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Featured researches published by Mingkai Peng.


PLOS ONE | 2015

Refining Hypertension Surveillance to Account for Potentially Misclassified Cases

Mingkai Peng; Guanmin Chen; Lisa M. Lix; Finlay A. McAlister; Karen Tu; Norm R.C. Campbell; Brenda R. Hemmelgarn; Lawrence W. Svenson; Hude Quan; Hypertension Outcomes Surveillance Team

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.


Medical Care | 2017

Constructing Episodes of Inpatient Care: How to Define Hospital Transfer in Hospital Administrative Health Data?

Mingkai Peng; Bing Li; Danielle A. Southern; Cathy A. Eastwood; Hude Quan

Background: Hospital administrative health data create separate records for each hospital stay of patients. Treating a hospital transfer as a readmission could lead to biased results in health service research. Methods: This is a cross-sectional study. We used the hospital discharge abstract database in 2013 from Alberta, Canada. Transfer cases were defined by transfer institution code and were used as the reference standard. Four time gaps between 2 hospitalizations (6, 9, 12, and 24 h) and 2 day gaps between hospitalizations [same day (up to 24 h), ⩽1 d (up to 48 h)] were used to identify transfer cases. We compared the sensitivity and positive predictive value (PPV) of 6 definitions across different categories of sex, age, and location of residence. Readmission rates within 30 days were compared after episodes of care were defined at the different time gaps. Results: Among the 6 definitions, sensitivity ranged from 93.3% to 98.7% and PPV ranged from 86.4% to 96%. The time gap of 9 hours had the optimal balance of sensitivity and PPV. The time gaps of same day (up to 24 h) and 9 hours had comparable 30-day readmission rates as the transfer indicator after defining episode of care. Conclusions: We recommend the use of a time gap of 9 hours between 2 hospitalizations to define hospital transfer in inpatient databases. When admission or discharge time is not available in the database, a time gap of same day (up to 24 h) can be used to define hospital transfer.


PLOS ONE | 2015

Chinese Herbal Therapy and Western Drug Use, Belief and Adherence for Hypertension Management in the Rural Areas of Heilongjiang Province, China

Xia Li; Mingkai Peng; Yuan Li; Zheng Kang; Yanhua Hao; Hong Sun; Lijun Gao; Mingli Jiao; Qunhong Wu; Hude Quan

Background Traditional Chinese medicine (TCM) including Chinese herbal therapy has been widely practiced in China. However, little is known about Chinese herbal therapy use for hypertension management, which is one of the most prevalent chronic conditions in China. Thus we described Chinese herbal therapy and western drug users, beliefs, hypertension knowledge, and Chinese herbal and western drug adherence and determinants of Chinese herbal therapy use among patients with hypertension in rural areas of Heilongjiang Province, China. Methodology and Principal Findings This face-to-face cross sectional survey included 665 hypertensive respondents aged 30 years or older in rural areas of Heilongjiang Province, China. Of 665 respondents, 39.7% were male, 27.4% were aged 65 years or older. At the survey, 14.0% reported using Chinese herbal therapy and 71.3% reported using western drug for hypertension management. A majority of patients had low level of treatment adherence (80.6% for the Chinese herbal therapy users and 81.2% for the western drug users). When respondents felt that their blood pressure was under control, 72.0% of the Chinese herbal therapy users and 69.2% of the western drug users sometimes stopped taking their medicine. Hypertensive patients with high education level or better quality of life are more likely use Chinese herbal therapy. Conclusions and Significance Majority of patients diagnosed with hypertension use western drugs to control blood pressure. Chinese herbal therapy use was associated with education level and quality of life.


