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BACKGROUND: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record-based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them. OBJECTIVE: The purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool. METHODS: We iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations. RESULTS: We extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns. CONCLUSIONS: We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system.
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INTRODUCTION: The rising cost of providing healthcare services creates an extreme pressure to know what works best in medicine. Traditional methods of generating clinical evidence are expensive and time consuming. The availability of electronic clinical data generated during routine patient encounters provides an opportunity to use that information to generate new clinical evidence. However, electronic clinical data is frequently marred by inadequate quality that impedes such secondary uses. This study provides a proof-of-concept and tests the classification accuracy of ClinicalTime-a temporal query system-to identify patient cohorts in clinical databases. METHODS: we randomly selected a sample of medical records from the MIMIC-II database, an anonymized database of intensive care patients. Records were manually classified as having an acute kidney injury or not according to the AKIN criteria. Those records were then blindly classified using ClinicalTime to represent the AKIN criteria. Classification accuracy was measured. RESULTS: ClinicalTime correctly classified 88% of all patients, with a sensitivity of 0.93 and specificity of 0.84. Its performance was superior to simply using ICD-9 codes, which correctly classified 66% of all patients. CONCLUSIONS: ClinicalTime, a temporal query system, is a valid method to add to the currently available ones to identify patient phenotypes in patient databases and, thus, improving our ability to re-use routinely collected electronic clinical data for secondary purposes.
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We report a female newborn with type II mucolipidoses. This condition is characterized clinically by Hurler like features, progressive psychomotor retardation and death during the first or second year of life. Most cases present during the first year of life, with poor weight gain and coarse facies features. The cause of this rare autosomal recessive hereditary disease is the deficiency of the enzyme N-acetylglucosamine-1-phosphotransferase, required for the synthesis of mannose-6-phosphate, the ligand that allows the transport of acid hydrolases into lysosomes. The patient had clinical features commonly found in mucolipidosis II, including disproportionate dwarfism, retarded psychomotor development, coarse facies features, gibbous and restricted joint mobility. The diagnosis was proved by an extremely elevated activity of lysosomal enzymes in the serum, secondary to non-regulated secretion and subsequent intracellular depletion of these proteins. The child suffered recurrent pneumonia and died at 22 months of age.