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Rodríguez-Fernández A, Visos-Varela I, Zapata-Cachafeiro M, Pintos-Rodríguez S, García-Álvarez RM, Herdeiro TM, Piñeiro-Lamas M, Figueiras A, Salgado-Barreira Á. Outpatient glucocorticoid use and COVID-19 outcomes: a population-based study. Inflammopharmacology 2024; 32:2305-2315. [PMID: 38698179 PMCID: PMC11300658 DOI: 10.1007/s10787-024-01474-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 03/30/2024] [Indexed: 05/05/2024]
Abstract
INTRODUCTION Owing to controversy information surrounds effect of glucocorticoids on the evolution of COVID-19, we evaluate the effects of outpatient glucocorticoid use on the severity and progression of COVID-19 and risk of infection and analyse the effect of window of exposure and dose. METHODS We conducted a population-based case - control study, involving 4 substudies: (i) Hospitalisation; (ii) Mortality, using subjects hospitalised with a PCR + as cases and subjects without a PCR + as controls; (iii) Progression, including subjects with a PCR + (hospitalised versus non-hospitalised); and (iv) Susceptibility, with all subjects with a PCR + and subjects without a PCR + . Adjusted odds ratios (ORa) and their 95% confidence intervals (95% CI) were calculated. RESULTS The outpatient glucocorticoid use was associated with an increased risk of hospitalisation (aOR 1.79; 95% CI 1.56-2.05), mortality (aOR 2.30; 95% CI 1.68-3.15), progression (aOR 1.69; 95% CI 1.43-2.00) and susceptibility (aOR 1.29, 95% CI 1.19-1.41). Furthermore, the effects was observed to be greater at higher doses and the closer that drug use approached the outcome date, with an almost fourfold increase in mortality among users in the previous month (aOR 3.85; 95% CI 2.63-5.62). CONCLUSIONS According to the results of this real-world data study, outpatient glucocorticoid use should be considered in making decisions about intrahospital treatment.
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Affiliation(s)
- Almudena Rodríguez-Fernández
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Rúa de San Francisco, S/N, 15782, Santiago de Compostela (A Coruña), Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Irene Visos-Varela
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Rúa de San Francisco, S/N, 15782, Santiago de Compostela (A Coruña), Spain
| | - Maruxa Zapata-Cachafeiro
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Rúa de San Francisco, S/N, 15782, Santiago de Compostela (A Coruña), Spain.
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Santiago de Compostela, Spain.
- Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain.
| | - Samuel Pintos-Rodríguez
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Rúa de San Francisco, S/N, 15782, Santiago de Compostela (A Coruña), Spain
| | - Rosa M García-Álvarez
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Rúa de San Francisco, S/N, 15782, Santiago de Compostela (A Coruña), Spain
- Service of Preventive Medicine and Public Health. Clinic Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | - Teresa M Herdeiro
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, 3810-193, Aveiro, Portugal
| | - María Piñeiro-Lamas
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Adolfo Figueiras
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Rúa de San Francisco, S/N, 15782, Santiago de Compostela (A Coruña), Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Ángel Salgado-Barreira
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Rúa de San Francisco, S/N, 15782, Santiago de Compostela (A Coruña), Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain
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Benny D, Giacobini M, Catalano A, Costa G, Gnavi R, Ricceri F. A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data. JMIR Public Health Surveill 2024; 10:e52353. [PMID: 39024001 PMCID: PMC11294776 DOI: 10.2196/52353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/31/2024] [Accepted: 05/16/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions. OBJECTIVE This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support. METHODS We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support. RESULTS We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4. CONCLUSIONS When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.
