Journal
GENERAL HOSPITAL PSYCHIATRY
Volume 77, Issue -, Pages 37-39Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.genhosppsych.2022.04.011
Keywords
PHQ-9; Neuropsychiatry; Major depressive disorder
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Funding
- Cumming School of Medicine
- Alberta Health Services through the Calgary Health Trust
- Cuthbertson & Fischer Chair in Pediatric Mental Health
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The study assessed whether machine learning methods could improve the predictive performance of the PHQ-9 for depression in patients with neurological disease. The results showed that the traditional approach to PHQ-9 scoring and interpretation remains clinically appropriate.
Objective: The study objective was to assess whether machine learning methods could improve predictive performance of the PHQ-9 for depression in patients with neurological disease. Specifically, we assessed whether a predictive algorithm deriving from all nine items could outperform the tradition of summing the items and applying a cut-point. Method: Data from the NEEDS Study was used (n = 825). Demographic data, PHQ-9 scores, and MDD diagnoses (via the SCID) were obtained. Logistic LASSO, logistic regression, and non-parametric ROC analyses were performed. The ROC curve was used to identify the optimal cut-point for regression-derived predictive algorithms using the Youden method. Results: The traditional approach to PHQ-9 scoring had a classification accuracy of 85.1% (sensitivity: 84.5%; specificity: 85.2%). The logistic LASSO regression model had a classification accuracy of 85.6% (sensitivity: 83.3%; specificity: 86.1%). The logistic regression model had a classification accuracy of 85.8% (sensitivity: 91.4%; specificity: 84.8%). Both models had similar areas under the curve values (logistic LASSO: 0.9097; logistic regression: 0.9026). Conclusions: The current cut-off threshold approach to PHQ-9 scoring and interpretation remains clinically appropriate.
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