Puk33 Patient Insights for Complement 3 Glomerulopathy (C3g) Using Social Media Listening

Abstract

model developed for national decision-making. We replicated an existing PLS model of this type to evaluate the advantages and disadvantages of different software. Methods: The model was originally created for a NICE clinical guideline on hyperphosphataemia in chronic kidney disease (CG157). It was built in visual basic for applications (VBA). PLS was chosen because it allowed the model to translate continuous biochemical measures from trials into effects on patient-relevant outcomes. We replicated the VBA model in Simul8 ® , a dedicated simulation environment, to explore differences in build-time, results, run-time and usability. Results: The Simul8 ® implementation was slower than the original VBA model when simulating 8 cohorts of 10,000 patients (approximately 12 minutes versus 7.5 minutes). The model structure required the use of multiple patient-level variables ('labels') in Simul8 ® , which are known to affect run-time. When event-logs were recorded for debugging, both VBA and Simul8 ® took considerably longer and Simul8 ® crashed frequently. Model validation was challenging due to the stochastic nature of PLS. When potentially non-trivial differences emerged between implementations, it was difficult to determine whether these were due to technical errors, Monte-Carlo error, or bias from different pseudo-random number generators, an area in which VBA is known to perform poorly. Object-oriented VBA was difficult for new users to grasp and coding errors were difficult to identify. The Simul8 ® interface allowed for patients' routes through the model to be easily visualised and tracked; however, some simple operations (e.g. lookups across categories) required convoluted coding. Conclusions: It is not clear that dedicated simulation software provides meaningful benefits over a generic scripting language for PLS. We are extending this project to encompass further replications in R and discretely integrated condition event simulation (DICE).

Unveiling the humanistic burden of C3 glomerulopathy in Italy: a narrative medicine multicentre project

“…Organizational and structural improvements: encouraging systematic collaboration among healthcare centres in the area and facilitating early diagnosis were highlighted. This reinforces ndings from a previous survey [14] where patients and caregivers perceived delayed diagnosis. Lack of detailed information about C3G was also mentioned by patients and caregivers, leading to emotional stress.…”

“…Nevertheless, narratives illustrate that both patients and caregivers can establish a new daily routine while living with the disease, evidenced by the prevalence of restitution [33] narratives. Despite this, C3G maintains a signi cant impact on patients' QoL, particularly relatively to social and work activities, as highlighted by the self-reported loss of an average 52.7% of working days per year, in line with ndings from previous studies [14].…”

“…Consistent with a previous study [14], the symptoms most frequently reported by patients, such as swelling and fatigue, have received minimal attention in the existing literature. In particular, fatigue remains poorly addressed despite its crucial impact on work activities and overall quality of life.…”

“…Previous research has highlighted the lack of evidence on the humanistic burden of C3G in the literature [14,15]; additionally, studies on other kidney diseases have emphasised the critical importance of systematically evaluating the impact of these conditions on quality of life (QoL) [16], including the use of patient-reported outcomes (PROs), as healthcare professionals may face challenges in recognising and understanding symptoms that are not reported in the literature and patients' coping mechanisms with the disease [17].…”

Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators

“…Overall, the data set of reviewed studies comprises a total of 40 indications or conditions from a variety of different disease areas (among them cancer, neurological diseases, auto-immune diseases, gastrointestinal diseases, and various others as detailed in Appendix A ). Notably, it includes rare diseases, such as complement 3 glomerulopathy ( 40 ) or SLC6A1 disorder ( 41 ), which often pose challenges to established research methods due to relatively small patient populations. This variety indicates the suitability of SML as a highly flexible and versatile method to elicit patient experience data across a wide range of disease areas, irrespective of their prevalence.…”