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Quality of reporting of outcomes in phase III studies of pulmonary tuberculosis: a systematic review.

ABSTRACT: BACKGROUND:Despite more than 60 years of clinical trials, tuberculosis (TB) still causes a high global burden of mortality and morbidity. Treatment currently requires multiple drugs in combination, taken over a prolonged period. New drugs are needed to shorten treatment duration, prevent resistance and reduce adverse events. However, to improve on current methodology in drug development, a more complete understanding of the existing clinical evidence base is required. METHODS:A systematic review was undertaken to summarise outcomes reported in phase III trials of patients with newly diagnosed pulmonary TB. A systematic search of databases (PubMed, MEDLINE, EMBASE, CENTRAL and LILACs) was conducted on 30 November 2017 to retrieve relevant peer-reviewed articles. Reference lists of included studies were also searched. This systematic review considered all reported outcomes. RESULTS:Of 248 included studies, 229 considered "on-treatment" outcomes whilst 148 reported "off-treatment" outcomes. There was wide variation and ambiguity in the definition of reported outcomes, including their relationship to treatment and in the time points evaluated. Additional challenges were observed regarding the analysis approach taken (per protocol versus intention to treat) and the varying durations of "intensive" and "continuation" phases of treatment. Bacteriological outcomes were most frequently reported but radiological and clinical data were often included as an implicit or explicit component of the overall definition of outcome. CONCLUSIONS:Terminology used to define long-term outcomes in phase III trials is inconsistent, reflecting evolving differences in protocols and practices. For successful future cumulative meta-analysis, the findings of this review suggest that greater availability of individual patient data and the development of a core outcome set would be desirable. In the meantime, we propose a simple and logical approach which should facilitate combination of key evidence and inform improvements in the methodology of TB drug development and clinical trials.

SUBMITTER: Bonnett LJ

PROVIDER: S-EPMC5822642 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Quality of reporting of outcomes in phase III studies of pulmonary tuberculosis: a systematic review.

Bonnett Laura Jayne LJ Ken-Dror Gie G Davies Geraint Rhys GR

Trials 20180221 1

<h4>Background</h4>Despite more than 60 years of clinical trials, tuberculosis (TB) still causes a high global burden of mortality and morbidity. Treatment currently requires multiple drugs in combination, taken over a prolonged period. New drugs are needed to shorten treatment duration, prevent resistance and reduce adverse events. However, to improve on current methodology in drug development, a more complete understanding of the existing clinical evidence base is required.<h4>Methods</h4>A sy ...[more]

PMID: 29467027

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Project description:BackgroundSurvival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context.MethodsSystematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria.ResultsSeventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively.ConclusionThe quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

Project description:BACKGROUND:Epithelial ovarian cancer (OC) remains a significant cause of morbidity and mortality for women worldwide. Patients may experience a multitude of disease- and treatment-related symptoms that can impact quality of life (QOL) and should be measured and reported in clinical trials. This systematic review investigated the adequacy of reporting of QOL in randomized phase III trials in OC in both the first-line and recurrent disease setting. MATERIALS AND METHODS:A systematic review of MEDLINE and EMBASE identified randomized clinical trials of systemic therapy in OC from 1980 to 2014. The adequacy of reporting QOL was evaluated with respect to adherence to established guidelines on reporting QOL in clinical trials and the recent recommendations on the inclusion of patient-reported outcomes in clinical trials from the Fifth Ovarian Cancer Consensus Conference. RESULTS:Of 3,247 abstracts, 35 studies, including 24,664 patients, met inclusion criteria. Twenty-two trials (63%) were in the first-line setting, with 13 (37%) in the recurrent setting. The inclusion of QOL assessments increased from 2% (1980s) to 62% (2010+). Quality of life was a co-primary endpoint in only one trial.Minimal clinically important differences in QOL were defined in eight trials (23%), with results included in the abstract in 37% and article in 86%. Compliance was reported in 26 trials (74%), with 13 trials (37%) reporting specifically how they dealt with missing data. Only seven trials reported the reasons for missing data (20%).Group results were published in 29 trials (83%), with 6 (17%) reporting individual patient results. Results were more commonly reported as a mean overall score (21 trials; 60%), with specific domain scores in only 9 trials (26%). No studies reported QOL beyond progression or included predefined context-specific endpoints based on objectives of treatment (i.e., palliation/cure/maintenance) and the patient population. Duration of benefit of palliative chemotherapy was reported in only one study. CONCLUSION:Inclusion and reporting of QOL as a trial endpoint has improved in phase III trials in OC, but there are still significant shortfalls that need to be addressed in future trials. IMPLICATIONS FOR PRACTICE:The impact of treatment on quality of life (QOL) is an important consideration in patients with ovarian cancer for whom treatment is often given with palliative intent. Both the disease and treatment impact a patient's QOL and require careful evaluation in clinical trials. Matching the QOL questions to the patient population of interest is critical. Similar rigor to that used to assess progression-based endpoints is essential to guide clinical decisions. This systematic review demonstrated that although the inclusion and reporting of QOL as a trial endpoint has improved in phase III trials there are still significant shortfalls that need to be addressed in future trials.

