Software Tools For Systematic Literature Review In Medicine: A Review And Feature Analysis

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.

Correction: Web-Based Software Tools for Systematic Literature Review in Medicine: Systematic Search and Feature Analysis.

[This corrects the article DOI: 10.2196/33219.].

Web-Based Software Tools for Systematic Literature Review in Medicine: Systematic Search and Feature Analysis.

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.

Efficacy of antiviral therapies for COVID-19: a systematic review of randomized controlled trials.

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.

Characteristics, treatment, and outcomes of Myasthenia Gravis in COVID-19 patients: A systematic review.

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.

Outcomes of acute respiratory distress syndrome in COVID-19 patients compared to the general population: a systematic review and meta-analysis.

INTRODUCTION: Acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) often leads to mortality. Outcomes of patients with COVID-19-related ARDS compared to ARDS unrelated to COVID-19 is not well characterized. AREAS COVERED: We performed a systematic review of PubMed, Scopus, and MedRxiv 11/1/2019 to 3/1/2021, including studies comparing outcomes in COVID-19-related ARDS (COVID-19 group) and ARDS unrelated to COVID-19 (ARDS group). Outcomes investigated were duration of mechanical ventilation-free days, intensive care unit (ICU) length-of-stay (LOS), hospital LOS, and mortality. Random effects models were fit for each outcome measure. Effect sizes were reported as pooled median differences of medians (MDMs), mean differences (MDs), or odds ratios (ORs). EXPERT OPINION: Ten studies with 2,281 patients met inclusion criteria (COVID-19: 861 [37.7%], ARDS: 1420 [62.3%]). There were no significant differences between the COVID-19 and ARDS groups for median number of mechanical ventilator-free days (MDM: -7.0 [95% CI: -14.8; 0.7], p = 0.075), ICU LOS (MD: 3.1 [95% CI: -5.9; 12.1], p = 0.501), hospital LOS (MD: 2.5 [95% CI: -5.6; 10.7], p = 0.542), or all-cause mortality (OR: 1.25 [95% CI: 0.78; 1.99], p = 0.361). Compared to the general ARDS population, results did not suggest worse outcomes in COVID-19-related ARDS.