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

Abstract

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.