Project description:BackgroundAutomated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports.MethodsWe searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score.ResultsOf the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality.ConclusionsThere is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies.

Project description:BackgroundDiabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine.ObjectiveWe aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies.MethodsWe searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility.ResultsAfter screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant.ConclusionsThe reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.

Project description:AIMS:We conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagnosis, with a specific focus on the reporting of information concerning the participants on which the diagnostic task was evaluated on. METHOD:Medline Core Clinical Journals were searched for studies published between July 2015 and July 2018. Two reviewers independently screened the retrieved articles, a third reviewer resolved any discrepancies. An extraction list was developed from the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline. Two reviewers independently extracted the data from the eligible articles. Third and fourth reviewers checked, verified the extracted data as well as resolved any discrepancies between the reviewers. RESULTS:The search results yielded 161 papers, of which 28 conformed to the eligibility criteria. Detail of data source was reported in 24 of the 28 papers. For all of the papers, the set of patients on which the ML-based diagnostic system was evaluated was partitioned from a larger dataset, and the method for deriving such set was always reported. Information on the diagnostic/non-diagnostic classification was reported well (23/28). The least reported items were the use of reporting guideline (0/28), distribution of disease severity (8/28 patient flow diagram (10/28) and distribution of alternative diagnosis (10/28). A large proportion of studies (23/28) had a delay between the conduct of the reference standard and ML tests, while one study did not and four studies were unclear. For 15 studies, it was unclear whether the evaluation group corresponded to the setting in which the ML test will be applied to. CONCLUSION:All studies in this review failed to use reporting guidelines, and a large proportion of them lacked adequate detail on participants, making it difficult to replicate, assess and interpret study findings. PROSPERO REGISTRATION NUMBER:CRD42018099167.

Project description:BackgroundThere has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation.MethodsMEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains.Results209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59-73%). Median compliance was highest for the model description domain (100%, IQR 80-100%) and lower for the study (71%, IQR 63-86%), dataset (63%, IQR 50-67%) and performance (60%, IQR 50-70%) description domains.ConclusionThis systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing-most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis-that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field.Systematic review registration[www.crd.york.ac.uk/prospero/], identifier [CRD42022279214].

Project description:BackgroundSafe, more efficacious treatments are needed to address the considerable morbidity and mortality associated with pulmonary tuberculosis (TB). However, the current practice in TB therapeutics trials is to use composite binary outcomes, which in the absence of standardization may inflate false positive and negative errors in evaluating regimens. The lack of standardization of outcomes is a barrier to the identification of highly efficacious regimens and the introduction of innovative methodologies METHODS: We conducted a systematic review of trials designed to advance new pulmonary TB drugs or regimens for regulatory approval and inform practice guidelines. Trials were primarily identified from the WHO International Clinical Trial Registry Platform (ICTRP). Only trials that collected post-treatment follow-up data and enrolled at least 100 patients were included. Protocols and Statistical Analysis Plans (SAP) for eligible trials from 1995 to the present were obtained from trial investigators. Details of outcome data, both explicit and implied, were abstracted and organized into three broad categories: favorable, unfavorable, and not assessable. Within these categories, individual trial definitions were recorded and collated, and areas of broad consensus and disagreement were identified and described.ResultsFrom 2205 trials in any way related to TB, 51 were selected for protocol and SAP review, from which 31 were both eligible and had accessible documentation. Within the three designated categories, we found broad consensus in the definitions of favorable and unfavorable outcomes, although specific details were not always provided, and when explicitly addressed, were heterogeneous. Favorable outcomes were handled the most consistently but were widely variable with respect to specification. In some cases, the same events were defined differently by different protocols, particularly in distinguishing unfavorable from not assessable events. Death was often interpreted as conditional on cause. Patients who did not complete the study because of withdrawal or loss to follow-up presented a particular challenge to consistent interpretation and analytic treatment of outcomes.ConclusionsIn a review of 31 clinical trials, we found that outcome definitions were heterogeneous, highlighting the need to establish clearer specification and a move towards universal standardization of outcomes across pulmonary TB trials. The ICH E9 (R1) addendum provides guidelines for undertaking and achieving this goal.Prospero registrationPROSPERO CRD42020197993 . Registration 11 August 2020.

