Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision
| Autor: | Peter Haddawy, Saranath Lawpoolsri, Viravarn Luvira, Sopon Iamsirithaworn, Chaitawat Sa-ngamuang, Watcharapong Piyaphanee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Bacterial Diseases
Male Viral Diseases Medical Doctors Health Care Providers Fevers Viral Nonstructural Proteins Pathology and Laboratory Medicine Dengue Fever Dengue fever Dengue Bayes' theorem 0302 clinical medicine Zoonoses Epidemiology Medicine and Health Sciences Medical Personnel 030212 general & internal medicine Medical diagnosis lcsh:Public aspects of medicine Middle Aged Thailand Clinical Laboratory Sciences Professions Clinical Laboratories Infectious Diseases Predictive value of tests Female Network Analysis Research Article Neglected Tropical Diseases Adult Computer and Information Sciences medicine.medical_specialty lcsh:Arctic medicine. Tropical medicine Adolescent Fever lcsh:RC955-962 030231 tropical medicine MEDLINE Immunologic Tests Sensitivity and Specificity Young Adult 03 medical and health sciences Signs and Symptoms Diagnostic Medicine Predictive Value of Tests Physicians medicine Humans Leptospirosis Intensive care medicine business.industry Public Health Environmental and Occupational Health Bayesian network Bayes Theorem lcsh:RA1-1270 Tropical Diseases medicine.disease Influenza Health Care Infectious disease (medical specialty) People and Places Population Groupings business |
| Zdroj: | PLoS Neglected Tropical Diseases, Vol 12, Iss 6, p e0006573 (2018) PLoS Neglected Tropical Diseases |
| ISSN: | 1935-2735 1935-2727 |
| Popis: | Differentiating dengue patients from other acute febrile illness patients is a great challenge among physicians. Several dengue diagnosis methods are recommended by WHO. The application of specific laboratory tests is still limited due to high cost, lack of equipment, and uncertain validity. Therefore, clinical diagnosis remains a common practice especially in resource limited settings. Bayesian networks have been shown to be a useful tool for diagnostic decision support. This study aimed to construct Bayesian network models using basic demographic, clinical, and laboratory profiles of acute febrile illness patients to diagnose dengue. Data of 397 acute undifferentiated febrile illness patients who visited the fever clinic of the Bangkok Hospital for Tropical Diseases, Thailand, were used for model construction and validation. The two best final models were selected: one with and one without NS1 rapid test result. The diagnostic accuracy of the models was compared with that of physicians on the same set of patients. The Bayesian network models provided good diagnostic accuracy of dengue infection, with ROC AUC of 0.80 and 0.75 for models with and without NS1 rapid test result, respectively. The models had approximately 80% specificity and 70% sensitivity, similar to the diagnostic accuracy of the hospital’s fellows in infectious disease. Including information on NS1 rapid test improved the specificity, but reduced the sensitivity, both in model and physician diagnoses. The Bayesian network model developed in this study could be useful to assist physicians in diagnosing dengue, particularly in regions where experienced physicians and laboratory confirmation tests are limited. Author summary In many parts of the world, dengue diagnosis still relies on clinical manifestation and basic laboratory tests. However, clinical manifestation of dengue patients is usually unspecific especially during the early stage, which makes accurate dengue diagnosis a challenge for physicians. Recently, the World Health Organization has recommended the use of the NS1 antigen rapid test to assist in dengue diagnosis. Unfortunately, the rapid test is still only in limited use due to its cost and relatively low sensitivity. In this study we constructed Bayesian Network (BN) models from available data and used them to predict the probability of dengue infection. Our BN models provided good performance in making dengue diagnosis, comparable with physician’s diagnosis. Information on NS1 rapid test could improve the diagnostic performance by improving specificity of dengue diagnosis. This study suggested that BN models can be useful to assist physicians in dengue diagnosis, particularly in areas where experienced physicians and laboratory confirmation tests are limited. |
| Databáze: | OpenAIRE |
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