ABSTRACT
This article reports the present situation of Brazilian health care in genetics for Orofacial Cleft (OFC) and 22q11.2 Deletions Syndrome (22q11.2 DS) based on research conducted by Brazil's Craniofacial Project (BCFP). Established in 2003, BCFP is a voluntary and cooperative network aiming to investigate the health care of people with these diseases and other craniofacial anomalies. The initiatives and research results are presented in four sections: (a) a comprehensive report of the Brazilian public health system in craniofacial genetics; (b) multicentric studies developed on OFC and 22q11.2 DS; (c) education strategies focused on addressing these conditions for both population and health-care professionals; and (d) the nosology through the Brazilian Database on Craniofacial Anomalies (BDCA). Since 2006, BDCA uses a standardized method with detailed clinical data collection, which allows for conducting studies on nosology, genotype-phenotype correlations, and natural history; data can also contribute to public policies. Currently, the BDCA stores data on 1,724 individuals, including 1,351 (78.36%) who were primarily admitted due to OFC and 373 (21.63%) with clinical suspicion of 22q11.2 DS. Chromosomal abnormalities/genomic imbalances were represented by 92/213 (43.19%) individuals with syndromic OFC, including 43 with 22q11.2 DS, which indicates the need for chromosomal microarray analysis in this group. The nosologic diversity reinforces that monitoring clinical is the best strategy for etiological investigation. BCFP's methodology has introduced the possibility of increasing scientific knowledge and genetic diagnosis of OFC and 22q11.2 DS to in turn improve health care and policies for this group of diseases.
Subject(s)
Cleft Lip , Cleft Palate , DiGeorge Syndrome , Brazil , Cleft Lip/genetics , Cleft Palate/genetics , DiGeorge Syndrome/diagnosis , DiGeorge Syndrome/genetics , Genomics , HumansABSTRACT
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.
Subject(s)
Conjunctivitis, Allergic/diagnosis , Mass Screening , Neural Networks, Computer , Surveys and Questionnaires/standards , Adolescent , Age Factors , Algorithms , Brazil/epidemiology , Child , Child, Preschool , Conjunctivitis, Allergic/epidemiology , Epidemiologic Methods , Female , Humans , MaleABSTRACT
OBJETIVO: Avaliar sensibilidade e especificidade de questionário escrito de varredura para alergia ocular por análises multivariáveis. Comparação entre os modelos de análise multivariável; elaboração de rede neural artificial para varreduras futuras. MÉTODOS: Estudo observacional, transversal realizado na UNIFESP, com 48 pacientes portadores de conjuntivite alérgica e 54 crianças sadias. A idade variou entre 3 e 14 anos, sem restrição quanto ao sexo, doença alérgica sistêmica ou tratamento. Aplicou-se questionário desenvolvido e procedeu-se a uma análise estatística multivariável. Por fim, elaborou-se uma rede neural artificial. RESULTADOS: A média de idade dos participantes foi 8,4 anos (4 a 13 anos), com predomínio do sexo masculino (60,8 por cento). A pontuação média do questionário foi 10 (0 a 18). A média do escore do grupo controle foi inferior ao grupo estudo (p<0,001). O diagnóstico de alergia foi melhorado em 68,8 por cento com a inclusão da questão 5. Não houve concordância entre os diagnósticos clínico e o efetuado pelo questionário (kappa = 0,337, p=0,071). Somente a questão 5 apresentou boas sensibilidade (85,4 por cento) e especificidade (85,1 por cento). O ponto de corte capaz de separar doentes de não-doentes foi 10 (sensibilidade= 77,0 por cento, especificidade= 79,6 por cento, acurácia de 85,7 por cento). A rede neural foi capaz de predizer os pacientes alérgicos em 100 por cento dos casos, com 7 das 15 questões. CONCLUSÕES: Conseguiu-se chegar a um modelo confiável, utilizando apenas sete itens, tornando fácil a aplicação do QE em larga escala.
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 percent). 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 percent. 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 percent) and specificity (85.1 percent). The cutoff point to separate allergic patients was 10 (sensitivity= 77.08 percent and specificity= 79.63 percent). The artificial neural network predicted allergic diagnosis in 100 percent 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.
