Common Design and Data Elements Reported on Idiopathic Intracranial Hypertension Trials: A Systematic Review.

BACKGROUND: There are an increasing number of controlled clinical trials and prospective studies, ongoing and recently completed, regarding management options for idiopathic intracranial hypertension (IIH). We present a Common Design and Data Element (CDDE) analysis of controlled and prospective IIH studies with the aim of aligning essential design and recommending data elements in future trials and enhancing data synthesis potential in IIH trials. METHODS: We used PubMed and ClinicalTrials.gov to screen for ongoing and published trials assessing treatment modalities in people with IIH. After our search, we used the Nested Knowledge AutoLit platform to extract pertinent information regarding each study. We examined outputs from each study and synthesized the data elements to determine the degree of homogeneity between studies. RESULTS: The most CDDE for inclusion criteria was the modified Dandy criteria for diagnosis of IIH, used in 9/14 studies (64%). The most CDDE for outcomes was change in visual function, reported in 12/14 studies (86%). Evaluation of surgical procedures (venous sinus stenting, cerebrospinal fluid shunt placement, and others) was more common, seen in 9/14 studies (64%) as compared with interventions with medical therapy 6/14 (43%). CONCLUSIONS: Although all studies have similar focus to improve patient care, there was a high degree of inconsistency among studies regarding inclusion criteria, exclusion criteria, and outcomes measures. Furthermore, studies used different time frames to assess outcome data elements. This heterogeneity will make it difficult to achieve a consistent standard, and thus, making secondary analyses and meta-analyses less effective in the future. Consensus on design of trials is an unmet research need for IIH.

Mortality of COVID-19 in patients with hematological malignancies versus solid tumors: a systematic literature review and meta-analysis.

Cancer patients are more vulnerable to COVID-19 compared to the general population, but it remains unclear which types of cancer have the highest risk of COVID-19-related mortality. This study examines mortality rates for those with hematological malignancies (Hem) versus solid tumors (Tumor). PubMed and Embase were systematically searched for relevant articles using Nested Knowledge software (Nested Knowledge, St Paul, MN). Articles were eligible for inclusion if they reported mortality for Hem or Tumor patients with COVID-19. Articles were excluded if they were not published in English, non-clinical studies, had insufficient population/outcomes reporting, or were irrelevant. Baseline characteristics collected included age, sex, and comorbidities. Primary outcomes were all-cause and COVID-19-related in-hospital mortality. Secondary outcomes included rates of invasive mechanical ventilation (IMV) and intensive care unit (ICU) admission. Effect sizes from each study were computed as logarithmically transformed odds ratios (ORs) with random-effects, Mantel-Haenszel weighting. The between-study variance component of random-effects models was computed using restricted effects maximum likelihood estimation, and 95% confidence intervals (CIs) around pooled effect sizes were calculated using Hartung-Knapp adjustments. In total, 12,057 patients were included in the analysis, with 2,714 (22.5%) patients in the Hem group and 9,343 (77.5%) patients in the Tumor group. The overall unadjusted odds of all-cause mortality were 1.64 times higher in the Hem group compared to the Tumor group (95% CI: 1.30-2.09). This finding was consistent with multivariable models presented in moderate- and high-quality cohort studies, suggestive of a causal effect of cancer type on in-hospital mortality. Additionally, the Hem group had increased odds of COVID-19-related mortality compared to the Tumor group (OR = 1.86 [95% CI: 1.38-2.49]). There was no significant difference in odds of IMV or ICU admission between cancer groups (OR = 1.13 [95% CI: 0.64-2.00] and OR = 1.59 [95% CI: 0.95-2.66], respectively). Cancer is a serious comorbidity associated with severe outcomes in COVID-19 patients, with especially alarming mortality rates in patients with hematological malignancies, which are typically higher compared to patients with solid tumors. A meta-analysis of individual patient data is needed to better assess the impact of specific cancer types on patient outcomes and to identify optimal treatment strategies.

Common Data Elements Analysis of Mechanical Thrombectomy Clinical Trials for Acute Ischemic Stroke with Large Core Infarct.

BACKGROUND: Clinical trials addressing large core acute ischemic stroke (AIS) are ongoing across multiple international groups. Future development of clinical guidelines depends on meta-analyses of these trials calling for a degree of homogeneity of elements across the studies. This common data element study aims to provide an overview of key features of pertinent large core infarct trials. METHODS: PubMed and ClinicalTrials.gov databases were screened for published and ongoing clinical trials assessing mechanical thrombectomy in patients with AIS with large core infarct. Nested Knowledge AutoLit living review platform was utilized to categorize primary and secondary outcomes as well as inclusion and exclusion criteria for patient selection in the trials. RESULTS: The most reported data element was ASPECTS score but with varied definitions of what constitutes large core. Non-utility-weighted modified Rankin score (mRS) was reported in 6/7 studies as the primary outcome, while the utility-weighted mRS was the outcome of interest in the TESLA trial, all of them at the 3 months mark, with only LASTE looking for mRS shift at the 6 months mark. Secondary outcomes had more variations. Mortality is reported separately only in 4/7 trials, all at the 3­month mark. Additionally, the TENSION trial reported the frequency of serious adverse events, including mortality, at the 1­week and 12-month mark. DISCUSSION: Overall, in large core trials there is a large degree of heterogeneity in the collected data elements. Differences in definition and timepoints render reaching a unified standard difficult, which hinders high quality meta-analyses and cohesive evidence-driven synthesis.