Publication: Análisis de protocolos mixtos de evaluación del riesgo de sufrir diabetes no conocida en pacientes en Clínicas de Odontología
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2020
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Introducción
La diabetes mellitus es una epidemia global cuyas complicaciones presentan unas importantes morbilidades y mortalidad asociadas. La diabetes tipo 2 permanece frecuentemente sin diagnosticar, especialmente en sus estadios iniciales. Diferentes revisiones publicadas en los últimos años han señalado claramente la influencia bidireccional de la diabetes sobre las enfermedades periodontales, demostrando incluso que el tratamiento periodontal conduce a mejoras en el control glucémico en pacientes con diabetes. Esta interacción tiene importantes implicaciones para los profesionales de la salud, para los pacientes con diabetes y para la población general. A la luz de esta asociación, los profesionales de la salud bucodental desempeñan un papel importante en el manejo de los pacientes con diabetes, y entre sus implicaciones, destaca la posibilidad de utilizar herramientas de ”cribado” para identificar pacientes con riesgo elevado de padecer diabetes mellitus sin diagnosticar en la clínica dental.
Objetivos El objetivo principal de este estudio fue analizar la eficacia de un protocolo mixto de evaluación del riesgo en la detección de diabetes o prediabetes no conocida en el consultorio dental. Un segundo objetivo fue evaluar la capacidad diagnóstica de diferentes modelos a la hora de discriminar individuos sanos, prediabéticos, o diabéticos. Estos modelos diagnósticos han sido: FindRisc, FindRisc con Examen Periodontal Básico (EPB), y FindRisc con EPB con determinación ambulatoria de HbA1c mediante dispositivo portátil.
Materiales y métodos El estudio se diseñó como un estudio observacional y transversal para valorar un protocolo diagnóstico. Se reclutaron a 1143 adultos ≥ 40 años sin antecedentes de diabetes o prediabetes en 41 consultorios odontológicos generales de la Red de Clínicas de Investigación SEPA durante un período de un año. A cada paciente se le realizó el FindRisc, y a los pacientes con riesgo ligeramente elevado (puntuaciones ≥7), se les realizó la determinación ambulatoria de los niveles de hemoglobina glicosilada mediante un dispositivo portátil (A1Cnow+, Bayer Healthcare, Leverkusen, Germany). Se confeccionaron las curvas ROC (del inglés Receiver Operating Characteristic) usando modelos multivariables de regresión logística para evaluar la capacidad de cribado de los diferentes modelos predictivos usando la hiperglucemia confirmada (prediabetes o diabetes) como variable dependiente.
Resultados De la muestra estudiada, 97 sujetos (8,5%) fueron confirmados con diagnóstico de diabetes (n = 28; 2,5%) o prediabetes (n = 69; 6,0%). Al incluir solo los resultados del cuestionario FindRisc, el modelo reportó un área bajo la curva (AUC) de 0,866 (intervalo de confianza del 95% - IC [0,833; 0,900]). Este modelo mejoró significativamente cuando se agregó la determinación ambulatoria de la HbA1c (AUC de 0,961; IC del 95% [0,941; 0,980]; p <0,001).
Conclusiones El protocolo evaluado, que combina el cuestionario FindRisc y una determinación portátil de HbA1c, demostró ser factible de llevarse a cabo en una clínica dental y fue eficiente para identificar sujetos con diabetes o prediabetes no diagnosticada, lo que pudiera implicar importantes beneficios para la salud pública.
