 Hello, my name is Jakub Ruszkowski. Today I would like to present our findings on the key role of citation tracing, indeed evidence synthesis on the gastrointestinal symptoms prevalence in chronic kidney disease. Additionally, especially for all of you who are learning R, I will show a code to perform a basic meta-analysis of prevalence data. For background for introductory facts about chronic kidney disease. The chronic kidney disease, or CKD, is an umbrella term for any abnormalities of kidney structure or function that have implications for health and are present for at least three months. According to the estimated filtration function, CKD is divided into five stages, from G1 to G5. The last one means kidney failure. CKD is a growing health concern affecting millions of people worldwide. Contrary to its name, chronic kidney disease is appeared to be a systemic disease. It results from the disruption of a number of physiological processes as kidney function declines. Therefore, CKD patients suffer from such conditions as anemia, cardiovascular disease, or even bone disorder. As a consequence, CKD patients experience a wide range of symptoms, including gastrointestinal wall. But how much they are overlooked? As always, the answer should be found in a systematic review. The greatest and most up-to-date systematic review from April 2022 was based on a search of pre-databases and not very specified citation chasing. They provided meta-analysis on the prevalence of constipation, diarrhea, and bloating. But practicing picking the utility of such data was limited because all stages of CKD were merged and analyzed together. A half year later, we published a systematic review, fully dedicated to lower GI symptoms, and find a lot more data that enable us to take into account the stages of CKD. In this table, I compared findings from the previous systematic review and our work on the right. In the case of abdominal pain, we provided data that were completely missed in the previous systematic review. In the other cases, we analyzed data for thousands of patients more as in the case of bloating. What I love the most is dividing general outcomes into more specific ones, such as constipation into the low frequency of bowel movements, heart-stool consistency, or self-reported symptom. It is important because each of them can have other causes and also other health implications. So you can see, we were able to collect enough data to provide an estimated prevalence for many specific outcomes. In the case of constipation, we included half more patients than the previous systematic reviews. OK, so how did we succeed? And how did we get additional data that had been overlooked in the previous systematic review? The answer lies in data sources. Besides a very broad database search and a conventional citation chasing of included studies, we performed citation chasing of papers that introduced questionnaires that are used to assess symptoms in patients. Taken together, forward citation chasing of these articles added data for more than 2,000 patients, nearly 20% of all included studies in our analysis. How did we carry out this? Firstly, we listed questionnaires that ask about symptoms that we were interested in. Secondly, we found articles that introduced these questionnaires and listed their DOIs. Finally, we used the DOIs to search for studies that use these questionnaires in the CKD populations In practice, just copy and paste the list of DOIs on citation chaser page, click on the load button, and then go to the citation tab. The next step is to search for all citing articles while and down on the file. This file can be important for abstract screen into, let's say, Ryan. One of the free application for abstract and title screen. Currently, it is recommended to meta-analyze single proportions data such as prevalence using GLMM. It is easy to conduct such analysis with the meta package. It fits the random intercept logistic progression model for all learning R. Here I show the code that I use to meta-analyze data and to plot data. Although publication bias by its nature is mainly concerned with studies on the effectiveness of interventions, we have, of course, also done such analyzes. Firstly, I plotted a classic panel plot and checked the significance of the asymmetry of the peter's test. P value was high. However, this paper from 2018 introduced new methods to look for reporting biases. TOI plots, as you can see, and LFK index. They are frequently used in meta-analyses of prevalence data. In this case, LFK index was 0.1. So we found a minor positive reporting bias. It means that studies that reported higher prevalence of self-reported constipation were more probable to be published. We used the ALT meta package to perform sensitivity analysis with the first function. We tested whether the choice of a link function significantly affects the estimated prevalence of symptoms. And the next function was used because the two-step method to meta-analyze prevalence data is still popular. And there is a lot of controversy around the Freemont-UK transformation. That is mostly used to normalize the proportion distribution before pulling. We concluded, as you can see in this table, that the results are generally stable. GLMM with Coaching Link tends to provide a bit lower estimates while the two-step method has a bit higher values. If you are interested in such analysis, I strongly recommend the paper published in 2022 by Leif and Glin, the author of the ALT meta package. Finally, I would like to point out the main limitation we faced with. I hope that somebody from the participants of the conference will be interested in working on any of the challenging issues. Firstly, there is a lack of validated filter that can be used in search strategy for studies assessing the prevalence of disease or symptoms. I believe that it is also possible to automate both equation and optimization of search strategy. Secondly, there are no well-established one-step methods to meta-analyze multinomial, ordinal data, such as symptom severity or still consistency types. Since I'm not a programmer, I would love to see examples of code with the implementation of such models. Finally, it was really hard for me to find information on how to modify it found in the charts plotted with the meta package. Eventually, I had to change them with Corel. Moreover, I did not find a well-established method to plot multinomial data. Perhaps there are data artists with nice solutions to enhance standard forest plots. And take home-assert citation-chasing should not be limited to studies identified through database searches. If you conduct a meta-analysis of prevalence of symptom, perform citation-chasing of the questioners that use, that are used to assess the symptom. The prevalence of symptoms can be meta-analyzed very easily with the meta package. And there is a need for more research and more working examples in the area of the meta-analysis of multinomial and ordinal data. Thank you for your attention.