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Affiliation(s)
- Dayana Benny
- Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
- Modeling and Data Science, Department of Mathematics, University of Turin, Turin, Italy
| | - Mario Giacobini
- Data Analysis and Modeling Unit, Department of Veterinary Sciences, University of Turin, Turin, Italy
| | - Alberto Catalano
- Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
- Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Giuseppe Costa
- Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Roberto Gnavi
- Unit of Epidemiology, Regional Health Service, Local Health Unit Torino 3, Turin, Italy
| | - Fulvio Ricceri
- Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
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Mehrizi R, Golestani A, Malekpour MR, Karami H, Nasehi MM, Effatpanah M, Rezaee M, Shahali Z, Akbari Sari A, Daroudi R. Patterns of case fatality and hospitalization duration among nearly 1 million hospitalized COVID-19 patients covered by Iran Health Insurance Organization (IHIO) over two years of pandemic: An analysis of associated factors. PLoS One 2024; 19:e0298604. [PMID: 38394118 PMCID: PMC10889889 DOI: 10.1371/journal.pone.0298604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Different populations and areas of the world experienced diverse COVID-19 hospitalization and mortality rates. Claims data is a systematically recorded source of hospitalized patients' information that could be used to evaluate the disease management course and outcomes. We aimed to investigate the hospitalization and mortality patterns and associated factors in a huge sample of hospitalized patients. METHODS In this retrospective registry-based study, we utilized claim data from the Iran Health Insurance Organization (IHIO) consisting of approximately one million hospitalized patients across various hospitals in Iran over a 26-month period. All records in the hospitalization dataset with ICD-10 codes U07.1/U07.2 for clinically/laboratory confirmed COVID-19 were included. In this study, a case referred to one instance of a patient being hospitalized. If a patient experienced multiple hospitalizations within 30 days, those were aggregated into a single case. However, if hospitalizations had longer intervals, they were considered independent cases. The primary outcomes of study were general and intensive care unit (ICU) hospitalization periods and case fatality rate (CFR) at the hospital. Besides, various demographic and hospitalization-associated factors were analyzed to derive the associations with study outcomes using accelerated failure time (AFT) and logistic regression models. RESULTS A total number of 1 113 678 admissions with COVID-19 diagnosis were recorded by IHIO during the study period, defined as 917 198 cases, including 51.9% females and 48.1% males. The 61-70 age group had the highest number of cases for both sexes. Among defined cases, CFR was 10.36% (95% CI: 10.29-10.42). The >80 age group had the highest CFR (26.01% [95% CI: 25.75-26.27]). The median of overall hospitalization and ICU days were 4 (IQR: 3-7) and 5 (IQR: 2-8), respectively. Male patients had a significantly higher risk for mortality both generally (odds ratio (OR) = 1.36 [1.34-1.37]) and among ICU admitted patients (1.12 [1.09-1.12]). Among various insurance funds, Foreign Citizens had the highest risk of death both generally (adjusted OR = 2.06 [1.91-2.22]) and in ICU (aOR = 1.71 [1.51-1.92]). Increasing age groups was a risk of longer hospitalization, and the >80 age group had the highest risk for overall hospitalization period (median ratio = 1.52 [1.51-1.54]) and at ICU (median ratio = 1.17 [1.16-1.18]). Considering Tehran as the reference province, Sistan and Balcuchestan (aOR = 1.4 [1.32-1.48]), Alborz (aOR = 1.28 [1.22-1.35]), and Khorasan Razavi (aOR = 1.24 [1.20-1.28]) were the provinces with the highest risk of mortality in hospitalized patients. CONCLUSION Hospitalization data unveiled mortality and duration associations with variables, highlighting provincial outcome disparities in Iran. Using enhanced registry systems in conjunction with other studies, empowers policymakers with evidence for optimizing resource allocation and fortifying healthcare system resilience against future health challenges.
