Abstract
BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention. METHODS: Initially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample. RESULTS: The study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach's alpha = 0.72, criterion-related validity = 0.77). CONCLUSIONS: This five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples.