Developing an AKI Consensus Definition for Database Research: Findings From a Scoping Review and Expert Opinion Using a Delphi Process.

RATIONALE & OBJECTIVE: The KDIGO (Kidney Disease: Improving Global Outcomes) definition of acute kidney injury (AKI) is frequently used in studies to examine the epidemiology of AKI. This definition is variably interpreted and applied to routinely collected health care data. The aim of this study was to examine this variation and to achieve consensus in how AKI should be defined for research using routinely collected health care data. SOURCES OF EVIDENCE AND STUDY DESIGN: Scoping review via searching Medline and EMBASE for studies using health care data to examine AKI by using the KDIGO creatinine-based definition. An international panel of experts formed to participate in a modified Delphi process to attempt to generate consensus about how AKI should be defined when using routinely collected laboratory data. CHARTING METHODS AND ANALYTICAL APPROACH: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) extension for scoping reviews was followed. For the Delphi process, 2 rounds of questions were distributed via internet-based questionnaires to all participants with a prespecified cutoff of 75% agreement used to define consensus. RESULTS: The scoping review found 174 studies that met the inclusion criteria. The KDIGO definition was inconsistently applied, and the methods for application were poorly described. We found 58 (33%) of papers did not provide a definition of how the baseline creatinine value was determined, and only 34 (20%) defined recovery of kidney function. Of 55 invitees to the Delphi process, 35 respondents participated in round 1, and 25 participated in round 2. Some consensus was achieved in areas related to how to define the baseline creatinine value, which patients should be excluded from analysis of routinely collected laboratory data, and how persistent chronic kidney disease or nonrecovery of AKI should be defined. LIMITATIONS: The Delphi panel members predominantly came from the United Kingdom, the United States, and Canada, and there were low response rates for some questions in round 1. CONCLUSIONS: The current methods for defining AKI using routinely collected data are inconsistent and poorly described in the available literature. Experts could not achieve consensus for many aspects of defining AKI and describing its sequelae. The KDIGO guidelines should be extended to include a standardized definition for how AKI should be defined when using routinely collected data.

Improving the quality of reporting findings using computer data analysis applications in educational research in context.

Data analysis is an important step in the research process as it influences the quality and standard of reporting research findings. Based on a review of the content of 255 doctorate theses, the use of computer applications for data analysis in educational research was assessed. It was feasible to assess how extensively used and accepted computer packages had become in educational research using an aspect of the diffusion of innovations theory as part of the conceptual framework. The results showed that the use of computer applications to analyse data was more prevalent among researchers using quantitative and mixed-methods research methodologies than among qualitative educational researchers. Educational researchers have not yet fully adopted innovative computer data analysis techniques in their research. It is evident that they use traditional technologies more than computer applications in their research. Name dropping of the computer applications used without employing the language or visualisations features provided by the applications was rife. This article bridges the gap between methodological scholarship and the use of computer applications in data analysis. It illuminates the potential of computer software to enhance the quality of the reporting of findings. The article aims to contribute to improvements in the standard of research reporting and the attributes of the graduates. The practical methodological advice in this article is aimed at guiding researchers who consider using computer packages in data analysis, irrespective of their methodological orientation. It stimulates debate on the use of computer applications in data analysis.