Design and Development of a Minimum Data Set for Phenylketonuria Disease

Abstract

Introduction: Phenylketonuria is one of the most common autosomal recessive metabolic diseases, characterized by a wide range of neuropsychological and neurocognitive disorders. Without proper care, control, and management, this disease can lead to severe mental retardation and neurobehavioral disorders. Therefore, the objective of this study was to design and develop a Minimum Data Set (MDS) for phenylketonuria disease to introduce indicators effective in further management, control, and monitoring of this disease. Method: The present study was a descriptive-analytical one that was conducted in two stages. In the first stage, a comprehensive review of PubMed, Web of Science, and Scopus databases was performed to identify management and clinical data elements. Then, the necessary data elements were extracted from the studies and put into a questionnaire. In the second stage, 15 pediatricians, nutritionists, psychiatrists, and psychologists completed the designed questionnaire using the two-stage Delphi technique. The descriptive statistics as well as SPSS 23 were used to analyze the data. Results: A total of 133 management and clinical data elements were extracted from the studies. These data elements were divided into three groups of information and 14 categories. According to experts, consensus and collective agreement were reached on 125 data elements in 13 categories. The category of congenital defects was the only category all data elements of which were excluded in the study. Conclusion: Given the clinical and economic challenges that phenylketonuria patients face, determining the minimum data set can enable effective control and management of this disease, reduce costs, and improve management of information relevant to these patients.

Keywords

• minimum data set
• effective indicators
• management
• control
• phenylketonuria