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"titulo" => "Probabilistic cost-effectiveness analysis of the treatment of sleep apnea"
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"resumen" => "<span class="elsevierStyleSectionTitle">Objetivo</span><p class="elsevierStyleSimplePara elsevierViewall">En este trabajo se presenta la aplicación del análisis coste-efectividad de tipo probabilístico al tratamiento con presión continua en la vía respiratoria por vía nasal (<span class="elsevierStyleItalic">nasal continuous positive airway pressure</span>, nCPAP) del síndrome de la apnea obstructiva del sueño (SAOS).</p> <span class="elsevierStyleSectionTitle">Material y métodos</span><p class="elsevierStyleSimplePara elsevierViewall">La base del estudio es un modelo de Markov probabilístico. Éste se caracteriza porque las variables se introducen en forma de distribuciones. El modelo se procesa mediante 2.000 simulaciones de Monte Carlo, cada una de las cuales calcula el coste y la efectividad incrementales. El resultado se analiza mediante el plano coste-efectividad, la curva de aceptabilidad, el beneficio neto y el valor esperado de la información perfecta.</p> <span class="elsevierStyleSectionTitle">Resultados</span><p class="elsevierStyleSimplePara elsevierViewall">La razón coste-efectividad del tratamiento con nCPAP media calculada es de 5.480 €/año de vida ajustado por calidad (AVAC). Utilizando como umbral de eficiencia la cifra de 30.000 €/AVAC, el análisis probabilístico muestra que en el 98,5% de las simulaciones el tratamiento con nCPAP es una práctica eficiente. El valor esperado de la información perfecta muestra que el parámetro que origina más incertidumbre en el resultado es la ganancia en calidad de vida producida por el tratamiento.</p> <span class="elsevierStyleSectionTitle">Conclusiones</span><p class="elsevierStyleSimplePara elsevierViewall">El análisis de tipo probabilístico ratifica el resultado de los estudios deterministas que caracterizan el tratamiento con nCPAP como una intervención eficiente. La ventaja añadida es que permite situar la incertidumbre en términos cuantitativos; en este caso la probabilidad de equivocarse es inferior al 5%. Además, el estudio muestra que para reducir esa incertidumbre la investigación debe centrarse en la mejora de la información referente a la calidad de vida.</p>"
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"en" => array:2 [
"titulo" => "Abstract"
"resumen" => "<span class="elsevierStyleSectionTitle">Objective</span><p class="elsevierStyleSimplePara elsevierViewall">To describe the application of a probabilistic costeffectiveness analysis to nasal continuous positive airway passage (nCPAP) treatment of obstructive sleep apnea syndrome (OSAS).</p> <span class="elsevierStyleSectionTitle">Material and Methods</span><p class="elsevierStyleSimplePara elsevierViewall">The probabilistic model was constructed from a discrete Markov model. This probabilistic approach is characterized by the introduction of variables as probability distributions. The model performed 2,000 Monte Carlo simulations, and incremental costs and effectiveness were calculated in each. The results were analyzed through the costeffectiveness plane, the acceptability curve, the net benefit rule, and the expected value of perfect information (EVPI).</p> <span class="elsevierStyleSectionTitle">Results</span><p class="elsevierStyleSimplePara elsevierViewall">The mean cost-effectiveness ratio for nCPAP treatment was 5,480 €/QALY (quality-adjusted life year). Using an acceptability threshold of 30,000 €/QALY, the probabilistic analysis showed that nCPAP was the optimal treatment in 98.5% of the simulations. The EVPI showed that the parameter causing greatest uncertainty in the final results was the quality of life gain through nCPAP treatment.</p> <span class="elsevierStyleSectionTitle">Conclusions</span><p class="elsevierStyleSimplePara elsevierViewall">The results of our probabilistic analysis are endorsed by previous deterministic studies confirming that nCPAP treatment of OSAS is the most cost-effective strategy. An additional advantage of probabilistic analysis is that it allows uncertainty to be quantified; in the present case the probability of making the wrong decision was below 5%. Furthermore, this study reveals that to reduce uncertainty, research should center on improving information on quality of life.</p>"
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