Statistical Methods in Medical Research | 2018

MethodCompare: An R package to assess bias and precision in method comparison studies

Patrick Taffé; Mingkai Peng; Victoria Stagg; Tyler Williamson

Bland and Altman’s limits of agreement have been used in many clinical research settings to assess agreement between two methods of measuring a quantitative trait. However, when the variances of the measurement errors of the two methods are different, limits of agreement can be misleading. MethodCompare is an R package that implements a new statistical methodology, developed by Taffé in 2016. MethodCompare produces three new plots, the “bias plot”, the “precision plot”, and the “comparison plot” to visually evaluate the performance of the new measurement method against the reference method. The method is illustrated on three simulated examples. Note that the Taffé method assumes that there are several measurements from reference standard and possibly as few as one measurement from the new method for each individual.


International Journal for Population Data Science | 2018

Identifying Cases of Sleep Disorders through International Classification of Diseases (ICD) Codes in Administrative Data

Rachel J. Jolley; Zhiying Liang; Mingkai Peng; Sachin R. Pendharkar; Willis H. Tsai; Guanmin Chen; Cathy A. Eastwood; Hude Quan; Paul E. Ronksley

Abstract Objectives Prevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data. Methods A cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results From a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations. Conclusion Sleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed


International Journal for Population Data Science | 2018

Coding reliability and agreement of international classification of disease, 10th revision (ICD-10) codes in emergency department data

Mingkai Peng; Cathy A. Eastwood; Alicia Boxill; Rachel J. Jolley; Laura Rutherford; Karen Carlson; Stafford Dean; Hude Quan

Abstract Introduction Administrative health data from emergency departments play important roles in understanding health needs of the public and reasons for health care resource use. International Classification of Disease (ICD) diagnostic codes have been widely used to code reasons of clinical encounters for administrative purposes in emergency departments. Objective The purpose of the study is to examine the coding agreement and reliability of ICD diagnosis codes in emergency department records through auditing the routinely collected data. Methods We randomly sampled 1 percent of records (n=1636) between October and December 2013 from 11 emergency departments in Alberta, Canada. Auditors were employed to review the same chart and independently assign main diagnosis codes. We assessed coding agreement and reliability through comparison of codes assigned by auditors and hospital coders using proportion of agreement and Cohen’s kappa. Error analysis was conducted to review diagnosis codes with disagreement and categorized them into six groups. Results Overall, the agreement was 86.5% and 82.2% at 3 and 4 digits levels respectively, and reliability was 0.86 and 0.82 respectively. Variations of agreement and reliability were identified across different emergency departments. The major two categories of coding discrepancy were the use of different codes for same condition (23.6%) and the use of codes at different levels of specificity (20.9%). Conclusions Diagnosis codes in emergency departments show high agreement and reliability, although there are variations of coding quality across different hospitals. Stricter coding guidelines regarding the use of unspecified codes are needed to enhance coding consistency.


Stata Journal | 2017

biasplot: A package to effective plots to assess bias and precision in method comparison studies

Patrick Taffé; Mingkai Peng; Vicki Stagg; Tyler Williamson


Journal of Public Health | 2016

Methods of defining hypertension in electronic medical records: validation against national survey data

Mingkai Peng; Guanmin Chen; Gilaad G. Kaplan; Lisa M. Lix; Neil Drummond; Kelsey Lucyk; Stephanie Garies; Mark Lowerison; Samuel Weibe; Hude Quan


International Journal for Population Data Science | 2018

Ensemble-based Classification Models for Predicting Post-Operative Mortality Risk in Coronary Artery Disease

Anita Brobbey; Peter Faris; Alberto Nettel-Aguirre; Tyler Williamson; Samuel Wiebe; Mingkai Peng; Hude Quan; Tolulope T. Sajobi


International Journal for Population Data Science | 2018

Inferring sensitivity and specificity of phenotyping algorithms using positive and negative predictive value in validation study in observational health data

Mingkai Peng; Rosa Gini; Tyler Williamson

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Hude Quan

University of Calgary

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Peter Faris

Alberta Health Services

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Bing Li

Alberta Health Services

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Lisa M. Lix

University of Manitoba

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