1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China;
2. Cloud Computing Center, Chinese Academy of Sciences, Dongguan, China.
3. College of Business, University of Colorado, Colorado Springs, CO, USA.
4. Institute of Disease Control and Prevention, Academy of Military Medical Sciences, Beijing, China.
†These authors contributed equally to this work.
Background: Hypertension, an important risk factor for the health of human being, is often accompanied by various comorbidities. However, the incidence patterns of those comorbidities have not been widely studied.
Aim: Applying big-data techniques on a large collection of electronic medical records, we investigated sex-specific and age-specific detection rates of some important comorbidities of hypertension, and sketched their relationships to reveal the risk for hypertension patients.
Methods: We collected a total of 6,371,963 hypertension-related medical records from 106 hospitals in 72 cities throughout China. Those records were reported to a National Center for Disease Control in China between 2011 and 2013. Based on the comprehensive and geographically distributed data set, we identified the top 20 comorbidities of hypertension, and disclosed the sex-specific and age-specific patterns of those comorbidities. A comorbidities network was constructed based on the frequency of co-occurrence relationships among those comorbidities.
Results: The top four comorbidities of hypertension were coronary heart disease, diabetes, hyperlipemia, and arteriosclerosis, whose detection rates were 21.71% (21.49% for men vs 21.95% for women), 16.00% (16.24% vs 15.74%), 13.81% (13.86% vs 13.76%), and 12.66% (12.25% vs 13.08%), respectively. The age-specific detection rates of comorbidities showed five unique patterns and also indicated that nephropathy, uremia, and anemia were significant risks for patients under 39 years of age. On the other hand, coronary heart disease, diabetes, arteriosclerosis, hyperlipemia, and cerebral infarction were more likely to occur in older patients. The comorbidity network that we constructed indicated that the top 20 comorbidities of hypertension had strong co-occurrence correlations.
Conclusions: Hypertension patients can be aware of their risks of comorbidities based on our sex-specific results, age-specific patterns, and the comorbidity network. Our findings provide useful insights into the comorbidity prevention, risk assessment, and early warning for hypertension patients.
Keywords: Hypertension, Comorbidity, Electronic Medical Records, Detection Rate, Network Analysis.