COVID-19 Living Overview of Evidence repository is highly comprehensive and can be used as a single source for COVID-19 studies

Verdugo-Paiva, Francisca; Vergara, Camilo; Ávila, Camila; Castro, Juan Camilo Pardo de; Cid, Joan; Contreras, Vanessa; Jara, I.; Jiménez, Virginia Martín; Lee, M.H.; Muñoz, Mónica; Rojas-Gómez, Ana María; Rosón-Rodríguez, P.; Serrano-Arevalo, Karen; Silva-Ruz, I.; Vásquez-Laval, J.; Zambrano-Achig, Paula; Zavadzki, G.; Rada, Gabriel

doi:10.1016/j.jclinepi.2022.05.001

Cited by 20 publications

(16 citation statements)

References 20 publications

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“…ML offers the potential to reduce resource use, produce evidence syntheses in less time, and maintain or perhaps exceed the current expectations of transparency, reproducibility, and methodological rigor. One example is the training of binary classifiers to predict the relevance of unread studies without human assessment: Aum and Choe recently used a classifier to predict systematic review study designs [18], Stansfield and colleagues to update living reviews [19], and Verdugo-Paiva and colleagues to update an entire COVID-19 database [20].…”

Section: Evidence Synthesis and Machine Learningmentioning

confidence: 99%

The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study

et al. 2023

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Background Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and the need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews. Methods This protocol describes how we will answer two research questions using a retrospective study design: Is there a difference in resources used to produce reviews using recommended ML versus not using ML, and is there a difference in time-to-completion? We will also compare recommended ML use to non-recommended ML use that merely adds ML use to existing procedures. We will retrospectively include all reviews conducted at our institute from 1 August 2020, corresponding to the commission of the first review in our institute that used ML. Conclusion The results of this study will allow us to quantitatively estimate the effect of ML adoption on resource use and time-to-completion, providing our organization and others with better information to make high-level organizational decisions about ML.

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Section: Evidence Synthesis and Machine Learningmentioning

confidence: 99%

The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study

et al. 2023

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“…Two studies validated its reliability as a primary source, identifying 93% of relevant articles in 1 study and 99.67% in another (100% for RCTs). 9,10 This repository draws from 41 databases that are updated on a daily to weekly basis, including MEDLINE, Embase, the Cochrane Register, Clinicaltrials.gov, the World Health Organization (WHO) International Clinical Trials Registry Platform and MedRxiv.…”

Section: Search Strategymentioning

confidence: 99%

“…The Epistemonikos database is relatively new but has been recently validated as a comprehensive source for COVID-19 articles. 9 However, the use of automated tools and assessment by human reviewers can also lead to errors in the systematic review process. A further methodological limitation is that there were few head-to-head comparisons of active interventions.…”

Section: Limitationsmentioning

confidence: 99%

Antiviral drug treatment for nonsevere COVID-19: a systematic review and network meta-analysis

Pitre

Alstine

Chick

et al. 2022

CMAJ

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Most trials addressing the treatment of patients with COVID-19 have targeted patients admitted to hospital with severe or critical disease. 1 However, more recently, several treatments, including antiviral drugs, antidepressants, monoclonal anti bodies and inhaled corticosteroids, have been studied for patients with nonsevere COVID-19. 2 Preliminary evidence from ongoing or recently completed trials suggests that 2 novel antiviral drugsmolnupiravir and nirmatrelvir-ritonavir (Paxlovid) -may be effective at reducing risk of hospital admission. [3][4][5] To date, evidence on antiviral drugs for nonsevere COVID-19 has not been systematically synthesized or appraised. Furthermore, although efficacy data from trials of molnupiravir, nirmatrelvir-ritonavir and remdesivir are promising, no head-to-head trials have compared these drugs.A network meta-analysis allows for comparison of treatments that have not been compared in randomized controlled trials (RCTs), using pooled estimates from direct and indirect evidence.They can provide guidance to clinicians and evidence users in determining which treatments are superior. This is particularly important as health care systems attempt to prioritize access to effective COVID-19 treatments in the early stages of the disease.We sought to compare the effectiveness of antiviral drugs for patients with nonsevere COVID-19. MethodsWe conducted a systematic review and network meta-analysis, that included a rigorous appraisal of the evidence. We registered a protocol on Open Science Framework and uploaded the data used for this analysis (https://osf.io/zbcf9). We report our systematic review and network meta-analysis in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) extension statement for reporting of sys tematic reviews incorporating network meta-analyses. 6

