Visualizing adverse events in clinical trials using correspondence analysis with R-package visae

A visual safety risk evaluation tool in early clinical phases

Dynamic evaluation of safety risks across all stages of clinical development, including early trial phases, has become the norm with the evolution of risk management in pharmacovigilance. The need for practical tools directly applicable to early clinical assessment prompted the authors to pilot the customization of a common risk matrix to support proactive safety risk evaluation based on multiple dimensions. Once defined for their research area, the tailored safety risk matrix was applied to visualize a series of known risk profiles and examples, along with projected risk mitigation effects. This two-fold visualization on one simple graph (risks positioning, and effect of risk mitigation of those positions) was found to be particularly relevant in multi-disciplinary safety discussions during early clinical development stages. The visual tool provides useful snapshots of projected safety risk profiles, facilitating communication with multiple stakeholders involved in decisions throughout early clinical development. With the prospect of a simple visual instrument to add to their existing risk management toolbox and processes, clinical teams are also provided with a basic blueprint to apply and tailor based on risk dimensions related to their own research area.

Patient clusters based on demographics, clinical characteristics and cancer-related symptoms: A cross-sectional pilot study

PURPOSE

This study aimed to identify and preliminary validate distinct clusters of patients with cancer based on demographics, clinical characteristics, and symptoms and to inform future research on sample size requirements for achieving sufficient power in clustering analyses.

METHODS

This cross-sectional pilot study involved 114 patients with cancer from two hospitals in northern Italy. Data were collected on demographics, clinical characteristics, and 20 symptoms using the Edmonton Symptom Assessment System in October 2022. t-distributed stochastic neighbor embedding (t-SNE) was used to reduce the symptom data and demographics (e.g., age) into two components, which were then clustered using Ward's method. A Monte Carlo simulation was conducted based on the t-SNE components to estimate the sample size needed to achieve 80% power for different cluster solutions (k = 2, 3, 4).

RESULTS

Two distinct clusters were identified: Cluster 1 (Higher Symptom Burden Cluster) and Cluster 2 (Lower Symptom Burden Cluster). Cluster 1 patients had a higher prevalence of depression, anxiety, and drowsiness. Monte Carlo simulations indicated that 50 patients per cluster were sufficient for k = 2 clusters to achieve 80% power, whereas 90 patients per cluster were needed for k = 3 clusters and 120 patients per cluster for k = 4 clusters.

CONCLUSION

This study identified distinct patient clusters and provided preliminary evidence on the sample size required for clustering analyses in cancer research. Understanding patient clusters enables nurses to provide tailored interventions, potentially improving symptom management and overall patient care.

Analysis and visualization of the course and burden over time of adverse drug reactions (ADRs) attributed to TNFα-inhibitors in patients with inflammatory rheumatic diseases (IRDs)

BACKGROUND

We aimed to investigate course and burden over time of ADRs attributed to TNFα-inhibitors in IRD-patients, and whether Sankey diagrams and polar plots can visualize this.

RESEARCH DESIGN AND METHODS

Data on ADRs experienced during the Dutch Biologic Monitor (January 2017 till December 2022) were used in this study. We selected IRD-patients using a TNFα-inhibitor, reporting skin reactions/infections/injection site reactions and completing ≥3 questionnaires (i.e. the initial report and ≥2 follow-ups). Course was scored as worsening/improving/remaining stable/resolving and as (non-)recurrent. Patients scored burden from 1 (no burden) to 5 (very high burden). Sankey diagrams and polar plots visualized this.

RESULTS

202 patients were included, reporting 353 ADRs. Most skin reactions were stable (25.0%). Most infections resolved (50.8%). Injection site reactions were mostly recurrent (72.3%). Skin reactions and infections tended to decrease in burden . Infections had highest burden at start, which mostly decreased over time. Injection site reactions had a low and stable burden.

CONCLUSIONS

Skin reactions attributed to TNFα-inhibitors by IRD-patients are stable with a slightly decreasing burden over time. Infections have highest burden at start but resolved mostly. Injection site reactions have a low and stable burden. Sankey diagrams and polar plots are suitable to visualize this.