PURPOSE: To evaluate sensibility and specificity of a screening questionnaire with multivariable analysis, compare them and elaborate an artificial neural network for future screenings. METHODS: Observational, transversal study performed at UNIFESP, with 48 patients with allergic conjunctivitis and 54 children without the disease. Their age ranged between 3 and 14 years and there was no restriction related to gender, systemic allergy or treatment. The questionnaire was applied and multivariable statistical analysis was performed. Finally, an artificial neural network was elaborated. RESULTS: Mean age was 8.4 years (7-13) and male gender was more frequent (60.7%). Mean score was 10.04 (0-18), and it was higher in the study group (p < 0.001). Allergic diagnosis was increased with the inclusion of the fifth question in 68.8%. Kappa coefficient was low (0.337; p = 0.071) and showed no agreement between diagnosis made by the questionnaire and clinical examination. Only the question number five had good sensitivity (85.4%) and specificity (85.1%). The cutoff point to separate allergic patients was 10 (sensitivity = 77.08% and specificity = 79.63%). The artificial neural network predicted allergic diagnosis in 100% using 7 of the 15 existent items. CONCLUSIONS: An efficient model was developed using seven questions, in a manner that its application might be easy to large populations.
PURPOSE: To evaluate sensibility and specificity of a screening questionnaire with multivariable analysis, compare them and elaborate an artificial neural network for future screenings. METHODS: Observational, transversal study performed at UNIFESP, with 48 patients with allergic conjunctivitis and 54 children without the disease. Their age ranged between 3 and 14 years and there was no restriction related to gender, systemic allergy or treatment. The questionnaire was applied and multivariable statistical analysis was performed. Finally, an artificial neural network was elaborated. RESULTS: Mean age was 8.4 years (7-13) and male gender was more frequent (60.7%). Mean score was 10.04 (0-18), and it was higher in the study group (p < 0.001). Allergic diagnosis was increased with the inclusion of the fifth question in 68.8%. Kappa coefficient was low (0.337; p = 0.071) and showed no agreement between diagnosis made by the questionnaire and clinical examination. Only the question number five had good sensitivity (85.4%) and specificity (85.1%). The cutoff point to separate allergicpatients was 10 (sensitivity = 77.08% and specificity = 79.63%). The artificial neural network predicted allergic diagnosis in 100% using 7 of the 15 existent items. CONCLUSIONS: An efficient model was developed using seven questions, in a manner that its application might be easy to large populations.