Introduction Diabetes mellitus is a global epidemic whose complications have significantly associated morbidities and mortality. Type 2 diabetes often remains undiagnosed, especially in its early stages. Different works published in recent years have clearly indicated the bidirectional influence of diabetes on periodontal diseases, even demonstrating that periodontal treatment leads to improvements in glycemic control in patients with diabetes. This interaction has important implications for healthcare professionals, for patients with diabetes and for the general population. In the light of this association, oral health professionals may play an important role in the management of patients with diabetes, and its implications include the possibility of using screening tools to identify patients at high risk of suffering from undiagnosed diabetes mellitus in the dental clinic. Objectives The main objective of this study was to analyze the efficacy of a mixed risk assessment protocol in the detection of unknown diabetes or prediabetes in the dental office. A second objective was to evaluate the diagnostic capacity of different models when discriminating healthy, prediabetic, or diabetic individuals. These diagnostic models have been: FindRisc, FindRisc with Basic Periodontal Examination (BPE), and FindRisc with BPE with ambulatory HbA1c determination using a portable device. Materials and methods The study was designed as an observational and cross-sectional study to assess a diagnostic protocol. 1,143 adults ≥ 40 years of age were recruited, with no history of diabetes or prediabetes, from 41 general dental offices of the SEPA Research Clinic Network over a period of one year. Each patient underwent the FindRisc, and patients with slightly high risk (scores ≥7) had outpatient determination of glycosylated hemoglobin levels using a portable device (A1Cnow +, Bayer Healthcare, Leverkusen, Germany). ROC (Receiver Operating Characteristic) curves were constructed using multivariate logistic regression models to assess the screening capacity of the different predictive models using confirmed hyperglycemia (prediabetes or diabetes) as the dependent variable. Results Of the sample studied, 97 subjects (8,5%) were confirmed with a diagnosis of diabetes (n = 28; 2,5%) or prediabetes (n = 69; 6,0%). By including only the results of the FindRisc questionnaire, the model reported an area under the curve (AUC) of 0,866 (95% confidence interval - CI [0,833, 0,900]). This model was significantly improved when ambulatory HbA1c measurement was added (AUC 0,961, 95% CI [0,941, 0,980], p <0,001). The protocol evaluated, which combines the FindRisc questionnaire and a portable HbA1c determination, proved to be feasible to be carried out in a dental clinic and was efficient in identifying subjects with undiagnosed diabetes or prediabetes, which could imply important benefits for public health.
Introduction Diabetes mellitus is a global epidemic whose complications have significantly associated morbidities and mortality. Type 2 diabetes often remains undiagnosed, especially in its early stages. Different works published in recent years have clearly indicated the bidirectional influence of diabetes on periodontal diseases, even demonstrating that periodontal treatment leads to improvements in glycemic control in patients with diabetes. This interaction has important implications for healthcare professionals, for patients with diabetes and for the general population. In the light of this association, oral health professionals may play an important role in the management of patients with diabetes, and its implications include the possibility of using screening tools to identify patients at high risk of suffering from undiagnosed diabetes mellitus in the dental clinic. Objectives The main objective of this study was to analyze the efficacy of a mixed risk assessment protocol in the detection of unknown diabetes or prediabetes in the dental office. A second objective was to evaluate the diagnostic capacity of different models when discriminating healthy, prediabetic, or diabetic individuals. These diagnostic models have been: FindRisc, FindRisc with Basic Periodontal Examination (BPE), and FindRisc with BPE with ambulatory HbA1c determination using a portable device. Materials and methods The study was designed as an observational and cross-sectional study to assess a diagnostic protocol. 1,143 adults ≥ 40 years of age were recruited, with no history of diabetes or prediabetes, from 41 general dental offices of the SEPA Research Clinic Network over a period of one year. Each patient underwent the FindRisc, and patients with slightly high risk (scores ≥7) had outpatient determination of glycosylated hemoglobin levels using a portable device (A1Cnow +, Bayer Healthcare, Leverkusen, Germany). ROC (Receiver Operating Characteristic) curves were constructed using multivariate logistic regression models to assess the screening capacity of the different predictive models using confirmed hyperglycemia (prediabetes or diabetes) as the dependent variable. Results Of the sample studied, 97 subjects (8,5%) were confirmed with a diagnosis of diabetes (n = 28; 2,5%) or prediabetes (n = 69; 6,0%). By including only the results of the FindRisc questionnaire, the model reported an area under the curve (AUC) of 0,866 (95% confidence interval - CI [0,833, 0,900]). This model was significantly improved when ambulatory HbA1c measurement was added (AUC 0,961, 95% CI [0,941, 0,980], p <0,001). The protocol evaluated, which combines the FindRisc questionnaire and a portable HbA1c determination, proved to be feasible to be carried out in a dental clinic and was efficient in identifying subjects with undiagnosed diabetes or prediabetes, which could imply important benefits for public health.
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Trabajo Fin de Master encuadrado en la línea de investigación Etiología y patogenia de las enfermedades periodontales y periimplantarias.
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Unesco subjects
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