary outcome - the proportion of patients who died or had persisting organ dysfunction at day 28 - was significantly higher in patients who received vitamin C (p = .01). The authors had hypothesized that vitamin C would have a beneficial effect, although the prior evidence for benefit was weak. There was no prior evidence for a harmful effect of high-dose vitamin C. Consequently, the pretest probability for harm was low. The sample size was calculated assuming a 10% absolute risk difference, which was optimistic. Overestimating the effect size when calculating the sample size leads to low power. For these reasons, we should be skeptical that vitamin C causes harm in septic patients, despite the significant result. p-values and confidence intervals are probabilities concerning the chance of obtaining the observed data. However, we are more interested in the chance the intervention has a real effect on the outcome. That is to say, we are more interested in whether the hypothesis is true. A Bayesian approach allows us to estimate the false positive risk, which is the post-test probability there is no effect of the intervention. The false positive risk for the LOVIT trial (calculated from the published summary data using uniform priors for the parameter values) is 70%. Most likely, high-dose vitamin C does not cause harm in septic patients. Most likely it has no effect at all. If there is an effect, it is probably small and most likely beneficial.

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Background: In medical research, null hypothesis significance testing (NHST) is the dominant framework for statistical inference. NHST involves calculating P-values and confidence intervals to quantify the evidence against the null hypothesis of no effect. However, P-values and confidence intervals cannot tell us the probability that the hypothesis is true. In contrast, false-positive risk (FPR) and false-negative risk (FNR) are post-test probabilities concerning the truth of the hypothesis, that is to say, the probability a real effect exists. Methods: We calculated the FPR or FNR for 53 individual multicentre trials in critical care based on a pretest probability of 0.5 that the hypothesis was true. Results: For trials reporting statistical significance, the FPR varied between 0.1% and 57.6%. For trials reporting non-significance, the FNR varied between 1.7% and 36.9%. Twenty-six of 47 trials (55.3%) reporting non-significance provided strong or very strong evidence in favour of the null hypothesis; the remaining trials provided limited evidence. There was no obvious relationship between the P-value and the FNR. Conclusions: The FPR and FNR showed marked variability, indicating that the probability of a real or absent treatment effect differed substantially between trials. Only one trial reporting statistical significance provided convincing evidence of a real treatment effect, and nearly half of all trials reporting non-significance provided limited evidence for the absence of a treatment effect. Our findings suggest that the quality of evidence from multicentre trials in critical care is highly variable.

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AIM: We sought to describe the aetiology, demographics and outcomes of patients with pneumonia undergoing venovenous extracorporeal membrane oxygenation (VV-ECMO) in Aotearoa New Zealand. METHODS: Retrospective observational study. RESULTS: Between January 2004 and August 2020, 133 patients underwent VV-ECMO for pneumonia. This VV-ECMO cohort is representative of the geographic and ethnic distribution of the population of Aotearoa New Zealand. Six-month survival was 85/133 (64%). A primary viral aetiology was identified in 63/133 cases (47%) with bacterial co-infection present in 34/63 viral pneumonias (54%). Primary bacterial pneumonia was identified in 48/133 cases (36%). Twenty-three (17%) of 133 patients developed necrotising pneumonia. The most commonly identified microorganisms were influenza A, Staphylococcus aureus and Streptococcus pneumoniae. Infection with Staphylococcus aureus or Streptococcus species was strongly associated with necrotising pneumonia (OR 10.18, 95% CI 3.52-37.13, P<0.0001). Necrotising pneumonia was more common in Maori and Pacific Peoples than in other ethnic groups (OR 3.08, 95% CI 1.16-7.96, P=0.02). DISCUSSION: Outcomes from VV-ECMO for pneumonia in Aotearoa New Zealand are comparable to large international series. Although the use of VV-ECMO was matched to the ethnic distribution of the population of Aotearoa New Zealand, Maori may have reduced access because they have higher rates of pneumonia than non-Maori.

