The standards for reporting of diagnostic accuracy for studies in journal or conference abstracts (STARD for Abstracts) was developed for guiding the reporting of abstracts of diagnostic accuracy studies, which was published in BMJ in August 2017. The study mainly introduced and interpreted the items of STARD for Abstracts, in order to help domestic researchers to perform and report the abstracts of diagnostic accuracy studies by STARD for Abstracts.
The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) has been widely accepted in the assessment of diagnostic accuracy and quality. However, it is not suitable for assessing risk of bias in studies comparing diagnostic accuracy. The current common practice in systematic reviews is to derive comparative accuracy from non-comparative diagnostic accuracy studies, which is inherently biased. The QUADAS group developed the QUADAS-Compare (QUADAS-C) tool for assessing the risk of bias in comparative diagnostic accuracy studies. It was officially launched in October 2021. QUADAS-C retains the same 4-domain structure as QUADAS-2: patient selection, index test, reference standard, and flow and timing. It also includes an additional 14 signaling questions and 4 risk of bias questions. This allows researchers to identify high-quality research evidence and avoid bias in research design and conduct. This article interpreted the basic situation, evaluation items, evaluation standards, and usage methods and procedures associated with QUADAS-C to provide references for domestic users.
The method of network meta-analysis of diagnostic test accuracy is in the exploratory stage. We had explored and introduced several methods of network meta-analysis of diagnostic test accuracy before. Based on example, we introduce ANOVA model for performing network meta-analysis of diagnostic test accuracy step-by-step.
Objective To evaluate the accuracy of soluble triggering receptor expressed on myeloid cells-1 ( sTREM-1) as a diagnostic index for ventilator-associated pneumonia ( VAP) . Methods We searched the PubMed, EMBase, Cochrane Library,Wanfang Database, CNKI and VIP for clinical trials which assessed the diagnosis accuracy of sTREM-1 for VAP. The methodological quality of each study was assessed by the quality assessment for studies of diagnostic accuracy ( QUADAS) tool. The Meta-disc software was used to conduct merger analyses on sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio. The heterogeneity test was performed and summary receiver operating characteristic ( SROC) curve was completed. Results 8 studies were included ( 180 VAP patients and 224 non-VAP patients) . The value of merger sensitivity, specificity, and diagnostic odds ratio were 0. 80, 0. 74, and 13. 89, respectively. The area under of SROC curve was 0. 857, with Q point at 0. 788. Conclusion sTREM-1 showed moderate accuracy for VAP diagnosis in adult mechanically ventilated patients, which should be combined with other diagnostic markers to further improve the sensitivity and specificity.
Objective To compare the effectiveness of robot-assisted and traditional freehand screw placement in the treatment of atlantoaxial dislocation. Methods The clinical data of 55 patients with atlantoaxial dislocation who met the selection criteria between January 2021 and January 2024 were retrospectively analyzed. According to different screw placement methods, they were divided into the traditional group (using the traditional freedhand screw placement, 31 cases) and the robot group (using the Mazor X robot-assisted screw placement, 24 cases). There was no significant difference in gender, age, body mass index, etiology, and preoperative visual analogue scale (VAS) score, cervical spine Japanese Orthopaedic Association (JOA) score between the two groups (P>0.05). The operation time, intraoperative blood loss, operation cost, and intraoperative complications were recorded and compared between the two groups. The VAS score and cervical spine JOA score were used to evaluate the improvement of pain and cervical spinal cord function before operation and at 1 month after operation. CT examination was performed at 3 days after operation, and the accuracy of screw placement was evaluated according to Neo grading criteria. Results All the 55 patients successfully completed the operation. The operation time, intraoperative blood loss, and operation cost in the robot group were significantly higher than those in the traditional group (P<0.05). A total of 220 C1 and C2 pedicle screws were inserted in the two groups, and 94 were inserted in the robot group, with an accuracy rate of 95.7%, among them, 2 were inserted by traditional freehand screw placement due to bleeding caused by intraoperative slip. And 126 pedicle screws were inserted in the traditional group, with an accuracy rate of 87.3%, which was significantly lower than that in the robot group (P<0.05). There were 1 case of venous plexus injury in the robot group and 3 cases in the traditional group, which improved after pressure hemostasis treatment. No other intraoperative complication such as vertebral artery injury or spinal cord injury occurred in both groups. All patients were followed up 4-16 months with an average of 6.6 months, and there was no significant difference in the follow-up time between the two groups (P>0.05). Postoperative neck pain significantly relieved in both groups, and neurological symptoms relieved to varying degrees. The VAS score and cervicle spine JOA score of both groups significantly improved at 1 month after operation when compared with preoperative scores (P<0.05), and there was no significant difference in the score change between the two groups (P>0.05). Conclusion In the treatment of atlantoaxial dislocation, the accuracy of robot-assisted screw placement is superior to the traditional freedhand screw placement.
