Are large randomised controlled trials in severe sepsis and septic shock statistically disadvantaged by repeated inadvertent underestimates of required sample size?

Wong, Joshua L. C.; Mason, Alexina J.; Gordon, Anthony; Brett, Stephen

doi:10.1136/bmjopen-2017-020068

Cited by 13 publications

(12 citation statements)

References 23 publications

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“…This translates into differences in the benefits derived from specific therapy application, which in turn handicaps sample size calculation. This can lead to the trial having less statistical power than initially planned, increasing the risk of a type two error and this is the origin of unexpected results 3. It questions the use of subgroups in an attempt to extract some kind of useful information in negative RCTs 4…”

Section: Introductionmentioning

confidence: 99%

The premature closure of ROMPA clinical trial: mortality reduction in septic shock by plasma adsorption

et al. 2019

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ObjectivesCoupled Plasma Filtration and Adsorption (CPFA) use in septic shock remains controversial. The objective is to clarify whether the application of high doses of CPFA in addition to the current clinical practice could reduce hospital mortality in septic shock patients in Intensive Care Units at 28 days and at 90 days follow-up.DesignWe designed a prospective randomised clinical trial, Reducción de la Mortalidad Plasma-Adsorción (ROMPA), to demonstrate an absolute mortality reduction of 20% (α=0.05; 1-β=0.8; n=190 (95×2)).SettingBeing aware of the pitfalls associated with previous medical device trials, we developed a training programme to improve CPFA use (especially clotting problems). The protocol was approved by the ethics committees of all participating centres. Circumstances beyond our control produced a change in recruitment conditions unacceptable to ROMPA researchers and the trial was discontinued.ParticipantsBy closure, five centres from an initial 10 fulfilled the necessary trial criteria, with 49 patients included, 30 in the control group (CG) and 19 in the intervention group (IG).InterventionCPFA.Main outcome measuresHospital mortality at 28 days and 90 days follow-up.ResultsAfter 28 days, 14 patients died (46.7%) from the CG and 11 (57.9%) from the IG, not reaching statistical significance (p=0.444). At 90 days, 19 patients had died (63.3%) from the CG and 11 patients (57.9%) from the IG, (p=0.878). The adjustment by propensity score or the use of the Kaplan-Meier technique failed to achieve statistical difference, neither by Intention to Treat nor by the Actual Intervention Received.ConclusionWe herewith present the results gained from the prematurely closed trial. The results are inconclusive due to low statistical power but we consider that this data is of interest for the scientific community and potentially necessary for any ensuing debate.RegisterNCT02357433 in clinicaltrials.gov.

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Section: Introductionmentioning

confidence: 99%

The premature closure of ROMPA clinical trial: mortality reduction in septic shock by plasma adsorption

et al. 2019

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“…There are several recently completed or ongoing randomized clinical trials that explored if increased water intake ameliorates ADPKD progression. One of them is a PREVENT-ADPKD pilot trial [ACTRN12614001216606 ( Wong et al, 2018 )], which should be completed in 2021, and another study is “Determining feasibility of Randomisation to high vs. ad libitum water INtake in polycystic Kidney disease” [DRINK; NCT02933268 ( El-Damanawi et al, 2018 )]. The DRINK trial has already been completed, and adult ADPKD patients with high water intake (HWI) during 8weeks showed lower urine osmolality and higher urine volume compared with patients with ad libitum water intake.…”

Section: Dietary Interventions In Pkdmentioning

confidence: 99%

Insights Into the Molecular Mechanisms of Polycystic Kidney Diseases

et al. 2021

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Autosomal dominant (AD) and autosomal recessive (AR) polycystic kidney diseases (PKD) are severe multisystem genetic disorders characterized with formation and uncontrolled growth of fluid-filled cysts in the kidney, the spread of which eventually leads to the loss of renal function. Currently, there are no treatments for ARPKD, and tolvaptan is the only FDA-approved drug that alleviates the symptoms of ADPKD. However, tolvaptan has only a modest effect on disease progression, and its long-term use is associated with many side effects. Therefore, there is still a pressing need to better understand the fundamental mechanisms behind PKD development. This review highlights current knowledge about the fundamental aspects of PKD development (with a focus on ADPKD) including the PC1/PC2 pathways and cilia-associated mechanisms, major molecular cascades related to metabolism, mitochondrial bioenergetics, and systemic responses (hormonal status, levels of growth factors, immune system, and microbiome) that affect its progression. In addition, we discuss new information regarding non-pharmacological therapies, such as dietary restrictions, which can potentially alleviate PKD.

