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Your search keyword '"Quan, Hude"' showing total 10 results
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10 results on '"Quan, Hude"'

1. Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol

Author

Wu, Guosong, primary, Eastwood, Cathy, additional, Zeng, Yong, additional, Quan, Hude, additional, Long, Quan, additional, Zhang, Zilong, additional, Ghali, William A., additional, Bakal, Jeffrey, additional, Boussat, Bastien, additional, Flemons, Ward, additional, Forster, Alan, additional, Southern, Danielle A., additional, Knudsen, Søren, additional, Popowich, Brittany, additional, and Xu, Yuan, additional

Published
2022
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5. Influence of Using Different Databases and ‘Look Back’ Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers

Author

Chen, Guanmin, Lix, Lisa, Tu, Karen, Hemmelgarn, Brenda R., Campbell, Norm R. C., McAlister, Finlay A., and Quan, Hude

Subjects
Male, Canada, Time Factors, Medical Doctors, Patients, Databases, Factual, Endocrine Disorders, Health Care Providers, Blood Pressure, Gastroenterology and Hepatology, Comorbidity, Cardiovascular Medicine, Vascular Medicine, Geographical locations, Alberta, Endocrinology, Physicians, Outpatients, Medicine and Health Sciences, Diabetes Mellitus, Humans, Aged, Retrospective Studies, Inpatients, Liver Diseases, Research, Health Services, Middle Aged, Patient Discharge, Health Care, Professions, Cardiovascular Diseases, Metabolic Disorders, People and Places, Hypertension, North America, Population Groupings, Female, Medical Informatics, Research Article
Abstract

Objective To determine the data sources and ‘look back’ intervals to define comorbidities. Data Sources Hospital discharge abstracts database (DAD), physician claims, population registry and death registry from April 1, 1994 to March 31, 2010 in Alberta, Canada. Study Design Newly-diagnosed hypertension cases from 1997 to 2008 fiscal years were identified and followed up to 12 years. We defined comorbidities using data sources and duration of retrospective observation (6 months, 1 year, 2 years, and 3 years). The C-statistics for logistic regression and concordance index (CI) for Cox model of mortality and cardiovascular disease hospitalization were used to evaluate discrimination performance for each approach of defining comorbidities. Principal Findings The comorbidities prevalence became higher with a longer duration. Using DAD alone underestimated the prevalence by about 75%, compared to using both DAD and physician claims. The C-statistic and CI were highest when both DAD and physician claims were used, and model performance improved when observation duration increased from 6 months to one year or longer. Conclusion The comorbidities prevalence is greatly impacted by the data source and duration of retrospective observation. A combination of DAD and physicians claims with at least one year observation duration improves predictions for cardiovascular disease and one-year mortality outcome model performance.

Published
2016

10. Refining Hypertension Surveillance to Account for Potentially Misclassified Cases.

Author

Peng, Mingkai, Chen, Guanmin, Lix, Lisa M., McAlister, Finlay A., Tu, Karen, Campbell, Norm R., Hemmelgarn, Brenda R., Svenson, Lawrence W., Quan, Hude, and null, null

Subjects
HYPERTENSION, BIOACCUMULATION, PHYSICIANS, HOSPITAL care, LOGISTIC regression analysis
Abstract

Administrative health data have been used in hypertension surveillance using the 1H2P method: the International Classification of Disease (ICD) hypertension diagnosis codes were recorded in at least 1 hospitalization or 2 physician claims within 2 year-period. Accumulation of false positive cases over time using the 1H2P method could result in the overestimation of hypertension prevalence. In this study, we developed and validated a new reclassification method to define hypertension cases using regularized logistic regression with the age, sex, hypertension and comorbidities in physician claims, and diagnosis of hypertension in hospital discharge data as independent variables. A Bayesian method was then used to adjust the prevalence estimated from the reclassification method. We evaluated the hypertension prevalence in data from Alberta, Canada using the currently accepted 1H2P method and these newly developed methods. The reclassification method with Bayesian adjustment produced similar prevalence estimates as the 1H2P method. This supports the continued use of the 1H2P method as a simple and practical way to conduct hypertension surveillance using administrative health data. [ABSTRACT FROM AUTHOR]

Published
2015
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