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  1. Fulltext Validity of International Classification of Diseases (ICD) coding for dengue infections in hospital discharge records in Malaysia
    Woon YL, Lee KY, Mohd Anuar SFZ, Goh PP, Lim TO
    BMC Health Serv Res, 2018 04 20;18(1):292.
    PMID: 29678172 DOI: 10.1186/s12913-018-3104-z
    BACKGROUND: Hospitalization due to dengue illness is an important measure of dengue morbidity. However, limited studies are based on administrative database because the validity of the diagnosis codes is unknown. We validated the International Classification of Diseases, 10th revision (ICD) diagnosis coding for dengue infections in the Malaysian Ministry of Health's (MOH) hospital discharge database.

    METHODS: This validation study involves retrospective review of available hospital discharge records and hand-search medical records for years 2010 and 2013. We randomly selected 3219 hospital discharge records coded with dengue and non-dengue infections as their discharge diagnoses from the national hospital discharge database. We then randomly sampled 216 and 144 records for patients with and without codes for dengue respectively, in keeping with their relative frequency in the MOH database, for chart review. The ICD codes for dengue were validated against lab-based diagnostic standard (NS1 or IgM).

    RESULTS: The ICD-10-CM codes for dengue had a sensitivity of 94%, modest specificity of 83%, positive predictive value of 87% and negative predictive value 92%. These results were stable between 2010 and 2013. However, its specificity decreased substantially when patients manifested with bleeding or low platelet count.

    CONCLUSION: The diagnostic performance of the ICD codes for dengue in the MOH's hospital discharge database is adequate for use in health services research on dengue.

    Matched MeSH terms: Hospital Records/statistics & numerical data*
  2. How well can signs and symptoms predict AMI in the Malaysian population?
    Bulgiba AM, Razaz M
    Int J Cardiol, 2005 Jun 22;102(1):87-93.
    PMID: 15939103
    The aim of the study was to use data from an electronic medical record system (EMR) to look for factors that would help us diagnose acute myocardial infarction (AMI) with the ultimate aim of using these factors in a decision support system for chest pain. We extracted 887 records from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. We cleaned the data, extracted 69 possible variables and performed univariate and multivariate analysis. From the univariate analysis we find that 22 variables are significantly associated with a diagnosis of AMI. However, multiple logistic regression reveals that only 9 of these 22 variables are significantly related to a diagnosis of AMI. Race (Indian), male sex, sudden onset of persistent crushing pain, associated sweating and a history of diabetes mellitus are significant predictors of AMI. Pain that is relieved by other means and history of heart disease on treatment are important predictors of a diagnosis other than AMI. The degree of accuracy is high at 80.5%. There are 13 factors that are significant in the univariate analysis but are not among the nine significant factors in the multivariate analysis. These are location of pain, associated palpitations, nausea and vomiting; pain relieved by rest, pain aggravated by posture, cough, inspiration and exertion; age more than 40, being a smoker and abnormal chest wall and face examination. We believe that these findings can have important applications in the design of an intelligent decision support system for use in medical care as the predictive capability can be further refined with the use of intelligent computational techniques.
    Matched MeSH terms: Hospital Records/statistics & numerical data
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