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Michelle Y. Wang, Alfredo A. Sadun; Importance Of History In The Diagnosis Of Neuro-ophthalmic Diseases. Invest. Ophthalmol. Vis. Sci. 2011;52(14):3871.
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To study the importance of the chief complaint, the decision-tree history and a systemic approach as effective diagnostic tools in neuro-ophthalmology.
A prospective study consisting of one hundred and thirty five consecutive new patients seen by one neuro-ophthalmologist (AAS) at the Doheny Eye Institute between January 2009 and April 2009. A decision-tree analysis was utilized by the same specialist in evaluation of all subjects. A forced choice diagnosis was made sequentially based on the patient’s chief complaint, ocular history, eye examination and testing. Diagnoses were grouped into afferent, central nervous system (CNS), efferent, orbit, pupil, and other neuro-ophthalmic diseases. T-test was used to analyze data.
The percentages of correct diagnosis by chief complaint and history alone for afferent, CNS, efferent, orbit, pupil, and other neuro-ophthalmic diseases were 84, 100, 86, 80, 50 and 100 respectively. The percentages of correct diagnosis revealed by examination after history failed to establish this were, for afferent, CNS, efferent, orbit, pupil, and other neuro-ophthalmic diseases, 16, 0, 14, 20, 50, and 0 respectively. The number of correct diagnosis by chief complaint alone was remarkable (52%). However, when a decision tree history was added to chief complaint, an average of 83% diagnoses was correctly made (p=0.003).
Many physicians and physicians-in-training rely heavily on sophisticated testing and imaging in diagnosing. Some of those tests are costly. More importantly, unnecessary testing can produce type II errors and subsequent unnecessary, costly and dangerous further work up. Although the current study is a small pilot project, it demonstrates the importance of the history as obtained in a decision-tree fashion that entails formulating an initial diagnosis as a testable hypothesis, which further questions or examination can confirm or reject. This form of decision-tree analysis allows for an efficient systematic investigation of complicated cases.
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