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BACKGROUND: Multicentre RCTs are widely used by critical care researchers to answer important clinical questions. However, few trials evaluating mortality outcomes report statistically significant results. We hypothesised that the low proportion of trials reporting statistically significant differences for mortality outcomes is plausibly explained by lower-than-expected effect sizes combined with a low proportion of participants who could realistically benefit from studied interventions. METHODS: We reviewed multicentre trials in critical care published over a 10-yr period in the New England Journal of Medicine, the Journal of the American Medical Association, and the Lancet. To test our hypothesis, we analysed the results using a Bayesian model to investigate the relationship between the proportion of effective interventions and the proportion of statistically significant results for prior distributions of effect size and trial participant susceptibility. RESULTS: Five of 54 trials (9.3%) reported a significant difference in mortality between the control and the intervention groups. The median expected and observed differences in absolute mortality were 8.0% and 2.0%, respectively. Our modelling shows that, across trials, a lower-than-expected effect size combined with a low proportion of potentially susceptible participants is consistent with the observed proportion of trials reporting significant differences even when most interventions are effective. CONCLUSIONS: When designing clinical trials, researchers most likely overestimate true population effect sizes for critical care interventions. Bayesian modelling demonstrates that that it is not necessarily the case that most studied interventions lack efficacy. In fact, it is plausible that many studied interventions have clinically important effects that are missed.

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The normal blood lactate level is 0-2 mmol/L, and a value above 3-5 mmol/L is variably used to define hyperlactatemia. In cardiac surgical patients, hyperlactatemia can arise from both hypoxic and non-hypoxic mechanisms. The major non-hypoxic mechanism is likely stress-induced accelerated aerobic metabolism, in which elevated lactate results from a mass effect on the lactate/pyruvate equilibrium. The lactate/pyruvate ratio is normal (<20) in this circumstance. Hyperlactatemia can also result from impaired global or regional oxygen delivery, in which case the lactate/pyruvate ratio is typically elevated (>20). Lactate is a strong anion that is virtually fully dissociated at physiological pH. As such, increased lactate concentration reduces the strong ion difference and exerts an acidifying effect on the blood. Hyperlactatemia in cardiac surgery patients has been categorized as either early or late onset. Early-onset hyperlactatemia is that which develops in the operating room or very early following intensive care unit (ICU) admission. Early-onset hyperlactatemia is strongly associated with adverse outcome and probably arises as a consequence of both hypoxic (e.g., microcirculatory shock) and non-hypoxic (accelerated aerobic metabolism) mechanisms. By contrast, late-onset hyperlactatemia is a benign, self-limiting condition that typically arises within 6-12 hours of ICU admission and spontaneously resolves within 24 hours. Late onset hyperlactatemia occurs in the absence of any evidence of global or regional tissue hypoxia. The mechanism of late onset hyperlactatemia is not understood. Hyperlactatemia is a common accompaniment to treatment with ß2-agonists such as epinephrine. Epinephrine-induced hyperlactatemia is thought to be due to accelerated aerobic metabolism and requires no specific intervention. Irrespective of the cause, the presence of hyperlactatemia should trigger a search for remedial causes of impaired tissue oxygenation, bearing in mind that normal-or even supranormal-indices of global oxygen delivery may exist despite regional tissue hypoperfusion.

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The incidence of life-threatening anaphylactic reactions related to anesthesia is approximately 1 in 6000 anesthetics administered, and is associated with mortality as high as 5%. In such cases the use of extracorporeal membrane oxygenation (ECMO) in the setting of refractory shock following anaphylaxis may be life saving. Abdominal compartment syndrome (ACS) itself and in this case complicating ECMO support, is a potentially devastating complication of high-volume resuscitation. Decompressive laparotomy is the treatment of choice for ACS. We present a patient treated with venoarterial ECMO for refractory shock following anaphylaxis who developed ACS that was successfully treated with urgent decompressive laparotomy performed in the intensive care unit. This case report highlights the role of abdominal compartment syndrome as a rare but potentially fatal cause of low circuit flow in ECMO-supported patients and proposes a stepwise approach to decision making in this setting. Urgent decompressive laparotomy is potentially lifesaving in this circumstance, and should be urgently considered once other causes of low ECMO flow have been excluded.

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