ABSTRACT
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.
ABSTRACT
IntroductionSystematic reviews (SRs) are central to evaluating therapies but have high costs in time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depends on performing and presenting evidence according to established best practices. In order to provide a basis for comparing between software tools that support SR, we performed a feature-by-feature comparison of SR tools.MethodsWe searched for SR tools by reviewing any such tool listed the Systematic Review Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional, and required no coding and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in four previous reviews of SR tools;we also added five features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency/replicability. Then, two reviewers assigned binary ‘present/absent' assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements.ResultsOf 53 SR tools found, 29 were excluded, leaving 24 for assessment. Thirty features were assessed across six classes, and the inter-observer agreement was 86 percent. DistillerSR (Evidence Partners;n = 26/30, 87%), Nested Knowledge (Nested Knowledge;n = 25/30, 83%), and EPPI-Reviewer Web (EPPI-Centre;n = 24/30, 80%) support the most features followed by Giotto Compliance (Giotto Compliance;n = 23/30, 77%), LitStream (ICF;n = 22/30, 73%), and SRDB.PRO (VTS Software;n = 21/30, 70%). Seven tools support fewer than half of all features assessed: RobotAnalyst, SyRF, Data ion Assistant, SWIFT-Review, SR-Accelerator, RobotReviewer, and COVID-NMA. Notably, only 10 tools (42%) support direct search, 7 (29%) offer dual extraction, and 13 (54%) offer living/updatable reviews.ConclusionsDistillerSR, EPPI-Reviewer Web, and Nested Knowledge each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.
ABSTRACT
[This corrects the article DOI: 10.2196/33219.].
ABSTRACT
BACKGROUND: Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depend on performing and presenting evidence according to established best practices. OBJECTIVE: This study aims to provide a basis for comparing and selecting between web-based software tools that support SR, by conducting a feature-by-feature comparison of SR tools. METHODS: We searched for SR tools by reviewing any such tool listed in the SR Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional and required no coding, and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in 4 previous reviews of SR tools; we also added 5 features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency and replicability. Then, 2 reviewers assigned binary present or absent assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements. RESULTS: Of the 53 SR tools found, 55% (29/53) were excluded, leaving 45% (24/53) for assessment. In total, 30 features were assessed across 6 classes, and the interobserver agreement was 86.46%. Giotto Compliance (27/30, 90%), DistillerSR (26/30, 87%), and Nested Knowledge (26/30, 87%) support the most features, followed by EPPI-Reviewer Web (25/30, 83%), LitStream (23/30, 77%), JBI SUMARI (21/30, 70%), and SRDB.PRO (VTS Software) (21/30, 70%). Fewer than half of all the features assessed are supported by 7 tools: RobotAnalyst (National Centre for Text Mining), SRDR (Agency for Healthcare Research and Quality), SyRF (Systematic Review Facility), Data Abstraction Assistant (Center for Evidence Synthesis in Health), SR Accelerator (Institute for Evidence-Based Healthcare), RobotReviewer (RobotReviewer), and COVID-NMA (COVID-NMA). Notably, of the 24 tools, only 10 (42%) support direct search, only 7 (29%) offer dual extraction, and only 13 (54%) offer living/updatable reviews. CONCLUSIONS: DistillerSR, Nested Knowledge, and EPPI-Reviewer Web each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.
ABSTRACT
BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to pose a significant threat to public health worldwide. The purpose of this study was to review current evidence obtained from randomized clinical trials on the efficacy of antivirals for COVID-19 treatment. METHODS: A systematic literature search was performed using PubMed to identify randomized controlled trials published up to September 4, 2021 that examined the efficacy of antivirals for COVID-19 treatment. Studies that were not randomized controlled trials or that did not include treatment of COVID-19 with approved antivirals were excluded. Risk of bias was assessed using the Scottish Intercollegiate Guidelines Network (SIGN) method. Due to study heterogeneity, inferential statistics were not performed and data were expressed as descriptive statistics. RESULTS: Of the 2,284 articles retrieved, 31 (12,440 patients) articles were included. Overall, antivirals were more effective when administered early in the disease course. No antiviral treatment demonstrated efficacy at reducing COVID-19 mortality. Sofosbuvir/daclatasvir results suggested clinical improvement, although statistical power was low. Remdesivir exhibited efficacy in reducing time to recovery, but results were inconsistent across trials. CONCLUSIONS: Although select antivirals have exhibited efficacy to improve clinical outcomes in COVID-19 patients, none demonstrated efficacy in reducing mortality. Larger RCTs are needed to conclusively establish efficacy.
Subject(s)
COVID-19 Drug Treatment , Antiviral Agents/therapeutic use , Humans , Randomized Controlled Trials as Topic , SARS-CoV-2ABSTRACT
OBJECTIVE: Recent studies suggest that the clinical course and outcomes of patients with coronavirus disease 2019 (COVID-19) and myasthenia gravis (MG) are highly variable. We performed a systematic review of the relevant literature with a key aim to assess the outcomes of invasive ventilation, mortality, and hospital length of stay (HLoS) for patients presenting with MG and COVID-19. METHODS: We searched the PubMed, Scopus, Web of Science, and MedRxiv databases for original articles that reported patients with MG and COVID-19. We included all clinical studies that reported MG in patients with confirmed COVID-19 cases via RT-PCR tests. We collected data on patient background characteristics, symptoms, time between MG and COVID-19 diagnosis, MG and COVID-19 treatments, HLoS, and mortality at last available follow-up. We reported summary statistics as counts and percentages or mean±SD. When necessary, inverse variance weighting was used to aggregate patient-level data and summary statistics. RESULTS: Nineteen studies with 152 patients (mean age 54.4 ± 12.7 years; 79/152 [52.0%] female) were included. Hypertension (62/141, 44.0%) and diabetes (30/141, 21.3%) were the most common comorbidities. The mean time between the diagnosis of MG and COVID-19 was7.0 ± 6.3 years. Diagnosis of COVID-19 was confirmed in all patients via RT-PCR tests. Fever (40/59, 67.8%) and ptosis (9/55, 16.4%) were the most frequent COVID-19 and MG symptoms, respectively. Azithromycin and ceftriaxone were the most common COVID-19 treatments, while prednisone and intravenous immunoglobulin were the most common MG treatments. Invasive ventilation treatment was required for 25/59 (42.4%) of patients. The mean HLoS was 18.2 ± 9.9 days. The mortality rate was 18/152 (11.8%). CONCLUSION: This report provides an overview of the characteristics, treatment, and outcomes of MG in COVID-19 patients. Although COVID-19 may exaggerate the neurological symptoms and worsens the outcome in MG patients, we did not find enough evidence to support this notion. Further studies with larger numbers of patients with MG and COVID-19 are needed to better assess the clinical outcomes in these patients.