Project description:BACKGROUND:Electrochemotherapy is becoming a well-established treatment for malignancies of skin and non-skin origin and its use is widening across Europe. The technique was developed and optimized from solid experimental and clinical evidence. A consensus document is now warranted to formalize reporting results, which should strengthen evidence-based practice recommendations. This consensus should be derived from high quality clinical data collection, clinical expertise and summarizing patient feedback. The first step, which is addressed in this paper, aims to critically analyze the quality of published studies and to provide the recommendations for reporting clinical trials on electrochemotherapy. METHODS:The quality of reporting in published studies on electrochemotherapy was analyzed in order to produce procedure specific reporting recommendations. A comprehensive literature search of studies published from 2006 to 2015 was performed followed by qualitative analysis of manuscripts assessing for 47 quality criteria grouped into four major clusters: (1) trial design, (2) description of patient population, (3) description of treatment delivery and patient outcome, (4) analysis of results and their interpretation. The summary measure during literature assessment was the proportion of studies fulfilling each manuscript quality criteria. RESULTS:A total of 56 studies were screened, from the period 2006 to 2015, of which 33 were included in the qualitative analysis, with a total of 1215 patients. Overall, the quality of reporting was highly variable. Twenty-four reports (73%) were single-center, non-comparative studies, and only 15 (45%) were prospective in nature (only 2 of them were entered into a clinical trials registry). Electrochemotherapy technique was consistently reported, with most studies (31/33) adhering closely to published standard operating procedures. The quality of reporting the patient population was variable among the analyzed studies, with only between 45% and 100% achieving dedicated quality criteria. Reporting of treatment delivery and patient outcome was also highly variable with studies only fulfilling between 3% and 100%. Finally, reporting study results critically varied, fulfilling from 27% to 100% of the quality criteria. Based on the critical issues emerging from this analysis, recommendations and minimal requirements for reporting clinical data on electrochemotherapy were prepared and summarized into a checklist. CONCLUSIONS:There is an increasing body of published clinical data on electrochemotherapy, but more high quality clinical data are needed. Published papers often lack accurate description of study population, treatment delivery as well as patient outcome. Our recommendations, provided in the form of a summary checklist, are intended to ameliorate data reporting in future studies on electrochemotherapy and help researchers to provide a solid evidence basis for clinical practice.

Project description:BackgroundPhase II clinical trials are a cornerstone of the development in experimental treatments They work as a "filter" for phase III trials confirmation. Surprisingly the attrition ratio in Phase III trials in oncology is significantly higher than in any other medical specialty. This suggests phase II trials in oncology fail to achieve their goal. Objective The present study aims at estimating the quality of reporting in published oncology phase II clinical trials.Data sourcesA literature review was conducted among all phase II and phase II/III clinical trials published during a 5-year period (2010-2015).Study eligibility criteriaAll articles electronically published by three randomly-selected oncology journals with Impact-Factors>4 were included: Journal of Clinical Oncology, Annals of Oncology and British Journal of Cancer.InterventionQuality of reporting was assessed using the Key Methodological Score.Results557 articles were included. 315 trials were single-arm studies (56.6%), 193 (34.6%) were randomized and 49 (8.8%) were non-randomized multiple-arm studies. The Methodological Score was equal to 0 (lowest level), 1, 2, 3 (highest level) respectively for 22 (3.9%), 119 (21.4%), 270 (48.5%) and 146 (26.2%) articles. The primary end point is almost systematically reported (90.5%), while sample size calculation is missing in 66% of the articles. 3 variables were independently associated with reporting of a high standard: presence of statistical design (p-value <0.001), multicenter trial (p-value = 0.012), per-protocol analysis (p-value <0.001).LimitationsScreening was mainly performed by a sole author. The Key Methodological Score was based on only 3 items, making grey zones difficult to translate.Conclusions & implications of key findingsThis literature review highlights the existence of gaps concerning the quality of reporting. It therefore raised the question of the suitability of the methodology as well as the quality of these trials, reporting being incomplete in the corresponding articles.

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Quality of reporting of outcomes in phase III studies of pulmonary tuberculosis: a systematic review.

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Quality of reporting of outcomes in phase III studies of pulmonary tuberculosis: a systematic review.

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