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“…The search first targeted systematic and rapid reviews (SR/RRs) of potentially eligible RCTs in two electronic COVID-19 databases (WHO COVID-19 Global literature on coronavirus disease database 9 , the Living OVerview of Evidence (L.OVE) platform) 10 and the COVID-END inventory 11 of best evidence syntheses for clinical management to rapidly identify eligible SR/RRs [May 2 to 3, 2021] ( Online supplement 2 ). Reviewers checked the included study lists of the identified English language SR/RRs for potentially eligible RCTs and checked all ongoing RCT records from the SR/RRs for results (July 15, 2021).…”

Section: Searchmentioning

confidence: 99%

“…The search first targeted systematic and rapid reviews (SR/RRs) of potentially eligible RCTs in two electronic COVID-19 databases (WHO COVID-19 Global literature on coronavirus disease database 9 , the Living OVerview of Evidence (L.OVE) platform) 10 and the COVID-END inventory 11 Registry Platform (ICTRP) 12 as the database was not accessible to our information scientist during this rapid review. We did not apply restrictions to publication status or language in the search strategy.…”

Section: Searchmentioning

confidence: 99%

Noninvasive ventilation strategies for patients with severe or critical COVID-19: A rapid review of clinical outcomes

Kelly

Wells

2022

Preprint

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ObjectivesTo examine whether high flow nasal oxygen (HFNO), continuous positive airway pressure (CPAP), or noninvasive ventilation (NIV) strategies impact mortality, the need for invasive mechanical ventilation (IMV), or hospital and intensive care unit (ICU) length of stay compared to standard oxygen therapy (SOT) or each other in patients with severe or critical COVID-19 with acute hypoxemic respiratory failure.MethodsA rapid review of randomized controlled trials (RCTs) identified through published systematic and rapid reviews supplemented with a search of bibliographic databases. RCTs were eligible if they compared HFNO, CPAP, or NIV to SOT or another ventilation strategy. Studies were screened, selected, and extracted by a single reviewer and checked by a second reviewer. We assessed risk of bias of included studies using the Cochrane ‘Risk of bias’ tool and used the grading of recommendations, assessment, development, and evaluation (GRADE) approach to judge the certainty of the evidence for mortality, need for IMV, and hospital and ICU length of stay. We sought RCT evidence for non-COVID-19 patients with acute hypoxemic respiratory failure and acute respiratory distress to inform additional comparisons and to supplement the available data for COVID-19.ResultsA total of 5 RCTs comparing ventilation strategies in patients with severe or critical COVID-19 were included. Patient and study characteristics were extracted and evidence and certainty of evidence assessments were completed for comparisons of HFNO and CPAP to standard oxygen therapy and NIV and CPAP to HFNO. An additional 22 RCTs of non-COVID-19 patients were also included and considered.Results from meta-analysis suggest reductions in mortality and IMV with HFNO (RR mortality 0.87 (0.66-1.13), IMV 0.89 (0.77-1.03); low quality evidence) or CPAP (RR mortality 0.87 (0.64-1.18) low quality evidence, IMV 0.81 (0.67-0.98) moderate quality evidence) compared to SOT. Helmet NIV may reduce IMV (RR 0.69 (0.43-1.09)) and CPAP may reduce IMV (RR 0.69 (0.43-1.09)) and hospital (1.67 days fewer (5.43 fewer-2.09 more) or ICU length of stay (1.02 days fewer (3.97 fewer-1.93 more)) compared to HFNO (low quality evidence).ConclusionsThis rapid systematic review highlights the available evidence to support the use of noninvasive ventilation strategies including high flow nasal oxygen, noninvasive ventiltaion (e.g., BiPAP), or CPAP in hospitalized patients with severe or critical COVID-19 and acute hypoxemic respiratory failure who do not need emergent intubation. Findings based on moderate to very low certainty evidence suggest that noninvasive ventilation may be considered as an alternative to standard oxygen therapy to reduce hypoxemia and dyspnea. Additional high quality RCTs are warranted to reduce uncertainty and to fill in important knowledge gaps.

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COVID-19 Living Overview of Evidence repository is highly comprehensive and can be used as a single source for COVID-19 studies

Cited by 20 publications

References 20 publications

The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study

The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study

Antiviral drug treatment for nonsevere COVID-19: a systematic review and network meta-analysis

Noninvasive ventilation strategies for patients with severe or critical COVID-19: A rapid review of clinical outcomes

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