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Affiliation(s)
- Reza Mehrizi
- National Center for Health Insurance Research, Tehran, Iran
| | - Ali Golestani
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Reza Malekpour
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Karami
- National Center for Health Insurance Research, Tehran, Iran
| | - Mohammad Mahdi Nasehi
- National Center for Health Insurance Research, Tehran, Iran
- Pediatric Neurology Research Center, Research Institute for Children Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Effatpanah
- National Center for Health Insurance Research, Tehran, Iran
- School of Medicine, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Rezaee
- National Center for Health Insurance Research, Tehran, Iran
- Department of Orthopedics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Shahali
- National Center for Health Insurance Research, Tehran, Iran
| | - Ali Akbari Sari
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Rajabali Daroudi
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Mehrizi R, Golestani A, Malekpour MR, Karami H, Nasehi MM, Effatpanah M, Ranjbaran H, Shahali Z, Sari AA, Daroudi R. Drug prescription patterns and their association with mortality and hospitalization duration in COVID-19 patients: insights from big data. Front Public Health 2023; 11:1280434. [PMID: 38164450 PMCID: PMC10758044 DOI: 10.3389/fpubh.2023.1280434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024] Open
Abstract
Background Different medication prescription patterns have been associated with varying course of disease and outcomes in COVID-19. Health claims data is a rich source of information on disease treatment and outcomes. We aimed to investigate drug prescription patterns and their association with mortality and hospitalization via insurance data for a relatively long period of the pandemic in Iran. Methods We retrieved hospitalized patients' data from Iran Health Insurance Organization (IHIO) spanning 26 months (2020-2022) nationwide. Included were patients with ICD-10 codes U07.1/U07.2 for confirmed/suspected COVID-19. A case was defined as a single hospitalization event for an individual patient. Multiple hospitalizations of a patient within a 30-day interval were aggregated into a single case, while hospitalizations with intervals exceeding 30 days were treated as independent cases. The Anatomical Therapeutic Chemical (ATC) was used for medications classification. The two main study outcomes were general and intensive care unit (ICU) hospitalization periods and mortality. Besides, various demographic and clinical associate factors were analyzed to derive the associations with medication prescription patterns and study outcomes using accelerated failure time (AFT) and logistic regression models. Results During the 26 months of the study period, 1,113,678 admissions with COVID-19 diagnosis at hospitals working in company with IHIO were recorded. 917,198 cases were detected from the database, among which 51.91% were females and 48.09% were males. Among the main groups of medications, antithrombotics (55.84% [95% CI: 55.74-55.94]), corticosteroids (54.14% [54.04-54.24]), and antibiotics (42.22% [42.12-42.32]) were the top used medications among cases with COVID-19. Investigation of the duration of hospitalization based on main medication groups showed antithrombotics (adjusted median ratio = 0.94 [0.94-0.95]) were significantly associated with shorter periods of overall hospitalization. Also, antithrombotics (adjusted odds ratio = 0.74 [95%CI, 0.73-0.76]), corticosteroids (0.97 [0.95-0.99]), antivirals (0.82 [0.80-0.83]), and ACE inhibitor/ARB (0.79 [0.77-0.80]) were significantly associated with lower mortality. Conclusion Over 2 years of investigation, antithrombotics, corticosteroids, and antibiotics were the top medications for hospitalized patients with COVID-19. Trends in medication prescription varied based on various factors across the country. Medication prescriptions could potentially significantly impact the trends of mortality and hospitalization during epidemics, thereby affecting both health and economic burdens.
