Systematic reporting of medical kidney biopsies.
Ontology highlight
ABSTRACT:
SUBMITTER: Leh S
PROVIDER: S-EPMC8757430 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo identify and describe the content of templates for reporting prehospital major incident medical management.DesignSystematic literature review according to PRISMA guidelines.Data sourcesPubMed/MEDLINE, EMBASE, CINAHL, Scopus and Web of Knowledge. Grey literature was also searched.Eligibility criteria for selected studiesTemplates published after 1 January 1990 and up to 19 March 2012. Non-English language literature, except Scandinavian; literature without an available abstract; and literature reporting only psychological aspects were excluded.ResultsThe main database search identified 8497 articles, among which 8389 were excluded based on title and abstract. An additional 96 were excluded based on the full-text. The remaining 12 articles were included in the analysis. A total of 107 articles were identified in the grey literature and excluded. The reference lists for the included articles identified five additional articles. A relevant article published after completing the search was also included. In the 18 articles included in the study, 10 different templates or sets of data are described: 2 methodologies for assessing major incident responses, 3 templates intended for reporting from exercises, 2 guidelines for reporting in medical journals, 2 analyses of previous disasters and 1 Utstein-style template.ConclusionsMore than one template exists for generating reports. The limitations of the existing templates involve internal and external validity, and none of them have been tested for feasibility in real-life incidents.Trial registrationThe review is registered in PROSPERO (registration number: CRD42012002051).
| S-EPMC3733314 | biostudies-literature
Project description:IntroductionIn 2010, a total of 385 natural disasters killed more than 297 000 people worldwide and affected over 217 million others. More standardised reporting of major incident management have been advocated in the previous years. Prevention, mitigation, preparedness and major incident response may be improved through collection and analysis of high-quality standardised data on medical management of major incidents. Standardised data may elevate the level of scientific evidence within disaster medicine research.Methods and analysisA systematic literature review will be conducted to identify templates for reporting pre-hospital major incident medical management. The first set of entry terms aims to describe major incidents published during the last 20 years. The second set aims to focus the number of search results from the first set to those publications that describe templates based on data collections from these major incidents. Predefined free search phases will be combined with the first two sets.Ethics and disseminationThe results will be submitted for publication in an open access, peer-reviewed scientific journal. The PRISMA checklist will be applied. No ethics approval is considered indicated, as this is a literature review only.Registration detailsThis review is registered in PROSPERO (registration number: CRD42012002051).
| S-EPMC3346945 | biostudies-literature
Project description:BackgroundThe field of Artificial Intelligence (AI) holds transformative potential in medicine. However, the lack of universal reporting guidelines poses challenges in ensuring the validity and reproducibility of published research studies in this field.MethodsBased on a systematic review of academic publications and reporting standards demanded by both international consortia and regulatory stakeholders as well as leading journals in the fields of medicine and medical informatics, 26 reporting guidelines published between 2009 and 2023 were included in this analysis. Guidelines were stratified by breadth (general or specific to medical fields), underlying consensus quality, and target research phase (preclinical, translational, clinical) and subsequently analyzed regarding the overlap and variations in guideline items.ResultsAI reporting guidelines for medical research vary with respect to the quality of the underlying consensus process, breadth, and target research phase. Some guideline items such as reporting of study design and model performance recur across guidelines, whereas other items are specific to particular fields and research stages.ConclusionsOur analysis highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines. Overall, this comprehensive overview could help researchers and public stakeholders reinforce quality standards for increased reliability, reproducibility, clinical validity, and public trust in AI research in healthcare. This could facilitate the safe, effective, and ethical translation of AI methods into clinical applications that will ultimately improve patient outcomes.
| S-EPMC11009315 | biostudies-literature
Project description: Not available
| S-EPMC1117768 | biostudies-literature
Project description:Acute kidney injury (AKI) is a highly heterogeneous, common, and potentially devastating condition associated with markedly increased hospital length of stay, cost, mortality, and morbidity. Expanding the role for kidney biopsies in AKI may offer fresh insights into disease heterogeneity, molecular mechanisms, and therapeutic targets. A number of challenges face investigators and clinicians considering research biopsies in AKI: ensuring patient safety, ensuring the ethical conduct of research studies, and maximizing the scientific yield of the kidney tissue obtained. The societal benefits of research that lead to novel strategies for preventing and treating AKI would be enormous. Rethinking our current approach to the role of kidney biopsy for AKI diagnosis and research may be a major step toward the promise of personalized medicine in nephrology.