By comparing the diagnostic accuracy of two or more tests in the same study, the one with the higher diagnostic accuracy can be screened. Therefore, it is extremely important to conduct the comparative diagnostic test accuracy study. This paper introduced the concept of the comparative diagnostic test accuracy study, compared it with single diagnostic test accuracy study, and described its role, study design, statistical analysis, current status, and challenges.
ObjectiveTo study the method of rapid and accurate measurement of body temperature in dense population during the coronavirus disease 2019 pandemic.MethodsFrom January 27th to February 8th, 2020, subjects were respectively measured with two kinds of non-contact infrared thermometers (blue thermometer and red one) to measure the temperature of forehead, neck, and inner side of forearm under the conditions of 4–6℃ (n=152), 7–10℃ (n=103), and 11–25℃ (n=209), while the temperature of axillary was measured with mercury thermometer under the same conditions. Taking the mercury thermometer temperature as the gold standard, the measurement results with non-contact infrared thermometers were compared.ResultsAt 7–10℃, there was no statistical difference among the forehead temperatures measured by the two non-contact infrared thermometers and the axillary temperature (P>0.05); there was no difference among the temperature measured by blue thermometer on forehead, neck, and inner side of forearm (P>0.05); no difference was found between the temperature measured by the red thermometer on forehead and inner side of forearm (P>0.05), while there was statistical difference between the temperatures measured by the red thermometer on forehead and neck (P<0.05). Under the environment of 11−25℃, there was no statistical difference among the forehead temperatures measured by the two infrared thermometers and the axillary temperature (P>0.05); the difference between the temperatures of forehead and inner side of forearm measured by the blue thermometer was statistically significant (P<0.05), while no difference appeared between the forehead and neck temperatures measured by the blue thermometer (P>0.05); there was no statistical difference among the temperatures of three body regions mentioned above measured by the red thermometer (P>0.05). According to the manual, the allowable fluctuation range of the blue thermometer was 0.3℃, and that of the red one was 0.2℃. The mean differences in measured values between different measured sites of the two products were within the allowable fluctuation range. Therefore, the differences had no clinical significance in the environment of 7–25℃. Under the environment of 4–6℃, the detection rate of blue thermometer was 2.2% and that of the red one was 19.1%.ConclusionsThere is no clinical difference between the temperature measured by mercury thermometer and the temperature measured by temperature guns at 7–10 or 11–25℃, so temperature guns can be widely used. In order to maintain the maximum distance between the measuring and the measured persons and reduce the infection risk, it is recommended to choose the inner forearm for temperature measurement. Under the environment of ambient temperature 4–6℃, the detection rate of non-contact electronic temperature gun is low, requiring taking thermal measures for the instrument.
Previous methods of grading evidence for systematic reviews of diagnostic test accuracy have generally focused on assessing the certainty (quality) of evidence at the level of diagnostic indicators. When the question is not limited to follow the diagnostic test accuracy results themselves, the grading results may be inaccurate due to the lack of consideration of the downstream effects of the test accuracy in specific settings. To address these challenges, the GRADE working group conducted a series of studies focused on updating methods to explore or simulate important downstream effects of diagnostic test accuracy outcomes within a contextual framework. This paper aimed to introduce advances in the contextual framework of the GRADE approach to rate the certainty of evidence from systematic reviews of single diagnostic test accuracy.
With the development of artificial intelligence, machine learning has been widely used in diagnosis of diseases. It is crucial to conduct diagnostic test accuracy studies and evaluate the performance of models reasonably to improve the accuracy of diagnosis. For machine learning-based diagnostic test accuracy studies, this paper introduces the principles of study design in the aspects of target conditions, selection of participants, diagnostic tests, reference standards and ethics.
The correct and reasonable statistical analysis method can make the results of comparative diagnosis test accuracy more convincing. In this paper, the accuracy of diagnostic tests is divided into 2 forms: binary-scale outcomes and ordinal-scale/continuous-scale outcomes. Taking diagnostic indicators such as sensitivity, specificity, receiver operating characteristic (ROC) curves and area under curve (AUC) values as entry points, combined with examples, this paper introduced how to compare the diagnostic results of tests by parameter estimation and hypothesis testing, with the aim of providing references for the comparative diagnosis test accuracy.