Delayed cerebral ischemia (DCI) is a severe complication following aneurysmal subarachnoid hemorrhage (aSAH), linked to poor functional outcomes and prolonged intensive care unit (ICU) stays. Timely DCI diagnosis is crucial but remains challenging. Dysregulated blood glucose, commonly observed after aSAH, may impair the constant glucose supply that is vital for brain function, potentially contributing to DCI. This study aimed to assess whether glucose indices could help identify at-risk patients and improve DCI detection. This retrospective, single-center observational study examined 151 aSAH patients between 2016 and 2019. Additionally, 70 of these (46.4%) developed DCI and 81 did not (no-DCI). To determine the value of glycemic indices for DCI, they were analyzed separately in patients in the period before (pre-DCI) and after DCI (post-DCI). The time-weighted average glucose (TWAG, p = 0.024), mean blood glucose (p = 0.033), and novel time-unified dysglycemic rate (TUDR140, calculated as the ratio of dysglycemic to total periods within a glucose target range of 70–140 mg/dL, p = 0.042), showed significantly higher values in the pre-DCI period of the DCI group than in the no-DCI group. In the time-series analysis, significant increases in TWAG and TUDR140 were observed at the DCI onset. In conclusion, DCI patients showed elevated blood glucose levels before and a further increase at the DCI onset. Prospective studies are needed to confirm these findings, as this retrospective, single-center study cannot completely exclude confounders and limitations. In the future blood glucose indices might become valuable parameters in multiparametric models to identify patients at risk and detect DCI onset earlier.

Purpose: In the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be utilized to support the new study design, but of course, inclusion also carries the risk of bias in the study results. Methods: To combine historical and randomized controls we investigate the Fill-it-up-design, which in the first step checks the comparability of the historical and randomized controls performing an equivalence pre-test. If equivalence is confirmed, the historical control data will be included in the new RCT. If equivalence cannot be confirmed, the historical controls will not be considered at all and the randomization of the original study will be extended. We are investigating the performance of this study design in terms of type I error rate and power. Results: We demonstrate how many patients need to be recruited in each of the two steps in the Fill-it-up-design and show that the family wise error rate of the design is kept at 5 $$\%$$% . The maximum sample size of the Fill-it-up-design is larger than that of the single-stage design without historical controls and increases as the heterogeneity between the historical controls and the concurrent controls increases. Conclusion: The two-stage Fill-it-up-design represents a frequentist method for including historical control data for various study designs. As the maximum sample size of the design is larger, a robust prior belief is essential for its use. The design should therefore be seen as a way out in exceptional situations where a hybrid design is considered necessary.

Background: Puumala hantavirus (PUUV) causes nephropathia epidemica (NE), an endemic form of transient acute renal injury (AKI). Serological testing is the mainstay of diagnosis. It was the aim of the present study to assist decision-making for serological testing by constructing a simple tool that predicts the likelihood of PUUV positivity. Methods: We conducted a comparative cohort study of all PUUV-tested cases at Aachen University tertiary care center in Germany between mid-2013 and mid-2021. N = 293 qualified for inclusion; N = 30 had a positive test result and clinical NE; N = 263 were negative. Two predictive point scores, the Aachen PUUV Score (APS) 1 and 2, respectively, were derived with the aid of logistic regression and receiver operating characteristic (ROC) analysis by determining the presence of four admission parameters. For internal validation, the internal Monte Carlo method was applied. In addition, partial external validation was performed using an independent historic cohort of N = 41 positive cases of NE. Results: APS1 is recommended for clinical use as it estimated the probability of PUUV positivity in the entire medical population tested. With a range from 0 to 6 points, it yielded an area under the curve of 0.94 by allotting 2 points each for fever or headache and 1 point each for AKI or LDH>300 U/L. A point sum of 0–2 safely predicted negativity for PUUV, as was confirmed in the NE validation cohort. Conclusion: Here, we present a novel, easy-to-use tool to guide the diagnostic management of suspected PUUV infection/NE and to safely avoid unnecessary serological testing, as indicated by point sum class 0–2. Since 67% of the cohort fell into this stratum, half of the testing should be avoidable in the future.

Background: In clinical research, the most appropriate way to assess the effect of an intervention is to conduct a randomized controlled trial (RCT). In the field of rare diseases, conducting an RCT is challenging, resulting in a low rate of clinical trials, with a high frequency of early termination and unpublished trials. The aim of the EPISTOP trial was to compare outcomes in infants with tuberous sclerosis (TSC) who received vigabatrin preventively before the seizures onset with those who received it conventionally after. The study was designed as a prospective, multicentre, randomized clinical trial. However, ethics committees at four centres did not approve this RCT design, resulting in an open-label trial (OLT) in these four centres and an RCT in the other six centres. In this paper, we re-analyse the data from the EPISTOP trial using methods to investigate the influence of allocation bias on the results of the EPISTOP trial. Method: A bias-corrected analysis is used to support and strengthen the published results. We included a term representing the effect of selection bias as an influencing factor on the corresponding endpoint in the statistical model. Thus, the treatment effect estimates for the primary endpoint of time to first seizure and additional secondary endpoints are adjusted for the bias effect. Result: The bias-corrected analyses for the primary endpoint show that the estimated hazard ratio and associated confidence intervals are in a very similar range (original analysis: HR 2.91, 95%-CI [1.11 to 7.67], p-value 0.0306; bias-corrected analysis: HR 2.89, 95%-CI [1.10 to 7.58], p-value 0.0316). This was also the case for the secondary endpoints. Conclusion: The statistical re-analysis of the raw trial data therefore supports the published results and confirms that there is no additional bias introduced by randomization, thereby increasing the value of the results. However, this highlights that this aspect needs to be considered in future trials, especially in rare diseases, to avoid additional biases in an already small sample size where it may be difficult to reach significance.