Hude Quan

Bio: Hude Quan is an academic researcher from University of Calgary. The author has contributed to research in topics: Population & Health care. The author has an hindex of 68, co-authored 406 publications receiving 28034 citations. Previous affiliations of Hude Quan include University of Alberta & Foothills Medical Centre.

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Abstract: Objectives:Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define C

Updating and Validating the Charlson Comorbidity Index and Score for Risk Adjustment in Hospital Discharge Abstracts Using Data From 6 Countries

Abstract: With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data.

A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data.

Abstract: Background:Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the El

Assessing Validity of ICD-9-CM and ICD-10 Administrative Data in Recording Clinical Conditions in a Unique Dually Coded Database

Abstract: The World Health Organization adopted the first version of the International Classification of Diseases (ICD) in 1900 to internationally monitor and compare mortality statistics and causes of death. Since then, the classification has been revised periodically to accommodate new knowledge of disease and health. The sixth revision, published in 1949, was more radical than the previous five revisions because this edition made it possible to record information from patient charts to compile morbidity statistics. Subsequent revisions were made in 1958 (7th Edition), in 1968 (8th Edition), and in 1979 (9th Edition). The United States modified ICD-9 by specifying many categories and extending coding rubrics to describe the clinical picture in more detail. These modifications resulted in the publication of ICD-9 Clinical Modification (ICD-9-CM) in 1979 for coding diagnoses in patient charts (Commission on Professional and Hospital Activities 1986). The latest version, ICD-10, was introduced in 1992 (World Health Organization 1992). The major differences between the ICD-10 and ICD-9-CM coding systems are: (1) the tabular list in ICD-10 has 21 categories of disease compared with 19 categories in ICD-9-CM and the category of diseases of the nervous system and sense organs in ICD-9-CM is divided into three categories in ICD-10, including diseases of the nervous system, diseases of the eye and adnexa, and diseases of the ear and mastoid process; and (2) the codes in ICD-10 are alphanumeric while codes in ICD-9-CM are numeric. Each code in ICD-10 starts with a letter (i.e., A–Z), followed by two numeric digits, a decimal, and a digit (e.g., acute bronchiolitis due to respiratory syncytial virus is J21.0). In contrast, codes in ICD-9-CM begin with three digit numbers (i.e., 001–999), that are followed by a decimal and up to two digits (e.g., acute bronchiolitis due to respiratory syncytial virus is 466.11). Canada, Australia, Germany, and other countries have enhanced ICD-10 by adding more specific codes and released country-specific ICD-10 versions, such as ICD-10-Canada (ICD-10-CA; Canadian Institute for Health Information 2003). However, ICD-10-CA has maintained its comparability with ICD-10. The basic ICD-10 structure, scope, content, and definition of existing codes are not altered in ICD-10-CA. This means that none of the ICD-10 codes are relocated or deleted. ICD-10-CA mainly extends code character levels, from third and fourth levels of ICD-10 to fourth, fifth, or sixth character levels (e.g., from I15.0 for renovascular hypertension to I15.00 for benign renovascular hypertension and I15.01 for malignant renovascular hypertension). A few additions of third- and fourth-level codes were also included in ICD-10-CA in a manner consistent with the existing classification. All of these additional codes are indicated with red maple leaf symbols in ICD-10-CA coding manuals. To continuously study the health care system and investigate or monitor population health status with ICD-10 data, it is imperative to assess errors that could occur in the process of creating administrative data due to the introduction of the new coding system, ICD-10. We conducted this study to evaluate the validity of ICD-10 administrative hospital discharge data and to determine whether there were improvements in the validity compared with the validity of ICD-9-CM data. To achieve this aim, we reviewed randomly selected charts coded using ICD-10 at four Canadian teaching hospitals, determined the presence or absence of recorded conditions, and then separately recoded the same charts using ICD-9-CM. Then we assessed the agreement between originally coded ICD-10 administrative and chart review data, and the recoded ICD-9-CM administrative data and chart review data for recording the same conditions. This permitted us to compare the accuracy of ICD-10 data relative to the chart review data, with the accuracy of ICD-9-CM data relative to the chart review data for these conditions.

The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials

Abstract: Flaws in the design, conduct, analysis, and reporting of randomised trials can cause the effect of an intervention to be underestimated or overestimated. The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate

Cochrane Handbook for Systematic Reviews of Interventions

Abstract: The Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Abstract: Objectives:Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define C

CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials

Abstract: Overwhelming evidence shows the quality of reporting of randomised controlled trials (RCTs) is not optimal. Without transparent reporting, readers cannot judge the reliability and validity of trial findings nor extract information for systematic reviews. Recent methodological analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which are considered the gold standard for evaluating interventions because of their ability to minimise or avoid bias. A group of scientists and editors developed the CONSORT (Consolidated Standards of Reporting Trials) statement to improve the quality of reporting of RCTs. It was first published in 1996 and updated in 2001. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have endorsed the CONSORT statement. The statement facilitates critical appraisal and interpretation of RCTs. During the 2001 CONSORT revision, it became clear that explanation and elaboration of the principles underlying the CONSORT statement would help investigators and others to write or appraise trial reports. A CONSORT explanation and elaboration article was published in 2001 alongside the 2001 version of the CONSORT statement. After an expert meeting in January 2007, the CONSORT statement has been further revised and is published as the CONSORT 2010 Statement. This update improves the wording and clarity of the previous checklist and incorporates recommendations related to topics that have only recently received recognition, such as selective outcome reporting bias. This explanatory and elaboration document-intended to enhance the use, understanding, and dissemination of the CONSORT statement-has also been extensively revised. It presents the meaning and rationale for each new and updated checklist item providing examples of good reporting and, where possible, references to relevant empirical studies. Several examples of flow diagrams are included. The CONSORT 2010 Statement, this revised explanatory and elaboration document, and the associated website (www.consort-statement.org) should be helpful resources to improve reporting of randomised trials.