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Affiliation(s)
- Reza Mehrizi
- National Center for Health Insurance Research, Tehran, Iran
| | - Ali Golestani
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Reza Malekpour
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Karami
- National Center for Health Insurance Research, Tehran, Iran
| | - Mohammad Mahdi Nasehi
- National Center for Health Insurance Research, Tehran, Iran
- Pediatric Neurology Research Center, Research Institute for Children Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Effatpanah
- National Center for Health Insurance Research, Tehran, Iran
- School of Medicine, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Ranjbaran
- National Center for Health Insurance Research, Tehran, Iran
- Immunogenetics Research Center, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Zahra Shahali
- National Center for Health Insurance Research, Tehran, Iran
| | - Ali Akbari Sari
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Rajabali Daroudi
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Ziad E, Sadat S, Farzadfar F, Malekpour MR. Prescription pattern analysis of Type 2 Diabetes Mellitus: a cross-sectional study in Isfahan, Iran. BioData Min 2023; 16:29. [PMID: 37864248 PMCID: PMC10588025 DOI: 10.1186/s13040-023-00344-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 09/20/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Patients with Type 2 Diabetes Mellitus (T2DM) are at a higher risk of polypharmacy and more susceptible to irrational prescriptions; therefore, pharmacological therapy patterns are important to be monitored. The primary objective of this study was to highlight current prescription patterns in T2DM patients and compare them with existing Standards of Medical Care in Diabetes. The second objective was to analyze whether age and gender affect prescription patterns. METHOD This cross-sectional study was conducted using the Iran Health Insurance Organization (IHIO) prescription database. It was mined by an Association Rule Mining (ARM) technique, FP-Growth, in order to find co-prescribed drugs with anti-diabetic medications. The algorithm was implemented at different levels of the Anatomical Therapeutic Chemical (ATC) classification system, which assigns different codes to drugs based on their anatomy, pharmacological, therapeutic, and chemical properties to provide an in-depth analysis of co-prescription patterns. RESULTS Altogether, the prescriptions of 914,652 patients were analyzed, of whom 91,505 were found to have diabetes. According to our results, prescribing Lipid Modifying Agents (C10) (56.3%), Agents Acting on The Renin-Angiotensin System (C09) (48.9%), Antithrombotic Agents (B01) (35.7%), and Beta Blocking Agents (C07) (30.1%) were meaningfully associated with the prescription of Drugs Used in Diabetes. Our study also revealed that female diabetic patients have a higher lift for taking Thyroid Preparations, and the older the patients were, the more they were prone to take neuropathy-related medications. Additionally, the results suggest that there are gender differences in the association between aspirin and diabetes drugs, with the differences becoming less pronounced in old age. CONCLUSIONS Almost all of the association rules found in this research were clinically meaningful, proving the potential of ARM for co-prescription pattern discovery. Moreover, implementing level-based ARM was effective in detecting difficult-to-spot rules. Additionally, the majority of drugs prescribed by physicians were consistent with the Standards of Medical Care in Diabetes.
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Affiliation(s)
- Elnaz Ziad
- School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Islamic Republic of Iran
| | - Somayeh Sadat
- Centre for Analytics and Artificial Intelligence Engineering, University of Toronto, Toronto, Canada.
| | - Farshad Farzadfar
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Mohammad-Reza Malekpour
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
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Zhou S, Zhang A, Liao H, Liu Z, Yang F. Pathological Interplay and Clinical Complications between COVID-19 and Cardiovascular Diseases: An Overview in 2023. Cardiology 2023; 149:60-70. [PMID: 37586358 PMCID: PMC11251668 DOI: 10.1159/000533359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/10/2023] [Indexed: 08/18/2023]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) involves all organs of the body, of which the interaction with cardiovascular diseases is the most important. SUMMARY Numerous studies have reported that COVID-19 patients complicated with cardiovascular comorbidities (hypertension, coronary heart disease, chronic heart failure (HF), cerebrovascular disease) are more likely to develop into critical illness and have higher mortality. Conversely, COVID-19 may also cause myocardial injury in patients through various pathological mechanisms such as direct virus attack on cardiomyocytes, overactivation of immune response, microthrombus formation, which may lead to fatal acute ST-segment elevation myocardial infarction, arrhythmia, acute worsening of chronic HF, etc. In addition, the symptoms of the so-called long-COVID may remain in some patients who survived the acute viral infection. Positional tachycardia has been widely reported, and cardiovascular autonomic disorders are thought to play a pathogenic role. KEY MESSAGE The review summarizes the interaction between COVID-19 and cardiovascular disease in terms of pathological mechanism, clinical features, and sequelae. Therapeutic and rehabilitation programs after COVID-19 infection are compiled and need to be further standardized in the future.
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Affiliation(s)
- Sufeng Zhou
- Department of Cardiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Anchen Zhang
- Department of Cardiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hua Liao
- Department of Cardiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhebo Liu
- Department of Cardiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feiyan Yang
- Department of Cardiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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