Psycho-Oncology · 2020 · 10 citations · 25 references
MedicineOncologyPediatricsCancer Survivorship CarePediatric OncologyPsychometricsChild AssessmentChild Clinical PsychologyEmotional Distress AssessmentPediatric Health AssessmentPsychological TestingCriterion Cutoff SelectionCutoff PointsDistress Severity GradingGeneralized Linear Mixed ModelsPediatric Distress AssessmentSymptom Assessment ToolsSymptom Distress MeasurementValidated Screening Instruments
OBJECTIVE: Although there is enthusiasm for identifying and treating psychosocial problems in childhood cancer patients, there are few validated instruments to help providers identify at-risk children for further assessment. The study objective was to evaluate the sensitivity and specificity of the self-report pediatric Distress Thermometer Rating Scale (Peds DTRS) in childhood cancer survivors and identify a threshold score to help providers classify pediatric patients. METHODS: We evaluated 54 children 7-17 years old using 178 Peds DTRS longitudinal data points from the cohort that was used for the original pediatric adaptation of the DTRS. We compared Peds DTRS scores against two established standardized measures using a generalized linear mixed model to deal with the dependency in the longitudinal data to estimate ROC curves and related statistics. RESULTS: Results indicate that a score of 3 is a reasonable cutoff to identify distress with children 7-17 years old. This cutoff yielded high sensitivity (87.0%) and specificity (79.7%) using the PedsQL Emotional Domain score as the standard. Similar results were obtained using the CDI as the standard, but we are cautious as very few CDI scores reached the cutoff criterion. Exploratory analysis highlighted clinical factors that correlate with increased distress measured using the Peds DTRS. CONCLUSIONS: The Peds DTRS is a very brief, convenient, and rapid screening tool for global distress in children. Further investigation of the Peds DTRS and other tools can improve the ability of providers to prevent and treat the negative emotional consequences of cancer and improve the quality of survivorship.
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Fitting Linear Mixed-Effects Models Using lme4
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