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Automating Identification of Multiple Chronic Conditions in Clinical Practice Guidelines.

Leung TI, Jalal H, Zulman DM, Dumontier M, Owens DK, Musen MA, Goldstein MK - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs.Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others.Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

View Article: PubMed Central - PubMed

Affiliation: Department of Veterans Affairs, VA Palo Alto Health Care System, Palo Alto, CA ; Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA ; Division of General Medical Disciplines, Stanford University, Stanford, CA.

ABSTRACT
Many clinical practice guidelines (CPGs) are intended to provide evidence-based guidance to clinicians on a single disease, and are frequently considered inadequate when caring for patients with multiple chronic conditions (MCC), or two or more chronic conditions. It is unclear to what degree disease-specific CPGs provide guidance about MCC. In this study, we develop a method for extracting knowledge from single-disease chronic condition CPGs to determine how frequently they mention commonly co-occurring chronic diseases. We focus on 15 highly prevalent chronic conditions. We use publicly available resources, including a repository of guideline summaries from the National Guideline Clearinghouse to build a text corpus, a data dictionary of ICD-9 codes from the Medicare Chronic Conditions Data Warehouse (CCW) to construct an initial list of disease terms, and disease synonyms from the National Center for Biomedical Ontology to enhance the list of disease terms. First, for each disease guideline, we determined the frequency of comorbid condition mentions (a disease-comorbidity pair) by exactly matching disease synonyms in the text corpus. Then, we developed an annotated reference standard using a sample subset of guidelines. We used this reference standard to evaluate our approach. Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs. Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others. Sixty-one (29.0%) of 210 possible disease-comorbidity pairs occurred zero times; for example, no guideline on chronic kidney disease mentioned depression, while heart failure guidelines mentioned ischemic heart disease the most frequently. Our method adequately identifies comorbid chronic conditions in CPG recommendations with precision 0.82, recall 0.75, and F-measure 0.78. Our work identifies knowledge currently embedded in the free text of clinical practice guideline recommendations and provides an initial view of the extent to which CPGs mention common comorbid conditions. Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

No MeSH data available.


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Development of annotation guide and reference standard
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f2-2091334: Development of annotation guide and reference standard

Mentions: We developed a reference standard to identify comorbid disease concepts in a sample subset of CPG text (Figure 2). In the sample, we randomly selected one guideline for each chronic condition for a total of 15 guidelines manually annotated. As an annotation guide for manual annotation, comorbid disease mentions in each guideline, included (1) explicit comorbid disease mentions, including common synonyms (e.g. left ventricular systolic failure for congestive heart failure) or guideline-defined acronyms (e.g. CHF for chronic heart failure) and (2) statements that referenced or described a mention of one of the 15 chronic conditions, or for which the mention could be inferred from the clinical context of the statement. For example, one coronary artery disease guideline states: “Individuals with established cardiovascular disease, who also have chronic renal disease or diabetes with complications, or target organ damage may be considered for treatment at the lower threshold of systolic 130 mm Hg and/or diastolic 80 mm Hg.”27 This statement mentions a description clinically consistent with hypertension, even if hypertension is not explicitly stated.


Automating Identification of Multiple Chronic Conditions in Clinical Practice Guidelines.

Leung TI, Jalal H, Zulman DM, Dumontier M, Owens DK, Musen MA, Goldstein MK - AMIA Jt Summits Transl Sci Proc (2015)

Development of annotation guide and reference standard
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525235&req=5

f2-2091334: Development of annotation guide and reference standard
Mentions: We developed a reference standard to identify comorbid disease concepts in a sample subset of CPG text (Figure 2). In the sample, we randomly selected one guideline for each chronic condition for a total of 15 guidelines manually annotated. As an annotation guide for manual annotation, comorbid disease mentions in each guideline, included (1) explicit comorbid disease mentions, including common synonyms (e.g. left ventricular systolic failure for congestive heart failure) or guideline-defined acronyms (e.g. CHF for chronic heart failure) and (2) statements that referenced or described a mention of one of the 15 chronic conditions, or for which the mention could be inferred from the clinical context of the statement. For example, one coronary artery disease guideline states: “Individuals with established cardiovascular disease, who also have chronic renal disease or diabetes with complications, or target organ damage may be considered for treatment at the lower threshold of systolic 130 mm Hg and/or diastolic 80 mm Hg.”27 This statement mentions a description clinically consistent with hypertension, even if hypertension is not explicitly stated.

Bottom Line: Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs.Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others.Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

View Article: PubMed Central - PubMed

Affiliation: Department of Veterans Affairs, VA Palo Alto Health Care System, Palo Alto, CA ; Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA ; Division of General Medical Disciplines, Stanford University, Stanford, CA.

ABSTRACT
Many clinical practice guidelines (CPGs) are intended to provide evidence-based guidance to clinicians on a single disease, and are frequently considered inadequate when caring for patients with multiple chronic conditions (MCC), or two or more chronic conditions. It is unclear to what degree disease-specific CPGs provide guidance about MCC. In this study, we develop a method for extracting knowledge from single-disease chronic condition CPGs to determine how frequently they mention commonly co-occurring chronic diseases. We focus on 15 highly prevalent chronic conditions. We use publicly available resources, including a repository of guideline summaries from the National Guideline Clearinghouse to build a text corpus, a data dictionary of ICD-9 codes from the Medicare Chronic Conditions Data Warehouse (CCW) to construct an initial list of disease terms, and disease synonyms from the National Center for Biomedical Ontology to enhance the list of disease terms. First, for each disease guideline, we determined the frequency of comorbid condition mentions (a disease-comorbidity pair) by exactly matching disease synonyms in the text corpus. Then, we developed an annotated reference standard using a sample subset of guidelines. We used this reference standard to evaluate our approach. Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs. Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others. Sixty-one (29.0%) of 210 possible disease-comorbidity pairs occurred zero times; for example, no guideline on chronic kidney disease mentioned depression, while heart failure guidelines mentioned ischemic heart disease the most frequently. Our method adequately identifies comorbid chronic conditions in CPG recommendations with precision 0.82, recall 0.75, and F-measure 0.78. Our work identifies knowledge currently embedded in the free text of clinical practice guideline recommendations and provides an initial view of the extent to which CPGs mention common comorbid conditions. Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

No MeSH data available.


Related in: MedlinePlus