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“…Patterns in disease history data could probably be used effectively in trial design to detect a difference between groups and to avoid use of noncomparable subgroups. 32 Furthermore, the effect of previous disease history-both diagnosis and the temporal order of diagnoses-on patient outcomes could reflect a transition of physiological systems that predisposes individuals not only to certain diagnoses but also to certain outcomes. This could explain why some survivors of sepsis return to their baseline performance status whereas others progressively decline (a decline not predicted by hospital course).…”

Section: Discussionmentioning

confidence: 99%

Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records

Nielsen

Thorsen-Meyer

Belling

et al. 2019

The Lancet Digital Health

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Background Intensive-care units (ICUs) treat the most critically ill patients, which is complicated by the heterogeneity of the diseases that they encounter. Severity scores based mainly on acute physiology measures collected at ICU admission are used to predict mortality, but are non-specific, and predictions for individual patients can be inaccurate. We investigated whether inclusion of long-term disease history before ICU admission improves mortality predictions.Methods Registry data for long-term disease histories for more than 230 000 Danish ICU patients were used in a neural network to develop an ICU mortality prediction model. Long-term disease histories and acute physiology measures were aggregated to predict mortality risk for patients for whom both registry and ICU electronic patient record data were available. We compared mortality predictions with admission scores on the Simplified Acute Physiology Score (SAPS) II, the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II, and the best available multimorbidity score, the Multimorbidity Index. An external validation set from an additional hospital was acquired after model construction to confirm the validity of our model. During initial model development data were split into a training set (85%) and an independent test set (15%), and a five-fold cross-validation was done during training to avoid overfitting. Neural networks were trained for datasets with disease history of 1 month, 3 months, 6 months, 1 year, 2⋅5 years, 5 years, 7⋅5 years, 10 years, and 23 years before ICU admission.Findings Mortality predictions with a model based solely on disease history outperformed the Multimorbidity Index (Matthews correlation coefficient 0⋅265 vs 0⋅065), and performed similarly to SAPS II and APACHE II (Matthews correlation coefficient with disease history, age, and sex 0•326 vs 0•347 and 0•300 for SAPS II and APACHE II, respectively). Diagnoses up to 10 years before ICU admission affected current mortality prediction. Aggregation of previous disease history and acute physiology measures in a neural network yielded the most precise predictions of in-hospital mortality (Matthews correlation coefficient 0⋅391 for in-hospital mortality compared with 0⋅347 with SAPS II and 0⋅300 with APACHE II). These results for the aggregated model were validated in an external independent dataset of 1528 patients (Matthews correlation coefficient for prediction of in-hospital mortality 0⋅341).Interpretation Longitudinal disease-spectrum-wide data available before ICU admission are useful for mortality prediction. Disease history can be used to differentiate mortality risk between patients with similar vital signs with more precision than SAPS II and APACHE II scores. Machine learning models can be deconvoluted to generate novel understandings of how ICU patient features from long-term and short-term events interact with each other. Explainable machine learning models are key in clinical settings, and our results emphasise how to progress towards the transformatio...

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Are large randomised controlled trials in severe sepsis and septic shock statistically disadvantaged by repeated inadvertent underestimates of required sample size?

Cited by 13 publications

References 23 publications

The premature closure of ROMPA clinical trial: mortality reduction in septic shock by plasma adsorption

The premature closure of ROMPA clinical trial: mortality reduction in septic shock by plasma adsorption

Insights Into the Molecular Mechanisms of Polycystic Kidney Diseases

Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records

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