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

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...

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Autores principales: Feng, Jia, Mu, Xiao-min, Ma, Ling-ling, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729838/
https://www.ncbi.nlm.nih.gov/pubmed/33291317
http://dx.doi.org/10.3390/ijerph17239119
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author Feng, Jia
Mu, Xiao-min
Ma, Ling-ling
Wang, Wei
author_facet Feng, Jia
Mu, Xiao-min
Ma, Ling-ling
Wang, Wei
author_sort Feng, Jia
collection PubMed
description 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.
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spelling pubmed-77298382020-12-12 Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records Feng, Jia Mu, Xiao-min Ma, Ling-ling Wang, Wei Int J Environ Res Public Health Article 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. MDPI 2020-12-06 2020-12 /pmc/articles/PMC7729838/ /pubmed/33291317 http://dx.doi.org/10.3390/ijerph17239119 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Feng, Jia
Mu, Xiao-min
Ma, Ling-ling
Wang, Wei
Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records
title Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records
title_full Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records
title_fullStr Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records
title_full_unstemmed Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records
title_short Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records
title_sort comorbidity patterns of older lung cancer patients in northeast china: an association rules analysis based on electronic medical records
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729838/
https://www.ncbi.nlm.nih.gov/pubmed/33291317
http://dx.doi.org/10.3390/ijerph17239119
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