Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records

Abstract

Purposes: This study aims to identify the comorbidity patterns of older men with lung cancer in China.

Methods: We analyzed the electronic medical records (EMRs) of lung cancer patients over age 65 in the Jilin Province of China. The data studied were obtained from 20 hospitals of Jilin Province in 2018. In total, 1510 patients were identified. We conducted a rank-frequency analysis and social network analysis to identify the predominant comorbidities and comorbidity networks. We applied the association rules to mine the comorbidity combination with the values of confidence and lift. A heatmap was utilized to visualize the rules.

Results: Our analyses discovered that (1) there were 31 additional medical conditions in older patients with lung cancer. The most frequent comorbidities were pneumonia, cerebral infarction, and hypertension. (2) The network-based analysis revealed seven subnetworks. (3) The association rules analysis provided 41 interesting rules. The results revealed that hypertension, ischemic cardiomyopathy, and pneumonia are the most frequent comorbid combinations. Heart failure may not have a strong implicating role in these comorbidity patterns. Cerebral infarction was rarely combined with other diseases. In addition, glycoprotein metabolism disorder comorbid with hyponatremia or hypokalemia increased the risk of anemia by more than eight times in older lung cancer patients.

Conclusions: This study provides evidence on the comorbidity patterns of older men with lung cancer in China. Understanding the comorbidity patterns of older patients with lung cancer can assist clinicians in their diagnoses and contribute to developing healthcare policies, as well as allocating resources.

Keywords: aging; association rules; chronic disease management; comorbidity; lung cancer.

Figure 1

(a) Distribution (blue) of the number of comorbidities across all patients, where the x-axis represents the number of patients having n comorbidities ($0\le n\le 9$) and the cumulative proportion distribution across all patients (orange). (b) Distributions of the numbers of comorbidities for men and women.

Figure 2

Distribution of the number of patients for each disease (blue) and the cumulative proportion (orange).

Figure 3

The average number of comorbidities for each co-occurring disease, ordered based on the prevalence of each disease observed in the study.

Figure 4

A network representation of comorbidities. Each node represents a disease, where nodes with the same shape denote a morbidity possessing the same International Classification of Diseases 10th revision (ICD-10) classification code, and an edge connects two nodes if patients were observed with this comorbidity. The size of a node is proportional to the frequency of the disease, and the width of an edge is proportional to the number of patients with the comorbidity.

Figure 5

A heatmap showing the support values in the 41 derived association rules, where the rows are for the rules and the columns are for XX morbidities.

Figure 6

Heatmaps for the confidence and lift values observed in the 41 derived association rules. (a) The rows are for rules, and the columns are for XX morbidities. (b) Defined similar to (a).