Outcome Measurement Instruments (OMI) are essential tools for evaluating patients' experiences or health status, but their quality levels often vary. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) provide methodological guidance for selecting high-quality OMIs, which is widely recognized. In recent years, there has been a sharp increase in the number of systematic reviews of OMI based on COSMIN. However, many published related systematic reviews often fail to fully report key information, significantly impacting the reproducibility and interpretability of such systematic reviews, thus affecting the dissemination and application of their results. As the PRISMA 2020 does not include all necessary information for reporting such systematic reviews, scholars have developed a new reporting standard, "PRISMA-COSMIN for OMIs 2024", to assist researchers in writing and reporting systematic reviews of OMI in a clear, detailed, and transparent manner. This article introduces and interprets this guideline through an example paper, aiming to help domestic scholars better understand and effectively apply the guideline, thereby enhancing the overall quality of systematic reviews of OMI conducted in China.
Postoperative infection outbreak (PIO) is a concentrated manifestation of the harmfulness of hospital-acquired infections, and how to effectively control the occurrence of PIO promptly is currently the focus of infection control. At present, some infection prevention personnel lack emergency response experience and ability to investigate suspected sources of infection in the face of infection clusters.
To develop an investigation form for PIO, and identify the sources of infection in the early stage to prevent outbreaks.
From January to May 2023, Worldwide Database for Nosocomial Outbreaks (WDNO), PubMed, CNKI, and other databases were searched for studies related to PIO. Data extraction and annotation were performed. After cleaning the title and abstract text information of infection control literature downloaded from the PubMed database, a large-scale training set was constructed to build a word vector model based on the Skip-Gram method. Preliminary identification of risk factors for PIO was summarized based on the classification results of the word vector model constructed. Subsequently, 15 consulting experts were invited to evaluate the importance of PIO risk factors using a modified Delphi method. The Analytic Hierarchy Process (AHP) was adopted to calculate weight and combined weight for the primary and secondary indicators to determine the possible sources of infection outbreaks. Based on this, an investigation form for PIO was constructed.
A total of 203 PIO-related studies were finally included. A total of 15 experts were consulted by the modified Delphi method, and all the questionnaires for the two rounds of expert consultation were returned with a positive coefficient of 100.00%. The average authoritative coefficient (Cr) of 15 experts was (0.86±0.10), the average familiarity (Cs) was (0.81±0.10), and the average judgment (Ca) was (0.92±0.10). Kendall's W coefficient (Kendall's W) of concordance after two rounds of expert consultation was 0.351 (P<0.005), which showed that the consensus of expert opinion was good, indicating a good consensus among expert opinions. After two rounds of the modified Delphi method, 4 primary indicators and 19 secondary indicators were constructed. Within the indicator system, the weights of the 7 primary indicators ranged from 22.23% to 27.50%, and the combined weights of the secondary indicators ranged from 3.32% to 6.29%. Based on this, the PIO investigation questionnaire was developed, primarily including five aspects of patient basic information, infection status, perioperative factors, personnel, and environment.
Based on epidemiological characteristics and expert consultation, a form used for PIO investigation was constructed. The content covers the main risk factors and critical points that may lead to outbreaks, providing a reference for identifying potential sources of PIO.
Fatigue is prevalent in patients with chronic kidney disease (CKD) and closely associated with reduced quality of life and increased mortality. Currently, Patient-reported Outcome Measures (PROMs) are commonly employed to assess fatigue. However, these measures exhibit variations in format. There is no consensus about how to choose an appropriate one to use in clinical practice.
Systematically assess the advantages, disadvantages, and target populations of various scales to provide clinical practitioners with a reference for choosing appropriate assessment tools.
A systematic search of database like PubMed, Web of Science, CNKI, VIP, and Wanfang Data was conducted for literatures about fatigue assessment in patients with CKD, from January 2018 to April 2024. The data were independently screened and extracted by two researchers, and by comparing the assessment methods, advantages and disadvantages of each scale to inform the choice of fatigue assessment scales in different CKD populations.
The study reveals that CKD-related fatigue scales can be categorized into general fatigue scales and specific population fatigue scale. All these scales employ Likert scale for fatigue assessment. We found that health status survey instruments and the Dialysis Symptom Index Scale (DSI) were suitable for screening fatigue symptoms in CKD; the Piper Fatigue Scale-Revised (PFS-R) with multidimensional scale was the most commonly used in clinical practice and a promising scale for the assessment of fatigue in CKD because it could clearly differentiate between the degree and dimensions of fatigue.
There are many fatigue scales to assess fatigue for patients with CKD. Clinical practitioners should consider the characteristics of the CKD study population comprehensively and choose the appropriate scale for fatigue assessment.
