Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network

Abstract

Objective: The study aims to identify distinct population-specific comorbidity progression patterns, timely detect potential comorbidities, and gain better understanding of the progression of comorbid conditions among patients.

Methods: This work presents a comorbidity progression analysis framework that utilizes temporal comorbidity networks (TCN) for patient stratification and comorbidity prediction. We propose a TCN construction approach that utilizes longitudinal, temporal diagnosis data of patients to construct their TCN. Subsequently, we employ the TCN for patient stratification by conducting preliminary analysis, and typical prescription analysis to uncover potential comorbidity progression patterns in different patient groups. Finally, we propose an innovative comorbidity prediction method by utilizing the distance-matched temporal comorbidity network (TCN-DM). This method identifies similar patients with disease prevalence and disease transition patterns and combines their diagnosis information with that of the current patient to predict potential comorbidity at the patient's next visit.

Results: This study validated the capability of the framework using a real-world dataset MIMIC-III, with heart failure (HF) as interested disease to investigate comorbidity progression in HF patients. With TCN, this study can identify four significant distinctive HF subgroups, revealing the progression of comorbidities in patients. Furthermore, compared to other methods, TCN-DM demonstrated better predictive performance with F1-Score values ranging from 0.454 to 0.612, showcasing its superiority.

Conclusions: This study can identify comorbidity patterns for individuals and population, and offer promising prediction for future comorbidity developments in patients.

Keywords: Comorbidity prediction; Comorbidity progression; Electronic health records; Patient stratification; Temporal comorbidity network.

Fig. 1

The framework of this study

Fig. 2

Temporal comorbidity network construction workflow diagram

Fig. 3

An example of network and its degree centrality for each node

Fig. 4

A schematic diagram of node and edge matching

Fig. 5

A schematic diagram of comorbidity prediction

Fig. 6

Temporal comorbidity networks construction results. a and b are the individual’s and population’s TCN for heart failure patients, respectively, illustrating the relationships between various comorbidities over time

Fig. 7

Patient clustering results for different values of K

Fig. 8

Population’s temporal comorbidity network of each sub-phenotype

Fig. 9

Kaplan–Meier estimation of the survival probability of four sub-phenotypes

Fig. 10

The UpSet plot of frequent prescriptions (a) and population’s TCN of patients who use glucagon (b)