| S-EPMC5753426 | biostudies-literature
Project description:BackgroundMany publications report the prevalence of chronic kidney disease (CKD) in the general population. Comparisons across studies are hampered as CKD prevalence estimations are influenced by study population characteristics and laboratory methods.MethodsFor this systematic review, two researchers independently searched PubMed, MEDLINE and EMBASE to identify all original research articles that were published between 1 January 2003 and 1 November 2014 reporting the prevalence of CKD in the European adult general population. Data on study methodology and reporting of CKD prevalence results were independently extracted by two researchers.ResultsWe identified 82 eligible publications and included 48 publications of individual studies for the data extraction. There was considerable variation in population sample selection. The majority of studies did not report the sampling frame used, and the response ranged from 10 to 87%. With regard to the assessment of kidney function, 67% used a Jaffe assay, whereas 13% used the enzymatic assay for creatinine determination. Isotope dilution mass spectrometry calibration was used in 29%. The CKD-EPI (52%) and MDRD (75%) equations were most often used to estimate glomerular filtration rate (GFR). CKD was defined as estimated GFR (eGFR) <60 mL/min/1.73 m(2) in 92% of studies. Urinary markers of CKD were assessed in 60% of the studies. CKD prevalence was reported by sex and age strata in 54 and 50% of the studies, respectively. In publications with a primary objective of reporting CKD prevalence, 39% reported a 95% confidence interval.ConclusionsThe findings from this systematic review showed considerable variation in methods for sampling the general population and assessment of kidney function across studies reporting CKD prevalence. These results are utilized to provide recommendations to help optimize both the design and the reporting of future CKD prevalence studies, which will enhance comparability of study results.
| S-EPMC4514069 | biostudies-literature
Project description:AimsWe 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.MethodMedline 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.ResultsThe 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.ConclusionAll 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 numberCRD42018099167.
| S-EPMC7103817 | biostudies-literature
Project description:Background and objectivesUnfavorable histology on procurement biopsies is the most common reason for deceased donor kidney discard. We sought to assess the reproducibility of procurement biopsy findings.Design, setting, participants, & measurementsWe compiled a continuous cohort of deceased donor kidneys transplanted at our institution from 1/1/2006 to 12/31/2016 that had at least one procurement biopsy performed, and excluded cases with missing biopsy reports and those used in multiorgan transplants. Suboptimal histology was defined as the presence of advanced sclerosis in greater than or equal to one biopsy compartment (glomeruli, tubules/interstitium, vessels). We calculated κ coefficients to assess agreement in optimal versus suboptimal classification between sequential biopsy reports for kidneys that underwent multiple procurement biopsies and used time-to-event analysis to evaluate the association between first versus second biopsies and patient and allograft survival.ResultsOf the 1011 kidneys included in our cohort, 606 (60%) had multiple procurement biopsies; 98% had first biopsy performed at another organ procurement organization and their second biopsy performed locally. Categorical agreement was highest for vascular disease (κ=0.17) followed by interstitial fibrosis and tubular atrophy (κ=0.12) and glomerulosclerosis (κ=0.12). Overall histologic agreement (optimal versus suboptimal) was κ=0.15. First biopsy histology had no association with allograft survival in unadjusted or adjusted analyses. However, second biopsy optimal histology was associated with a higher probability of death-censored allograft survival, even after adjusting for donor and recipient factors (adjusted hazard ratio, 0.50; 95% confidence interval, 0.34 to 0.75; P=0.001).ConclusionsDeceased donor kidneys that underwent multiple procurement biopsies often displayed substantial differences in histologic categorization in sequential biopsies, and there was no association between first biopsy findings and post-transplant outcomes.
| S-EPMC7015101 | biostudies-literature
Project description:IntroductionStudies are needed to assess the quality of transcriptome analysis in paired human tissue samples preserved by different methods and different gene amplification platforms to enable data comparisons across experimenters.MethodsRNA was extracted from kidney biopsies, either submerged in RNA-stabilizing solution (RSS) or stored in formalin-fixed, paraffin-embedded (FFPE) blocks. RNA quality and integrity were compared. Gene expression of the common rejection module and other immune cell genes were quantified for both tissue preservation methods in the same sample using conventional quantitative polymerase chain reaction (QPCR) by 2 different commercial platforms, (fluidigm [FD]) or barcoded-oligos (nanostring [NS]).ResultsRNA quality was inferior in FFPE tissues. Despite this, gene expression for 19 measured genes on the same sample, stored in FFPE or RSS, were strongly correlated on the FD (r = 0.81) or NS platforms (r = 0.82). For the same samples, interplatform gene expression correlations were excellent (r = 0.80) for RSS and moderate (r = 0.66) for FFPE. Significant differences in gene expression were confirmed on both platforms (FD: P = 1.1E-03; NS: P = 2.5E-04) for biopsy-confirmed acute rejection.ConclusionOur study provided supportive evidence that despite a low RNA quality of archival FFPE kidney transplantation tissue, small quantities of this tissue can be obtained from existing paraffin blocks to provide a viable and rich biospecimen source for focused gene expression assays. In addition, reliable and reproducible gene expression evaluation can be performed on these FFPE tissues using either a QPCR-based or a barcoded-oligo approach, which provides opportunities for collaborative analytics.
| S-EPMC5976814 | biostudies-literature
Project description:Pre transplantation Kidney Biopsies
| PRJNA888648 | ENA